API#

Python API#

Warning

The use of Single Prim classes (a particular case of the Prims classes for a single prim) is discouraged as they will be removed in future versions. Use Prims classes (formerly Prim Views) instead.

Prims

`Articulation`	High level wrapper to deal with prims (one or many) that have the Root Articulation API applied and their attributes/properties
`ClothPrim`	The view class for cloth prims.
`DeformablePrim`	The view class for deformable prims.
`GeometryPrim`	High level wrapper to deal with geom prims (one or many) as well as their attributes/properties.
`ParticleSystem`	Provides high level functions to deal with particle systems (1 or more particle systems) as well as its attributes/ properties.
`RigidPrim`	Provides high level functions to deal with prims (one or many) that have Rigid Body API applied to them as well as their attributes/properties.
`SdfShapePrim`	High level functions to deal with geometry prims that provide their Signed Distance Field (SDF).
`XFormPrim`	Provides high level functions to deal with a Xform prim view (one or many) and its descendants as well as its attributes/properties.

Single Prims

`SingleArticulation`	High level wrapper to deal with an articulation prim (only one articulation prim) and its attributes/properties.
`SingleClothPrim`	Cloth primitive object provide functionalities to create and control cloth parameters
`SingleDeformablePrim`	Deformable primitive object provide functionalities to create and control deformable objects
`SingleGeometryPrim`	High level wrapper to deal with a Geom prim (only one geometry prim) and its attributes/properties.
`SingleParticleSystem`	A wrapper around PhysX particle system.
`SingleRigidPrim`	High level wrapper to deal with a rigid body prim (only one rigid body prim) and its attributes/properties.
`SingleXFormPrim`	Provides high level functions to deal with an Xform prim (only one Xform prim) and its attributes/properties

Prims#

Bases: XFormPrim

High level wrapper to deal with prims (one or many) that have the Root Articulation API applied and their attributes/properties

This class wraps all matching articulations found at the regex provided at the prim_paths_expr argument

Note

Each prim will have xformOp:orient, xformOp:translate and xformOp:scale only post-init, unless it is a non-root articulation link.

Warning

The articulation view object must be initialized in order to be able to operate on it. See the initialize method for more details.

Parameters:

prim_paths_expr (Union[str, List[str]]) – prim paths regex to encapsulate all prims that match it. example: “/World/Env[1-5]/Franka” will match /World/Env1/Franka, /World/Env2/Franka..etc. (a non regex prim path can also be used to encapsulate one rigid prim).
name (str, optional) – shortname to be used as a key by Scene class. Note: needs to be unique if the object is added to the Scene. Defaults to “articulation_prim_view”.
positions (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – default positions in the world frame of the prims. shape is (N, 3). Defaults to None, which means left unchanged.
translations (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – default translations in the local frame of the prims (with respect to its parent prims). shape is (N, 3). Defaults to None, which means left unchanged.
orientations (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – default quaternion orientations in the world/ local frame of the prims (depends if translation or position is specified). quaternion is scalar-first (w, x, y, z). shape is (N, 4).
scales (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – local scales to be applied to the prim’s dimensions in the view. shape is (N, 3). Defaults to None, which means left unchanged.
visibilities (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – set to false for an invisible prim in the stage while rendering. shape is (N,). Defaults to None.
reset_xform_properties (bool, optional) – True if the prims don’t have the right set of xform properties (i.e: translate, orient and scale) ONLY and in that order. Set this parameter to False if the object were cloned using using the cloner api in isaacsim.core.cloner. Defaults to True.
enable_residual_reports (bool optional) – Setting to True will enable using the residual reporting APIs. Defaults to False.

Example:

>>> import isaacsim.core.utils.stage as stage_utils
>>> from isaacsim.core.cloner import GridCloner
>>> from isaacsim.core.prims import Articulation
>>> from pxr import UsdGeom
>>>
>>> usd_path = "/home/<user>/Documents/Assets/Robots/Franka/franka_alt_fingers.usd"
>>> env_zero_path = "/World/envs/env_0"
>>> num_envs = 5
>>>
>>> # load the Franka Panda robot USD file
>>> stage_utils.add_reference_to_stage(usd_path, prim_path=f"{env_zero_path}/panda")  # /World/envs/env_0/panda
>>>
>>> # clone the environment (num_envs)
>>> cloner = GridCloner(spacing=1.5)
>>> cloner.define_base_env(env_zero_path)
>>> UsdGeom.Xform.Define(stage_utils.get_current_stage(), env_zero_path)
>>> cloner.clone(source_prim_path=env_zero_path, prim_paths=cloner.generate_paths("/World/envs/env", num_envs))
>>>
>>> # wrap all articulations
>>> prims = Articulation(prim_paths_expr="/World/envs/env.*/panda", name="franka_panda_view")
>>> prims
<isaacsim.core.prims.articulation.Articulation object at 0x7ff174054b20>

property num_dof: int#

Number of DOF of the articulations

Returns:: maximum number of DOFs for the articulations in the view
Return type:: int

Example:

>>> prims.num_dof
9

property num_bodies: int#

Number of rigid bodies (links) of the articulations

Returns:: maximum number of rigid bodies for the articulations in the view
Return type:: int

Example:

>>> prims.num_bodies
12

property num_shapes: int#

Number of rigid shapes of the articulations

Returns:: maximum number of rigid shapes for the articulations in the view
Return type:: int

Example:

>>> prims.num_shapes
17

property num_joints: int#

Number of joints of the articulations

Returns:: number of joints of the articulations in the view
Return type:: int

property num_fixed_tendons: int#

Number of fixed tendons of the articulations

Returns:: maximum number of fixed tendons for the articulations in the view
Return type:: int

Example:

>>> prims.num_fixed_tendons
0

property body_names: List[str]#

List of prim names for each rigid body (link) of the articulations

Returns:: ordered names of bodies that corresponds to links for the articulations in the view
Return type:: List[str]

Example:

>>> prims.body_names
['panda_link0', 'panda_link1', 'panda_link2', 'panda_link3', 'panda_link4', 'panda_link5',
 'panda_link6', 'panda_link7', 'panda_link8', 'panda_hand', 'panda_leftfinger', 'panda_rightfinger']

property dof_names: List[str]#

List of prim names for each DOF of the articulations

Returns:: ordered names of joints that corresponds to degrees of freedom for the articulations in the view
Return type:: List[str]

Example:

>>> prims.dof_names
['panda_joint1', 'panda_joint2', 'panda_joint3', 'panda_joint4', 'panda_joint5',
 'panda_joint6', 'panda_joint7', 'panda_finger_joint1', 'panda_finger_joint2']

property joint_names: List[str]#

List of prim names for each joint of the articulations

Returns:: ordered names of joints that corresponds to degrees of freedom for the articulations in the view
Return type:: List[str]

is_physics_handle_valid() → bool#

Check if articulation view’s physics handler is initialized

Warning

If the physics handler is not valid many of the methods that requires PhysX will return None.

Returns:: False if .initialize() needs to be called again for the physics handle to be valid. Otherwise True
Return type:: bool

Example:

>>> prims.is_physics_handle_valid()
True

get_body_index(body_name: str) → int#

Get a ridig body (link) index in the articulation view given its name

Parameters:: body_name (str) – name of the ridig body to query
Returns:: index of the rigid body in the articulation buffers
Return type:: int

Example:

>>> # get the index of the left finger: panda_leftfinger
>>> prims.get_body_index("panda_leftfinger")
10

get_dof_index(dof_name: str) → int#

Get a DOF index in the joint buffers given its name

Parameters:: dof_name (str) – name of the joint that corresponds to the degree of freedom to query
Returns:: index of the degree of freedom in the joint buffers
Return type:: int

Example:

>>> # get the index of the left finger joint: panda_finger_joint1
>>> prims.get_dof_index("panda_finger_joint1")
7

get_dof_types( dof_names: List[str] | None = None, ) → List[str]#

Get the DOF types given the DOF names

Parameters:: dof_names (List[str], optional) – names of the joints that corresponds to the degrees of freedom to query. Defaults to None.
Returns:: types of the joints that corresponds to the degrees of freedom. Types can be invalid, translation or rotation.
Return type:: List[str]

Example:

>>> # get all DOF types
>>> prims.get_dof_types()
[<DofType.Rotation: 0>, <DofType.Rotation: 0>, <DofType.Rotation: 0>,
 <DofType.Rotation: 0>, <DofType.Rotation: 0>, <DofType.Rotation: 0>,
 <DofType.Rotation: 0>, <DofType.Translation: 1>, <DofType.Translation: 1>]
>>>
>>> # get only the finger DOF types: panda_finger_joint1 and panda_finger_joint2
>>> prims.get_dof_types(dof_names=["panda_finger_joint1", "panda_finger_joint2"])
[<DofType.Translation: 1>, <DofType.Translation: 1>]

get_dof_limits() → ndarray | Tensor#

Get the articulations DOFs limits (lower and upper)

Returns:: degrees of freedom position limits. Shape is (N, num_dof, 2). For the last dimension, index 0 corresponds to lower limits and index 1 corresponds to upper limits
Return type:: Union[np.ndarray, torch.Tensor, wp.array]

Example:

>>> # get DOF limits. Returned shape is (5, 9, 2) for the example: 5 envs, 9 DOFs
>>> prims.get_dof_limits()
[[[-2.8973  2.8973]
 [-1.7628  1.7628]
 [-2.8973  2.8973]
 [-3.0718 -0.0698]
 [-2.8973  2.8973]
 [-0.0175  3.7525]
 [-2.8973  2.8973]
 [ 0.      0.04  ]
 [ 0.      0.04  ]]
...
[[-2.8973  2.8973]
 [-1.7628  1.7628]
 [-2.8973  2.8973]
 [-3.0718 -0.0698]
 [-2.8973  2.8973]
 [-0.0175  3.7525]
 [-2.8973  2.8973]
 [ 0.      0.04  ]
 [ 0.      0.04  ]]]

get_drive_types() → ndarray | Tensor#

Get the articulations DOFs limits (lower and upper)

Returns:: degrees of freedom position limits. Shape is (N, num_dof). For the last dimension, index 0 corresponds to lower limits and index 1 corresponds to upper limits
Return type:: Union[np.ndarray, torch.Tensor, wp.array]

get_joint_index(joint_name: str) → int#

Get a joint index in the joint buffers given its name

Parameters:: joint_name (str) – name of the joint that corresponds to the index of the joint in the articulation
Returns:: index of the joint in the joint buffers
Return type:: int

get_link_index(link_name: str) → int#

Get a link index in the link buffers given its name

Parameters:: link_name (str) – name of the link that corresponds to the index of the link in the articulation
Returns:: index of the link in the link buffers
Return type:: int

Set the friction coefficients for articulation joints in the view

Search for “Joint Friction Coefficient” in PhysX docs for more details.

Parameters:

values (Union[np.ndarray, torch.Tensor, wp.array]) – friction coefficients for articulation joints in the view. shape (M, K).
indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
joint_indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – joint indices to specify which joints to manipulate. Shape (K,). Where K <= num of dofs. Defaults to None (i.e: all dofs).
joint_names (Optional[List[str]]) – joint names to specify which joints to manipulate (can’t be sppecified together with joint_indices). Shape (K,). Where K <= num of dofs. Defaults to None (i.e: all dofs).

Example:

>>> # set all joint friction coefficients to 0.05 for all envs
>>> prims.set_friction_coefficients(np.full((num_envs, prims.num_dof), 0.05))
>>>
>>> # set only the finger joint (panda_finger_joint1 (7) and panda_finger_joint2 (8)) friction coefficients
>>> # for the first, middle and last of the 5 envs to 0.05
>>> prims.set_friction_coefficients(np.full((3, 2), 0.05), indices=np.array([0,2,4]), joint_indices=np.array([7,8]))

Get the friction coefficients for the articulation joints in the view

Search for “Joint Friction Coefficient” in PhysX docs for more details.

Parameters:

indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
joint_indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – joint indices to specify which joints to query. Shape (K,). Where K <= num of dofs. Defaults to None (i.e: all dofs).
joint_names (Optional[List[str]]) – joint names to specify which joints to manipulate (can’t be sppecified together with joint_indices). Shape (K,). Where K <= num of dofs. Defaults to None (i.e: all dofs).
clone (Optional[bool]) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

joint friction coefficients for articulations in the view. shape (M, K).

Return type:

Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>> # get joint friction coefficients. Returned shape is (5, 9) for the example: 5 envs, 9 DOFs
>>> prims.get_friction_coefficients()
[[0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0.]]
>>>
>>> # get only the finger joint (panda_finger_joint1 (7) and panda_finger_joint2 (8)) friction coefficients
>>> # for the first, middle and last of the 5 envs. Returned shape is (3, 2)
>>> prims.get_friction_coefficients(indices=np.array([0,2,4]), joint_indices=np.array([7,8]))
[[0. 0.]
 [0. 0.]
 [0. 0.]]

Set armatures for articulation joints in the view

Search for “Joint Armature” in PhysX docs for more details.

Parameters:

values (Union[np.ndarray, torch.Tensor, wp.array]) – armatures for articulation joints in the view. shape (M, K).
indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
joint_indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – joint indices to specify which joints to manipulate. Shape (K,). Where K <= num of dofs. Defaults to None (i.e: all dofs).
joint_names (Optional[List[str]]) – joint names to specify which joints to manipulate (can’t be sppecified together with joint_indices). Shape (K,). Where K <= num of dofs. Defaults to None (i.e: all dofs).

Example:

>>> # set all joint armatures to 0.05 for all envs
>>> prims.set_armatures(np.full((num_envs, prims.num_dof), 0.05))
>>>
>>> # set only the finger joint (panda_finger_joint1 (7) and panda_finger_joint2 (8)) armatures
>>> # for the first, middle and last of the 5 envs to 0.05
>>> prims.set_armatures(np.full((3, 2), 0.05), indices=np.array([0,2,4]), joint_indices=np.array([7,8]))

Get armatures for articulation joints in the view

Search for “Joint Armature” in PhysX docs for more details.

Parameters:

indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
joint_indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – joint indices to specify which joints to query. Shape (K,). Where K <= num of dofs. Defaults to None (i.e: all dofs).
joint_names (Optional[List[str]]) – joint names to specify which joints to manipulate (can’t be sppecified together with joint_indices). Shape (K,). Where K <= num of dofs. Defaults to None (i.e: all dofs).
clone (Optional[bool]) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

joint armatures for articulations in the view. shape (M, K).

Return type:

Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>> # get joint armatures. Returned shape is (5, 9) for the example: 5 envs, 9 DOFs
>>> prims.get_armatures()
[[0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0.]]
>>>
>>> # get only the finger joint (panda_finger_joint1 (7) and panda_finger_joint2 (8)) armatures
>>> # for the first, middle and last of the 5 envs. Returned shape is (3, 2)
>>> prims.get_armatures(indices=np.array([0,2,4]), joint_indices=np.array([7,8]))
[[0. 0.]
 [0. 0.]
 [0. 0.]]

get_articulation_body_count() → int#

Get the number of rigid bodies (links) of the articulations

Returns:: maximum number of rigid bodies (links) in the articulation
Return type:: int

Example:

>>> prims.get_articulation_body_count()
12

Set the joint position targets for the implicit Proportional-Derivative (PD) controllers

Note

This is an independent method for controlling joints. To apply multiple targets (position, velocity, and/or effort) in the same call, consider using the apply_action method

Parameters:

positions (Optional[Union[np.ndarray, torch.Tensor, wp.array]]) – joint position targets for the implicit PD controller. shape is (M, K).
indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
joint_indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – joint indices to specify which joints to manipulate. Shape (K,). Where K <= num of dofs. Defaults to None (i.e: all dofs).
joint_names (Optional[List[str]]) – joint names to specify which joints to manipulate (can’t be sppecified together with joint_indices). Shape (K,). Where K <= num of dofs. Defaults to None (i.e: all dofs).

Hint

High stiffness makes the joints snap faster and harder to the desired target, and higher damping smoothes but also slows down the joint’s movement to target

For position control, set relatively high stiffness and low damping (to reduce vibrations)

Example:

>>> # apply the target positions (to move all the robot joints) to the indicated values.
>>> # Since there are 5 envs, the joint positions are repeated 5 times
>>> positions = np.tile(np.array([0.0, -1.0, 0.0, -2.2, 0.0, 2.4, 0.8, 0.04, 0.04]), (num_envs, 1))
>>> prims.set_joint_position_targets(positions)
>>>
>>> # close the robot fingers: panda_finger_joint1 (7) and panda_finger_joint2 (8) to 0.0
>>> # for the first, middle and last of the 5 envs
>>> positions = np.tile(np.array([0.0, 0.0]), (3, 1))
>>> prims.set_joint_position_targets(positions, indices=np.array([0, 2, 4]), joint_indices=np.array([7, 8]))

Set the joint positions of articulations in the view

Warning

This method will immediately set (teleport) the affected joints to the indicated value. Use the set_joint_position_targets or the apply_action methods to control the articulation joints.

Parameters:

positions (Optional[Union[np.ndarray, torch.Tensor, wp.array]]) – joint positions of articulations in the view to be set to in the next frame. shape is (M, K).
indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
joint_indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – joint indices to specify which joints to manipulate. Shape (K,). Where K <= num of dofs. Defaults to None (i.e: all dofs).
joint_names (Optional[List[str]]) – joint names to specify which joints to manipulate (can’t be sppecified together with joint_indices). Shape (K,). Where K <= num of dofs. Defaults to None (i.e: all dofs).

Hint

This method belongs to the methods used to set the articulation kinematic states:

set_velocities (set_linear_velocities, set_angular_velocities), set_joint_positions, set_joint_velocities, set_joint_efforts

Example:

>>> # set all the articulation joints.
>>> # Since there are 5 envs, the joint positions are repeated 5 times
>>> positions = np.tile(np.array([0.0, -1.0, 0.0, -2.2, 0.0, 2.4, 0.8, 0.04, 0.04]), (num_envs, 1))
>>> prims.set_joint_positions(positions)
>>>
>>> # set only the fingers in closed position: panda_finger_joint1 (7) and panda_finger_joint2 (8) to 0.0
>>> # for the first, middle and last of the 5 envs
>>> positions = np.tile(np.array([0.0, 0.0]), (3, 1))
>>> prims.set_joint_positions(positions, indices=np.array([0, 2, 4]), joint_indices=np.array([7, 8]))

Set the joint velocity targets for the implicit Proportional-Derivative (PD) controllers

Note

This is an independent method for controlling joints. To apply multiple targets (position, velocity, and/or effort) in the same call, consider using the apply_action method

Parameters:

velocities (Optional[Union[np.ndarray, torch.Tensor, wp.array]]) – joint velocity targets for the implicit PD controller. shape is (M, K).
indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
joint_indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – joint indices to specify which joints to manipulate. Shape (K,). Where K <= num of dofs. Defaults to None (i.e: all dofs).
joint_names (Optional[List[str]]) – joint names to specify which joints to manipulate (can’t be sppecified together with joint_indices). Shape (K,). Where K <= num of dofs. Defaults to None (i.e: all dofs).

Hint

High stiffness makes the joints snap faster and harder to the desired target, and higher damping smoothes but also slows down the joint’s movement to target

For velocity control, stiffness must be set to zero with a non-zero damping

Example:

>>> # apply the target velocities for all the articulation joints to the indicated values.
>>> # Since there are 5 envs, the joint velocities are repeated 5 times
>>> velocities = np.tile(np.array([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]), (num_envs, 1))
>>> prims.set_joint_velocity_targets(velocities)
>>>
>>> # apply the fingers target velocities: panda_finger_joint1 (7) and panda_finger_joint2 (8) to -1.0
>>> # for the first, middle and last of the 5 envs
>>> velocities = np.tile(np.array([-0.1, -0.1]), (3, 1))
>>> prims.set_joint_velocity_targets(velocities, indices=np.array([0, 2, 4]), joint_indices=np.array([7, 8]))

Set the joint velocities of articulations in the view

Warning

This method will immediately set the affected joints to the indicated value. Use the set_joint_velocity_targets or the apply_action methods to control the articulation joints.

Parameters:

velocities (Optional[Union[np.ndarray, torch.Tensor, wp.array]]) – joint velocities of articulations in the view to be set to in the next frame. shape is (M, K).
indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
joint_indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – joint indices to specify which joints to manipulate. Shape (K,). Where K <= num of dofs. Defaults to None (i.e: all dofs).
joint_names (Optional[List[str]]) – joint names to specify which joints to manipulate (can’t be sppecified together with joint_indices). Shape (K,). Where K <= num of dofs. Defaults to None (i.e: all dofs).

Hint

This method belongs to the methods used to set the articulation kinematic states:

set_velocities (set_linear_velocities, set_angular_velocities), set_joint_positions, set_joint_velocities, set_joint_efforts

Example:

>>> # set the velocities for all the articulation joints to the indicated values.
>>> # Since there are 5 envs, the joint velocities are repeated 5 times
>>> velocities = np.tile(np.array([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]), (num_envs, 1))
>>> prims.set_joint_velocities(velocities)
>>>
>>> # set the fingers velocities: panda_finger_joint1 (7) and panda_finger_joint2 (8) to -0.1
>>> # for the first, middle and last of the 5 envs
>>> velocities = np.tile(np.array([-0.1, -0.1]), (3, 1))
>>> prims.set_joint_velocities(velocities, indices=np.array([0, 2, 4]), joint_indices=np.array([7, 8]))

Set the joint efforts of articulations in the view

Note

This method can be used for effort control. For this purpose, there must be no joint drive or the stiffness and damping must be set to zero.

Parameters:

efforts (Optional[Union[np.ndarray, torch.Tensor, wp.array]]) – efforts of articulations in the view to be set to in the next frame. shape is (M, K).
indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
joint_indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – joint indices to specify which joints to manipulate. Shape (K,). Where K <= num of dofs. Defaults to None (i.e: all dofs).
joint_names (Optional[List[str]]) – joint names to specify which joints to manipulate (can’t be sppecified together with joint_indices). Shape (K,). Where K <= num of dofs. Defaults to None (i.e: all dofs).

Hint

This method belongs to the methods used to set the articulation kinematic states:

set_velocities (set_linear_velocities, set_angular_velocities), set_joint_positions, set_joint_velocities, set_joint_efforts

Example:

>>> # set the efforts for all the articulation joints to the indicated values.
>>> # Since there are 5 envs, the joint efforts are repeated 5 times
>>> efforts = np.tile(np.array([10, 20, 30, 40, 50, 60, 70, 80, 90]), (num_envs, 1))
>>> prims.set_joint_efforts(efforts)
>>>
>>> # set the fingers efforts: panda_finger_joint1 (7) and panda_finger_joint2 (8) to 10
>>> # for the first, middle and last of the 5 envs
>>> efforts = np.tile(np.array([10, 10]), (3, 1))
>>> prims.set_joint_efforts(efforts, indices=np.array([0, 2, 4]), joint_indices=np.array([7, 8]))

Get the joint efforts of articulations in the view

This method will return the efforts set by the set_joint_efforts method

Parameters:

indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
joint_indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – joint indices to specify which joints to query. Shape (K,). Where K <= num of dofs. Defaults to None (i.e: all dofs).
joint_names (Optional[List[str]]) – joint names to specify which joints to manipulate (can’t be sppecified together with joint_indices). Shape (K,). Where K <= num of dofs. Defaults to None (i.e: all dofs).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

joint efforts of articulations in the view. Shape is (M, K).

Return type:

Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>> # get all applied joint efforts. Returned shape is (5, 9) for the example: 5 envs, 9 DOFs
>>> prims.get_applied_joint_efforts()
[[0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0.]]
>>>
>>> # get finger applied efforts: panda_finger_joint1 (7) and panda_finger_joint2 (8)
>>> # for the first, middle and last of the 5 envs. Returned shape is (3, 2)
>>> prims.get_applied_joint_efforts(indices=np.array([0, 2, 4]), joint_indices=np.array([7, 8]))
[[0. 0.]
 [0. 0.]
 [0. 0.]]

Returns the efforts computed/measured by the physics solver of the joint forces in the DOF motion direction

Parameters:

indices (Optional[Union[np.ndarray, List, torch.Tensor]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
joint_indices (Optional[Union[np.ndarray, List, torch.Tensor]], optional) – joint indices to specify which joints to query. Shape (K,). Where K <= num of dofs. Defaults to None (i.e: all dofs).
joint_names (Optional[List[str]]) – joint names to specify which joints to manipulate (can’t be sppecified together with joint_indices). Shape (K,). Where K <= num of dofs. Defaults to None (i.e: all dofs).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

computed joint efforts of articulations in the view. shape is (M, K).

Return type:

Union[np.ndarray, torch.Tensor]

Example:

>>> # get all measured joint efforts. Returned shape is (5, 9) for the example: 5 envs, 9 DOFs
>>> prims.get_measured_joint_efforts()
[[ 4.8250298e-05 -6.9073005e+00  5.3364405e-05  1.9157070e+01 -5.8759182e-05
   1.1863427e+00 -5.6388220e-05  5.1680300e-03 -5.1910817e-03]
 [ 4.8250298e-05 -6.9073005e+00  5.3364405e-05  1.9157070e+01 -5.8759182e-05
   1.1863427e+00 -5.6388220e-05  5.1680300e-03 -5.1910817e-03]
 [ 4.8254540e-05 -6.9072919e+00  5.3344327e-05  1.9157072e+01 -5.8761045e-05
   1.1863427e+00 -5.6405144e-05  5.1680212e-03 -5.1910840e-03]
 [ 4.8254540e-05 -6.9072919e+00  5.3344327e-05  1.9157072e+01 -5.8761045e-05
   1.1863427e+00 -5.6405144e-05  5.1680212e-03 -5.1910840e-03]
 [ 4.8250298e-05 -6.9073005e+00  5.3364405e-05  1.9157070e+01 -5.8759182e-05
   1.1863427e+00 -5.6388220e-05  5.1680300e-03  -5.1910817e-03]]
>>>
>>> # get finger measured joint efforts: panda_finger_joint1 (7) and panda_finger_joint2 (8)
>>> # for the first, middle and last of the 5 envs. Returned shape is (3, 2)
>>> prims.get_measured_joint_efforts(indices=np.array([0, 2, 4]), joint_indices=np.array([7, 8]))
[[ 0.00516803 -0.00519108]
 [ 0.00516802 -0.00519108]
 [ 0.00516803 -0.00519108]]

Get the measured joint reaction forces and torques (link incoming joint forces and torques) to external loads

Note

Since the name->index map for joints has not been exposed yet, it is possible to access the joint names and their indices through the articulation metadata.

prims._metadata.joint_names  # list of names
prims._metadata.joint_indices  # dict of name: index

To retrieve a specific row for the link incoming joint force/torque use joint_index + 1

Parameters:

indices (Optional[Union[np.ndarray, List, torch.Tensor]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
joint_indices (Optional[Union[np.ndarray, List, torch.Tensor]], optional) – link indices to specify which link’s incoming joints to query. Shape (K,). Where K <= num of links/bodies. Defaults to None (i.e: all dofs).
joint_names (Optional[List[str]]) – joint names to specify which joints to manipulate (can’t be sppecified together with joint_indices). Shape (K,). Where K <= num of dofs. Defaults to None (i.e: all dofs).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

joint forces and torques of articulations in the view. Shape is (M, num_joint + 1, 6). Column index 0 is the incoming joint of the base link. For the last dimension the first 3 values are for forces and the last 3 for torques

Return type:

Union[np.ndarray, torch.Tensor]

Example:

>>> # get all measured joint forces and torques. Returned shape is (5, 12, 6) for the example:
>>> # 5 envs, 9 DOFs (but 12 joints including the fixed and root joints)
>>> prims.get_measured_joint_forces()
[[[ 0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00]
  [ 1.49950760e+02  3.52353277e-06  5.62586996e-04  4.82502983e-05 -6.90729856e+00  2.69259126e-05]
  [-2.60467059e-05 -1.06778236e+02 -6.83844986e+01 -6.90730047e+00 -5.27759657e-05 -1.24897576e-06]
  [ 8.71209946e+01 -4.46646191e-05 -5.57951622e+01  5.33644052e-05 -2.45385647e+01  1.38957939e-05]
  [ 5.18576926e-05 -4.81099091e+01  6.07092705e+01  1.91570702e+01 -5.81023924e-05  1.46875891e-06]
  [-3.16910419e+01  2.31799815e-04  3.99901695e+01 -5.87591821e-05 -1.18634319e+00  2.24427877e-05]
  [-1.07621672e-04  1.53405371e+01 -1.54584875e+01  1.18634272e+00  6.09036942e-05 -1.60679410e-05]
  [-7.54189777e+00 -5.08146524e+00 -5.65130091e+00 -5.63882204e-05  3.88599992e-01 -3.49432468e-01]
  [ 4.74214745e+00 -3.19458222e+00  3.55281782e+00  5.58562024e-05  8.47946014e-03  7.64050474e-03]
  [ 4.07607269e+00  2.16406956e-01 -4.05131817e+00 -5.95658377e-04  1.14070829e-02  2.13965313e-06]
  [ 5.16803004e-03 -9.77545828e-02 -9.70939621e-02 -8.41282599e-12 -1.29066744e-12 -1.93477560e-11]
  [-5.19108167e-03  9.75882635e-02 -9.71064270e-02  8.41282859e-12  1.29066018e-12 -1.93477543e-11]]
 ...
 [[ 0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00]
  [ 1.49950760e+02  3.52353277e-06  5.62586996e-04  4.82502983e-05 -6.90729856e+00  2.69259126e-05]
  [-2.60467059e-05 -1.06778236e+02 -6.83844986e+01 -6.90730047e+00 -5.27759657e-05 -1.24897576e-06]
  [ 8.71209946e+01 -4.46646191e-05 -5.57951622e+01  5.33644052e-05 -2.45385647e+01  1.38957939e-05]
  [ 5.18576926e-05 -4.81099091e+01  6.07092705e+01  1.91570702e+01 -5.81023924e-05  1.46875891e-06]
  [-3.16910419e+01  2.31799815e-04  3.99901695e+01 -5.87591821e-05 -1.18634319e+00  2.24427877e-05]
  [-1.07621672e-04  1.53405371e+01 -1.54584875e+01  1.18634272e+00  6.09036942e-05 -1.60679410e-05]
  [-7.54189777e+00 -5.08146524e+00 -5.65130091e+00 -5.63882204e-05  3.88599992e-01 -3.49432468e-01]
  [ 4.74214745e+00 -3.19458222e+00  3.55281782e+00  5.58562024e-05  8.47946014e-03  7.64050474e-03]
  [ 4.07607269e+00  2.16406956e-01 -4.05131817e+00 -5.95658377e-04  1.14070829e-02  2.13965313e-06]
  [ 5.16803004e-03 -9.77545828e-02 -9.70939621e-02 -8.41282599e-12 -1.29066744e-12 -1.93477560e-11]
  [-5.19108167e-03  9.75882635e-02 -9.71064270e-02  8.41282859e-12  1.29066018e-12 -1.93477543e-11]]]
>>>
>>> # get measured joint forces and torques for the fingers for the first, middle and last of the 5 envs.
>>> # Returned shape is (3, 2, 6)
>>> metadata = prims._metadata
>>> joint_indices = 1 + np.array([
>>>     metadata.joint_indices["panda_finger_joint1"],
>>>     metadata.joint_indices["panda_finger_joint2"],
>>> ])
>>> joint_indices
[10 11]
>>> prims.get_measured_joint_forces(indices=np.array([0, 2, 4]), joint_indices=joint_indices)
[[[ 5.1680300e-03 -9.7754583e-02 -9.7093962e-02 -8.4128260e-12 -1.2906674e-12 -1.9347756e-11]
  [-5.1910817e-03  9.7588263e-02 -9.7106427e-02  8.4128286e-12  1.2906602e-12 -1.9347754e-11]]
 [[ 5.1680212e-03 -9.7754560e-02 -9.7093947e-02 -8.4141834e-12 -1.2907383e-12 -1.9348209e-11]
  [-5.1910840e-03  9.7588278e-02 -9.7106412e-02  8.4141869e-12  1.2907335e-12 -1.9348207e-11]]
 [[ 5.1680300e-03 -9.7754583e-02 -9.7093962e-02 -8.4128260e-12 -1.2906674e-12 -1.9347756e-11]
  [-5.1910817e-03  9.7588263e-02 -9.7106427e-02  8.4128286e-12  1.2906602e-12 -1.9347754e-11]]]

Get the joint positions of articulations in the view

Parameters:

indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
joint_indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – joint indices to specify which joints to query. Shape (K,). Where K <= num of dofs. Defaults to None (i.e: all dofs).
joint_names (Optional[List[str]]) – joint names to specify which joints to manipulate (can’t be sppecified together with joint_indices). Shape (K,). Where K <= num of dofs. Defaults to None (i.e: all dofs).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

joint positions of articulations in the view. Shape is (M, K).

Return type:

Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>> # get all joint positions. Returned shape is (5, 9) for the example: 5 envs, 9 DOFs
>>> prims.get_joint_positions()
[[ 1.1999921e-02 -5.6962633e-01  1.3219320e-08 -2.8105433e+00  6.8276213e-06
   3.0301569e+00  7.3234755e-01  3.9912373e-02  3.9999999e-02]
 [ 1.1999921e-02 -5.6962633e-01  1.3219320e-08 -2.8105433e+00  6.8276213e-06
   3.0301569e+00  7.3234755e-01  3.9912373e-02  3.9999999e-02]
 [ 1.1999921e-02 -5.6962633e-01  1.3220056e-08 -2.8105433e+00  6.8276104e-06
   3.0301569e+00  7.3234755e-01  3.9912373e-02  3.9999999e-02]
 [ 1.1999921e-02 -5.6962633e-01  1.3220056e-08 -2.8105433e+00  6.8276104e-06
   3.0301569e+00  7.3234755e-01  3.9912373e-02  3.9999999e-02]
 [ 1.1999921e-02 -5.6962633e-01  1.3219320e-08 -2.8105433e+00  6.8276213e-06
   3.0301569e+00  7.3234755e-01  3.9912373e-02  3.9999999e-02]]
>>>
>>> # get finger joint positions: panda_finger_joint1 (7) and panda_finger_joint2 (8)
>>> # for the first, middle and last of the 5 envs. Returned shape is (3, 2)
>>> prims.get_joint_positions(indices=np.array([0, 2, 4]), joint_indices=np.array([7, 8]))
[[0.03991237 0.04      ]
 [0.03991237 0.04      ]
 [0.03991237 0.04      ]]

Get the joint velocities of articulations in the view

Parameters:

indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
joint_indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – joint indices to specify which joints to query. Shape (K,). Where K <= num of dofs. Defaults to None (i.e: all dofs).
joint_names (Optional[List[str]]) – joint names to specify which joints to manipulate (can’t be sppecified together with joint_indices). Shape (K,). Where K <= num of dofs. Defaults to None (i.e: all dofs).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

joint velocities of articulations in the view. Shape is (M, K).

Return type:

Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>> # get all joint velocities. Returned shape is (5, 9) for the example: 5 envs, 9 DOFs
>>> prims.get_joint_velocities()
[[ 1.9010375e-06 -7.6763844e-03 -2.1396865e-07  1.1063669e-02 -4.6333633e-05
   3.4824573e-02  8.8469200e-02  5.4033857e-04  1.0287426e-05]
 [ 1.9010375e-06 -7.6763844e-03 -2.1396865e-07  1.1063669e-02 -4.6333633e-05
   3.4824573e-02  8.8469200e-02  5.4033857e-04  1.0287426e-05]
 [ 1.9010074e-06 -7.6763779e-03 -2.1403629e-07  1.1063648e-02 -4.6333400e-05
   3.4824558e-02  8.8469170e-02  5.4033566e-04  1.0287110e-05]
 [ 1.9010074e-06 -7.6763779e-03 -2.1403629e-07  1.1063648e-02 -4.6333400e-05
   3.4824558e-02  8.8469170e-02  5.4033566e-04  1.0287110e-05]
 [ 1.9010375e-06 -7.6763844e-03 -2.1396865e-07  1.1063669e-02 -4.6333633e-05
   3.4824573e-02  8.8469200e-02  5.4033857e-04  1.0287426e-05]]
>>>
>>> # get finger joint velocities: panda_finger_joint1 (7) and panda_finger_joint2 (8)
>>> # for the first, middle and last of the 5 envs. Returned shape is (3, 2)
>>> prims.get_joint_velocities(indices=np.array([0, 2, 4]), joint_indices=np.array([7, 8]))
[[5.4033857e-04 1.0287426e-05]
 [5.4033566e-04 1.0287110e-05]
 [5.4033857e-04 1.0287426e-05]]

apply_action( control_actions: ArticulationActions, indices: ndarray | List | Tensor | warp.array | None = None, ) → None#

Apply joint positions (targets), velocities (targets) and/or efforts to control an articulation

Note

This method can be used instead of the separate set_joint_position_targets, set_joint_velocity_targets and set_joint_efforts

Parameters:

control_actions (ArticulationActions) – actions to be applied for next physics step.
indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Hint

High stiffness makes the joints snap faster and harder to the desired target, and higher damping smoothes but also slows down the joint’s movement to target

For position control, set relatively high stiffness and low damping (to reduce vibrations)
For velocity control, stiffness must be set to zero with a non-zero damping
For effort control, stiffness and damping must be set to zero

Example:

>>> from isaacsim.core.utils.types import ArticulationActions
>>>
>>> # move all the articulation joints to the indicated position.
>>> # Since there are 5 envs, the joint positions are repeated 5 times
>>> positions = np.tile(np.array([0.0, -1.0, 0.0, -2.2, 0.0, 2.4, 0.8, 0.04, 0.04]), (num_envs, 1))
>>> action = ArticulationActions(joint_positions=positions)
>>> prims.apply_action(action)
>>>
>>> # close the robot fingers: panda_finger_joint1 (7) and panda_finger_joint2 (8) to 0.0
>>> # for the first, middle and last of the 5 envs
>>> positions = np.tile(np.array([0.0, 0.0]), (3, 1))
>>> action = ArticulationActions(joint_positions=positions, joint_indices=np.array([7, 8]))
>>> prims.apply_action(action, indices=np.array([0, 2, 4]))

get_applied_actions( clone: bool = True, ) → ArticulationActions#

Get the last applied actions

Parameters:: clone (bool, optional) – True to return clones of the internal buffers. Otherwise False. Defaults to True.
Returns:: current applied actions (i.e: current position targets and velocity targets)
Return type:: ArticulationActions

Example:

>>> # last applied action: joint_positions -> [0.0, -1.0, 0.0, -2.2, 0.0, 2.4, 0.8, 0.04, 0.04].
>>> # Returned shape is (5, 9) for the example: 5 envs, 9 DOFs
>>> actions = prims.get_applied_actions()
>>> actions
<isaacsim.core.utils.types.ArticulationActions object at 0x7f28af31d870>
>>> actions.joint_positions
[[ 0.   -1.    0.   -2.2   0.    2.4   0.8   0.04  0.04]
 [ 0.   -1.    0.   -2.2   0.    2.4   0.8   0.04  0.04]
 [ 0.   -1.    0.   -2.2   0.    2.4   0.8   0.04  0.04]
 [ 0.   -1.    0.   -2.2   0.    2.4   0.8   0.04  0.04]
 [ 0.   -1.    0.   -2.2   0.    2.4   0.8   0.04  0.04]]
>>> actions.joint_velocities
[[0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0.]]
>>> actions.joint_efforts
[[0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0.]]

Set poses of prims in the view with respect to the world’s frame.

Warning

This method will change (teleport) the prim poses immediately to the indicated value

Parameters:

positions (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – positions in the world frame of the prim. shape is (M, 3). Defaults to None, which means left unchanged.
orientations (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – quaternion orientations in the world frame of the prims. quaternion is scalar-first (w, x, y, z). shape is (M, 4). Defaults to None, which means left unchanged.
indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
usd (bool, optional) – True to query from usd. Otherwise False to query from Fabric data. Defaults to True.

Hint

This method belongs to the methods used to set the prim state

Example:

>>> # reposition all articulations in row (x-axis)
>>> positions = np.zeros((num_envs, 3))
>>> positions[:,0] = np.arange(num_envs)
>>> orientations = np.tile(np.array([1.0, 0.0, 0.0, 0.0]), (num_envs, 1))
>>> prims.set_world_poses(positions, orientations)
>>>
>>> # reposition only the articulations for the first, middle and last of the 5 envs in column (y-axis)
>>> positions = np.zeros((3, 3))
>>> positions[:,1] = np.arange(3)
>>> orientations = np.tile(np.array([1.0, 0.0, 0.0, 0.0]), (3, 1))
>>> prims.set_world_poses(positions, orientations, indices=np.array([0, 2, 4]))

Get the poses of the prims in the view with respect to the world’s frame.

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.
usd (bool, optional) – True to query from usd. Otherwise False to query from Fabric data. Defaults to True.

Returns:

first index is positions in the world frame of the prims. shape is (M, 3). Second index is quaternion orientations in the world frame of the prims. Quaternion is scalar-first (w, x, y, z). shape is (M, 4).

Return type:

Union[Tuple[np.ndarray, np.ndarray], Tuple[torch.Tensor, torch.Tensor], Tuple[wp.indexedarray, wp.indexedarray]]

Example:

>>> # get all articulation poses with respect to the world's frame.
>>> # Returned shape is position (5, 3) and orientation (5, 4) for the example: 5 envs
>>> positions, orientations = prims.get_world_poses()
>>> positions
[[ 1.5000000e+00 -7.5000000e-01 -2.8610229e-08]
 [ 1.5000000e+00  7.5000000e-01 -2.8610229e-08]
 [-4.5299529e-08 -7.5000000e-01 -2.8610229e-08]
 [-4.5299529e-08  7.5000000e-01 -2.8610229e-08]
 [-1.5000000e+00 -7.5000000e-01 -2.8610229e-08]]
>>> orientations
[[1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]]
>>>
>>> # get only the articulation poses with respect to the world's frame for the first, middle and last of the 5 envs.
>>> # Returned shape is position (3, 3) and orientation (3, 4) for the example: 3 envs selected
>>> positions, orientations = prims.get_world_poses(indices=np.array([0, 2, 4]))
>>> positions
[[ 1.5000000e+00 -7.5000000e-01 -2.8610229e-08]
 [-4.5299529e-08 -7.5000000e-01 -2.8610229e-08]
 [-1.5000000e+00 -7.5000000e-01 -2.8610229e-08]]
>>> orientations
[[1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]]

Get prim poses in the view with respect to the local frame (the prim’s parent frame).

Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view)
Returns:: first index is positions in the local frame of the prims. shape is (M, 3). Second index is quaternion orientations in the local frame of the prims. Quaternion is scalar-first (w, x, y, z). shape is (M, 4).
Return type:: Union[Tuple[np.ndarray, np.ndarray], Tuple[torch.Tensor, torch.Tensor], Tuple[wp.indexedarray, wp.indexedarray]]

Example:

>>> # get all articulation poses with respect to the local frame.
>>> # Returned shape is position (5, 3) and orientation (5, 4) for the example: 5 envs
>>> positions, orientations = prims.get_local_poses()
>>> positions
[[ 0.0000000e+00  0.0000000e+00 -2.8610229e-08]
 [ 0.0000000e+00  0.0000000e+00 -2.8610229e-08]
 [-4.5299529e-08  0.0000000e+00 -2.8610229e-08]
 [-4.5299529e-08  0.0000000e+00 -2.8610229e-08]
 [ 0.0000000e+00  0.0000000e+00 -2.8610229e-08]]
>>> orientations
[[1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]]
>>>
>>> # get only the articulation poses with respect to the local frame for the first, middle and last of the 5 envs.
>>> # Returned shape is position (3, 3) and orientation (3, 4) for the example: 3 envs selected
>>> positions, orientations = prims.get_local_poses(indices=np.array([0, 2, 4]))
>>> positions
[[ 0.0000000e+00  0.0000000e+00 -2.8610229e-08]
 [-4.5299529e-08  0.0000000e+00 -2.8610229e-08]
 [ 0.0000000e+00  0.0000000e+00 -2.8610229e-08]]
>>> orientations
[[1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]]

Set prim poses in the view with respect to the local frame (the prim’s parent frame).

Warning

This method will change (teleport) the prim poses immediately to the indicated value

Parameters:

translations (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – translations in the local frame of the prims (with respect to its parent prim). shape is (M, 3). Defaults to None, which means left unchanged.
orientations (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – quaternion orientations in the local frame of the prims. quaternion is scalar-first (w, x, y, z). shape is (M, 4). Defaults to None, which means left unchanged.
indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Hint

This method belongs to the methods used to set the prim state

Example:

>>> # reposition all articulations
>>> positions = np.zeros((num_envs, 3))
>>> positions[:,0] = np.arange(num_envs)
>>> orientations = np.tile(np.array([1.0, 0.0, 0.0, 0.0]), (num_envs, 1))
>>> prims.set_local_poses(positions, orientations)
>>>
>>> # reposition only the articulations for the first, middle and last of the 5 envs
>>> positions = np.zeros((3, 3))
>>> positions[:,1] = np.arange(3)
>>> orientations = np.tile(np.array([1.0, 0.0, 0.0, 0.0]), (3, 1))
>>> prims.set_local_poses(positions, orientations, indices=np.array([0, 2, 4]))

Set the linear and angular velocities of the prims in the view at once.

The method does this through the PhysX API only. It has to be called after initialization

Warning

This method will immediately set the articulation state

Parameters:

velocities (Optional[Union[np.ndarray, torch.Tensor, wp.array]]) – linear and angular velocities respectively to set the rigid prims to. shape is (M, 6).
indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Hint

This method belongs to the methods used to set the articulation kinematic state:

set_velocities (set_linear_velocities, set_angular_velocities), set_joint_positions, set_joint_velocities, set_joint_efforts

Example:

>>> # set each articulation linear velocity to (1., 1., 1.) and angular velocity to (.1, .1, .1)
>>> velocities = np.ones((num_envs, 6))
>>> velocities[:,3:] = 0.1
>>> prims.set_velocities(velocities)
>>>
>>> # set only the articulation velocities for the first, middle and last of the 5 envs
>>> velocities = np.ones((3, 6))
>>> velocities[:,3:] = 0.1
>>> prims.set_velocities(velocities, indices=np.array([0, 2, 4]))

Get the linear and angular velocities of prims in the view.

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view)
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

linear and angular velocities of the prims in the view concatenated. shape is (M, 6). For the last dimension the first 3 values are for linear velocities and the last 3 for angular velocities

Return type:

Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>> # get all articulation velocities. Returned shape is (5, 6) for the example: 5 envs, linear (3) and angular (3)
>>> prims.get_velocities()
[[0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0.]]
>>>
>>> # get only the articulation velocities for the first, middle and last of the 5 envs.
>>> # Returned shape is (3, 6) for the example: 3 envs selected, linear (3) and angular (3)
>>> prims.get_velocities(indices=np.array([0, 2, 4]))
[[0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0.]]

Set the linear velocities of the prims in the view

The method does this through the PhysX API only. It has to be called after initialization. Note: This method is not supported for the gpu pipeline. set_velocities method should be used instead.

Warning

This method will immediately set the articulation state

Parameters:

velocities (Optional[Union[np.ndarray, torch.Tensor, wp.array]]) – linear velocities to set the rigid prims to. shape is (M, 3).
indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Hint

This method belongs to the methods used to set the articulation kinematic state:

set_velocities (set_linear_velocities, set_angular_velocities), set_joint_positions, set_joint_velocities, set_joint_efforts

Example:

>>> # set each articulation linear velocity to (1.0, 1.0, 1.0)
>>> velocities = np.ones((num_envs, 3))
>>> prims.set_linear_velocities(velocities)
>>>
>>> # set only the articulation linear velocities for the first, middle and last of the 5 envs
>>> velocities = np.ones((3, 3))
>>> prims.set_linear_velocities(velocities, indices=np.array([0, 2, 4]))

Get the linear velocities of prims in the view.

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view)
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

linear velocities of the prims in the view. shape is (M, 3).

Return type:

Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>> # get all articulation linear velocities. Returned shape is (5, 3) for the example: 5 envs, linear (3)
>>> prims.get_linear_velocities()
[[0. 0. 0.]
 [0. 0. 0.]
 [0. 0. 0.]
 [0. 0. 0.]
 [0. 0. 0.]]
>>>
>>> # get only the articulation linear velocities for the first, middle and last of the 5 envs.
>>> # Returned shape is (3, 3) for the example: 3 envs selected, linear (3)
>>> prims.get_linear_velocities(indices=np.array([0, 2, 4]))
[[0. 0. 0.]
 [0. 0. 0.]
 [0. 0. 0.]]

Set the angular velocities of the prims in the view

The method does this through the physx API only. It has to be called after initialization. Note: This method is not supported for the gpu pipeline. set_velocities method should be used instead.

Warning

This method will immediately set the articulation state

Parameters:

velocities (Optional[Union[np.ndarray, torch.Tensor, wp.array]]) – angular velocities to set the rigid prims to. shape is (M, 3).
indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Hint

This method belongs to the methods used to set the articulation kinematic state:

set_velocities (set_linear_velocities, set_angular_velocities), set_joint_positions, set_joint_velocities, set_joint_efforts

Example:

>>> # set each articulation linear velocity to (0.1, 0.1, 0.1)
>>> velocities = np.full((num_envs, 3), fill_value=0.1)
>>> prims.set_angular_velocities(velocities)
>>>
>>> # set only the articulation linear velocities for the first, middle and last of the 5 envs
>>> velocities = np.full((3, 3), fill_value=0.1)
>>> prims.set_angular_velocities(velocities, indices=np.array([0, 2, 4]))

Get the angular velocities of prims in the view.

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view)
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

angular velocities of the prims in the view. shape is (M, 3).

Return type:

Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>> # get all articulation angular velocities. Returned shape is (5, 3) for the example: 5 envs, angular (3)
>>> prims.get_angular_velocities()
[[0. 0. 0.]
 [0. 0. 0.]
 [0. 0. 0.]
 [0. 0. 0.]
 [0. 0. 0.]]
>>>
>>> # get only the articulation angular velocities for the first, middle and last of the 5 envs
>>> # Returned shape is (5, 3) for the example: 3 envs selected, angular (3)
>>> prims.get_angular_velocities(indices=np.array([0, 2, 4]))
[[0. 0. 0.]
 [0. 0. 0.]
 [0. 0. 0.]]

Get physics solver position residuals for articulations. This is the residual across all joints that are part of articulations.: The solver residuals are computed according to impulse variation normalized by the effective mass.

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view)
report_max (Optional[bool]) – whether to report max or RMS residual. Defaults to True, i.e. max criteria

Returns:

Solver residuals for rigid bodies of the view

Return type:

Union[np.ndarray, torch.Tensor, wp.indexedarray]

Get physics solver velocity residuals for articulations. This is the residual across all joints that are part of articulations.: The solver residuals are computed according to impulse variation normalized by the effective mass.

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view)
report_max (Optional[bool]) – whether to report max or RMS residual. Defaults to True, i.e. max criteria

Returns:

Solver residuals for rigid bodies of the view

Return type:

Union[np.ndarray, torch.Tensor, wp.indexedarray]

Set the joints default state (joint positions, velocities and efforts) to be applied after each reset.

Note

The default states will be set during post-reset (e.g., calling .post_reset() or world.reset() methods)

Parameters:

positions (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – default joint positions. shape is (N, num of dofs). Defaults to None.
velocities (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – default joint velocities. shape is (N, num of dofs). Defaults to None.
efforts (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – default joint efforts. shape is (N, num of dofs). Defaults to None.

Example:

>>> # configure default joint states for all articulations
>>> positions = np.tile(np.array([0.0, -1.0, 0.0, -2.2, 0.0, 2.4, 0.8, 0.04, 0.04]), (num_envs, 1))
>>> prims.set_joints_default_state(
...     positions=positions,
...     velocities=np.zeros((num_envs, prims.num_dof)),
...     efforts=np.zeros((num_envs, prims.num_dof))
... )
>>>
>>> # set default states during post-reset
>>> prims.post_reset()

get_joints_default_state() → JointsState#

Get the default joint states defined with the set_joints_default_state method

Returns:: an object that contains the default joint states
Return type:: JointsState

Example:

>>> # returned shape is (5, 9) for the example: 5 envs, 9 DOFs
>>> states = prims.get_joints_default_state()
>>> states
<isaacsim.core.utils.types.JointsState object at 0x7fc2c174fd90>
>>> states.positions
[[ 0.   -1.    0.   -2.2   0.    2.4   0.8   0.04  0.04]
 [ 0.   -1.    0.   -2.2   0.    2.4   0.8   0.04  0.04]
 [ 0.   -1.    0.   -2.2   0.    2.4   0.8   0.04  0.04]
 [ 0.   -1.    0.   -2.2   0.    2.4   0.8   0.04  0.04]
 [ 0.   -1.    0.   -2.2   0.    2.4   0.8   0.04  0.04]]
>>> states.velocities
[[0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0.]]
>>> states.efforts
[[0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0.]]

get_joints_state() → JointsState#

Get the current joint states (positions and velocities)

Returns:: an object that contains the current joint positions and velocities
Return type:: JointsState

Example:

>>> # returned shape is (5, 9) for the example: 5 envs, 9 DOFs
>>> states = prims.get_joints_state()
>>> states
<isaacsim.core.utils.types.JointsState object at 0x7fc1a23a82e0>
>>> states.positions
[[ 1.1999921e-02 -5.6962633e-01  1.3219320e-08 -2.8105433e+00  6.8276213e-06
   3.0301569e+00  7.3234755e-01  3.9912373e-02  3.9999999e-02]
 [ 1.1999921e-02 -5.6962633e-01  1.3219320e-08 -2.8105433e+00  6.8276213e-06
   3.0301569e+00  7.3234755e-01  3.9912373e-02  3.9999999e-02]
 [ 1.1999921e-02 -5.6962633e-01  1.3220056e-08 -2.8105433e+00  6.8276104e-06
   3.0301569e+00  7.3234755e-01  3.9912373e-02  3.9999999e-02]
 [ 1.1999921e-02 -5.6962633e-01  1.3220056e-08 -2.8105433e+00  6.8276104e-06
   3.0301569e+00  7.3234755e-01  3.9912373e-02  3.9999999e-02]
 [ 1.1999921e-02 -5.6962633e-01  1.3219320e-08 -2.8105433e+00  6.8276213e-06
   3.0301569e+00  7.3234755e-01  3.9912373e-02  3.9999999e-02]]
>>> states.velocities
[[ 1.9010375e-06 -7.6763844e-03 -2.1396865e-07  1.1063669e-02 -4.6333633e-05
   3.4824573e-02  8.8469200e-02  5.4033857e-04  1.0287426e-05]
 [ 1.9010375e-06 -7.6763844e-03 -2.1396865e-07  1.1063669e-02 -4.6333633e-05
   3.4824573e-02  8.8469200e-02  5.4033857e-04  1.0287426e-05]
 [ 1.9010074e-06 -7.6763779e-03 -2.1403629e-07  1.1063648e-02 -4.6333400e-05
   3.4824558e-02  8.8469170e-02  5.4033566e-04  1.0287110e-05]
 [ 1.9010074e-06 -7.6763779e-03 -2.1403629e-07  1.1063648e-02 -4.6333400e-05
   3.4824558e-02  8.8469170e-02  5.4033566e-04  1.0287110e-05]
 [ 1.9010375e-06 -7.6763844e-03 -2.1396865e-07  1.1063669e-02 -4.6333633e-05
   3.4824573e-02  8.8469200e-02  5.4033857e-04  1.0287426e-05]]

Get effort modes for articulations in the view

Parameters:

indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
joint_indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – joint indices to specify which joints to query. Shape (K,). Where K <= num of dofs. Defaults to None (i.e: all dofs).
joint_names (Optional[List[str]]) – joint names to specify which joints to manipulate (can’t be sppecified together with joint_indices). Shape (K,). Where K <= num of dofs. Defaults to None (i.e: all dofs).

Returns:

Returns a List of size (M, K) indicating the effort modes: acceleration or force

Return type:

List

Example:

>>> # get the effort mode for all joints
>>> prims.get_effort_modes()
[['acceleration', 'acceleration', 'acceleration', 'acceleration', 'acceleration', 'acceleration', 'acceleration', 'acceleration', 'acceleration'],
 ['acceleration', 'acceleration', 'acceleration', 'acceleration', 'acceleration', 'acceleration', 'acceleration', 'acceleration', 'acceleration'],
 ['acceleration', 'acceleration', 'acceleration', 'acceleration', 'acceleration', 'acceleration', 'acceleration', 'acceleration', 'acceleration'],
 ['acceleration', 'acceleration', 'acceleration', 'acceleration', 'acceleration', 'acceleration', 'acceleration', 'acceleration', 'acceleration'],
 ['acceleration', 'acceleration', 'acceleration', 'acceleration', 'acceleration', 'acceleration', 'acceleration', 'acceleration', 'acceleration']]
>>>
>>> # get only the finger joints effort modes for the first, middle and last of the 5 envs
>>> prims.get_effort_modes(indices=np.array([0, 2, 4]), joint_indices=np.array([7, 8]))
[['acceleration', 'acceleration'], ['acceleration', 'acceleration'], ['acceleration', 'acceleration']]

Set effort modes for articulations in the view

Parameters:

mode (str) – effort mode to be applied to prims in the view: acceleration or force.
indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
joint_indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – joint indices to specify which joints to manipulate. Shape (K,). Where K <= num of dofs. Defaults to None (i.e: all dofs).
joint_names (Optional[List[str]]) – joint names to specify which joints to manipulate (can’t be sppecified together with joint_indices). Shape (K,). Where K <= num of dofs. Defaults to None (i.e: all dofs).

Example:

>>> # set the effort mode for all joints to 'force'
>>> prims.set_effort_modes("force")
>>>
>>> # set only the finger joints effort mode to 'force' for the first, middle and last of the 5 envs
>>> prims.set_effort_modes("force", indices=np.array([0, 2, 4]), joint_indices=np.array([7, 8]))

Set maximum efforts for articulation in the view

Parameters:

values (Union[np.ndarray, torch.Tensor, wp.array]) – maximum efforts for articulations in the view. shape (M, K).
indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
joint_indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – joint indices to specify which joints to manipulate. Shape (K,). Where K <= num of dofs. Defaults to None (i.e: all dofs).
joint_names (Optional[List[str]]) – joint names to specify which joints to manipulate (can’t be sppecified together with joint_indices). Shape (K,). Where K <= num of dofs. Defaults to None (i.e: all dofs).

Example:

>>> # set the max efforts for all the articulation joints to the indicated values.
>>> # Since there are 5 envs, the joint efforts are repeated 5 times
>>> max_efforts = np.tile(np.array([10000, 9000, 8000, 7000, 6000, 5000, 4000, 1000, 1000]), (num_envs, 1))
>>> prims.set_max_efforts(max_efforts)
>>>
>>> # set the fingers max efforts: panda_finger_joint1 (7) and panda_finger_joint2 (8) to 1000
>>> # for the first, middle and last of the 5 envs
>>> max_efforts = np.tile(np.array([1000, 1000]), (3, 1))
>>> prims.set_max_efforts(max_efforts, indices=np.array([0, 2, 4]), joint_indices=np.array([7, 8]))

Get the maximum efforts for articulation in the view

Parameters:

indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
joint_indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – joint indices to specify which joints to query. Shape (K,). Where K <= num of dofs. Defaults to None (i.e: all dofs).
joint_names (Optional[List[str]]) – joint names to specify which joints to manipulate (can’t be sppecified together with joint_indices). Shape (K,). Where K <= num of dofs. Defaults to None (i.e: all dofs).
clone (Optional[bool]) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

maximum efforts for articulations in the view. shape (M, K).

Return type:

Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>> # get all joint maximum efforts. Returned shape is (5, 9) for the example: 5 envs, 9 DOFs
>>> prims.get_max_efforts()
[[5220. 5220. 5220. 5220.  720.  720.  720.  720.  720.]
 [5220. 5220. 5220. 5220.  720.  720.  720.  720.  720.]
 [5220. 5220. 5220. 5220.  720.  720.  720.  720.  720.]
 [5220. 5220. 5220. 5220.  720.  720.  720.  720.  720.]
 [5220. 5220. 5220. 5220.  720.  720.  720.  720.  720.]]
>>>
>>> # get finger joint maximum efforts: panda_finger_joint1 (7) and panda_finger_joint2 (8)
>>> # for the first, middle and last of the 5 envs. Returned shape is (3, 2)
>>> prims.get_max_efforts(indices=np.array([0, 2, 4]), joint_indices=np.array([7, 8]))
[[720. 720.]
 [720. 720.]
 [720. 720.]]

Set maximum velocities for articulation in the view

Parameters:

values (Union[np.ndarray, torch.Tensor, wp.array]) – maximum velocities for articulations in the view. shape (M, K).
indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
joint_indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – joint indices to specify which joints to manipulate. Shape (K,). Where K <= num of dofs. Defaults to None (i.e: all dofs).
joint_names (Optional[List[str]]) – joint names to specify which joints to manipulate (can’t be sppecified together with joint_indices). Shape (K,). Where K <= num of dofs. Defaults to None (i.e: all dofs).

Get the maximum joint velocities for articulation dofs in the view

Parameters:

indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
joint_indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – joint indices to specify which joints to query. Shape (K,). Where K <= num of dofs. Defaults to None (i.e: all dofs).
joint_names (Optional[List[str]]) – joint names to specify which joints to manipulate (can’t be sppecified together with joint_indices). Shape (K,). Where K <= num of dofs. Defaults to None (i.e: all dofs).
clone (Optional[bool]) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

maximum joint velocities for articulations dofs in the view. shape (M, K).

Return type:

Union[np.ndarray, torch.Tensor, wp.indexedarray]

Set the implicit Proportional-Derivative (PD) controller’s Kps (stiffnesses) and Kds (dampings) of articulations in the view

Parameters:

kps (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – stiffness of the drives. shape is (M, K). Defaults to None.
kds (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – damping of the drives. shape is (M, K).. Defaults to None.
indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
joint_indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – joint indices to specify which joints to manipulate. Shape (K,). Where K <= num of dofs. Defaults to None (i.e: all dofs).
joint_names (Optional[List[str]]) – joint names to specify which joints to manipulate (can’t be sppecified together with joint_indices). Shape (K,). Where K <= num of dofs. Defaults to None (i.e: all dofs).
save_to_usd (bool, optional) – True to save the gains in the usd. otherwise False.

Example:

>>> # set the gains (stiffnesses and dampings) for all the articulation joints to the indicated values.
>>> # Since there are 5 envs, the gains are repeated 5 times
>>> stiffnesses = np.tile(np.array([100000, 100000, 100000, 100000, 80000, 80000, 80000, 50000, 50000]), (num_envs, 1))
>>> dampings = np.tile(np.array([8000, 8000, 8000, 8000, 5000, 5000, 5000, 2000, 2000]), (num_envs, 1))
>>> prims.set_gains(kps=stiffnesses, kds=dampings)
>>>
>>> # set the fingers gains (stiffnesses and dampings): panda_finger_joint1 (7) and panda_finger_joint2 (8)
>>> # to 50000 and 2000 respectively for the first, middle and last of the 5 envs
>>> stiffnesses = np.tile(np.array([50000, 50000]), (3, 1))
>>> dampings = np.tile(np.array([2000, 2000]), (3, 1))
>>> prims.set_gains(kps=stiffnesses, kds=dampings, indices=np.array([0, 2, 4]), joint_indices=np.array([7, 8]))

Get the implicit Proportional-Derivative (PD) controller’s Kps (stiffnesses) and Kds (dampings) of articulations in the view

Parameters:

indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
joint_indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – joint indices to specify which joints to query. Shape (K,). Where K <= num of dofs. Defaults to None (i.e: all dofs).
joint_names (Optional[List[str]]) – joint names to specify which joints to manipulate (can’t be sppecified together with joint_indices). Shape (K,). Where K <= num of dofs. Defaults to None (i.e: all dofs).
clone (bool, optional) – True to return clones of the internal buffers. Otherwise False. Defaults to True.

Returns:

stiffness and damping of articulations in the view respectively. shapes are (M, K).

Return type:

Tuple[Union[np.ndarray, torch.Tensor], Union[np.ndarray, torch.Tensor], Union[wp.indexedarray, wp.index]]

Example:

>>> # get all joint stiffness and damping. Returned shape is (5, 9) for the example: 5 envs, 9 DOFs
>>> stiffnesses, dampings = prims.get_gains()
>>> stiffnesses
[[60000. 60000. 60000. 60000. 25000. 15000.  5000.  6000.  6000.]
 [60000. 60000. 60000. 60000. 25000. 15000.  5000.  6000.  6000.]
 [60000. 60000. 60000. 60000. 25000. 15000.  5000.  6000.  6000.]
 [60000. 60000. 60000. 60000. 25000. 15000.  5000.  6000.  6000.]
 [60000. 60000. 60000. 60000. 25000. 15000.  5000.  6000.  6000.]]
>>> dampings
[[3000. 3000. 3000. 3000. 3000. 3000. 3000. 1000. 1000.]
 [3000. 3000. 3000. 3000. 3000. 3000. 3000. 1000. 1000.]
 [3000. 3000. 3000. 3000. 3000. 3000. 3000. 1000. 1000.]
 [3000. 3000. 3000. 3000. 3000. 3000. 3000. 1000. 1000.]
 [3000. 3000. 3000. 3000. 3000. 3000. 3000. 1000. 1000.]]
>>>
>>> # get finger joints stiffness and damping: panda_finger_joint1 (7) and panda_finger_joint2 (8)
>>> # for the first, middle and last of the 5 envs. Returned shape is (3, 2)
>>> stiffnesses, dampings = prims.get_gains(indices=np.array([0, 2, 4]), joint_indices=np.array([7, 8]))
>>> stiffnesses
[[6000. 6000.]
 [6000. 6000.]
 [6000. 6000.]]
>>> dampings
[[1000. 1000.]
 [1000. 1000.]
 [1000. 1000.]]

Switch control mode between "position", "velocity", or "effort" for all joints

This method will set the implicit Proportional-Derivative (PD) controller’s Kps (stiffnesses) and Kds (dampings), defined via the set_gains method, of the selected articulations and joints according to the following rule:

Control mode	Stiffnesses	Dampings
`"position"`	Kps	Kds
`"velocity"`	0	Kds
`"effort"`	0	0

Parameters:

mode (str) – control mode to switch the articulations specified to. It can be "position", "velocity", or "effort"
indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
joint_indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – joint indices to specify which joints to manipulate. Shape (K,). Where K <= num of dofs. Defaults to None (i.e: all dofs).
joint_names (Optional[List[str]]) – joint names to specify which joints to manipulate (can’t be sppecified together with joint_indices). Shape (K,). Where K <= num of dofs. Defaults to None (i.e: all dofs).

Example:

>>> # set 'velocity' as control mode for all joints
>>> prims.switch_control_mode("velocity")
>>>
>>> # set 'effort' as control mode only for the fingers: panda_finger_joint1 (7) and panda_finger_joint2 (8)
>>> # for the first, middle and last of the 5 envs
>>> prims.switch_control_mode("effort", indices=np.array([0, 2, 4]), joint_indices=np.array([7, 8]))

switch_dof_control_mode( mode: str, dof_index: int, indices: ndarray | List | Tensor | warp.array | None = None, ) → None#

Switch control mode between "position", "velocity", or "effort" for the specified DOF

This method will set the implicit Proportional-Derivative (PD) controller’s Kps (stiffnesses) and Kds (dampings), defined via the set_gains method, of the selected DOF according to the following rule:

Control mode	Stiffnesses	Dampings
`"position"`	Kps	Kds
`"velocity"`	0	Kds
`"effort"`	0	0

Parameters:

mode (str) – control mode to switch the DOF specified to. It can be "position", "velocity" or "effort"
dof_index (int) – dof index to switch the control mode of.
indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Example:

>>> # set 'velocity' as control mode for the panda_joint1 (0) joint for all envs
>>> prims.switch_dof_control_mode("velocity", dof_index=0)
>>>
>>> # set 'effort' as control mode for the panda_joint1 (0) for the first, middle and last of the 5 envs
>>> prims.switch_dof_control_mode("effort", dof_index=0, indices=np.array([0, 2, 4]))

Set the solver (position) iteration count for the articulations

The solver iteration count determines how accurately contacts, drives, and limits are resolved. Search for Solver Iteration Count in PhysX docs for more details.

Warning

Setting a higher number of iterations may improve the fidelity of the simulation, although it may affect its performance.

Parameters:

counts (Union[np.ndarray, torch.Tensor, wp.array]) – number of iterations for the solver. Shape (M,).
indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Example:

>>> # set the position iteration count for all envs
>>> prims.set_solver_position_iteration_counts(np.full((num_envs,), 64))
>>>
>>> # set only the position iteration count for the first, middle and last of the 5 envs
>>> prims.set_solver_position_iteration_counts(np.full((3,), 64), indices=np.array([0, 2, 4]))

Get the solver (position) iteration count for the articulations

The solver iteration count determines how accurately contacts, drives, and limits are resolved. Search for Solver Iteration Count in PhysX docs for more details.

Parameters:: indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
Returns:: position iteration count. Shape (M,).
Return type:: Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>> # get all position iteration count. Returned shape is (5,) for the example: 5 envs
>>> prims.get_solver_position_iteration_counts()
[32 32 32 32 32]
>>>
>>> # get the position iteration count for the first, middle and last of the 5 envs. Returned shape is (3,)
>>> prims.get_solver_position_iteration_counts(indices=np.array([0, 2, 4]))
[32 32 32]

Set the solver (velocity) iteration count for the articulations

The solver iteration count determines how accurately contacts, drives, and limits are resolved. Search for Solver Iteration Count in PhysX docs for more details.

Warning

Setting a higher number of iterations may improve the fidelity of the simulation, although it may affect its performance.

Parameters:

counts (Union[np.ndarray, torch.Tensor, wp.array]) – number of iterations for the solver. Shape (M,).
indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Example:

>>> # set the velocity iteration count for all envs
>>> prims.set_solver_velocity_iteration_counts(np.full((num_envs,), 64))
>>>
>>> # set only the velocity iteration count for the first, middle and last of the 5 envs
>>> prims.set_solver_velocity_iteration_counts(np.full((3,), 64), indices=np.array([0, 2, 4]))

Get the solver (velocity) iteration count for the articulations

The solver iteration count determines how accurately contacts, drives, and limits are resolved. Search for Solver Iteration Count in PhysX docs for more details.

Parameters:: indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
Returns:: velocity iteration count. Shape (M,).
Return type:: Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>> # get all velocity iteration count. Returned shape is (5,) for the example: 5 envs
>>> prims.get_solver_velocity_iteration_counts()
[32 32 32 32 32]
>>>
>>> # get the velocity iteration count for the first, middle and last of the 5 envs. Returned shape is (3,)
>>> prims.get_solver_velocity_iteration_counts(indices=np.array([0, 2, 4]))
[32 32 32]

Set the mass-normalized kinetic energy below which the articulation may participate in stabilization

Search for Stabilization Threshold in PhysX docs for more details

Parameters:

thresholds (Union[np.ndarray, torch.Tensor, wp.array]) – stabilization thresholds to be applied. Shape (M,).
indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Example:

>>> # set the stabilization threshold for all envs
>>> prims.set_stabilization_thresholds(np.full((num_envs,), 0.005))
>>>
>>> # set only the stabilization threshold for the first, middle and last of the 5 envs
>>> prims.set_stabilization_thresholds(np.full((3,), 0.0051), indices=np.array([0, 2, 4]))

Get the mass-normalized kinetic energy below which the articulations may participate in stabilization

Search for Stabilization Threshold in PhysX docs for more details

Parameters:: indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
Returns:: stabilization threshold. Shape (M,).
Return type:: Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>> # get all stabilization thresholds. Returned shape is (5,) for the example: 5 envs
>>> prims.get_solver_velocity_iteration_counts()
[0.001 0.001 0.001 0.001 0.001]
>>>
>>> # get the stabilization thresholds for the first, middle and last of the 5 envs. Returned shape is (3,)
>>> prims.get_solver_velocity_iteration_counts(indices=np.array([0, 2, 4]))
[0.001 0.001 0.001]

Set the enable self collisions flag (physxArticulation:enabledSelfCollisions)

Parameters:

flags (Union[np.ndarray, torch.Tensor, wp.array]) – true to enable self collision. otherwise false. shape (M,)
indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Example:

>>> # enable the self collisions flag for all envs
>>> prims.set_enabled_self_collisions(np.full((num_envs,), True))
>>>
>>> # enable the self collisions flag only for the first, middle and last of the 5 envs
>>> prims.set_enabled_self_collisions(np.full((3,), True), indices=np.array([0, 2, 4]))

Get the enable self collisions flag (physxArticulation:enabledSelfCollisions) for all articulations

Parameters:: indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
Returns:: self collisions flags (boolean interpreted as int). shape (M,)
Return type:: Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>> # get all self collisions flags. Returned shape is (5,) for the example: 5 envs
>>> prims.get_enabled_self_collisions()
[0 0 0 0 0]
>>>
>>> # get the self collisions flags for the first, middle and last of the 5 envs. Returned shape is (3,)
>>> prims.get_enabled_self_collisions(indices=np.array([0, 2, 4]))
[0 0 0]

Set the threshold for articulations to enter a sleep state

Search for Articulations and Sleeping in PhysX docs for more details

Parameters:

thresholds (Union[np.ndarray, torch.Tensor, wp.array]) – sleep thresholds to be applied. shape (M,).
indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Example:

>>> # set the sleep threshold for all envs
>>> prims.set_sleep_thresholds(np.full((num_envs,), 0.01))
>>>
>>> # set only the sleep threshold for the first, middle and last of the 5 envs
>>> prims.set_sleep_thresholds(np.full((3,), 0.01), indices=np.array([0, 2, 4]))

Get the threshold for articulations to enter a sleep state

Search for Articulations and Sleeping in PhysX docs for more details

Parameters:: indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
Returns:: sleep thresholds. shape (M,).
Return type:: Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>> # get all sleep thresholds. Returned shape is (5,) for the example: 5 envs
>>> prims.get_sleep_thresholds()
[0.005 0.005 0.005 0.005 0.005]
>>>
>>> # get the sleep thresholds for the first, middle and last of the 5 envs. Returned shape is (3,)
>>> prims.get_sleep_thresholds(indices=np.array([0, 2, 4]))
[0.005 0.005 0.005]

get_jacobian_shape() → ndarray | Tensor | warp.array#

Get the Jacobian matrix shape of a single articulation

The Jacobian matrix maps the joint space velocities of a DOF to it’s cartesian and angular velocities

The shape of the Jacobian depends on the number of links (rigid bodies), DOFs, and whether the articulation base is fixed (e.g., robotic manipulators) or not (e.g,. mobile robots).

Fixed articulation base: (num_bodies - 1, 6, num_dof)
Non-fixed articulation base: (num_bodies, 6, num_dof + 6)

Each body has 6 values in the Jacobian representing its linear and angular motion along the three coordinate axes. The extra 6 DOFs in the last dimension, for non-fixed base cases, correspond to the linear and angular degrees of freedom of the free root link

Returns:: shape of jacobian for a single articulation.
Return type:: Union[np.ndarray, torch.Tensor, wp.array]

Example:

>>> # for the Franka Panda (a robotic manipulator with fixed base):
>>> # - num_bodies: 12
>>> # - num_dof: 9
>>> prims.get_jacobian_shape()
(11, 6, 9)

get_mass_matrix_shape() → ndarray | Tensor | warp.array#

Get the mass matrix shape of a single articulation

The mass matrix contains the generalized mass of the robot depending on the current configuration

The shape of the max matrix depends on the number of DOFs as well as whether the articulation is fixed-base or floating-base. For fixed-base articulation the shape is (num_dof, num_dof) while for floating-base articulation the shape is (num_dof + 6, num_dof + 6)

Returns:: shape of mass matrix for a single articulation.
Return type:: Union[np.ndarray, torch.Tensor, wp.array]

Example:

>>> # for the Franka Panda:
>>> # - num_dof: 9
>>> prims.get_mass_matrix_shape()
(9, 9)
>>> # for Ant robot:
>>> # - num_dof: 8
>>> prims.get_mass_matrix_shape()
    (14, 14)

Get the Jacobian matrices of articulations in the view

Note

The first dimension corresponds to the amount of wrapped articulations while the last 3 dimensions are the Jacobian matrix shape. Refer to the get_jacobian_shape method for details about the Jacobian matrix shape

Parameters:

indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

Jacobian matrices of articulations in the view. Shape is (M, jacobian_shape).

Return type:

Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>> # get the Jacobian matrices. Returned shape is (5, 11, 6, 9) for the example: 5 envs, 12 links, 9 DOFs
>>> prims.get_jacobians()
[[[[ 4.2254178e-09  0.0000000e+00  0.0000000e+00 ...  0.0000000e+00  0.0000000e+00  0.0000000e+00]
   [ 1.2093576e-08  0.0000000e+00  0.0000000e+00 ...  0.0000000e+00  0.0000000e+00  0.0000000e+00]
   [-6.0873992e-16  0.0000000e+00  0.0000000e+00 ...  0.0000000e+00  0.0000000e+00  0.0000000e+00]
   [ 1.4458647e-07  0.0000000e+00  0.0000000e+00 ...  0.0000000e+00  0.0000000e+00  0.0000000e+00]
   [-1.8178657e-10  0.0000000e+00  0.0000000e+00 ...  0.0000000e+00  0.0000000e+00  0.0000000e+00]
   [ 9.9999976e-01  0.0000000e+00  0.0000000e+00 ...  0.0000000e+00  0.0000000e+00  0.0000000e+00]]
  ...
  [[-4.5089945e-02  8.1210062e-02 -3.8495898e-02 ...  2.8108317e-02  0.0000000e+00 -4.9317405e-02]
   [ 4.2863289e-01  9.7436900e-04  4.0475106e-01 ...  2.4577195e-03  0.0000000e+00  9.9807423e-01]
   [ 6.5973169e-09 -4.2914307e-01 -2.1542320e-02 ...  2.8352857e-02  0.0000000e+00 -3.7625343e-02]
   [ 1.4458647e-07 -1.1999309e-02 -5.3927803e-01 ...  7.0976764e-01  0.0000000e+00  0.0000000e+00]
   [-1.8178657e-10  9.9992776e-01 -6.4710006e-03 ...  8.5178167e-03  0.0000000e+00  0.0000000e+00]
   [ 9.9999976e-01 -3.8743019e-07  8.4210289e-01 ... -7.0438433e-01  0.0000000e+00  0.0000000e+00]]]]

Get the mass matrices of articulations in the view

Note

The first dimension corresponds to the amount of wrapped articulations while the last 2 dimensions are the mass matrix shape. Refer to the get_mass_matrix_shape method for details about the mass matrix shape

Parameters:

indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

mass matrices of articulations in the view. Shape is (M, mass_matrix_shape).

Return type:

Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>> # get the mass matrices. Returned shape is (5, 9, 9) for the example: 5 envs, 9 DOFs for a fixed-based articulation
>>> prims.get_mass_matrices()
[[[ 5.0900602e-01  1.1794259e-06  4.2570841e-01 -1.6387942e-06 -3.1573933e-02
   -1.9736715e-06 -3.1358242e-04 -6.0441834e-03  6.0441834e-03]
  [ 1.1794259e-06  1.0598221e+00  7.4729815e-07 -4.2621672e-01  2.3612277e-08
   -4.9647894e-02 -2.9080724e-07 -1.8432185e-04  1.8432130e-04]
  ...
  [-6.0441834e-03 -1.8432185e-04 -5.7159867e-03  4.0070520e-04  9.6930371e-04
    1.2324301e-04  2.5264668e-10  1.4055224e-02  0.0000000e+00]
  [ 6.0441834e-03  1.8432130e-04  5.7159867e-03 -4.0070404e-04 -9.6930366e-04
   -1.2324269e-04 -3.6906206e-10  0.0000000e+00  1.4055224e-02]]]

Get the Coriolis and centrifugal forces (joint DOF forces required to counteract Coriolis and centrifugal forces for the given articulation state) of articulations in the view

Search for Coriolis and Centrifugal Forces in PhysX docs for more details

Parameters:

indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
joint_indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – joint indices to specify which joints to query. Shape (K,). Where K <= num of dofs for fixed-based arituclations and K <= num of dofs + 6 for floating-based articulations. Defaults to None (i.e: all dofs).
joint_names (Optional[List[str]]) – joint names to specify which joints to manipulate (can’t be sppecified together with joint_indices). Shape (K,). Where K <= num of dofs. Defaults to None (i.e: all dofs).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

Coriolis and centrifugal forces of articulations in the view. Shape is (M, K).

Return type:

Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>> # get all coriolis and centrifugal forces. Returned shape is (5, 9) for the example: 5 envs, 9 DOFs for a fixed-based articulation
>>> prims.get_coriolis_and_centrifugal_forces()
[[ 1.6842524e-06 -1.8269569e-04  5.2162073e-07 -9.7677548e-05  3.0365106e-07
   6.7375149e-06  6.1105780e-08 -4.6237556e-06 -4.1627968e-06]
 [ 1.6842524e-06 -1.8269569e-04  5.2162073e-07 -9.7677548e-05  3.0365106e-07
   6.7375149e-06  6.1105780e-08 -4.6237556e-06 -4.1627968e-06]
 [ 1.6842561e-06 -1.8269687e-04  5.2162375e-07 -9.7677454e-05  3.0365084e-07
   6.7375931e-06  6.1106007e-08 -4.6237533e-06 -4.1627954e-06]
 [ 1.6842561e-06 -1.8269687e-04  5.2162375e-07 -9.7677454e-05  3.0365084e-07
   6.7375931e-06  6.1106007e-08 -4.6237533e-06 -4.1627954e-06]
 [ 1.6842524e-06 -1.8269569e-04  5.2162073e-07 -9.7677548e-05  3.0365106e-07
   6.7375149e-06  6.1105780e-08 -4.6237556e-06 -4.1627968e-06]]
>>>
>>> # get finger joint coriolis and centrifugal forces: panda_finger_joint1 (7) and panda_finger_joint2 (8)
>>> # for the first, middle and last of the 5 envs. Returned shape is (3, 2)
>>> prims.get_coriolis_and_centrifugal_forces(indices=np.array([0, 2, 4]), joint_indices=np.array([7, 8]))
[[-4.6237556e-06 -4.1627968e-06]
 [-4.6237533e-06 -4.1627954e-06]
 [-4.6237556e-06 -4.1627968e-06]]

Get the generalized gravity forces (joint DOF forces required to counteract gravitational forces for the given articulation pose) of articulations in the view

Search for Generalized Gravity Force in PhysX docs for more details

Parameters:

indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
joint_indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – joint indices to specify which joints to query. Shape (K,). Where K <= num of dofs for fixed-based arituclations and K <= num of dofs + 6 for floating-based articulations. Defaults to None (i.e: all dofs).
joint_names (Optional[List[str]]) – joint names to specify which joints to manipulate (can’t be sppecified together with joint_indices). Shape (K,). Where K <= num of dofs. Defaults to None (i.e: all dofs).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

generalized gravity forces of articulations in the view. Shape is (M, K).

Return type:

Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>>

>>> # get all generalized gravity forces. Returned shape is (5, 9) for the example: 5 envs, 9 DOFs
>>> prims.get_generalized_gravity_forces()
[[ 1.32438602e-08 -6.90832138e+00 -1.08629465e-05  1.91585541e+01  5.13810664e-06
   1.18674076e+00  8.01788883e-06  5.18786255e-03 -5.18784765e-03]
 [ 1.32438602e-08 -6.90832138e+00 -1.08629465e-05  1.91585541e+01  5.13810664e-06
   1.18674076e+00  8.01788883e-06  5.18786255e-03 -5.18784765e-03]
 [ 1.32438585e-08 -6.90830994e+00 -1.08778477e-05  1.91585541e+01  5.14090061e-06
   1.18674052e+00  8.02161412e-06  5.18786255e-03 -5.18784765e-03]
 [ 1.32438585e-08 -6.90830994e+00 -1.08778477e-05  1.91585541e+01  5.14090061e-06
   1.18674052e+00  8.02161412e-06  5.18786255e-03 -5.18784765e-03]
 [ 1.32438602e-08 -6.90832138e+00 -1.08629465e-05  1.91585541e+01  5.13810664e-06
   1.18674076e+00  8.01788883e-06  5.18786255e-03 -5.18784765e-03]]
>>>
>>> # get finger joint generalized gravity forces: panda_finger_joint1 (7) and panda_finger_joint2 (8)
>>> # for the first, middle and last of the 5 envs. Returned shape is (3, 2)
>>> prims.get_generalized_gravity_forces(indices=np.array([0, 2, 4]), joint_indices=np.array([7, 8]))
[[ 0.00518786 -0.00518785]
 [ 0.00518786 -0.00518785]
 [ 0.00518786 -0.00518785]]

Get rigid body masses of articulations in the view

Parameters:

indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
body_indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – body indices to specify which bodies to query. Shape (K,). Where K <= num of bodies. Defaults to None (i.e: all bodies).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

rigid body masses of articulations in the view. Shape is (M, K).

Return type:

Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>> # get all body masses. Returned shape is (5, 12) for the example: 5 envs, 12 rigid bodies
>>> prims.get_body_masses()
[[2.8142028  2.3599997  2.3795187  2.6498823  2.6948018  2.981282
  1.1285807  0.40529126 0.1  0.5583305  0.01405522 0.01405522]
 [2.8142028  2.3599997  2.3795187  2.6498823  2.6948018  2.981282
  1.1285807  0.40529126 0.1  0.5583305  0.01405522 0.01405522]
 [2.8142028  2.3599997  2.3795187  2.6498823  2.6948018  2.981282
  1.1285807  0.40529126 0.1  0.5583305  0.01405522 0.01405522]
 [2.8142028  2.3599997  2.3795187  2.6498823  2.6948018  2.981282
  1.1285807  0.40529126 0.1  0.5583305  0.01405522 0.01405522]
 [2.8142028  2.3599997  2.3795187  2.6498823  2.6948018  2.981282
  1.1285807  0.40529126 0.1  0.5583305  0.01405522 0.01405522]]
>>>
>>> # get finger body masses: panda_leftfinger (10) and panda_rightfinger (11)
>>> # for the first, middle and last of the 5 envs. Returned shape is (3, 2)
>>> prims.get_body_masses(indices=np.array([0, 2, 4]), body_indices=np.array([10, 11]))
[[0.01405522 0.01405522]
 [0.01405522 0.01405522]
 [0.01405522 0.01405522]]

Get rigid body inverse masses of articulations in the view

Parameters:

indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
body_indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – body indices to specify which bodies to query. Shape (K,). Where K <= num of bodies. Defaults to None (i.e: all bodies).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

rigid body inverse masses of articulations in the view. Shape is (M, K).

Return type:

Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>> # get all body inverse masses. Returned shape is (5, 12) for the example: 5 envs, 12 rigid bodies
>>> prims.get_body_inv_masses()
[[ 0.35534042  0.42372888  0.42025304  0.37737525  0.3710848  0.33542618  0.8860687
   2.4673615  10. 1.7910539  71.14793  71.14793]
 [ 0.35534042  0.42372888  0.42025304  0.37737525  0.3710848  0.33542618  0.8860687
   2.4673615  10. 1.7910539  71.14793  71.14793]
 [ 0.35534042  0.42372888  0.42025304  0.37737525  0.3710848  0.33542618  0.8860687
   2.4673615  10. 1.7910539  71.14793  71.14793]
 [ 0.35534042  0.42372888  0.42025304  0.37737525  0.3710848  0.33542618  0.8860687
   2.4673615  10. 1.7910539  71.14793  71.14793]
 [ 0.35534042  0.42372888  0.42025304  0.37737525  0.3710848  0.33542618  0.8860687
   2.4673615  10. 1.7910539  71.14793  71.14793]]
>>>
>>> # get finger body inverse masses: panda_leftfinger (10) and panda_rightfinger (11)
>>> # for the first, middle and last of the 5 envs. Returned shape is (3, 2)
>>> prims.get_body_inv_masses(indices=np.array([0, 2, 4]), body_indices=np.array([10, 11]))
[[71.14793 71.14793]
 [71.14793 71.14793]
 [71.14793 71.14793]]

Get rigid body center of mass (COM) of articulations in the view.

Parameters:

indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
body_indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – body indices to specify which bodies to query. Shape (K,). Where K <= num of bodies. Defaults to None (i.e: all bodies).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

rigid body center of mass positions and orientations of articulations in the view. Position shape is (M, K, 3), orientation shape is (M, k, 4).

Return type:

Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>> # get all body center of mass. Returned shape is (5, 12, 3) for positions and (5, 12, 4) for orientations
>>> # for the example: 5 envs, 12 rigid bodies
>>> positions, orientations = prims.get_body_coms()
>>> positions
[[[0. 0. 0.]
  [0. 0. 0.]
  ...
  [0. 0. 0.]
  [0. 0. 0.]]]
>>> orientations
[[[1. 0. 0. 0.]
  [1. 0. 0. 0.]
  ...
  [1. 0. 0. 0.]
  [1. 0. 0. 0.]]]
>>>
>>> # get finger body center of mass: panda_leftfinger (10) and panda_rightfinger (11) for the first,
>>> # middle and last of the 5 envs. Returned shape is (3, 2, 3) for positions and (3, 2, 4) for orientations
>>> positions, orientations = prims.get_body_coms(indices=np.array([0, 2, 4]), body_indices=np.array([10, 11]))
>>> positions
[[[0. 0. 0.]
  [0. 0. 0.]]
 [[0. 0. 0.]
  [0. 0. 0.]]
 [[0. 0. 0.]
  [0. 0. 0.]]]
>>> orientations
[[[1. 0. 0. 0.]
  [1. 0. 0. 0.]]
 [[1. 0. 0. 0.]
  [1. 0. 0. 0.]]
 [[1. 0. 0. 0.]
  [1. 0. 0. 0.]]]

Get rigid body inertias of articulations in the view

Parameters:

indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
body_indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – body indices to specify which bodies to query. Shape (K,). Where K <= num of bodies. Defaults to None (i.e: all bodies).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

rigid body inertias of articulations in the view. Shape is (M, K, 9).

Return type:

Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>> # get all body inertias. Returned shape is (5, 12, 9) for the example: 5 envs, 12 rigid bodies
>>> prims.get_body_inertias()
[[[1.2988697e-06  0.0  0.0  0.0  1.6535528e-06  0.0  0.0  0.0  2.0331163e-06]
  [1.8686389e-06  0.0  0.0  0.0  1.4378986e-06  0.0  0.0  0.0  9.0681192e-07]
  ...
  [4.2041304e-10  0.0  0.0  0.0  3.9026365e-10  0.0  0.0  0.0  1.3347495e-10]
  [4.2041304e-10  0.0  0.0  0.0  3.9026365e-10  0.0  0.0  0.0  1.3347495e-10]]]
>>>
>>> # get finger body inertias: panda_leftfinger (10) and panda_rightfinger (11)
>>> # for the first, middle and last of the 5 envs. Returned shape is (3, 2, 9)
>>> prims.get_body_inertias(indices=np.array([0, 2, 4]), body_indices=np.array([10, 11]))
[[[4.2041304e-10  0.0  0.0  0.0  3.9026365e-10  0.0  0.0  0.0  1.3347495e-10]
  [4.2041304e-10  0.0  0.0  0.0  3.9026365e-10  0.0  0.0  0.0  1.3347495e-10]]
 ...
 [[4.2041304e-10  0.0  0.0  0.0  3.9026365e-10  0.0  0.0  0.0  1.3347495e-10]
  [4.2041304e-10  0.0  0.0  0.0  3.9026365e-10  0.0  0.0  0.0  1.3347495e-10]]]

Get rigid body inverse inertias of articulations in the view

Parameters:

indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
body_indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – body indices to specify which bodies to query. Shape (K,). Where K <= num of bodies. Defaults to None (i.e: all bodies).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

rigid body inverse inertias of articulations in the view. Shape is (M, K, 9).

Return type:

Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>> # get all body inverse inertias. Returned shape is (5, 12, 9) for the example: 5 envs, 12 rigid bodies
>>> prims.get_body_inv_inertias()
[[[7.6990012e+05  0.0  0.0  0.0  6.0475844e+05  0.0  0.0  0.0  4.9185578e+05]
  [5.3514888e+05  0.0  0.0  0.0  6.9545931e+05  0.0  0.0  0.0  1.1027645e+06]
  ...
  [2.3786132e+09  0.0  0.0  0.0  2.5623703e+09  0.0  0.0  0.0  7.4920422e+09]
  [2.3786132e+09  0.0  0.0  0.0  2.5623703e+09  0.0  0.0  0.0  7.4920422e+09]]]
>>>
>>> # get finger body inverse inertias: panda_leftfinger (10) and panda_rightfinger (11)
>>> # for the first, middle and last of the 5 envs. Returned shape is (3, 2, 9)
>>> prims.get_body_inv_inertias(indices=np.array([0, 2, 4]), body_indices=np.array([10, 11]))
[[[2.3786132e+09  0.0  0.0  0.0  2.5623703e+09  0.0  0.0  0.0  7.4920422e+09]
  [2.3786132e+09  0.0  0.0  0.0  2.5623703e+09  0.0  0.0  0.0  7.4920422e+09]]
 ...
 [[2.3786132e+09  0.0  0.0  0.0  2.5623703e+09  0.0  0.0  0.0  7.4920422e+09]
  [2.3786132e+09  0.0  0.0  0.0  2.5623703e+09  0.0  0.0  0.0  7.4920422e+09]]]

Get whether the rigid bodies of articulations in the view have gravity disabled or not

Parameters:

indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
body_indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – body indices to specify which bodies to query. Shape (K,). Where K <= num of bodies. Defaults to None (i.e: all bodies).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

rigid body gravity activation of articulations in the view. Shape is (M, K).

Return type:

Union[np.ndarray, torch.Tensor, wp.indexedarray]

Set body masses for articulation bodies in the view

Parameters:

values (Union[np.ndarray, torch.Tensor, wp.array]) – body masses for articulations in the view. shape (M, K).
indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
body_indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – body indices to specify which bodies to manipulate. Shape (K,). Where K <= num of bodies. Defaults to None (i.e: all bodies).

Example:

>>> # set the masses for all the articulation rigid bodies to the indicated values.
>>> # Since there are 5 envs, the masses are repeated 5 times
>>> masses = np.tile(np.array([1.2, 1.1, 1.0, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.2]), (num_envs, 1))
>>> prims.set_body_masses(masses)
>>>
>>> # set the fingers masses: panda_leftfinger (10) and panda_rightfinger (11) to 0.2
>>> # for the first, middle and last of the 5 envs
>>> masses = np.tile(np.array([0.2, 0.2]), (3, 1))
>>> prims.set_body_masses(masses, indices=np.array([0, 2, 4]), body_indices=np.array([10, 11]))

Set body inertias for articulation bodies in the view.

Parameters:

values (Union[np.ndarray, torch.Tensor, wp.array]) – body inertias for articulations in the view. shape (M, K, 9).
indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
body_indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – body indices to specify which bodies to manipulate. Shape (K,). Where K <= num of bodies. Defaults to None (i.e: all bodies).

Example:

>>> # set the inertias for all the articulation rigid bodies to the indicated values.
>>> # Since there are 5 envs, the inertias are repeated 5 times
>>> inertias = np.tile(np.array([0.1, 0.0, 0.0, 0.0, 0.1, 0.0, 0.0, 0.0, 0.1]), (num_envs, prims.num_bodies, 1))
>>> prims.set_body_inertias(inertias)
>>>
>>> # set the fingers inertias: panda_leftfinger (10) and panda_rightfinger (11) to 0.2
>>> # for the first, middle and last of the 5 envs
>>> inertias = np.tile(np.array([0.1, 0.0, 0.0, 0.0, 0.1, 0.0, 0.0, 0.0, 0.1]), (3, 2, 1))
>>> prims.set_body_inertias(inertias, indices=np.array([0, 2, 4]), body_indices=np.array([10, 11]))

Set body center of mass (COM) positions and orientations for articulation bodies in the view.

Parameters:

positions (Union[np.ndarray, torch.Tensor, wp.array]) – body center of mass positions for articulations in the view. shape (M, K, 3).
orientations (Union[np.ndarray, torch.Tensor, wp.array]) – body center of mass orientations for articulations in the view. shape (M, K, 4).
indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
body_indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – body indices to specify which bodies to manipulate. Shape (K,). Where K <= num of bodies. Defaults to None (i.e: all bodies).

Example:

>>> # set the center of mass for all the articulation rigid bodies to the indicated values.
>>> # Since there are 5 envs, the inertias are repeated 5 times
>>> positions = np.tile(np.array([0.01, 0.02, 0.03]), (num_envs, prims.num_bodies, 1))
>>> orientations = np.tile(np.array([1.0, 0.0, 0.0, 0.0]), (num_envs, prims.num_bodies, 1))
>>> prims.set_body_coms(positions, orientations)
>>>
>>> # set the fingers center of mass: panda_leftfinger (10) and panda_rightfinger (11) to 0.2
>>> # for the first, middle and last of the 5 envs
>>> positions = np.tile(np.array([0.01, 0.02, 0.03]), (3, 2, 1))
>>> orientations = np.tile(np.array([1.0, 0.0, 0.0, 0.0]), (3, 2, 1))
>>> prims.set_body_coms(positions, orientations, indices=np.array([0, 2, 4]), body_indices=np.array([10, 11]))

Set body gravity activation articulation bodies in the view.

Parameters:

values (Union[np.ndarray, torch.Tensor, wp.array]) – body gravity activation for articulations in the view. shape (M, K).
indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
body_indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – body indices to specify which bodies to manipulate. Shape (K,). Where K <= num of bodies. Defaults to None (i.e: all bodies).

Get the stiffness of fixed tendons for articulations in the view

Search for Fixed Tendon in PhysX docs for more details

Parameters:

indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

fixed tendon stiffnesses of articulations in the view. Shape is (M, K).

Return type:

Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>> # get the fixed tendon stiffnesses
>>> # for the ShadowHand articulation that has 4 fixed tendons (prims.num_fixed_tendons)
>>> prims.get_fixed_tendon_stiffnesses()
[[0. 0. 0. 0.]
 [0. 0. 0. 0.]
 [0. 0. 0. 0.]
 [0. 0. 0. 0.]
 [0. 0. 0. 0.]]

Get the dampings of fixed tendons for articulations in the view

Search for Fixed Tendon in PhysX docs for more details

Parameters:

indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

fixed tendon dampings of articulations in the view. Shape is (M, K).

Return type:

Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>> # get the fixed tendon dampings
>>> # for the ShadowHand articulation that has 4 fixed tendons (prims.num_fixed_tendons)
>>> prims.get_fixed_tendon_dampings()
[[0. 0. 0. 0.]
 [0. 0. 0. 0.]
 [0. 0. 0. 0.]
 [0. 0. 0. 0.]
 [0. 0. 0. 0.]]

Get the limit stiffness of fixed tendons for articulations in the view

Search for Fixed Tendon in PhysX docs for more details

Parameters:

indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

fixed tendon stiffnesses of articulations in the view. Shape is (M, K).

Return type:

Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>> # get the fixed tendon limit stiffnesses
>>> # for the ShadowHand articulation that has 4 fixed tendons (prims.num_fixed_tendons)
>>> prims.get_fixed_tendon_limit_stiffnesses()
[[0. 0. 0. 0.]
 [0. 0. 0. 0.]
 [0. 0. 0. 0.]
 [0. 0. 0. 0.]
 [0. 0. 0. 0.]]

Get the limits of fixed tendons for articulations in the view

Search for Fixed Tendon in PhysX docs for more details

Parameters:

indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

fixed tendon stiffnesses of articulations in the view. Shape is (M, K, 2).

Return type:

Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>> # get the fixed tendon limits
>>> # for the ShadowHand articulation that has 4 fixed tendons (prims.num_fixed_tendons)
>>> prims.get_fixed_tendon_limits()
[[[-0.001  0.001] [-0.001  0.001] [-0.001  0.001] [-0.001  0.001]]
 [[-0.001  0.001] [-0.001  0.001] [-0.001  0.001] [-0.001  0.001]]
 [[-0.001  0.001] [-0.001  0.001] [-0.001  0.001] [-0.001  0.001]]
 [[-0.001  0.001] [-0.001  0.001] [-0.001  0.001] [-0.001  0.001]]
 [[-0.001  0.001] [-0.001  0.001] [-0.001  0.001] [-0.001  0.001]]]

Get the rest length of fixed tendons for articulations in the view

Search for Fixed Tendon in PhysX docs for more details

Parameters:

indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

fixed tendon stiffnesses of articulations in the view. Shape is (M, K).

Return type:

Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>> # get the fixed tendon rest lengths
>>> # for the ShadowHand articulation that has 4 fixed tendons (prims.num_fixed_tendons)
>>> prims.get_fixed_tendon_rest_lengths()
[[0. 0. 0. 0.]
 [0. 0. 0. 0.]
 [0. 0. 0. 0.]
 [0. 0. 0. 0.]
 [0. 0. 0. 0.]]

Get the offsets of fixed tendons for articulations in the view

Search for Fixed Tendon in PhysX docs for more details

Parameters:

indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

fixed tendon stiffnesses of articulations in the view. Shape is (M, K).

Return type:

Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>> # get the fixed tendon offsets
>>> # for the ShadowHand articulation that has 4 fixed tendons (prims.num_fixed_tendons)
>>> prims.get_fixed_tendon_offsets()
[[0. 0. 0. 0.]
 [0. 0. 0. 0.]
 [0. 0. 0. 0.]
 [0. 0. 0. 0.]
 [0. 0. 0. 0.]]

Set fixed tendon properties for articulations in the view

Search for Fixed Tendon in PhysX docs for more details

Parameters:

stiffnesses (Union[np.ndarray, torch.Tensor, wp.array]) – fixed tendon stiffnesses for articulations in the view. shape (M, K).
dampings (Union[np.ndarray, torch.Tensor, wp.array]) – fixed tendon dampings for articulations in the view. shape (M, K).
limit_stiffnesses (Union[np.ndarray, torch.Tensor, wp.array]) – fixed tendon limit stiffnesses for articulations in the view. shape (M, K).
limits (Union[np.ndarray, torch.Tensor, wp.array]) – fixed tendon limits for articulations in the view. shape (M, K, 2).
rest_lengths (Union[np.ndarray, torch.Tensor, wp.array]) – fixed tendon rest lengths for articulations in the view. shape (M, K).
offsets (Union[np.ndarray, torch.Tensor, wp.array]) – fixed tendon offsets for articulations in the view. shape (M, K).
indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Example:

>>> # set the limit stiffnesses and dampings
>>> # for the ShadowHand articulation that has 4 fixed tendons (prims.num_fixed_tendons)
>>> limit_stiffnesses = np.full((num_envs, prims.num_fixed_tendons), fill_value=10.0)
>>> dampings = np.full((num_envs, prims.num_fixed_tendons), fill_value=0.1)
>>> prims.set_fixed_tendon_properties(dampings=dampings, limit_stiffnesses=limit_stiffnesses)

pause_motion() → None#: Pauses the motion of all articulations wrapped under the Articulation.

resume_motion()#: Resumes the motion of all articulations wrapped under the Articulation using the position and velocity dof targets cached when pause_motion was called.

initialize( physics_sim_view: omni.physics.tensors.SimulationView | None = None, ) → None#

Create a physics simulation view if not passed and set other properties using the PhysX tensor API

Note

For this particular class, calling this method will do nothing

Parameters:: physics_sim_view (omni.physics.tensors.SimulationView, optional) – current physics simulation view. Defaults to None.

Example:

>>> prims.initialize()

Apply visual material to the prims and optionally their prim descendants.

Parameters:

visual_materials (Union[VisualMaterial, List[VisualMaterial]]) – visual materials to be applied to the prims. Currently supports PreviewSurface, OmniPBR and OmniGlass. If a list is provided then its size has to be equal the view’s size or indices size. If one material is provided it will be applied to all prims in the view.
weaker_than_descendants (Optional[Union[bool, List[bool]]], optional) – True if the material shouldn’t override the descendants materials, otherwise False. Defaults to False. If a list of visual materials is provided then a list has to be provided with the same size for this arg as well.
indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Raises:

Exception – length of visual materials != length of prims indexed
Exception – length of visual materials != length of weaker descendants bools arg

Example:

>>> from isaacsim.core.api.materials import OmniGlass
>>>
>>> # create a dark-red glass visual material
>>> material = OmniGlass(
...     prim_path="/World/material/glass",  # path to the material prim to create
...     ior=1.25,
...     depth=0.001,
...     thin_walled=False,
...     color=np.array([0.5, 0.0, 0.0])
... )
>>> prims.apply_visual_materials(material)

property count: int#

Returns:: Number of prims encapsulated in this view.
Return type:: int

Example:

>>> prims.count
5

get_applied_visual_materials( indices: ndarray | list | Tensor | warp.array | None = None, ) → List[VisualMaterial]#

Get the current applied visual materials

Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
Returns:: a list of the current applied visual materials to the prims if its type is currently supported.
Return type:: List[VisualMaterial]

Example:

>>> # get all applied visual materials. Returned size is 5 for the example: 5 envs
>>> prims.get_applied_visual_materials()
[<isaacsim.core.api.materials.omni_glass.OmniGlass object at 0x7f829c165de0>,
 <isaacsim.core.api.materials.omni_glass.OmniGlass object at 0x7f829c165de0>,
 <isaacsim.core.api.materials.omni_glass.OmniGlass object at 0x7f829c165de0>,
 <isaacsim.core.api.materials.omni_glass.OmniGlass object at 0x7f829c165de0>,
 <isaacsim.core.api.materials.omni_glass.OmniGlass object at 0x7f829c165de0>]
>>>
>>> # get the applied visual materials for the first, middle and last of the 5 envs. Returned size is 3
>>> prims.get_applied_visual_materials(indices=np.array([0, 2, 4]))
[<isaacsim.core.api.materials.omni_glass.OmniGlass object at 0x7f829c165de0>,
 <isaacsim.core.api.materials.omni_glass.OmniGlass object at 0x7f829c165de0>,
 <isaacsim.core.api.materials.omni_glass.OmniGlass object at 0x7f829c165de0>]

get_default_state() → XFormPrimViewState#

Get the default states (positions and orientations) defined with the set_default_state method

Returns:: returns the default state of the prims that is used after each reset.
Return type:: XFormPrimViewState

Example:

>>> state = prims.get_default_state()
>>> state
<isaacsim.core.utils.types.XFormPrimViewState object at 0x7f82f73e3070>
>>> state.positions
[[ 1.5  -0.75  0.  ]
 [ 1.5   0.75  0.  ]
 [ 0.   -0.75  0.  ]
 [ 0.    0.75  0.  ]
 [-1.5  -0.75  0.  ]]
>>> state.orientations
[[1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]]

Get prim scales in the view with respect to the local frame (the parent’s frame).

Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
Returns:: scales applied to the prim’s dimensions in the local frame. shape is (M, 3).
Return type:: Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>> # get all prims scales with respect to the local frame.
>>> # Returned shape is (5, 3) for the example: 5 envs
>>> prims.get_local_scales()
[[1. 1. 1.]
 [1. 1. 1.]
 [1. 1. 1.]
 [1. 1. 1.]
 [1. 1. 1.]]
>>>
>>> # get only the prims scales with respect to the local frame for the first, middle and last of the 5 envs.
>>> # Returned shape is (3, 3) for the example: 3 envs selected
>>> prims.get_local_scales(indices=np.array([0, 2, 4]))
[[1. 1. 1.]
 [1. 1. 1.]
 [1. 1. 1.]]

Returns the current visibilities of the prims in stage.

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Returns:

Shape (M,) with type bool, where each item holds True: if the prim is visible in stage. False otherwise.

Return type:

Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>> # get all visibilities. Returned shape is (5,) for the example: 5 envs
>>> prims.get_visibilities()
[ True  True  True  True  True]
>>>
>>> # get the visibilities for the first, middle and last of the 5 envs. Returned shape is (3,)
>>> prims.get_visibilities(indices=np.array([0, 2, 4]))
[ True  True  True]

Get prim scales in the view with respect to the world’s frame

Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
Returns:: scales applied to the prim’s dimensions in the world frame. shape is (M, 3).
Return type:: Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>> # get all prims scales with respect to the world's frame.
>>> # Returned shape is (5, 3) for the example: 5 envs
>>> prims.get_world_scales()
[[1. 1. 1.]
 [1. 1. 1.]
 [1. 1. 1.]
 [1. 1. 1.]
 [1. 1. 1.]]
>>>
>>> # get only the prims scales with respect to the world's frame for the first, middle and last of the 5 envs.
>>> # Returned shape is (3, 3) for the example: 3 envs selected
>>> prims.get_world_scales(indices=np.array([0, 2, 4]))
[[1. 1. 1.]
 [1. 1. 1.]
 [1. 1. 1.]]

property initialized: bool#

Check if prim view is initialized

Returns:: True if the view object was initialized (after the first call of .initialize()). False otherwise.
Return type:: bool

Example:

>>> # given an initialized articulation view
>>> prims.initialized
True

property is_non_root_articulation_link: bool#: Returns: bool: True if the prim corresponds to a non root link in an articulation. Otherwise False.

is_valid( indices: ndarray | list | Tensor | warp.array | None = None, ) → bool#

Check that all prims have a valid USD Prim

Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
Returns:: True if all prim paths specified in the view correspond to a valid prim in stage. False otherwise.
Return type:: bool

Example:

>>> prims.is_valid()
True

is_visual_material_applied( indices: ndarray | list | Tensor | warp.array | None = None, ) → List[bool]#

Check if there is a visual material applied

Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
Returns:: True if there is a visual material applied is applied to the corresponding prim in the view. False otherwise.
Return type:: List[bool]

Example:

>>> # given a visual material that is applied only to the first and the last environment
>>> prims.is_visual_material_applied()
[True, False, False, False, True]
>>>
>>> # check for the first, middle and last of the 5 envs
>>> prims.is_visual_material_applied(indices=np.array([0, 2, 4]))
[True, False, True]

property name: str#: Returns: str: name given to the prims view when instantiating it.

post_reset() → None#

Reset the prims to its default state

Example:

>>> prims.post_reset()

property prim_paths: List[str]#

Returns:: list of prim paths in the stage encapsulated in this view.
Return type:: List[str]

Example:

>>> prims.prim_paths
['/World/envs/env_0', '/World/envs/env_1', '/World/envs/env_2', '/World/envs/env_3', '/World/envs/env_4']

property prims: List[pxr.Usd.Prim]#

Returns:: List of USD Prim objects encapsulated in this view.
Return type:: List[Usd.Prim]

Example:

>>> prims.prims
[Usd.Prim(</World/envs/env_0>), Usd.Prim(</World/envs/env_1>), Usd.Prim(</World/envs/env_2>),
 Usd.Prim(</World/envs/env_3>), Usd.Prim(</World/envs/env_4>)]

Set the default state of the prims (positions and orientations), that will be used after each reset.

Note

The default states will be set during post-reset (e.g., calling .post_reset() or world.reset() methods)

Parameters:

positions (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – positions in the world frame of the prim. shape is (M, 3). Defaults to None, which means left unchanged.
orientations (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – quaternion orientations in the world frame of the prim. quaternion is scalar-first (w, x, y, z). shape is (M, 4). Defaults to None, which means left unchanged.
indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Example:

>>> # configure default states for all prims
>>> positions = np.zeros((num_envs, 3))
>>> positions[:, 0] = np.arange(num_envs)
>>> orientations = np.tile(np.array([1.0, 0.0, 0.0, 0.0]), (num_envs, 1))
>>> prims.set_default_state(positions=positions, orientations=orientations)
>>>
>>> # set default states during post-reset
>>> prims.post_reset()

Set prim scales in the view with respect to the local frame (the prim’s parent frame)

Parameters:

scales (Optional[Union[np.ndarray, torch.Tensor, wp.array]]) – scales to be applied to the prim’s dimensions in the view. shape is (M, 3).
indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Example:

>>> # set the scale for all prims. Since there are 5 envs, the scale is repeated 5 times
>>> scales = np.tile(np.array([1.0, 0.75, 0.5]), (num_envs, 1))
>>> prims.set_local_scales(scales)
>>>
>>> # set the scale for the first, middle and last of the 5 envs
>>> scales = np.tile(np.array([1.0, 0.75, 0.5]), (3, 1))
>>> prims.set_local_scales(scales, indices=np.array([0, 2, 4]))

Set the visibilities of the prims in stage

Parameters:

visibilities (Union[np.ndarray, torch.Tensor, wp.array]) – flag to set the visibilities of the usd prims in stage. Shape (M,). Where M <= size of the encapsulated prims in the view.
indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Defaults to None (i.e: all prims in the view).

Example:

>>> # make all prims not visible in the stage
>>> prims.set_visibilities(visibilities=[False] * num_envs)

Bases: XFormPrim

The view class for cloth prims.

property count: int#: Returns: int: cloth counts.

property max_springs_per_cloth: int#: Returns: int: maximum number of springs per cloth.

property max_particles_per_cloth: int#: Returns: int: maximum number of particles per cloth.

is_physics_handle_valid() → bool#

Returns:: True if the physics handle of the view is valid (i.e physics is initialized for the view). Otherwise False.
Return type:: bool

initialize( physics_sim_view: omni.physics.tensors.SimulationView | None = None, ) → None#

Create a physics simulation view if not passed and creates a rigid body view in physX.

Parameters:: physics_sim_view (omni.physics.tensors.SimulationView, optional) – current physics simulation view. Defaults to None.

Sets the particle world positions for the cloths indicated by the indices.

Parameters:

positions (Union[np.ndarray, torch.Tensor]) – particle positions with the shape (M, max_particles_per_cloth, 3).
indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which cloth prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

get_world_positions( indices: ndarray | list | Tensor | None = None, clone: bool = True, ) → ndarray | Tensor#

Gets the particle world positions for the cloths indicated by the indices.

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which cloth prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

position tensor with shape (M, max_particles_per_cloth, 3)

Return type:

Union[Tuple[np.ndarray, np.ndarray], Tuple[torch.Tensor, torch.Tensor]]

Sets the particle velocities for the cloths indicated by the indices.

Parameters:

velocities (Union[np.ndarray, torch.Tensor]) – particle velocities with the shape (M, max_particles_per_cloth, 3).
indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which cloth prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

get_velocities( indices: ndarray | list | Tensor | None = None, clone: bool = True, ) → ndarray | Tensor#

Gets the particle velocities for the cloths indicated by the indices.

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which cloth prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

velocity tensor with shape (M, max_particles_per_cloth, 3)

Return type:

Union[Tuple[np.ndarray, np.ndarray], Tuple[torch.Tensor, torch.Tensor]]

Sets the particle masses for the cloths indicated by the indices.

Parameters:

masses (Union[np.ndarray, torch.Tensor]) – cloth particle masses with the shape (M, max_particles_per_cloth, 3).
indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which cloth prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

get_particle_masses( indices: ndarray | list | Tensor | None = None, clone: bool = True, ) → ndarray | Tensor#

Gets the particle masses for the cloths indicated by the indices.

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which cloth prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

mass tensor with shape (M, max_particles_per_cloth)

Return type:

Union[Tuple[np.ndarray, np.ndarray], Tuple[torch.Tensor, torch.Tensor]]

Sets the spring stretch stiffness values for springs within the cloths indicated by the indices.

Parameters:

stiffness (Union[np.ndarray, torch.Tensor]) – cloth spring stiffness with the shape (M, max_springs_per_cloth).
indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which cloth prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

get_stretch_stiffnesses( indices: ndarray | list | Tensor | None = None, clone: bool = True, ) → ndarray | Tensor#

Gets the spring stretch stiffness for the cloths indicated by the indices.

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which cloth prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

stiffness tensor with shape (M, max_springs_per_cloth)

Return type:

Union[Tuple[np.ndarray, np.ndarray], Tuple[torch.Tensor, torch.Tensor]]

Sets the spring damping for the cloths indicated by the indices.

Parameters:

damping (Union[np.ndarray, torch.Tensor]) – cloth spring damping with the shape (M, max_springs_per_cloth).
indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which cloth prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

get_spring_dampings( indices: ndarray | list | Tensor | None = None, clone: bool = True, ) → ndarray | Tensor#

Gets the spring damping for the cloths indicated by the indices.

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which cloth prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

damping tensor with shape (M, max_springs_per_cloth)

Return type:

Union[Tuple[np.ndarray, np.ndarray], Tuple[torch.Tensor, torch.Tensor]]

Sets the pressures of the cloths indicated by the indices.

Parameters:

pressures (Union[np.ndarray, torch.Tensor]) – cloths pressure with shape (M, ).
indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which cloth prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Sets the self collision filters for the cloths indicated by the indices.

Parameters:

self_collision_filters (Union[np.ndarray, torch.Tensor]) – self collision filters with the shape (M, ).
indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which cloth prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Sets the self collision flags for the cloths indicated by the indices.

Parameters:

self_collisions (Union[np.ndarray, torch.Tensor]) – self collision flag with the shape (M, ).
indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which cloth prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Sets the particle group of the cloths indicated by the indices.

Parameters:

particle_groups (Union[np.ndarray, torch.Tensor]) – particle group with shape (M, ).
indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which cloth prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Sets a single value of damping to all the springs within cloths indicated by the indices.

Parameters:

values (Union[np.ndarray, torch.Tensor]) – cloth spring damping with the shape (M, ).
indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which cloth prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Sets a single value of stretch stiffnesses to all the springs within cloths indicated by the indices.

Parameters:

values (Union[np.ndarray, torch.Tensor]) – cloth spring stretch stiffness values with the shape (M, ).
indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which cloth prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Sets a single value of bend stiffnesses to all the springs within cloths indicated by the indices.

Parameters:

values (Union[np.ndarray, torch.Tensor]) – cloth spring bend stiffness values with the shape (M, ).
indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which cloth prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Sets a single value of shear stiffnesses to all the springs within cloths indicated by the indices.

Parameters:

values (Union[np.ndarray, torch.Tensor]) – cloth spring shear stiffness values with the shape (M, ).
indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which cloth prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

get_cloths_dampings( indices: ndarray | list | Tensor | None = None, clone: bool = True, ) → ndarray | Tensor#

Gets the value of damping set for all the springs within cloths indicated by the indices.

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which cloth prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

damping tensor with shape (M, )

Return type:

Union[Tuple[np.ndarray, np.ndarray], Tuple[torch.Tensor, torch.Tensor]]

get_cloths_stretch_stiffnesses( indices: ndarray | list | Tensor | None = None, clone: bool = True, ) → ndarray | Tensor#

Gets the value of stretch stiffness set to all the springs within cloths indicated by the indices.

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which cloth prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

stretch stiffness tensor with shape (M, )

Return type:

Union[Tuple[np.ndarray, np.ndarray], Tuple[torch.Tensor, torch.Tensor]]

get_cloths_bend_stiffnesses( indices: ndarray | list | Tensor | None = None, clone: bool = True, ) → ndarray | Tensor#

Gets the value of bend stiffness set to all the springs within cloths indicated by the indices.

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which cloth prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

bend stiffness tensor with shape (M, )

Return type:

Union[Tuple[np.ndarray, np.ndarray], Tuple[torch.Tensor, torch.Tensor]]

get_cloths_shear_stiffnesses( indices: ndarray | list | Tensor | None = None, clone: bool = True, ) → ndarray | Tensor#

Gets the value of shear stiffness set to all the springs within cloths indicated by the indices.

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which cloth prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

shear stiffness tensor with shape (M, )

Return type:

Union[Tuple[np.ndarray, np.ndarray], Tuple[torch.Tensor, torch.Tensor]]

get_self_collision_filters( indices: ndarray | list | Tensor | None = None, clone: bool = True, ) → ndarray | Tensor#

Gets the self collision filters for the cloths indicated by the indices.

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which cloth prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

the self collision filters tensor with shape (M, )

Return type:

Union[Tuple[np.ndarray, np.ndarray], Tuple[torch.Tensor, torch.Tensor]]

get_self_collisions( indices: ndarray | list | Tensor | None = None, clone: bool = True, ) → ndarray | Tensor#

Gets the self collision for the cloths indicated by the indices.

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which cloth prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

the self collision tensor with shape (M, )

Return type:

Union[Tuple[np.ndarray, np.ndarray], Tuple[torch.Tensor, torch.Tensor]]

get_pressures( indices: ndarray | list | Tensor | None = None, clone: bool = True, ) → ndarray | Tensor#

Gets the pressures of the cloths indicated by the indices.

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which cloth prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

cloths pressure with shape (M, ).

Return type:

Union[Tuple[np.ndarray, np.ndarray], Tuple[torch.Tensor, torch.Tensor]]

get_particle_groups( indices: ndarray | list | Tensor | None = None, clone: bool = True, ) → ndarray | Tensor#

Gets the particle groups of the cloths indicated by the indices.

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which cloth prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

particle groups with shape (M, ).

Return type:

Union[Tuple[np.ndarray, np.ndarray], Tuple[torch.Tensor, torch.Tensor]]

Apply visual material to the prims and optionally their prim descendants.

Parameters:

visual_materials (Union[VisualMaterial, List[VisualMaterial]]) – visual materials to be applied to the prims. Currently supports PreviewSurface, OmniPBR and OmniGlass. If a list is provided then its size has to be equal the view’s size or indices size. If one material is provided it will be applied to all prims in the view.
weaker_than_descendants (Optional[Union[bool, List[bool]]], optional) – True if the material shouldn’t override the descendants materials, otherwise False. Defaults to False. If a list of visual materials is provided then a list has to be provided with the same size for this arg as well.
indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Raises:

Exception – length of visual materials != length of prims indexed
Exception – length of visual materials != length of weaker descendants bools arg

Example:

>>> from isaacsim.core.api.materials import OmniGlass
>>>
>>> # create a dark-red glass visual material
>>> material = OmniGlass(
...     prim_path="/World/material/glass",  # path to the material prim to create
...     ior=1.25,
...     depth=0.001,
...     thin_walled=False,
...     color=np.array([0.5, 0.0, 0.0])
... )
>>> prims.apply_visual_materials(material)

get_applied_visual_materials( indices: ndarray | list | Tensor | warp.array | None = None, ) → List[VisualMaterial]#

Get the current applied visual materials

Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
Returns:: a list of the current applied visual materials to the prims if its type is currently supported.
Return type:: List[VisualMaterial]

Example:

>>> # get all applied visual materials. Returned size is 5 for the example: 5 envs
>>> prims.get_applied_visual_materials()
[<isaacsim.core.api.materials.omni_glass.OmniGlass object at 0x7f829c165de0>,
 <isaacsim.core.api.materials.omni_glass.OmniGlass object at 0x7f829c165de0>,
 <isaacsim.core.api.materials.omni_glass.OmniGlass object at 0x7f829c165de0>,
 <isaacsim.core.api.materials.omni_glass.OmniGlass object at 0x7f829c165de0>,
 <isaacsim.core.api.materials.omni_glass.OmniGlass object at 0x7f829c165de0>]
>>>
>>> # get the applied visual materials for the first, middle and last of the 5 envs. Returned size is 3
>>> prims.get_applied_visual_materials(indices=np.array([0, 2, 4]))
[<isaacsim.core.api.materials.omni_glass.OmniGlass object at 0x7f829c165de0>,
 <isaacsim.core.api.materials.omni_glass.OmniGlass object at 0x7f829c165de0>,
 <isaacsim.core.api.materials.omni_glass.OmniGlass object at 0x7f829c165de0>]

get_default_state() → XFormPrimViewState#

Get the default states (positions and orientations) defined with the set_default_state method

Returns:: returns the default state of the prims that is used after each reset.
Return type:: XFormPrimViewState

Example:

>>> state = prims.get_default_state()
>>> state
<isaacsim.core.utils.types.XFormPrimViewState object at 0x7f82f73e3070>
>>> state.positions
[[ 1.5  -0.75  0.  ]
 [ 1.5   0.75  0.  ]
 [ 0.   -0.75  0.  ]
 [ 0.    0.75  0.  ]
 [-1.5  -0.75  0.  ]]
>>> state.orientations
[[1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]]

Get prim poses in the view with respect to the local frame (the prim’s parent frame)

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Returns:

first index is translations in the local frame of the prims. shape is (M, 3).: second index is quaternion orientations in the local frame of the prims. quaternion is scalar-first (w, x, y, z). shape is (M, 4).

Return type:

Union[Tuple[np.ndarray, np.ndarray], Tuple[torch.Tensor, torch.Tensor], Tuple[wp.indexedarray, wp.indexedarray]]

Example:

>>> # get all prims poses with respect to the local frame.
>>> # Returned shape is position (5, 3) and orientation (5, 4) for the example: 5 envs
>>> positions, orientations = prims.get_local_poses()
>>> positions
[[ 1.5  -0.75  0.  ]
 [ 1.5   0.75  0.  ]
 [ 0.   -0.75  0.  ]
 [ 0.    0.75  0.  ]
 [-1.5  -0.75  0.  ]]
>>> orientations
[[1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]]
>>>
>>> # get only the prims poses with respect to the local frame for the first, middle and last of the 5 envs.
>>> # Returned shape is position (3, 3) and orientation (3, 4) for the example: 3 envs selected
>>> positions, orientations = prims.get_local_poses(indices=np.array([0, 2, 4]))
>>> positions
[[ 1.5  -0.75  0.  ]
 [ 0.   -0.75  0.  ]
 [-1.5  -0.75  0.  ]]
>>> orientations
[[1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]]

Get prim scales in the view with respect to the local frame (the parent’s frame).

Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
Returns:: scales applied to the prim’s dimensions in the local frame. shape is (M, 3).
Return type:: Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>> # get all prims scales with respect to the local frame.
>>> # Returned shape is (5, 3) for the example: 5 envs
>>> prims.get_local_scales()
[[1. 1. 1.]
 [1. 1. 1.]
 [1. 1. 1.]
 [1. 1. 1.]
 [1. 1. 1.]]
>>>
>>> # get only the prims scales with respect to the local frame for the first, middle and last of the 5 envs.
>>> # Returned shape is (3, 3) for the example: 3 envs selected
>>> prims.get_local_scales(indices=np.array([0, 2, 4]))
[[1. 1. 1.]
 [1. 1. 1.]
 [1. 1. 1.]]

Returns the current visibilities of the prims in stage.

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Returns:

Shape (M,) with type bool, where each item holds True: if the prim is visible in stage. False otherwise.

Return type:

Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>> # get all visibilities. Returned shape is (5,) for the example: 5 envs
>>> prims.get_visibilities()
[ True  True  True  True  True]
>>>
>>> # get the visibilities for the first, middle and last of the 5 envs. Returned shape is (3,)
>>> prims.get_visibilities(indices=np.array([0, 2, 4]))
[ True  True  True]

Get the poses of the prims in the view with respect to the world’s frame

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
usd (bool, optional) – True to query from usd. Otherwise False to query from Fabric data. Defaults to True.

Returns:

first index is positions in the world frame of the prims. shape is (M, 3).: second index is quaternion orientations in the world frame of the prims. quaternion is scalar-first (w, x, y, z). shape is (M, 4).

Return type:

Union[Tuple[np.ndarray, np.ndarray], Tuple[torch.Tensor, torch.Tensor], Tuple[wp.indexedarray, wp.indexedarray]]

Example:

>>> # get all prims poses with respect to the world's frame.
>>> # Returned shape is position (5, 3) and orientation (5, 4) for the example: 5 envs
>>> positions, orientations = prims.get_world_poses()
>>> positions
[[ 1.5  -0.75  0.  ]
 [ 1.5   0.75  0.  ]
 [ 0.   -0.75  0.  ]
 [ 0.    0.75  0.  ]
 [-1.5  -0.75  0.  ]]
>>> orientations
[[1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]]
>>>
>>> # get only the prims poses with respect to the world's frame for the first, middle and last of the 5 envs.
>>> # Returned shape is position (3, 3) and orientation (3, 4) for the example: 3 envs selected
>>> positions, orientations = prims.get_world_poses(indices=np.array([0, 2, 4]))
>>> positions
[[ 1.5  -0.75  0.  ]
 [ 0.   -0.75  0.  ]
 [-1.5  -0.75  0.  ]]
>>> orientations
[[1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]]

Get prim scales in the view with respect to the world’s frame

Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
Returns:: scales applied to the prim’s dimensions in the world frame. shape is (M, 3).
Return type:: Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>> # get all prims scales with respect to the world's frame.
>>> # Returned shape is (5, 3) for the example: 5 envs
>>> prims.get_world_scales()
[[1. 1. 1.]
 [1. 1. 1.]
 [1. 1. 1.]
 [1. 1. 1.]
 [1. 1. 1.]]
>>>
>>> # get only the prims scales with respect to the world's frame for the first, middle and last of the 5 envs.
>>> # Returned shape is (3, 3) for the example: 3 envs selected
>>> prims.get_world_scales(indices=np.array([0, 2, 4]))
[[1. 1. 1.]
 [1. 1. 1.]
 [1. 1. 1.]]

property initialized: bool#

Check if prim view is initialized

Returns:: True if the view object was initialized (after the first call of .initialize()). False otherwise.
Return type:: bool

Example:

>>> # given an initialized articulation view
>>> prims.initialized
True

property is_non_root_articulation_link: bool#: Returns: bool: True if the prim corresponds to a non root link in an articulation. Otherwise False.

is_valid( indices: ndarray | list | Tensor | warp.array | None = None, ) → bool#

Check that all prims have a valid USD Prim

Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
Returns:: True if all prim paths specified in the view correspond to a valid prim in stage. False otherwise.
Return type:: bool

Example:

>>> prims.is_valid()
True

is_visual_material_applied( indices: ndarray | list | Tensor | warp.array | None = None, ) → List[bool]#

Check if there is a visual material applied

Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
Returns:: True if there is a visual material applied is applied to the corresponding prim in the view. False otherwise.
Return type:: List[bool]

Example:

>>> # given a visual material that is applied only to the first and the last environment
>>> prims.is_visual_material_applied()
[True, False, False, False, True]
>>>
>>> # check for the first, middle and last of the 5 envs
>>> prims.is_visual_material_applied(indices=np.array([0, 2, 4]))
[True, False, True]

property name: str#: Returns: str: name given to the prims view when instantiating it.

post_reset() → None#

Reset the prims to its default state

Example:

>>> prims.post_reset()

property prim_paths: List[str]#

Returns:: list of prim paths in the stage encapsulated in this view.
Return type:: List[str]

Example:

>>> prims.prim_paths
['/World/envs/env_0', '/World/envs/env_1', '/World/envs/env_2', '/World/envs/env_3', '/World/envs/env_4']

property prims: List[pxr.Usd.Prim]#

Returns:: List of USD Prim objects encapsulated in this view.
Return type:: List[Usd.Prim]

Example:

>>> prims.prims
[Usd.Prim(</World/envs/env_0>), Usd.Prim(</World/envs/env_1>), Usd.Prim(</World/envs/env_2>),
 Usd.Prim(</World/envs/env_3>), Usd.Prim(</World/envs/env_4>)]

Set the default state of the prims (positions and orientations), that will be used after each reset.

Note

The default states will be set during post-reset (e.g., calling .post_reset() or world.reset() methods)

Parameters:

positions (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – positions in the world frame of the prim. shape is (M, 3). Defaults to None, which means left unchanged.
orientations (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – quaternion orientations in the world frame of the prim. quaternion is scalar-first (w, x, y, z). shape is (M, 4). Defaults to None, which means left unchanged.
indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Example:

>>> # configure default states for all prims
>>> positions = np.zeros((num_envs, 3))
>>> positions[:, 0] = np.arange(num_envs)
>>> orientations = np.tile(np.array([1.0, 0.0, 0.0, 0.0]), (num_envs, 1))
>>> prims.set_default_state(positions=positions, orientations=orientations)
>>>
>>> # set default states during post-reset
>>> prims.post_reset()

Set prim poses in the view with respect to the local frame (the prim’s parent frame)

Warning

This method will change (teleport) the prim poses immediately to the indicated value

Parameters:

translations (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – translations in the local frame of the prims (with respect to its parent prim). shape is (M, 3). Defaults to None, which means left unchanged.
orientations (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – quaternion orientations in the local frame of the prims. quaternion is scalar-first (w, x, y, z). shape is (M, 4). Defaults to None, which means left unchanged.
indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Hint

This method belongs to the methods used to set the prim state

Example:

>>> # reposition all prims
>>> positions = np.zeros((num_envs, 3))
>>> positions[:,0] = np.arange(num_envs)
>>> orientations = np.tile(np.array([1.0, 0.0, 0.0, 0.0]), (num_envs, 1))
>>> prims.set_local_poses(positions, orientations)
>>>
>>> # reposition only the prims for the first, middle and last of the 5 envs
>>> positions = np.zeros((3, 3))
>>> positions[:,1] = np.arange(3)
>>> orientations = np.tile(np.array([1.0, 0.0, 0.0, 0.0]), (3, 1))
>>> prims.set_local_poses(positions, orientations, indices=np.array([0, 2, 4]))

Set prim scales in the view with respect to the local frame (the prim’s parent frame)

Parameters:

scales (Optional[Union[np.ndarray, torch.Tensor, wp.array]]) – scales to be applied to the prim’s dimensions in the view. shape is (M, 3).
indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Example:

>>> # set the scale for all prims. Since there are 5 envs, the scale is repeated 5 times
>>> scales = np.tile(np.array([1.0, 0.75, 0.5]), (num_envs, 1))
>>> prims.set_local_scales(scales)
>>>
>>> # set the scale for the first, middle and last of the 5 envs
>>> scales = np.tile(np.array([1.0, 0.75, 0.5]), (3, 1))
>>> prims.set_local_scales(scales, indices=np.array([0, 2, 4]))

Set the visibilities of the prims in stage

Parameters:

visibilities (Union[np.ndarray, torch.Tensor, wp.array]) – flag to set the visibilities of the usd prims in stage. Shape (M,). Where M <= size of the encapsulated prims in the view.
indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Defaults to None (i.e: all prims in the view).

Example:

>>> # make all prims not visible in the stage
>>> prims.set_visibilities(visibilities=[False] * num_envs)

Set prim poses in the view with respect to the world’s frame

Warning

This method will change (teleport) the prim poses immediately to the indicated value

Parameters:

positions (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – positions in the world frame of the prims. shape is (M, 3). Defaults to None, which means left unchanged.
orientations (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – quaternion orientations in the world frame of the prims. quaternion is scalar-first (w, x, y, z). shape is (M, 4). Defaults to None, which means left unchanged.
indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
usd (bool, optional) – True to query from usd. Otherwise False to query from Fabric data. Defaults to True.

Hint

This method belongs to the methods used to set the prim state

Example:

>>> # reposition all prims in row (x-axis)
>>> positions = np.zeros((num_envs, 3))
>>> positions[:,0] = np.arange(num_envs)
>>> orientations = np.tile(np.array([1.0, 0.0, 0.0, 0.0]), (num_envs, 1))
>>> prims.set_world_poses(positions, orientations)
>>>
>>> # reposition only the prims for the first, middle and last of the 5 envs in column (y-axis)
>>> positions = np.zeros((3, 3))
>>> positions[:,1] = np.arange(3)
>>> orientations = np.tile(np.array([1.0, 0.0, 0.0, 0.0]), (3, 1))
>>> prims.set_world_poses(positions, orientations, indices=np.array([0, 2, 4]))

Bases: XFormPrim

The view class for deformable prims.

property count: int#: Returns: int: deformable counts.

property max_simulation_mesh_elements_per_body: int#: Returns: int: maximum number of simulation mesh elements per deformable body.

property max_simulation_mesh_vertices_per_body: int#: Returns: int: maximum number of simulation mesh vertices per deformable body.

property max_collision_mesh_elements_per_body: int#: Returns: int: maximum number of collision mesh elements per deformable body.

property max_collision_mesh_vertices_per_body: int#: Returns: int: maximum number of collision mesh vertices per deformable body.

is_physics_handle_valid() → bool#

Returns:: True if the physics handle of the view is valid (i.e physics is initialized for the view). Otherwise False.
Return type:: bool

initialize( physics_sim_view: omni.physics.tensors.SimulationView | None = None, ) → None#

Create a physics simulation view if not passed and creates a deformable body view in physX.

Parameters:: physics_sim_view (omni.physics.tensors.SimulationView, optional) – current physics simulation view. Defaults to None.

apply_deformable_materials( deformable_materials: DeformableMaterial | List[DeformableMaterial], indices: ndarray | list | Tensor | None = None, ) → None#

Used to apply deformable material to prims in the view.

Parameters:

deformable_materials (Union[DeformableMaterial, List[DeformableMaterial]]) – deformable materials to be applied to prims in the view. Note: if a physics material is not defined, the defaults will be used from PhysX. If a list is provided then its size has to be equal the view’s size or indices size. If one material is provided it will be applied to all prims in the view.
indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Raises:

Exception – length of physics materials != length of prims indexed

get_applied_deformable_materials( indices: ndarray | list | Tensor | None = None, ) → List[DeformableMaterial]#

Gets the applied deformable material to prims in the view.

Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
Returns:: the current applied deformable materials for prims in the view.
Return type:: List[DeformableMaterial]

Sets the nodal positions of the simulation mesh for the deformable bodies indicated by the indices.

Parameters:

positions (Union[np.ndarray, torch.Tensor]) – nodal positions with the shape (M, max_simulation_mesh_vertices_per_body, 3).
indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which deformable prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Sets the vertex velocities for the deformable bodies indicated by the indices.

Parameters:

velocities (Union[np.ndarray, torch.Tensor]) – vertex velocities with the shape (M, max_simulation_mesh_vertices_per_body, 3).
indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which deformable prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Sets the kinematic targets of the simulation mesh for the deformable bodies indicated by the indices.

Parameters:

positions (Union[np.ndarray, torch.Tensor]) – kinematic targets with the shape (M, max_simulation_mesh_vertices_per_body, 4).
indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which deformable prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Sets the simulation mesh element indices of the deformable bodies indicated by the indices.

Parameters:

values (Union[np.ndarray, torch.Tensor]) – element indices with the shape (M, ).
indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which deformable prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Sets the collision mesh element indices of the deformable bodies indicated by the indices.

Parameters:

values (Union[np.ndarray, torch.Tensor]) – element indices with the shape (M, ).
indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which deformable prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Sets the collision mesh vertices rest positions of the deformable bodies indicated by the indices.

Parameters:

values (Union[np.ndarray, torch.Tensor]) – vertices rest positions values with the shape (M, ).
indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which deformable prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Sets the simulation mesh vertices rest positions of the deformable bodies indicated by the indices.

Parameters:

values (Union[np.ndarray, torch.Tensor]) – vertices rest positions values with the shape (M, ).
indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which deformable prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Sets the sleep dampings values for deformable bodies indicated by the indices.

Parameters:

values (Union[np.ndarray, torch.Tensor]) – solver position iteration counts values with the shape (M, ).
indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which deformable prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Sets the sleep threshold values for deformable bodies indicated by the indices.

Parameters:

values (Union[np.ndarray, torch.Tensor]) – solver position iteration counts values with the shape (M, ).
indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which deformable prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Sets the settling threshold values for deformable bodies indicated by the indices.

Parameters:

values (Union[np.ndarray, torch.Tensor]) – solver position iteration counts values with the shape (M, ).
indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which deformable prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Sets the self collisions filter distance values for deformable bodies indicated by the indices.

Parameters:

values (Union[np.ndarray, torch.Tensor]) – solver position iteration counts values with the shape (M, ).
indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which deformable prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Sets the self collisions values for deformable bodies indicated by the indices.

Parameters:

values (Union[np.ndarray, torch.Tensor]) – solver position iteration counts values with the shape (M, ).
indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which deformable prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Sets values of the vertex velocity damping values to deformable bodies indicated by the indices.

Parameters:

values (Union[np.ndarray, torch.Tensor]) – solver position iteration counts values with the shape (M, ).
indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which deformable prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Sets values of the solver position iteration counts to deformable bodies indicated by the indices.

Parameters:

values (Union[np.ndarray, torch.Tensor]) – solver position iteration counts values with the shape (M, ).
indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which deformable prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

get_sleep_dampings( indices: ndarray | list | Tensor | None = None, clone: bool = True, ) → ndarray | Tensor#

Gets the sleep damping for the deformable bodies indicated by the indices.

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which deformable prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
clone (float, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

the sleep damping tensor with shape (M, )

Return type:

Union[Tuple[np.ndarray, np.ndarray], Tuple[torch.Tensor, torch.Tensor]]

get_sleep_thresholds( indices: ndarray | list | Tensor | None = None, clone: bool = True, ) → ndarray | Tensor#

Gets the sleep threshold for the deformable bodies indicated by the indices.

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which deformable prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
clone (float, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

the sleep threshold tensor with shape (M, )

Return type:

Union[Tuple[np.ndarray, np.ndarray], Tuple[torch.Tensor, torch.Tensor]]

get_settling_thresholds( indices: ndarray | list | Tensor | None = None, clone: bool = True, ) → ndarray | Tensor#

Gets the settling threshold for the deformable bodies indicated by the indices.

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which deformable prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
clone (float, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

the settling threshold tensor with shape (M, )

Return type:

Union[Tuple[np.ndarray, np.ndarray], Tuple[torch.Tensor, torch.Tensor]]

get_self_collision_filter_distances( indices: ndarray | list | Tensor | None = None, clone: bool = True, ) → ndarray | Tensor#

Gets the self collision filter distance for the deformable bodies indicated by the indices.

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which deformable prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
clone (float, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

the self collision filter distance tensor with shape (M, )

Return type:

Union[Tuple[np.ndarray, np.ndarray], Tuple[torch.Tensor, torch.Tensor]]

get_self_collisions( indices: ndarray | list | Tensor | None = None, clone: bool = True, ) → ndarray | Tensor#

Gets the self collision parameters for the deformable bodies indicated by the indices.

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which deformable prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

the self collision tensor with shape (M, )

Return type:

Union[Tuple[np.ndarray, np.ndarray], Tuple[torch.Tensor, torch.Tensor]]

get_vertex_velocity_dampings( indices: ndarray | list | Tensor | None = None, clone: bool = True, ) → ndarray | Tensor#

Gets the vertex velocity dampings of the deformable bodies indicated by the indices.

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which deformable prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
clone (float, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

deformable bodies vertex velocity dampings with shape (M, ).

Return type:

Union[Tuple[np.ndarray, np.ndarray], Tuple[torch.Tensor, torch.Tensor]]

get_solver_position_iteration_counts( indices: ndarray | list | Tensor | None = None, clone: bool = True, ) → ndarray | Tensor#

Gets the solver’s positional iteration counts of the deformable bodies indicated by the indices.

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which deformable prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
clone (int, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

solver’s positional iteration counts with shape (M, ).

Return type:

Union[Tuple[np.ndarray, np.ndarray], Tuple[torch.Tensor, torch.Tensor]]

get_simulation_mesh_nodal_positions( indices: ndarray | list | Tensor | None = None, clone: bool = True, ) → ndarray | Tensor#

Gets the nodal positions of the simulation mesh for the deformable bodies indicated by the indices.

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which deformable prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

position tensor with shape (M, max_simulation_mesh_vertices_per_body, 3)

Return type:

Union[Tuple[np.ndarray, np.ndarray], Tuple[torch.Tensor, torch.Tensor]]

get_simulation_mesh_nodal_velocities( indices: ndarray | list | Tensor | None = None, clone: bool = True, ) → ndarray | Tensor#

Gets the vertex velocities for the deformable bodies indicated by the indices.

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which deformable prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

velocity tensor with shape (M, max_simulation_mesh_vertices_per_body, 3)

Return type:

Union[Tuple[np.ndarray, np.ndarray], Tuple[torch.Tensor, torch.Tensor]]

get_simulation_mesh_kinematic_targets( indices: ndarray | list | Tensor | None = None, clone: bool = True, ) → ndarray | Tensor#

Gets the nodal kinematic targets of the simulation mesh for the deformable bodies indicated by the indices.

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which deformable prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

kinematic targets tensor,: with shape (M, max_simulation_mesh_vertices_per_body, 4) the first three components are the position targets and the last value (0 or 1) indicate whether the node is kinematically driven or not.

Return type:

Union[Tuple[np.ndarray, np.ndarray], Tuple[torch.Tensor, torch.Tensor]]

get_collision_mesh_nodal_positions( indices: ndarray | list | Tensor | None = None, clone: bool = True, ) → ndarray | Tensor#

Gets the nodal positions of the collision mesh for the deformable bodies indicated by the indices.

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which deformable prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

position tensor with shape (M, max_collision_mesh_vertices_per_body, 3)

Return type:

Union[Tuple[np.ndarray, np.ndarray], Tuple[torch.Tensor, torch.Tensor]]

get_simulation_mesh_indices( indices: ndarray | list | Tensor | None = None, clone: bool = True, ) → ndarray | Tensor#

Gets the simulation mesh element indices of the deformable bodies indicated by the indices.

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which deformable prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
clone (float, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

deformable bodies simulation mesh element indices: with shape (M, self.max_simulation_mesh_elements_per_body, 4).

Return type:

Union[Tuple[np.ndarray, np.ndarray], Tuple[torch.Tensor, torch.Tensor]]

get_collision_mesh_indices( indices: ndarray | list | Tensor | None = None, clone: bool = True, ) → ndarray | Tensor#

Gets the collision mesh element indices of the deformable bodies indicated by the indices.

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which deformable prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
clone (float, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

deformable bodies collision mesh element indices: with shape (M, self.max_collision_mesh_elements_per_body, 4).

Return type:

Union[Tuple[np.ndarray, np.ndarray], Tuple[torch.Tensor, torch.Tensor]]

get_simulation_mesh_rest_points( indices: ndarray | list | Tensor | None = None, clone: bool = True, ) → ndarray | Tensor#

Gets the simulation mesh rest points of the deformable bodies indicated by the indices.: rest point are the nodal positions with respect to the local prim transform, while the values returned by get_simulation_mesh_nodal_positions are the nodal positions with respect to the origin

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which deformable prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
clone (float, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

deformable bodies simulation mesh rest points: with shape (M, self.max_simulation_mesh_vertices_per_body, 3).

Return type:

Union[Tuple[np.ndarray, np.ndarray], Tuple[torch.Tensor, torch.Tensor]]

get_simulation_mesh_element_rest_poses( indices: ndarray | list | Tensor | None = None, clone: bool = True, ) → ndarray | Tensor#

Gets the simulation mesh rest poses for the deformable bodies indicated by the indices. This method will return the 3x3 matrix inv([x1-x0, x2-x0, x3-x0]) where x0, x1, x2, x3 are the rest points of the simulation mesh elements

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which deformable prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

simulation mesh rest poses with: shape (M, max_simulation_mesh_elements_per_body, 3, 3)

Return type:

Union[Tuple[np.ndarray, np.ndarray], Tuple[torch.Tensor, torch.Tensor]]

get_collision_mesh_element_rest_poses( indices: ndarray | list | Tensor | None = None, clone: bool = True, ) → ndarray | Tensor#

Gets the collision mesh rest poses for the deformable bodies indicated by the indices. This method will return the 3x3 matrix inv([x1-x0, x2-x0, x3-x0]) where x0, x1, x2, x3 are the rest points of collision mesh elements

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which deformable prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

collision mesh rest poses with shape (M, max_collision_mesh_elements_per_body, 3, 3)

Return type:

Union[Tuple[np.ndarray, np.ndarray], Tuple[torch.Tensor, torch.Tensor]]

get_simulation_mesh_element_rotations( indices: ndarray | list | Tensor | None = None, clone: bool = True, ) → ndarray | Tensor#

Gets the simulation mesh element-wise rotations as quaternions for the deformable bodies indicated by the indices.

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which deformable prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

simulation mesh element-wise rotations with shape (M, max_simulation_mesh_elements_per_body, 4)

Return type:

Union[Tuple[np.ndarray, np.ndarray], Tuple[torch.Tensor, torch.Tensor]]

get_collision_mesh_element_rotations( indices: ndarray | list | Tensor | None = None, clone: bool = True, ) → ndarray | Tensor#

Gets the collision mesh element-wise rotations as quaternions for the deformable bodies indicated by the indices.

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which deformable prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

collision mesh rotations with shape (M, max_collision_mesh_elements_per_body, 4)

Return type:

Union[Tuple[np.ndarray, np.ndarray], Tuple[torch.Tensor, torch.Tensor]]

get_simulation_mesh_element_deformation_gradients( indices: ndarray | list | Tensor | None = None, clone: bool = True, ) → ndarray | Tensor#

Gets the simulation mesh element-wise second-order deformation gradient tensors for the deformable bodies indicated by the indices. This method will return the simulation mesh element-wise deformation gradient of the deformable bodies

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which deformable prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

simulation mesh element-wise deformation gradients with shape (M, max_simulation_mesh_elements_per_body, 3, 3)

Return type:

Union[Tuple[np.ndarray, np.ndarray], Tuple[torch.Tensor, torch.Tensor]]

get_collision_mesh_element_deformation_gradients( indices: ndarray | list | Tensor | None = None, clone: bool = True, ) → ndarray | Tensor#

Gets the collision mesh element-wise second-order deformation gradient tensors for the deformable bodies indicated by the indices.

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which deformable prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

collision mesh deformation gradients with shape (M, max_collision_mesh_elements_per_body, 3, 3)

Return type:

Union[Tuple[np.ndarray, np.ndarray], Tuple[torch.Tensor, torch.Tensor]]

get_simulation_mesh_element_stresses( indices: ndarray | list | Tensor | None = None, clone: bool = True, ) → ndarray | Tensor#

Gets the simulation mesh element-wise second-order stress tensors for the deformable bodies indicated by the indices.

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which deformable prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

simulation mesh element-wise stresses with shape (M, max_simulation_mesh_elements_per_body, 3, 3)

Return type:

Union[Tuple[np.ndarray, np.ndarray], Tuple[torch.Tensor, torch.Tensor]]

get_collision_mesh_element_stresses( indices: ndarray | list | Tensor | None = None, clone: bool = True, ) → ndarray | Tensor#

Gets the collision mesh element-wise second-order stress tensors for bodies indicated by the indices. This method will return the collision mesh element-wise stresses of the deformable bodies

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which deformable prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

collision mesh stresses with shape (M, max_collision_mesh_elements_per_body, 3, 3)

Return type:

Union[Tuple[np.ndarray, np.ndarray], Tuple[torch.Tensor, torch.Tensor]]

Apply visual material to the prims and optionally their prim descendants.

Parameters:

visual_materials (Union[VisualMaterial, List[VisualMaterial]]) – visual materials to be applied to the prims. Currently supports PreviewSurface, OmniPBR and OmniGlass. If a list is provided then its size has to be equal the view’s size or indices size. If one material is provided it will be applied to all prims in the view.
weaker_than_descendants (Optional[Union[bool, List[bool]]], optional) – True if the material shouldn’t override the descendants materials, otherwise False. Defaults to False. If a list of visual materials is provided then a list has to be provided with the same size for this arg as well.
indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Raises:

Exception – length of visual materials != length of prims indexed
Exception – length of visual materials != length of weaker descendants bools arg

Example:

>>> from isaacsim.core.api.materials import OmniGlass
>>>
>>> # create a dark-red glass visual material
>>> material = OmniGlass(
...     prim_path="/World/material/glass",  # path to the material prim to create
...     ior=1.25,
...     depth=0.001,
...     thin_walled=False,
...     color=np.array([0.5, 0.0, 0.0])
... )
>>> prims.apply_visual_materials(material)

get_applied_visual_materials( indices: ndarray | list | Tensor | warp.array | None = None, ) → List[VisualMaterial]#

Get the current applied visual materials

Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
Returns:: a list of the current applied visual materials to the prims if its type is currently supported.
Return type:: List[VisualMaterial]

Example:

>>> # get all applied visual materials. Returned size is 5 for the example: 5 envs
>>> prims.get_applied_visual_materials()
[<isaacsim.core.api.materials.omni_glass.OmniGlass object at 0x7f829c165de0>,
 <isaacsim.core.api.materials.omni_glass.OmniGlass object at 0x7f829c165de0>,
 <isaacsim.core.api.materials.omni_glass.OmniGlass object at 0x7f829c165de0>,
 <isaacsim.core.api.materials.omni_glass.OmniGlass object at 0x7f829c165de0>,
 <isaacsim.core.api.materials.omni_glass.OmniGlass object at 0x7f829c165de0>]
>>>
>>> # get the applied visual materials for the first, middle and last of the 5 envs. Returned size is 3
>>> prims.get_applied_visual_materials(indices=np.array([0, 2, 4]))
[<isaacsim.core.api.materials.omni_glass.OmniGlass object at 0x7f829c165de0>,
 <isaacsim.core.api.materials.omni_glass.OmniGlass object at 0x7f829c165de0>,
 <isaacsim.core.api.materials.omni_glass.OmniGlass object at 0x7f829c165de0>]

get_default_state() → XFormPrimViewState#

Get the default states (positions and orientations) defined with the set_default_state method

Returns:: returns the default state of the prims that is used after each reset.
Return type:: XFormPrimViewState

Example:

>>> state = prims.get_default_state()
>>> state
<isaacsim.core.utils.types.XFormPrimViewState object at 0x7f82f73e3070>
>>> state.positions
[[ 1.5  -0.75  0.  ]
 [ 1.5   0.75  0.  ]
 [ 0.   -0.75  0.  ]
 [ 0.    0.75  0.  ]
 [-1.5  -0.75  0.  ]]
>>> state.orientations
[[1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]]

Get prim poses in the view with respect to the local frame (the prim’s parent frame)

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Returns:

first index is translations in the local frame of the prims. shape is (M, 3).: second index is quaternion orientations in the local frame of the prims. quaternion is scalar-first (w, x, y, z). shape is (M, 4).

Return type:

Union[Tuple[np.ndarray, np.ndarray], Tuple[torch.Tensor, torch.Tensor], Tuple[wp.indexedarray, wp.indexedarray]]

Example:

>>> # get all prims poses with respect to the local frame.
>>> # Returned shape is position (5, 3) and orientation (5, 4) for the example: 5 envs
>>> positions, orientations = prims.get_local_poses()
>>> positions
[[ 1.5  -0.75  0.  ]
 [ 1.5   0.75  0.  ]
 [ 0.   -0.75  0.  ]
 [ 0.    0.75  0.  ]
 [-1.5  -0.75  0.  ]]
>>> orientations
[[1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]]
>>>
>>> # get only the prims poses with respect to the local frame for the first, middle and last of the 5 envs.
>>> # Returned shape is position (3, 3) and orientation (3, 4) for the example: 3 envs selected
>>> positions, orientations = prims.get_local_poses(indices=np.array([0, 2, 4]))
>>> positions
[[ 1.5  -0.75  0.  ]
 [ 0.   -0.75  0.  ]
 [-1.5  -0.75  0.  ]]
>>> orientations
[[1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]]

Get prim scales in the view with respect to the local frame (the parent’s frame).

Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
Returns:: scales applied to the prim’s dimensions in the local frame. shape is (M, 3).
Return type:: Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>> # get all prims scales with respect to the local frame.
>>> # Returned shape is (5, 3) for the example: 5 envs
>>> prims.get_local_scales()
[[1. 1. 1.]
 [1. 1. 1.]
 [1. 1. 1.]
 [1. 1. 1.]
 [1. 1. 1.]]
>>>
>>> # get only the prims scales with respect to the local frame for the first, middle and last of the 5 envs.
>>> # Returned shape is (3, 3) for the example: 3 envs selected
>>> prims.get_local_scales(indices=np.array([0, 2, 4]))
[[1. 1. 1.]
 [1. 1. 1.]
 [1. 1. 1.]]

Returns the current visibilities of the prims in stage.

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Returns:

Shape (M,) with type bool, where each item holds True: if the prim is visible in stage. False otherwise.

Return type:

Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>> # get all visibilities. Returned shape is (5,) for the example: 5 envs
>>> prims.get_visibilities()
[ True  True  True  True  True]
>>>
>>> # get the visibilities for the first, middle and last of the 5 envs. Returned shape is (3,)
>>> prims.get_visibilities(indices=np.array([0, 2, 4]))
[ True  True  True]

Get the poses of the prims in the view with respect to the world’s frame

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
usd (bool, optional) – True to query from usd. Otherwise False to query from Fabric data. Defaults to True.

Returns:

first index is positions in the world frame of the prims. shape is (M, 3).: second index is quaternion orientations in the world frame of the prims. quaternion is scalar-first (w, x, y, z). shape is (M, 4).

Return type:

Union[Tuple[np.ndarray, np.ndarray], Tuple[torch.Tensor, torch.Tensor], Tuple[wp.indexedarray, wp.indexedarray]]

Example:

>>> # get all prims poses with respect to the world's frame.
>>> # Returned shape is position (5, 3) and orientation (5, 4) for the example: 5 envs
>>> positions, orientations = prims.get_world_poses()
>>> positions
[[ 1.5  -0.75  0.  ]
 [ 1.5   0.75  0.  ]
 [ 0.   -0.75  0.  ]
 [ 0.    0.75  0.  ]
 [-1.5  -0.75  0.  ]]
>>> orientations
[[1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]]
>>>
>>> # get only the prims poses with respect to the world's frame for the first, middle and last of the 5 envs.
>>> # Returned shape is position (3, 3) and orientation (3, 4) for the example: 3 envs selected
>>> positions, orientations = prims.get_world_poses(indices=np.array([0, 2, 4]))
>>> positions
[[ 1.5  -0.75  0.  ]
 [ 0.   -0.75  0.  ]
 [-1.5  -0.75  0.  ]]
>>> orientations
[[1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]]

Get prim scales in the view with respect to the world’s frame

Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
Returns:: scales applied to the prim’s dimensions in the world frame. shape is (M, 3).
Return type:: Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>> # get all prims scales with respect to the world's frame.
>>> # Returned shape is (5, 3) for the example: 5 envs
>>> prims.get_world_scales()
[[1. 1. 1.]
 [1. 1. 1.]
 [1. 1. 1.]
 [1. 1. 1.]
 [1. 1. 1.]]
>>>
>>> # get only the prims scales with respect to the world's frame for the first, middle and last of the 5 envs.
>>> # Returned shape is (3, 3) for the example: 3 envs selected
>>> prims.get_world_scales(indices=np.array([0, 2, 4]))
[[1. 1. 1.]
 [1. 1. 1.]
 [1. 1. 1.]]

property initialized: bool#

Check if prim view is initialized

Returns:: True if the view object was initialized (after the first call of .initialize()). False otherwise.
Return type:: bool

Example:

>>> # given an initialized articulation view
>>> prims.initialized
True

property is_non_root_articulation_link: bool#: Returns: bool: True if the prim corresponds to a non root link in an articulation. Otherwise False.

is_valid( indices: ndarray | list | Tensor | warp.array | None = None, ) → bool#

Check that all prims have a valid USD Prim

Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
Returns:: True if all prim paths specified in the view correspond to a valid prim in stage. False otherwise.
Return type:: bool

Example:

>>> prims.is_valid()
True

is_visual_material_applied( indices: ndarray | list | Tensor | warp.array | None = None, ) → List[bool]#

Check if there is a visual material applied

Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
Returns:: True if there is a visual material applied is applied to the corresponding prim in the view. False otherwise.
Return type:: List[bool]

Example:

>>> # given a visual material that is applied only to the first and the last environment
>>> prims.is_visual_material_applied()
[True, False, False, False, True]
>>>
>>> # check for the first, middle and last of the 5 envs
>>> prims.is_visual_material_applied(indices=np.array([0, 2, 4]))
[True, False, True]

property name: str#: Returns: str: name given to the prims view when instantiating it.

post_reset() → None#

Reset the prims to its default state

Example:

>>> prims.post_reset()

property prim_paths: List[str]#

Returns:: list of prim paths in the stage encapsulated in this view.
Return type:: List[str]

Example:

>>> prims.prim_paths
['/World/envs/env_0', '/World/envs/env_1', '/World/envs/env_2', '/World/envs/env_3', '/World/envs/env_4']

property prims: List[pxr.Usd.Prim]#

Returns:: List of USD Prim objects encapsulated in this view.
Return type:: List[Usd.Prim]

Example:

>>> prims.prims
[Usd.Prim(</World/envs/env_0>), Usd.Prim(</World/envs/env_1>), Usd.Prim(</World/envs/env_2>),
 Usd.Prim(</World/envs/env_3>), Usd.Prim(</World/envs/env_4>)]

Set the default state of the prims (positions and orientations), that will be used after each reset.

Note

The default states will be set during post-reset (e.g., calling .post_reset() or world.reset() methods)

Parameters:

positions (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – positions in the world frame of the prim. shape is (M, 3). Defaults to None, which means left unchanged.
orientations (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – quaternion orientations in the world frame of the prim. quaternion is scalar-first (w, x, y, z). shape is (M, 4). Defaults to None, which means left unchanged.
indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Example:

>>> # configure default states for all prims
>>> positions = np.zeros((num_envs, 3))
>>> positions[:, 0] = np.arange(num_envs)
>>> orientations = np.tile(np.array([1.0, 0.0, 0.0, 0.0]), (num_envs, 1))
>>> prims.set_default_state(positions=positions, orientations=orientations)
>>>
>>> # set default states during post-reset
>>> prims.post_reset()

Set prim poses in the view with respect to the local frame (the prim’s parent frame)

Warning

This method will change (teleport) the prim poses immediately to the indicated value

Parameters:

translations (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – translations in the local frame of the prims (with respect to its parent prim). shape is (M, 3). Defaults to None, which means left unchanged.
orientations (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – quaternion orientations in the local frame of the prims. quaternion is scalar-first (w, x, y, z). shape is (M, 4). Defaults to None, which means left unchanged.
indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Hint

This method belongs to the methods used to set the prim state

Example:

>>> # reposition all prims
>>> positions = np.zeros((num_envs, 3))
>>> positions[:,0] = np.arange(num_envs)
>>> orientations = np.tile(np.array([1.0, 0.0, 0.0, 0.0]), (num_envs, 1))
>>> prims.set_local_poses(positions, orientations)
>>>
>>> # reposition only the prims for the first, middle and last of the 5 envs
>>> positions = np.zeros((3, 3))
>>> positions[:,1] = np.arange(3)
>>> orientations = np.tile(np.array([1.0, 0.0, 0.0, 0.0]), (3, 1))
>>> prims.set_local_poses(positions, orientations, indices=np.array([0, 2, 4]))

Set prim scales in the view with respect to the local frame (the prim’s parent frame)

Parameters:

scales (Optional[Union[np.ndarray, torch.Tensor, wp.array]]) – scales to be applied to the prim’s dimensions in the view. shape is (M, 3).
indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Example:

>>> # set the scale for all prims. Since there are 5 envs, the scale is repeated 5 times
>>> scales = np.tile(np.array([1.0, 0.75, 0.5]), (num_envs, 1))
>>> prims.set_local_scales(scales)
>>>
>>> # set the scale for the first, middle and last of the 5 envs
>>> scales = np.tile(np.array([1.0, 0.75, 0.5]), (3, 1))
>>> prims.set_local_scales(scales, indices=np.array([0, 2, 4]))

Set the visibilities of the prims in stage

Parameters:

visibilities (Union[np.ndarray, torch.Tensor, wp.array]) – flag to set the visibilities of the usd prims in stage. Shape (M,). Where M <= size of the encapsulated prims in the view.
indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Defaults to None (i.e: all prims in the view).

Example:

>>> # make all prims not visible in the stage
>>> prims.set_visibilities(visibilities=[False] * num_envs)

Set prim poses in the view with respect to the world’s frame

Warning

This method will change (teleport) the prim poses immediately to the indicated value

Parameters:

positions (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – positions in the world frame of the prims. shape is (M, 3). Defaults to None, which means left unchanged.
orientations (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – quaternion orientations in the world frame of the prims. quaternion is scalar-first (w, x, y, z). shape is (M, 4). Defaults to None, which means left unchanged.
indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
usd (bool, optional) – True to query from usd. Otherwise False to query from Fabric data. Defaults to True.

Hint

This method belongs to the methods used to set the prim state

Example:

>>> # reposition all prims in row (x-axis)
>>> positions = np.zeros((num_envs, 3))
>>> positions[:,0] = np.arange(num_envs)
>>> orientations = np.tile(np.array([1.0, 0.0, 0.0, 0.0]), (num_envs, 1))
>>> prims.set_world_poses(positions, orientations)
>>>
>>> # reposition only the prims for the first, middle and last of the 5 envs in column (y-axis)
>>> positions = np.zeros((3, 3))
>>> positions[:,1] = np.arange(3)
>>> orientations = np.tile(np.array([1.0, 0.0, 0.0, 0.0]), (3, 1))
>>> prims.set_world_poses(positions, orientations, indices=np.array([0, 2, 4]))

Bases: XFormPrim

High level wrapper to deal with geom prims (one or many) as well as their attributes/properties.

This class wraps all matching geom prims found at the regex provided at the prim_paths_expr argument

Note

Each prim will have xformOp:orient, xformOp:translate and xformOp:scale only post-init, unless it is a non-root articulation link.

Warning

The geometry prim view object must be initialized in order to be able to operate on it. See the initialize method for more details.

Warning

Some methods require the prims to have the Physx Collision API. Instantiate the class with the collision parameter to a list of True values to apply the collision API.

Parameters:

prim_paths_expr (str) – prim paths regex to encapsulate all prims that match it. example: “/World/Env[1-5]/Microwave” will match /World/Env1/Microwave, /World/Env2/Microwave..etc. (a non regex prim path can also be used to encapsulate one XForm).
name (str, optional) – shortname to be used as a key by Scene class. Note: needs to be unique if the object is added to the Scene. Defaults to “geometry_prim_view”.
positions (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – default positions in the world frame of the prim. shape is (N, 3). Defaults to None, which means left unchanged.
translations (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – default translations in the local frame of the prims (with respect to its parent prims). shape is (N, 3). Defaults to None, which means left unchanged.
orientations (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – default quaternion orientations in the world/ local frame of the prim (depends if translation or position is specified). quaternion is scalar-first (w, x, y, z). shape is (N, 4). Defaults to None, which means left unchanged.
scales (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – local scales to be applied to the prim’s dimensions. shape is (N, 3). Defaults to None, which means left unchanged.
visibilities (Optional[Union[np.ndarray, torch.Tensor, wp.array], optional) – set to false for an invisible prim in the stage while rendering. shape is (N,). Defaults to None.
reset_xform_properties (bool, optional) – True if the prims don’t have the right set of xform properties (i.e: translate, orient and scale) ONLY and in that order. Set this parameter to False if the object were cloned using using the cloner api in isaacsim.core.cloner. Defaults to True.
collisions (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – Set to True if the geometry already have/ should have a collider (i.e not only a visual geometry). shape is (N,). Defaults to None.
track_contact_forces (bool, Optional) – if enabled, the view will track the net contact forces on each geometry prim in the view. Note that the collision flag should be set to True to report contact forces. Defaults to False.
prepare_contact_sensors (bool, Optional) – applies contact reporter API to the prim if it already does not have one. Defaults to False.
disable_stablization (bool, optional) – disables the contact stabilization parameter in the physics context. Defaults to True.
contact_filter_prim_paths_expr (Optional[List[str]], Optional) – a list of filter expressions which allows for tracking contact forces between the geometry prim and this subset through get_contact_force_matrix().
max_contact_count (int, optional) – maximum number of contact data to report when detailed contact information is needed

Example:

>>> import isaacsim.core.utils.stage as stage_utils
>>> from isaacsim.core.cloner import GridCloner
>>> from isaacsim.core.prims import GeometryPrim
>>> from pxr import UsdGeom
>>>
>>> env_zero_path = "/World/envs/env_0"
>>> num_envs = 5
>>>
>>> # clone the environment (num_envs)
>>> cloner = GridCloner(spacing=1.5)
>>> cloner.define_base_env(env_zero_path)
>>> UsdGeom.Xform.Define(stage_utils.get_current_stage(), env_zero_path)
>>> stage_utils.get_current_stage().DefinePrim(f"{env_zero_path}/Xform", "Xform")
>>> stage_utils.get_current_stage().DefinePrim(f"{env_zero_path}/Xform/Cube", "Cube")
>>> env_pos = cloner.clone(
...     source_prim_path=env_zero_path,
...     prim_paths=cloner.generate_paths("/World/envs/env", num_envs),
...     copy_from_source=True
... )
>>>
>>> # wrap the prims
>>> prims = GeometryPrim(
...     prim_paths_expr="/World/envs/env.*/Xform",
...     name="geometry_prim_view",
...     collisions=[True] * num_envs
... )
>>> prims
<isaacsim.core.prims.geometry_prim.GeometryPrim object at 0x7f372bb21630>

property geoms: List[pxr.UsdGeom.Gprim]#

Returns:: USD geom objects encapsulated.
Return type:: List[UsdGeom.Gprim]

Example:

>>> prims.geoms
[UsdGeom.Gprim(Usd.Prim(</World/envs/env_0/Xform>)), UsdGeom.Gprim(Usd.Prim(</World/envs/env_1/Xform>)),
 UsdGeom.Gprim(Usd.Prim(</World/envs/env_2/Xform>)), UsdGeom.Gprim(Usd.Prim(</World/envs/env_3/Xform>)),
 UsdGeom.Gprim(Usd.Prim(</World/envs/env_4/Xform>))]

initialize( physics_sim_view: omni.physics.tensors.SimulationView | None = None, ) → None#

Create a physics simulation view if not passed and set other properties using the PhysX tensor API

Note

If the rigid prim view has been added to the world scene (e.g., world.scene.add(prims)), it will be automatically initialized when the world is reset (e.g., world.reset()).

Warning

This method needs to be called after each hard reset (e.g., Stop + Play on the timeline) before interacting with any other class method.

Parameters:: physics_sim_view (omni.physics.tensors.SimulationView, optional) – current physics simulation view. Defaults to None.

Example:

>>> prims.initialize()

Set contact offsets for prims in the view.

Shapes whose distance is less than the sum of their contact offset values will generate contacts

Search for Advanced Collision Detection in PhysX docs for more details

Parameters:

offsets (Union[np.ndarray, torch.Tensor, wp.array]) – Contact offsets of the collision shapes. Allowed range [maximum(0, rest_offset), 0]. Default value is -inf, means default is picked by simulation based on the shape extent. Shape (M,).
indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Example:

>>> # set the contact offset for all the prims to the specified values.
>>> prims.set_contact_offsets(np.full(num_envs, 0.02))
>>>
>>> # set the contact offset for the first, middle and last of the 5 envs
>>> prims.set_contact_offsets(np.full(3, 0.02), indices=np.array([0, 2, 4]))

Get contact offsets for prims in the view.

Shapes whose distance is less than the sum of their contact offset values will generate contacts

Search for Advanced Collision Detection in PhysX docs for more details

Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
Returns:: Contact offsets of the collision shapes. Shape is (M,).
Return type:: Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>> # get the contact offsets of all prims. Returned shape is (5,).
>>> prims.get_contact_offsets()
[-inf -inf -inf -inf -inf]
>>>
>>> # get the contact offsets of the prims for the first, middle and last of the 5 envs
>>> prims.get_contact_offsets(indices=np.array([0, 2, 4]))
[-inf -inf -inf]

Set rest offsets for prims in the view.

Two shapes will come to rest at a distance equal to the sum of their rest offset values. If the rest offset is 0, they should converge to touching exactly

Search for Advanced Collision Detection in PhysX docs for more details

Warning

The contact offset must be positive and greater than the rest offset

Parameters:

offsets (Union[np.ndarray, torch.Tensor, wp.array]) – Rest offset of a collision shape. Allowed range [-max_float, contact_offset]. Default value is -inf, means default is picked by simulation. For rigid bodies its zero. Shape (M,).
indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Example:

>>> # set the rest offset for all the prims to the specified values.
>>> prims.set_rest_offsets(np.full(num_envs, 0.01))
>>>
>>> # set the rest offset for the first, middle and last of the 5 envs
>>> prims.set_rest_offsets(np.full(3, 0.01), indices=np.array([0, 2, 4]))

Get rest offsets for prims in the view.

Two shapes will come to rest at a distance equal to the sum of their rest offset values. If the rest offset is 0, they should converge to touching exactly

Search for Advanced Collision Detection in PhysX docs for more details

Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
Returns:: Rest offsets of the collision shapes. Shape is (M,).
Return type:: Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>> # get the rest offsets of all prims. Returned shape is (5,).
>>> prims.get_rest_offsets()
[-inf -inf -inf -inf -inf]
>>>
>>> # get the rest offsets of the prims for the first, middle and last of the 5 envs
>>> prims.get_rest_offsets(indices=np.array([0, 2, 4]))
[-inf -inf -inf]

Set torsional patch radii for prims in the view.

Search for “Torsional Patch Radius” in PhysX docs for more details

Parameters:

radii (Union[np.ndarray, torch.Tensor, wp.array]) – radius of the contact patch used to apply torsional friction. Allowed range [0, max_float]. shape is (M,).
indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Example:

>>> # set the torsional patch radius for all the prims to the specified values.
>>> prims.set_torsional_patch_radii(np.full(num_envs, 0.1))
>>>
>>> # set the torsional patch radius for the first, middle and last of the 5 envs
>>> prims.set_torsional_patch_radii(np.full(3, 0.1), indices=np.array([0, 2, 4]))

Get torsional patch radii for prims in the view.

Search for “Torsional Patch Radius” in PhysX docs for more details

Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
Returns:: radius of the contact patch used to apply torsional friction. shape is (M,).
Return type:: Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>> # get the torsional patch radius of all prims. Returned shape is (5,).
>>> prims.get_torsional_patch_radii()
[0. 0. 0. 0. 0.]
>>>
>>> # get the torsional patch radius of the prims for the first, middle and last of the 5 envs
>>> prims.get_torsional_patch_radii(indices=np.array([0, 2, 4]))
[0. 0. 0.]

Set minimum torsional patch radii for prims in the view.

Search for “Torsional Patch Radius” in PhysX docs for more details

Parameters:

radii (Union[np.ndarray, torch.Tensor, wp.array]) – minimum radius of the contact patch used to apply torsional friction. Allowed range [0, max_float]. shape is (M, ).
indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Example:

>>> # set the minimum torsional patch radius for all the prims to the specified values.
>>> prims.set_min_torsional_patch_radii(np.full(num_envs, 0.05))
>>>
>>> # set the minimum torsional patch radius for the first, middle and last of the 5 envs
>>> prims.set_min_torsional_patch_radii(np.full(3, 0.05), indices=np.array([0, 2, 4]))

get_min_torsional_patch_radii( indices: ndarray | list | Tensor | None = None, ) → ndarray | Tensor#

Get minimum torsional patch radii for prims in the view.

Search for “Torsional Patch Radius” in PhysX docs for more details

Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
Returns:: minimum radius of the contact patch used to apply torsional friction. shape is (M,).
Return type:: Union[np.ndarray, torch.Tensor]

Example:

>>> # get the minimum torsional patch radius of all prims. Returned shape is (5,).
>>> prims.get_min_torsional_patch_radii()
[0. 0. 0. 0. 0.]
>>>
>>> # get the minimum torsional patch radius of the prims for the first, middle and last of the 5 envs
>>> prims.get_min_torsional_patch_radii(indices=np.array([0, 2, 4]))
[0. 0. 0.]

set_collision_approximations( approximation_types: List[str], indices: ndarray | list | Tensor | warp.array | None = None, ) → None#

Set collision approximation types for prims in the view.

Approximation	Full name	Description
`"none"`	Triangle Mesh	The mesh geometry is used directly as a collider without any approximation
`"convexDecomposition"`	Convex Decomposition	A convex mesh decomposition is performed. This results in a set of convex mesh colliders
`"convexHull"`	Convex Hull	A convex hull of the mesh is generated and used as the collider
`"boundingSphere"`	Bounding Sphere	A bounding sphere is computed around the mesh and used as a collider
`"boundingCube"`	Bounding Cube	An optimally fitting box collider is computed around the mesh
`"meshSimplification"`	Mesh Simplification	A mesh simplification step is performed, resulting in a simplified triangle mesh collider
`"sdf"`	SDF Mesh	SDF (Signed-Distance-Field) use high-detail triangle meshes as collision shape
`"sphereFill"`	Sphere Approximation	A sphere mesh decomposition is performed. This results in a set of sphere colliders

Parameters:

approximation_types (List[str]) – approximations used for collision. List size == M or the size of the view.
indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Example:

>>> # set the collision approximations for all the prims to the specified values.
>>> prims.set_collision_approximations(["convexDecomposition"] * num_envs)
>>>
>>> # set the collision approximations for the first, middle and last of the 5 envs
>>> types = ["convexDecomposition", "convexHull", "meshSimplification"]
>>> prims.set_collision_approximations(types, indices=np.array([0, 2, 4]))

get_collision_approximations( indices: ndarray | list | Tensor | warp.array | None = None, ) → List[str]#

Get collision approximation types for prims in the view.

Approximation	Full name	Description
`"none"`	Triangle Mesh	The mesh geometry is used directly as a collider without any approximation
`"convexDecomposition"`	Convex Decomposition	A convex mesh decomposition is performed. This results in a set of convex mesh colliders
`"convexHull"`	Convex Hull	A convex hull of the mesh is generated and used as the collider
`"boundingSphere"`	Bounding Sphere	A bounding sphere is computed around the mesh and used as a collider
`"boundingCube"`	Bounding Cube	An optimally fitting box collider is computed around the mesh
`"meshSimplification"`	Mesh Simplification	A mesh simplification step is performed, resulting in a simplified triangle mesh collider
`"sdf"`	SDF Mesh	SDF (Signed-Distance-Field) use high-detail triangle meshes as collision shape
`"sphereFill"`	Sphere Approximation	A sphere mesh decomposition is performed. This results in a set of sphere colliders

Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
Returns:: approximations used for collision. size == M or size of the view.
Return type:: List[str]

Example:

>>> # get the collision approximation of all prims. Returned size is (5,).
>>> prims.get_collision_approximations()
['none', 'none', 'none', 'none', 'none']
>>>
>>> # get the collision approximation of the prims for the first, middle and last of the 5 envs
>>> prims.get_collision_approximations(indices=np.array([0, 2, 4]))
['none', 'none', 'none']

enable_collision( indices: ndarray | list | Tensor | warp.array | None = None, ) → None#

Enables collision on prims in the view.

Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Example:

>>> # enable the collision API for all prims
>>> prims.enable_collision()
>>>
>>> # enable the collision API for the prims for the first, middle and last of the 5 envs
>>> prims.enable_collision(indices=np.array([0, 2, 4]))

disable_collision( indices: ndarray | list | Tensor | warp.array | None = None, ) → None#

Disables collision on prims in the view.

Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Example:

>>> # disable the collision API for all prims
>>> prims.disable_collision()
>>>
>>> # disable the collision API for the prims for the first, middle and last of the 5 envs
>>> prims.disable_collision(indices=np.array([0, 2, 4]))

Queries if collision is enabled on prims in the view.

Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
Returns:: True if collision is enabled. Shape is (M,).
Return type:: Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>> # check if the collision is enabled for all prims. Returned size is (5,).
>>> prims.is_collision_enabled()
[ True  True  True  True  True]
>>>
>>> # check if the collision is enabled for the first, middle and last of the 5 envs
>>> prims.is_collision_enabled(indices=np.array([0, 2, 4]))
[ True  True  True]

apply_collision_apis( indices: ndarray | list | Tensor | warp.array | None = None, ) → None#

Apply the collision API to prims in the view and update internal variables

Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Example:

>>> # apply the collision API for all prims
>>> prims.apply_collision_apis()
>>>
>>> # apply the collision API for the first, middle and last of the 5 envs
>>> prims.apply_collision_apis(indices=np.array([0, 2, 4]))

Used to apply physics material to prims in the view and optionally its descendants.

Parameters:

physics_materials (Union[PhysicsMaterial, List[PhysicsMaterial]]) – physics materials to be applied to prims in the view. Physics material can be used to define friction, restitution..etc. Note: if a physics material is not defined, the defaults will be used from PhysX. If a list is provided then its size has to be equal the view’s size or indices size. If one material is provided it will be applied to all prims in the view.
weaker_than_descendants (Optional[Union[bool, List[bool]]], optional) – True if the material shouldn’t override the descendants materials, otherwise False. Defaults to False. If a list of visual materials is provided then a list has to be provided with the same size for this arg as well.
indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Raises:

Exception – length of physics materials != length of prims indexed
Exception – length of physics materials != length of weaker descendants arg

Example:

>>> from isaacsim.core.api.materials import PhysicsMaterial
>>>
>>> # create a rigid body physical material
>>> material = PhysicsMaterial(
...     prim_path="/World/physics_material/aluminum",  # path to the material prim to create
...     dynamic_friction=0.4,
...     static_friction=1.1,
...     restitution=0.1
... )
>>>
>>> # apply the material to all prims
>>> prims.apply_physics_materials(material)  # or [material] * num_envs
>>>
>>> # apply the collision API for the first, middle and last of the 5 envs
>>> prims.apply_physics_materials(material, indices=np.array([0, 2, 4]))

get_applied_physics_materials( indices: ndarray | list | Tensor | warp.array | None = None, ) → List[PhysicsMaterial]#

Get the applied physics material to prims in the view.

Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
Returns:: the current applied physics materials for prims in the view.
Return type:: List[PhysicsMaterial]

Example:

>>> # get the applied material for all prims
>>> prims.get_applied_physics_materials()
[<isaacsim.core.api.materials.physics_material.PhysicsMaterial object at 0x7f720859ece0>,
 <isaacsim.core.api.materials.physics_material.PhysicsMaterial object at 0x7f720859ece0>,
 <isaacsim.core.api.materials.physics_material.PhysicsMaterial object at 0x7f720859ece0>,
 <isaacsim.core.api.materials.physics_material.PhysicsMaterial object at 0x7f720859ece0>,
 <isaacsim.core.api.materials.physics_material.PhysicsMaterial object at 0x7f720859ece0>]
>>>
>>> # get the applied material for the first, middle and last of the 5 envs
>>> prims.get_applied_physics_materials(indices=np.array([0, 2, 4]))
[<isaacsim.core.api.materials.physics_material.PhysicsMaterial object at 0x7f720859ece0>,
 <isaacsim.core.api.materials.physics_material.PhysicsMaterial object at 0x7f720859ece0>,
 <isaacsim.core.api.materials.physics_material.PhysicsMaterial object at 0x7f720859ece0>]

If contact forces of the prims in the view are tracked, this method returns the net contact forces on prims. i.e., a matrix of dimension (self.count, 3)

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.
dt (float) – time step multiplier to convert the underlying impulses to forces. If the default value is used then the forces are in fact contact impulses

Returns:

Net contact forces of the prims with shape (M,3).

Return type:

Union[np.ndarray, torch.Tensor, wp.indexedarray]

If the object is initialized with filter_paths_expr list, this method returns the contact forces between the prims in the view and the filter prims. i.e., a matrix of dimension (self.count, self._contact_view.num_filters, 3) where num_filters is the determined according to the filter_paths_expr parameter.

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.
dt (float) – time step multiplier to convert the underlying impulses to forces. If the default value is used then the forces are in fact contact impulses

Returns:

Net contact forces of the prims with shape (M, self._contact_view.num_filters, 3).

Return type:

Union[np.ndarray, torch.Tensor, wp.indexedarray]

Get more detailed contact information between the prims in the view and the filter prims. Specifically, this method provides individual contact normals, contact points, contact separations as well as contact forces for each pair (the sum of which equals the forces that the get_contact_force_matrix method provides as the force aggregate of a pair) Given to the dynamic nature of collision between bodies, this method will provide buffers of contact data which are arranged sequentially for each pair. The starting index and the number of contact data points for each pair in this stream can be realized from pair_contacts_start_indices, and pair_contacts_count tensors. They both have a dimension of (self.num_shapes, self.num_filters) where filter_count is determined according to the filter_paths_expr parameter.

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.
dt (float) – time step multiplier to convert the underlying impulses to forces. If the default value is used then the forces are in fact contact impulses

Returns:

Tuple[Union[np.ndarray, torch.Tensor, wp.indexedarray], Union[np.ndarray, torch.Tensor, wp.indexedarray],: Union[np.ndarray, torch.Tensor, wp.indexedarray], Union[np.ndarray, torch.Tensor, wp.indexedarray], Union[np.ndarray, torch.Tensor, wp.indexedarray], Union[np.ndarray, torch.Tensor, wp.indexedarray]]: A set of buffers for normal forces with shape (max_contact_count, 1), points with shape (max_contact_count, 3), normals with shape (max_contact_count, 3), and distances with shape (max_contact_count, 1), as well as two tensors with shape (M, self.num_filters) to indicate the starting index and the number of contact data points per pair in the aforementioned buffers.

Gets friction data between the prims in the view and the filter prims. Specifically, this method provides frictional contact forces, and points. The data in reported for number of anchor points that includes tangential forces in a single tangent direction to contact normal. Given to the dynamic nature of collision between bodies, this method will provide buffers of friction data arranged sequentially for each pair. The starting index and the number of contact data points for each pair in this stream can be realized from pair_contacts_start_indices, and pair_contacts_count tensors. They both have a dimension of (self.num_shapes, self.num_filters) where filter_count is determined according to the filter_paths_expr parameter.

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indicies to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.
dt (float) – time step multiplier to convert the underlying impulses to forces. If the default value is used then the forces are in fact contact impulses

Returns:

Tuple[Union[np.ndarray, torch.Tensor, wp.indexedarray], Union[np.ndarray, torch.Tensor, wp.indexedarray],: Union[np.ndarray, torch.Tensor, wp.indexedarray], Union[np.ndarray, torch.Tensor, wp.indexedarray]]: A set of buffers for tangential forces per patch (at number of anchor points, each in a single directions) with shape (max_contact_count, 3), points with shape (max_contact_count, 3), as well as two tensors with shape (M, self.num_filters) to indicate the starting index and the number of contact data points per pair in the aforementioned buffers.

Apply visual material to the prims and optionally their prim descendants.

Parameters:

visual_materials (Union[VisualMaterial, List[VisualMaterial]]) – visual materials to be applied to the prims. Currently supports PreviewSurface, OmniPBR and OmniGlass. If a list is provided then its size has to be equal the view’s size or indices size. If one material is provided it will be applied to all prims in the view.
weaker_than_descendants (Optional[Union[bool, List[bool]]], optional) – True if the material shouldn’t override the descendants materials, otherwise False. Defaults to False. If a list of visual materials is provided then a list has to be provided with the same size for this arg as well.
indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Raises:

Exception – length of visual materials != length of prims indexed
Exception – length of visual materials != length of weaker descendants bools arg

Example:

>>> from isaacsim.core.api.materials import OmniGlass
>>>
>>> # create a dark-red glass visual material
>>> material = OmniGlass(
...     prim_path="/World/material/glass",  # path to the material prim to create
...     ior=1.25,
...     depth=0.001,
...     thin_walled=False,
...     color=np.array([0.5, 0.0, 0.0])
... )
>>> prims.apply_visual_materials(material)

property count: int#

Returns:: Number of prims encapsulated in this view.
Return type:: int

Example:

>>> prims.count
5

get_applied_visual_materials( indices: ndarray | list | Tensor | warp.array | None = None, ) → List[VisualMaterial]#

Get the current applied visual materials

Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
Returns:: a list of the current applied visual materials to the prims if its type is currently supported.
Return type:: List[VisualMaterial]

Example:

>>> # get all applied visual materials. Returned size is 5 for the example: 5 envs
>>> prims.get_applied_visual_materials()
[<isaacsim.core.api.materials.omni_glass.OmniGlass object at 0x7f829c165de0>,
 <isaacsim.core.api.materials.omni_glass.OmniGlass object at 0x7f829c165de0>,
 <isaacsim.core.api.materials.omni_glass.OmniGlass object at 0x7f829c165de0>,
 <isaacsim.core.api.materials.omni_glass.OmniGlass object at 0x7f829c165de0>,
 <isaacsim.core.api.materials.omni_glass.OmniGlass object at 0x7f829c165de0>]
>>>
>>> # get the applied visual materials for the first, middle and last of the 5 envs. Returned size is 3
>>> prims.get_applied_visual_materials(indices=np.array([0, 2, 4]))
[<isaacsim.core.api.materials.omni_glass.OmniGlass object at 0x7f829c165de0>,
 <isaacsim.core.api.materials.omni_glass.OmniGlass object at 0x7f829c165de0>,
 <isaacsim.core.api.materials.omni_glass.OmniGlass object at 0x7f829c165de0>]

get_default_state() → XFormPrimViewState#

Get the default states (positions and orientations) defined with the set_default_state method

Returns:: returns the default state of the prims that is used after each reset.
Return type:: XFormPrimViewState

Example:

>>> state = prims.get_default_state()
>>> state
<isaacsim.core.utils.types.XFormPrimViewState object at 0x7f82f73e3070>
>>> state.positions
[[ 1.5  -0.75  0.  ]
 [ 1.5   0.75  0.  ]
 [ 0.   -0.75  0.  ]
 [ 0.    0.75  0.  ]
 [-1.5  -0.75  0.  ]]
>>> state.orientations
[[1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]]

Get prim poses in the view with respect to the local frame (the prim’s parent frame)

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Returns:

first index is translations in the local frame of the prims. shape is (M, 3).: second index is quaternion orientations in the local frame of the prims. quaternion is scalar-first (w, x, y, z). shape is (M, 4).

Return type:

Union[Tuple[np.ndarray, np.ndarray], Tuple[torch.Tensor, torch.Tensor], Tuple[wp.indexedarray, wp.indexedarray]]

Example:

>>> # get all prims poses with respect to the local frame.
>>> # Returned shape is position (5, 3) and orientation (5, 4) for the example: 5 envs
>>> positions, orientations = prims.get_local_poses()
>>> positions
[[ 1.5  -0.75  0.  ]
 [ 1.5   0.75  0.  ]
 [ 0.   -0.75  0.  ]
 [ 0.    0.75  0.  ]
 [-1.5  -0.75  0.  ]]
>>> orientations
[[1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]]
>>>
>>> # get only the prims poses with respect to the local frame for the first, middle and last of the 5 envs.
>>> # Returned shape is position (3, 3) and orientation (3, 4) for the example: 3 envs selected
>>> positions, orientations = prims.get_local_poses(indices=np.array([0, 2, 4]))
>>> positions
[[ 1.5  -0.75  0.  ]
 [ 0.   -0.75  0.  ]
 [-1.5  -0.75  0.  ]]
>>> orientations
[[1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]]

Get prim scales in the view with respect to the local frame (the parent’s frame).

Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
Returns:: scales applied to the prim’s dimensions in the local frame. shape is (M, 3).
Return type:: Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>> # get all prims scales with respect to the local frame.
>>> # Returned shape is (5, 3) for the example: 5 envs
>>> prims.get_local_scales()
[[1. 1. 1.]
 [1. 1. 1.]
 [1. 1. 1.]
 [1. 1. 1.]
 [1. 1. 1.]]
>>>
>>> # get only the prims scales with respect to the local frame for the first, middle and last of the 5 envs.
>>> # Returned shape is (3, 3) for the example: 3 envs selected
>>> prims.get_local_scales(indices=np.array([0, 2, 4]))
[[1. 1. 1.]
 [1. 1. 1.]
 [1. 1. 1.]]

Returns the current visibilities of the prims in stage.

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Returns:

Shape (M,) with type bool, where each item holds True: if the prim is visible in stage. False otherwise.

Return type:

Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>> # get all visibilities. Returned shape is (5,) for the example: 5 envs
>>> prims.get_visibilities()
[ True  True  True  True  True]
>>>
>>> # get the visibilities for the first, middle and last of the 5 envs. Returned shape is (3,)
>>> prims.get_visibilities(indices=np.array([0, 2, 4]))
[ True  True  True]

Get the poses of the prims in the view with respect to the world’s frame

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
usd (bool, optional) – True to query from usd. Otherwise False to query from Fabric data. Defaults to True.

Returns:

first index is positions in the world frame of the prims. shape is (M, 3).: second index is quaternion orientations in the world frame of the prims. quaternion is scalar-first (w, x, y, z). shape is (M, 4).

Return type:

Union[Tuple[np.ndarray, np.ndarray], Tuple[torch.Tensor, torch.Tensor], Tuple[wp.indexedarray, wp.indexedarray]]

Example:

>>> # get all prims poses with respect to the world's frame.
>>> # Returned shape is position (5, 3) and orientation (5, 4) for the example: 5 envs
>>> positions, orientations = prims.get_world_poses()
>>> positions
[[ 1.5  -0.75  0.  ]
 [ 1.5   0.75  0.  ]
 [ 0.   -0.75  0.  ]
 [ 0.    0.75  0.  ]
 [-1.5  -0.75  0.  ]]
>>> orientations
[[1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]]
>>>
>>> # get only the prims poses with respect to the world's frame for the first, middle and last of the 5 envs.
>>> # Returned shape is position (3, 3) and orientation (3, 4) for the example: 3 envs selected
>>> positions, orientations = prims.get_world_poses(indices=np.array([0, 2, 4]))
>>> positions
[[ 1.5  -0.75  0.  ]
 [ 0.   -0.75  0.  ]
 [-1.5  -0.75  0.  ]]
>>> orientations
[[1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]]

Get prim scales in the view with respect to the world’s frame

Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
Returns:: scales applied to the prim’s dimensions in the world frame. shape is (M, 3).
Return type:: Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>> # get all prims scales with respect to the world's frame.
>>> # Returned shape is (5, 3) for the example: 5 envs
>>> prims.get_world_scales()
[[1. 1. 1.]
 [1. 1. 1.]
 [1. 1. 1.]
 [1. 1. 1.]
 [1. 1. 1.]]
>>>
>>> # get only the prims scales with respect to the world's frame for the first, middle and last of the 5 envs.
>>> # Returned shape is (3, 3) for the example: 3 envs selected
>>> prims.get_world_scales(indices=np.array([0, 2, 4]))
[[1. 1. 1.]
 [1. 1. 1.]
 [1. 1. 1.]]

property initialized: bool#

Check if prim view is initialized

Returns:: True if the view object was initialized (after the first call of .initialize()). False otherwise.
Return type:: bool

Example:

>>> # given an initialized articulation view
>>> prims.initialized
True

property is_non_root_articulation_link: bool#: Returns: bool: True if the prim corresponds to a non root link in an articulation. Otherwise False.

is_valid( indices: ndarray | list | Tensor | warp.array | None = None, ) → bool#

Check that all prims have a valid USD Prim

Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
Returns:: True if all prim paths specified in the view correspond to a valid prim in stage. False otherwise.
Return type:: bool

Example:

>>> prims.is_valid()
True

is_visual_material_applied( indices: ndarray | list | Tensor | warp.array | None = None, ) → List[bool]#

Check if there is a visual material applied

Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
Returns:: True if there is a visual material applied is applied to the corresponding prim in the view. False otherwise.
Return type:: List[bool]

Example:

>>> # given a visual material that is applied only to the first and the last environment
>>> prims.is_visual_material_applied()
[True, False, False, False, True]
>>>
>>> # check for the first, middle and last of the 5 envs
>>> prims.is_visual_material_applied(indices=np.array([0, 2, 4]))
[True, False, True]

property name: str#: Returns: str: name given to the prims view when instantiating it.

post_reset() → None#

Reset the prims to its default state

Example:

>>> prims.post_reset()

property prim_paths: List[str]#

Returns:: list of prim paths in the stage encapsulated in this view.
Return type:: List[str]

Example:

>>> prims.prim_paths
['/World/envs/env_0', '/World/envs/env_1', '/World/envs/env_2', '/World/envs/env_3', '/World/envs/env_4']

property prims: List[pxr.Usd.Prim]#

Returns:: List of USD Prim objects encapsulated in this view.
Return type:: List[Usd.Prim]

Example:

>>> prims.prims
[Usd.Prim(</World/envs/env_0>), Usd.Prim(</World/envs/env_1>), Usd.Prim(</World/envs/env_2>),
 Usd.Prim(</World/envs/env_3>), Usd.Prim(</World/envs/env_4>)]

Set the default state of the prims (positions and orientations), that will be used after each reset.

Note

The default states will be set during post-reset (e.g., calling .post_reset() or world.reset() methods)

Parameters:

positions (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – positions in the world frame of the prim. shape is (M, 3). Defaults to None, which means left unchanged.
orientations (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – quaternion orientations in the world frame of the prim. quaternion is scalar-first (w, x, y, z). shape is (M, 4). Defaults to None, which means left unchanged.
indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Example:

>>> # configure default states for all prims
>>> positions = np.zeros((num_envs, 3))
>>> positions[:, 0] = np.arange(num_envs)
>>> orientations = np.tile(np.array([1.0, 0.0, 0.0, 0.0]), (num_envs, 1))
>>> prims.set_default_state(positions=positions, orientations=orientations)
>>>
>>> # set default states during post-reset
>>> prims.post_reset()

Set prim poses in the view with respect to the local frame (the prim’s parent frame)

Warning

This method will change (teleport) the prim poses immediately to the indicated value

Parameters:

translations (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – translations in the local frame of the prims (with respect to its parent prim). shape is (M, 3). Defaults to None, which means left unchanged.
orientations (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – quaternion orientations in the local frame of the prims. quaternion is scalar-first (w, x, y, z). shape is (M, 4). Defaults to None, which means left unchanged.
indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Hint

This method belongs to the methods used to set the prim state

Example:

>>> # reposition all prims
>>> positions = np.zeros((num_envs, 3))
>>> positions[:,0] = np.arange(num_envs)
>>> orientations = np.tile(np.array([1.0, 0.0, 0.0, 0.0]), (num_envs, 1))
>>> prims.set_local_poses(positions, orientations)
>>>
>>> # reposition only the prims for the first, middle and last of the 5 envs
>>> positions = np.zeros((3, 3))
>>> positions[:,1] = np.arange(3)
>>> orientations = np.tile(np.array([1.0, 0.0, 0.0, 0.0]), (3, 1))
>>> prims.set_local_poses(positions, orientations, indices=np.array([0, 2, 4]))

Set prim scales in the view with respect to the local frame (the prim’s parent frame)

Parameters:

scales (Optional[Union[np.ndarray, torch.Tensor, wp.array]]) – scales to be applied to the prim’s dimensions in the view. shape is (M, 3).
indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Example:

>>> # set the scale for all prims. Since there are 5 envs, the scale is repeated 5 times
>>> scales = np.tile(np.array([1.0, 0.75, 0.5]), (num_envs, 1))
>>> prims.set_local_scales(scales)
>>>
>>> # set the scale for the first, middle and last of the 5 envs
>>> scales = np.tile(np.array([1.0, 0.75, 0.5]), (3, 1))
>>> prims.set_local_scales(scales, indices=np.array([0, 2, 4]))

Set the visibilities of the prims in stage

Parameters:

visibilities (Union[np.ndarray, torch.Tensor, wp.array]) – flag to set the visibilities of the usd prims in stage. Shape (M,). Where M <= size of the encapsulated prims in the view.
indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Defaults to None (i.e: all prims in the view).

Example:

>>> # make all prims not visible in the stage
>>> prims.set_visibilities(visibilities=[False] * num_envs)

Set prim poses in the view with respect to the world’s frame

Warning

This method will change (teleport) the prim poses immediately to the indicated value

Parameters:

positions (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – positions in the world frame of the prims. shape is (M, 3). Defaults to None, which means left unchanged.
orientations (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – quaternion orientations in the world frame of the prims. quaternion is scalar-first (w, x, y, z). shape is (M, 4). Defaults to None, which means left unchanged.
indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
usd (bool, optional) – True to query from usd. Otherwise False to query from Fabric data. Defaults to True.

Hint

This method belongs to the methods used to set the prim state

Example:

>>> # reposition all prims in row (x-axis)
>>> positions = np.zeros((num_envs, 3))
>>> positions[:,0] = np.arange(num_envs)
>>> orientations = np.tile(np.array([1.0, 0.0, 0.0, 0.0]), (num_envs, 1))
>>> prims.set_world_poses(positions, orientations)
>>>
>>> # reposition only the prims for the first, middle and last of the 5 envs in column (y-axis)
>>> positions = np.zeros((3, 3))
>>> positions[:,1] = np.arange(3)
>>> orientations = np.tile(np.array([1.0, 0.0, 0.0, 0.0]), (3, 1))
>>> prims.set_world_poses(positions, orientations, indices=np.array([0, 2, 4]))

Bases: object

Provides high level functions to deal with particle systems (1 or more particle systems) as well as its attributes/ properties. This object wraps all matching particle systems found at the regex provided at the prim_paths_expr. Note: not all the attributes of the PhysxSchema.PhysxParticleSystem is currently controlled with this view class Tensor API support will be added in the future to extend the functionality of this class to applications beyond cloth.

property count: int#: Returns: int: number of rigid shapes for the prims in the view.

property name: str#: Returns: str: name given to the view when instantiating it.

is_physics_handle_valid() → bool#

Returns:: True if the physics handle of the view is valid (i.e physics is initialized for the view). Otherwise False.
Return type:: bool

initialize( physics_sim_view: omni.physics.tensors.SimulationView | None = None, ) → None#

Create a physics simulation view if not passed and creates a Particle System View.

Parameters:: physics_sim_view (omni.physics.tensors.SimulationView, optional) – current physics simulation view. Defaults to None.

is_valid( indices: ndarray | list | Tensor | None = None, ) → bool#

Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
Returns:: True if all prim paths specified in the view correspond to a valid prim in stage. False otherwise.
Return type:: bool

post_reset() → None#: Resets the particles to their initial states.

apply_particle_materials( particle_materials: ParticleMaterial | List[ParticleMaterial], indices: ndarray | list | Tensor | None = None, ) → None#

Used to apply particle material to prims in the view.

Parameters:

particle_materials (Union[ParticleMaterial, List[ParticleMaterial]]) – particle materials to be applied to prims in the view. Note: if a physics material is not defined, the defaults will be used from PhysX. If a list is provided then its size has to be equal the view’s size or indices size. If one material is provided it will be applied to all prims in the view.
indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Raises:

Exception – length of physics materials != length of prims indexed

get_applied_particle_materials( indices: ndarray | list | Tensor | None = None, ) → List[ParticleMaterial]#

Gets the applied particle material to prims in the view.

Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
Returns:: the current applied particle materials for prims in the view.
Return type:: List[ParticleMaterial]

set_particle_contact_offsets( values: ndarray | Tensor, indices: ndarray | List | Tensor | None = None, ) → None#

Set the contact offset used for interactions between particles.

Note: Must be larger than solid and fluid rest offsets.

Parameters:

values (Optional[Union[np.ndarray, torch.Tensor]]) – The contact offset.
indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

set_solid_rest_offsets( values: ndarray | Tensor, indices: ndarray | List | Tensor | None = None, ) → None#

Set the rest offset used for solid-solid or solid-fluid particle interactions.

Note: Must be smaller than particle contact offset.

Parameters:

values (Optional[Union[np.ndarray, torch.Tensor]]) – solid rest offset to set particle systems to. shape is (M, ).
indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

set_fluid_rest_offsets( values: ndarray | Tensor, indices: ndarray | List | Tensor | None = None, ) → None#

Set the rest offset used for fluid-fluid particle interactions.

Note: Must be smaller than particle contact offset.

Parameters:

values (Optional[Union[np.ndarray, torch.Tensor]]) – fluid rest offset to set particle systems to. shape is (M, ).
indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

set_winds( values: ndarray | Tensor, indices: ndarray | List | Tensor | None = None, ) → None#

Set the winds velocities applied to the current particle system.

Parameters:

values (Optional[Union[np.ndarray, torch.Tensor]]) – The wind applied to the current particle system. shape is (M, 3).
indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

set_max_velocities( values: ndarray | Tensor, indices: ndarray | List | Tensor | None = None, ) → None#

Set the maximum particle velocity for particle systems.

Parameters:

values (Optional[Union[np.ndarray, torch.Tensor]]) – maximum particle velocity tensor to set particle systems to. shape is (M, ).
indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

set_max_depenetration_velocities( values: ndarray | Tensor, indices: ndarray | List | Tensor | None = None, ) → None#

Set the maximum velocity permitted to be introduced by the solver to depenetrate intersecting particles for particle systems.

Parameters:

values (Optional[Union[np.ndarray, torch.Tensor]]) – maximum particle velocity tensor to set particle systems to. shape is (M, ).
indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

set_rest_offsets( values: ndarray | Tensor, indices: ndarray | List | Tensor | None = None, ) → None#

Set the rest offset used for collisions with non-particle objects such as rigid or deformable bodies for particle systems.

Parameters:

values (Optional[Union[np.ndarray, torch.Tensor]]) – maximum particle velocity tensor to set particle systems to. shape is (M, ).
indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

set_contact_offsets( values: ndarray | Tensor, indices: ndarray | List | Tensor | None = None, ) → None#

Set the contact offset used for collisions with non-particle objects such as rigid or deformable bodies for particle systems.

Parameters:

values (Optional[Union[np.ndarray, torch.Tensor]]) – maximum particle velocity tensor to set particle systems to. shape is (M, ).
indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

set_solver_position_iteration_counts( values: ndarray | Tensor, indices: ndarray | List | Tensor | None = None, ) → None#

Set the number of solver iterations for position for particle systems.

Parameters:

values (Optional[Union[np.ndarray, torch.Tensor]]) – maximum particle velocity tensor to set particle systems to. shape is (M, ).
indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

set_max_neighborhoods( values: ndarray | Tensor, indices: ndarray | List | Tensor | None = None, ) → None#

Set the particle neighborhood size for particle systems.

Parameters:

values (Optional[Union[np.ndarray, torch.Tensor]]) – maximum particle velocity tensor to set particle systems to. shape is (M, ).
indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

set_global_self_collisions_enabled( values: ndarray | Tensor, indices: ndarray | List | Tensor | None = None, ) → None#

Enable self collisions to follow particle-object-specific settings for particle systems.

Parameters:

values (Optional[Union[np.ndarray, torch.Tensor]]) – maximum particle velocity tensor to set particle systems to. shape is (M, ).
indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

set_enable_ccds( values: ndarray | Tensor, indices: ndarray | List | Tensor | None = None, ) → None#

Enable continuous collision detection for particles for particle systems.

Parameters:

values (Optional[Union[np.ndarray, torch.Tensor]]) – maximum particle velocity tensor to set particle systems to. shape is (M, ).
indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

set_particle_systems_enabled( values: ndarray | Tensor, indices: ndarray | List | Tensor | None = None, ) → None#

Set enabling of the particle systems.

Parameters:

values (Optional[Union[np.ndarray, torch.Tensor]]) – maximum particle velocity tensor to set particle systems to. shape is (M, ).
indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

set_simulation_owners( values: Sequence[str], indices: ndarray | List | Tensor | None = None, ) → None#

Set the PhysicsScene that simulates particle systems.

Parameters:

values (Sequence[str]) – PhysicsScene list to set particle systems to. shape is (M, ).
indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

get_particle_contact_offsets( indices: ndarray | list | Tensor | None = None, clone: bool = True, ) → ndarray | Tensor#

Returns:

The contact offset used for interactions between particles in the view concatenated. shape is (M, ).

Return type:

Union[np.ndarray, torch.Tensor]

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view)
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

get_solid_rest_offsets( indices: ndarray | list | Tensor | None = None, clone: bool = True, ) → ndarray | Tensor#

Returns:

The rest offset used for solid-solid or solid-fluid particle interactions. shape is (M, ).

Return type:

Union[np.ndarray, torch.Tensor]

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view)
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

get_fluid_rest_offsets( indices: ndarray | list | Tensor | None = None, clone: bool = True, ) → ndarray | Tensor#

Returns:

The rest offset used for fluid-fluid particle interactions. shape is (M, ).

Return type:

Union[np.ndarray, torch.Tensor]

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view)
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

get_winds( indices: ndarray | list | Tensor | None = None, clone: bool = True, ) → ndarray | Tensor#

Returns:

The winds applied to the current particle system. shape is (M, 3).

Return type:

Union[np.ndarray, torch.Tensor]

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view)
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

get_max_velocities( indices: ndarray | list | Tensor | None = None, ) → ndarray | Tensor#

Returns:: The maximum particle velocities for each particle system. shape is (M, ).
Return type:: Union[np.ndarray, torch.Tensor]
Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view)

get_max_depenetration_velocities( indices: ndarray | list | Tensor | None = None, ) → ndarray | Tensor#

Returns:

The maximum velocity permitted to be introduced by the solver to: depenetrate intersecting particles for particle systems for each particle system. shape is (M, ).

Return type:

Union[np.ndarray, torch.Tensor]

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view)

get_rest_offsets( indices: ndarray | list | Tensor | None = None, ) → ndarray | Tensor#

Returns:: The rest offset used for collisions with non-particle objects for each particle system. shape is (M, ).
Return type:: Union[np.ndarray, torch.Tensor]
Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view)

get_contact_offsets( indices: ndarray | list | Tensor | None = None, ) → ndarray | Tensor#

Returns:: The contact offset used for collisions with non-particle objects for each particle system. shape is (M, ).
Return type:: Union[np.ndarray, torch.Tensor]
Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view)

get_solver_position_iteration_counts( indices: ndarray | list | Tensor | None = None, ) → ndarray | Tensor#

Returns:: The number of solver iterations for positions for each particle system. shape is (M, ).
Return type:: Union[np.ndarray, torch.Tensor]
Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view)

get_max_neighborhoods( indices: ndarray | list | Tensor | None = None, ) → ndarray | Tensor#

Returns:: The particle neighborhood size for each particle system. shape is (M, ).
Return type:: Union[np.ndarray, torch.Tensor]
Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view)

get_global_self_collisions_enabled( indices: ndarray | list | Tensor | None = None, ) → ndarray | Tensor#

Returns:

Whether self collisions to follow particle-object-specific settings: is enabled or disabled. for each particle system. shape is (M, ).

Return type:

Union[np.ndarray, torch.Tensor]

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view)

get_enable_ccds( indices: ndarray | list | Tensor | None = None, ) → ndarray | Tensor#

Returns:: Whether continuous collision detection for particles is enabled or disabled for each particle system. shape is (M, ).
Return type:: Union[np.ndarray, torch.Tensor]
Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view)

get_particle_systems_enabled( indices: ndarray | list | Tensor | None = None, ) → ndarray | Tensor#

Returns:: Whether particle system is enabled or not for each particle system. shape is (M, ).
Return type:: Union[np.ndarray, torch.Tensor]
Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view)

get_simulation_owners( indices: ndarray | list | Tensor | None = None, ) → Sequence[str]#

Returns:: The physics scene prim path attached to particle system. shape is (M, ).
Return type:: Sequence[str]
Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view)

Bases: XFormPrim

Provides high level functions to deal with prims (one or many) that have Rigid Body API applied to them as well as their attributes/properties.

This class wraps all matching rigid prims found at the regex provided at the prim_paths_expr argument

Note

Each prim will have xformOp:orient, xformOp:translate and xformOp:scale only post-init, unless it is a non-root articulation link.

If the prims do not already have the Rigid Body API applied to them before init, it will apply it.

Warning

The rigid prim view object must be initialized in order to be able to operate on it. See the initialize method for more details.

Parameters:

prim_paths_expr (Union[str, List[str]]) – prim paths regex to encapsulate all prims that match it. example: “/World/Env[1-5]/Cube” will match /World/Env1/Cube, /World/Env2/Cube..etc. (a non regex prim path can also be used to encapsulate one rigid prim). Additionally a list of regex can be provided. example [“/World/Env[1-5]/Cube”, “/World/Env[10-19]/Cube”].
name (str, optional) – shortname to be used as a key by Scene class. Note: needs to be unique if the object is added to the Scene. Defaults to “rigid_prim_view”.
positions (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – default positions in the world frame of the prims. shape is (N, 3). Defaults to None, which means left unchanged.
translations (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – default translations in the local frame of the prims (with respect to its parent prims). shape is (N, 3). Defaults to None, which means left unchanged.
orientations (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – default quaternion orientations in the world/ local frame of the prims (depends if translation or position is specified). quaternion is scalar-first (w, x, y, z). shape is (N, 4). Defaults to None, which means left unchanged.
scales (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – local scales to be applied to the prim’s dimensions in the view. shape is (N, 3). Defaults to None, which means left unchanged.
visibilities (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – set to false for an invisible prim in the stage while rendering. shape is (N,). Defaults to None.
reset_xform_properties (bool, optional) – True if the prims don’t have the right set of xform properties (i.e: translate, orient and scale) ONLY and in that order. Set this parameter to False if the object were cloned using using the cloner api in isaacsim.core.cloner. Defaults to True.
masses (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – mass in kg specified for each prim in the view. shape is (N,). Defaults to None.
densities (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – density in kg/m^3 specified for each prim in the view. shape is (N,). Defaults to None.
linear_velocities (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – default linear velocity of each prim in the view (to be applied in the first frame and on resets). Shape is (N, 3). Defaults to None.
angular_velocities (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – default angular velocity of each prim in the view (to be applied in the first frame and on resets). Shape is (N, 3). Defaults to None.
track_contact_forces (bool, Optional) – if enabled, the view will track the net contact forces on each rigid prim in the view
prepare_contact_sensors (bool, Optional) – if rigid prims in the view are not cloned from a prim in a prepared state, (although slow for large number of prims) this ensures that appropriate physics settings are applied on all the prim in the view.
disable_stablization (bool, optional) – disables the contact stabilization parameter in the physics context
contact_filter_prim_paths_expr (Optional[List[str]], Optional) – a list of filter expressions which allows for tracking contact forces between prims and this subset through get_contact_force_matrix().
max_contact_count (int, optional) – maximum number of contact data to report when detailed contact information is needed

Example:

>>> import isaacsim.core.utils.stage as stage_utils
>>> from isaacsim.core.cloner import GridCloner
>>> from isaacsim.core.prims import RigidPrim
>>> from pxr import UsdGeom
>>>
>>> env_zero_path = "/World/envs/env_0"
>>> num_envs = 5
>>>
>>> # clone the environment (num_envs)
>>> cloner = GridCloner(spacing=1.5)
>>> cloner.define_base_env(env_zero_path)
>>> UsdGeom.Xform.Define(stage_utils.get_current_stage(), env_zero_path)
>>> stage_utils.get_current_stage().DefinePrim(f"{env_zero_path}/Xform", "Xform")
>>> stage_utils.get_current_stage().DefinePrim(f"{env_zero_path}/Xform/Cube", "Cube")
>>> env_pos = cloner.clone(
...     source_prim_path=env_zero_path,
...     prim_paths=cloner.generate_paths("/World/envs/env", num_envs),
...     copy_from_source=True
... )
>>>
>>> # wrap the prims
>>> prims = RigidPrim(prim_paths_expr="/World/envs/env.*/Xform", name="rigid_prim_view")
>>> prims
<isaacsim.core.prims.rigid_prim.RigidPrim object at 0x7f9a23b8bb80>

property num_shapes: int#

Returns:: number of rigid shapes for the prims in the view.
Return type:: int

Example:

>>> prims.num_shapes
1

is_physics_handle_valid() → bool#

Check if rigid prim view’s physics handler is initialized

Warning

If the physics handler is not valid many of the methods that requires PhysX will return None.

Returns:: True if the physics handle of the view is valid (i.e physics is initialized for the view). Otherwise False.
Return type:: bool

Example:

>>> prims.is_physics_handle_valid()
True

Set poses of prims in the view with respect to the world’s frame.

Warning

This method will change (teleport) the prim poses immediately to the specified value

Parameters:

positions (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – positions in the world frame of the prim. shape is (M, 3). Defaults to None, which means left unchanged.
orientations (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – quaternion orientations in the world frame of the prims. quaternion is scalar-first (w, x, y, z). shape is (M, 4). Defaults to None, which means left unchanged.
indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
usd (bool, optional) – True to query from usd. Otherwise False to query from Fabric data. Defaults to True.

Hint

This method belongs to the methods used to set the prim state

Example:

>>> # reposition all rigid prims in row (x-axis)
>>> positions = np.zeros((num_envs, 3))
>>> positions[:,0] = np.arange(num_envs)
>>> orientations = np.tile(np.array([1.0, 0.0, 0.0, 0.0]), (num_envs, 1))
>>> prims.set_world_poses(positions, orientations)
>>>
>>> # reposition only the rigid prims for the first, middle and last of the 5 envs in column (y-axis)
>>> positions = np.zeros((3, 3))
>>> positions[:,1] = np.arange(3)
>>> orientations = np.tile(np.array([1.0, 0.0, 0.0, 0.0]), (3, 1))
>>> prims.set_world_poses(positions, orientations, indices=np.array([0, 2, 4]))

Get the poses of the prims in the view with respect to the world’s frame.

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.
usd (bool, optional) – True to query from usd. Otherwise False to query from Fabric data. Defaults to True.

Returns:

first index is positions in the world frame of the prims. shape is (M, 3). second index is quaternion orientations in the world frame of the prims. quaternion is scalar-first (w, x, y, z). shape is (M, 4).

Return type:

Union[Tuple[np.ndarray, np.ndarray], Tuple[torch.Tensor, torch.Tensor], Tuple[wp.indexedarray, wp.indexedarray]]

Example:

>>> # get all rigid prim poses with respect to the world's frame.
>>> # Returned shape is position (5, 3) and orientation (5, 4) for the example: 5 envs
>>> positions, orientations = prims.get_world_poses()
>>> positions
[[ 1.4999989e+00 -7.4999851e-01 -1.5118626e-07]
 [ 1.4999989e+00  7.5000149e-01 -2.5988294e-07]
 [-1.0017333e-06 -7.4999845e-01  7.6070329e-08]
 [-9.5906785e-07  7.5000149e-01  1.0593490e-07]
 [-1.5000011e+00 -7.4999851e-01  1.9655154e-07]]
>>> orientations
[[ 9.9999994e-01 -8.8168377e-07 -4.1946004e-07 -1.5067183e-08]
 [ 9.9999994e-01 -8.8691013e-07 -4.2665880e-07 -2.7188951e-09]
 [ 1.0000000e+00 -9.5171310e-07 -2.2615541e-07  5.5922797e-08]
 [ 1.0000000e+00 -8.9923367e-07 -1.4408238e-07  1.3476099e-08]
 [ 1.0000000e+00 -7.9806580e-07 -1.3064776e-07  5.3154917e-08]]
>>>
>>> # get only the rigid prim poses with respect to the world's frame for the first, middle and last of the 5 envs.
>>> # Returned shape is position (3, 3) and orientation (3, 4) for the example: 3 envs selected
>>> positions, orientations = prims.get_world_poses(indices=np.array([0, 2, 4]))
>>> positions
[[ 1.4999989e+00 -7.4999851e-01 -1.5118626e-07]
 [-1.0017333e-06 -7.4999845e-01  7.6070329e-08]
 [-1.5000011e+00 -7.4999851e-01  1.9655154e-07]]
>>> orientations
[[ 9.9999994e-01 -8.8168377e-07 -4.1946004e-07 -1.5067183e-08]
 [ 1.0000000e+00 -9.5171310e-07 -2.2615541e-07  5.5922797e-08]
 [ 1.0000000e+00 -7.9806580e-07 -1.3064776e-07  5.3154917e-08]]

Get prim poses in the view with respect to the local frame (the prim’s parent frame).

Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view)
Returns:: first index is positions in the local frame of the prims. shape is (M, 3). second index is quaternion orientations in the local frame of the prims. quaternion is scalar-first (w, x, y, z). shape is (M, 4).
Return type:: Union[Tuple[np.ndarray, np.ndarray], Tuple[torch.Tensor, torch.Tensor], Tuple[wp.indexedarray, wp.indexedarray]]

Example:

>>> # get all rigid prim poses with respect to the local frame.
>>> # Returned shape is position (5, 3) and orientation (5, 4) for the example: 5 envs
>>> positions, orientations = prims.get_local_poses()
>>> positions
[[-1.0728836e-06  1.4901161e-06 -1.5118626e-07]
 [-1.0728836e-06  1.4901161e-06 -2.5988294e-07]
 [-1.0017333e-06  1.5497208e-06  7.6070329e-08]
 [-9.5906785e-07  1.4901161e-06  1.0593490e-07]
 [-1.0728836e-06  1.4901161e-06  1.9655154e-07]]
>>> orientations
[[ 1.0000000e+00 -8.8174920e-07 -4.1949116e-07 -1.5068302e-08]
 [ 1.0000000e+00 -8.8696777e-07 -4.2668654e-07 -2.7190719e-09]
 [ 1.0000000e+00 -9.5164734e-07 -2.2613979e-07  5.5918935e-08]
 [ 1.0000000e+00 -8.9923157e-07 -1.4408204e-07  1.3476067e-08]
 [ 1.0000000e+00 -7.9806864e-07 -1.3064822e-07  5.3155105e-08]]
>>>
>>> # get only the rigid prim poses with respect to the local frame for the first, middle and last of the 5 envs.
>>> # Returned shape is position (3, 3) and orientation (3, 4) for the example: 3 envs selected
>>> positions, orientations = prims.get_local_poses(indices=np.array([0, 2, 4]))
>>> positions
[[-1.0728836e-06  1.4901161e-06 -1.5118626e-07]
 [-1.0017333e-06  1.5497208e-06  7.6070329e-08]
 [-1.0728836e-06  1.4901161e-06  1.9655154e-07]]
>>> orientations
[[ 1.0000000e+00 -8.8174920e-07 -4.1949116e-07 -1.5068302e-08]
 [ 1.0000000e+00 -9.5164734e-07 -2.2613979e-07  5.5918935e-08]
 [ 1.0000000e+00 -7.9806864e-07 -1.3064822e-07  5.3155105e-08]]

Set prim poses in the view with respect to the local frame (the prim’s parent frame).

Parameters:

translations (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – translations in the local frame of the prims (with respect to its parent prim). shape is (M, 3). Defaults to None, which means left unchanged.
orientations (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – quaternion orientations in the local frame of the prims. quaternion is scalar-first (w, x, y, z). shape is (M, 4). Defaults to None, which means left unchanged.
indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Example:

>>> # reposition all rigid prims
>>> positions = np.zeros((num_envs, 3))
>>> positions[:,0] = np.arange(num_envs)
>>> orientations = np.tile(np.array([1.0, 0.0, 0.0, 0.0]), (num_envs, 1))
>>> prims.set_local_poses(positions, orientations)
>>>
>>> # reposition only the rigid prims for the first, middle and last of the 5 envs
>>> positions = np.zeros((3, 3))
>>> positions[:,1] = np.arange(3)
>>> orientations = np.tile(np.array([1.0, 0.0, 0.0, 0.0]), (3, 1))
>>> prims.set_local_poses(positions, orientations, indices=np.array([0, 2, 4]))

Set the linear velocities of the prims in the view

The method does this through the PhysX API only. It has to be called after initialization. Note: This method is not supported for the gpu pipeline. set_velocities method should be used instead.

Warning

This method will immediately set the rigid prim state

Parameters:

velocities (Optional[Union[np.ndarray, torch.Tensor, wp.array]]) – linear velocities to set the rigid prims to. shape is (M, 3).
indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Hint

This method belongs to the methods used to set the rigid prim kinematic state:

set_velocities (set_linear_velocities, set_angular_velocities)

Example:

>>> # set each rigid prim linear velocity to (1.0, 1.0, 1.0)
>>> velocities = np.ones((num_envs, 3))
>>> prims.set_linear_velocities(velocities)
>>>
>>> # set only the rigid prim linear velocities for the first, middle and last of the 5 envs
>>> velocities = np.ones((3, 3))
>>> prims.set_linear_velocities(velocities, indices=np.array([0, 2, 4]))

Get the linear velocities of prims in the view.

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view)
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

linear velocities of the prims in the view. shape is (M, 3).

Return type:

Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>> # get all rigid prim linear velocities. Returned shape is (5, 3) for the example: 5 envs, linear (3)
>>> prims.get_linear_velocities()
[[0. 0. 0.]
 [0. 0. 0.]
 [0. 0. 0.]
 [0. 0. 0.]
 [0. 0. 0.]]
>>>
>>> # get only the rigid prim linear velocities for the first, middle and last of the 5 envs.
>>> # Returned shape is (3, 3) for the example: 3 envs selected, linear (3)
>>> prims.get_linear_velocities(indices=np.array([0, 2, 4]))
[[0. 0. 0.]
 [0. 0. 0.]
 [0. 0. 0.]]

Set the angular velocities of the prims in the view

The method does this through the physx API only. It has to be called after initialization. Note: This method is not supported for the gpu pipeline. set_velocities method should be used instead.

Warning

This method will immediately set the rigid prim state

Parameters:

velocities (Optional[Union[np.ndarray, torch.Tensor, wp.array]]) – angular velocities to set the rigid prims to. shape is (M, 3).
indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Hint

This method belongs to the methods used to set the rigid prim kinematic state:

set_velocities (set_linear_velocities, set_angular_velocities)

Example:

>>> # set each rigid prim linear velocity to (5.0, 5.0, 5.0)
>>> velocities = np.full((num_envs, 3), fill_value=5.0)
>>> prims.set_angular_velocities(velocities)
>>>
>>> # set only the rigid prim linear velocities for the first, middle and last of the 5 envs
>>> velocities = np.full((3, 3), fill_value=5.0)
>>> prims.set_angular_velocities(velocities, indices=np.array([0, 2, 4]))

Get the angular velocities of prims in the view.

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view)
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

angular velocities of the prims in the view. shape is (M, 3).

Return type:

Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>> # get all rigid prim angular velocities. Returned shape is (5, 3) for the example: 5 envs, angular (3)
>>> prims.get_angular_velocities()
[[0. 0. 0.]
 [0. 0. 0.]
 [0. 0. 0.]
 [0. 0. 0.]
 [0. 0. 0.]]
>>>
>>> # get only the rigid prim angular velocities for the first, middle and last of the 5 envs
>>> # Returned shape is (5, 3) for the example: 3 envs selected, angular (3)
>>> prims.get_angular_velocities(indices=np.array([0, 2, 4]))
[[0. 0. 0.]
 [0. 0. 0.]
 [0. 0. 0.]]

Set the linear and angular velocities of the prims in the view at once.

The method does this through the PhysX API only. It has to be called after initialization

Warning

This method will immediately set the rigid prim state

Parameters:

velocities (Optional[Union[np.ndarray, torch.Tensor, wp.array]]) – linear and angular velocities respectively to set the rigid prims to. shape is (M, 6).
indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Hint

This method belongs to the methods used to set the rigid prim kinematic state:

set_velocities (set_linear_velocities, set_angular_velocities)

Example:

>>> # set each rigid prim linear velocity to (1., 1., 1.) and angular velocity to (5., 5., 5.)
>>> velocities = np.ones((num_envs, 6))
>>> velocities[:,3:] = 5.0
>>> prims.set_velocities(velocities)
>>>
>>> # set only the rigid prim velocities for the first, middle and last of the 5 envs
>>> velocities = np.ones((3, 6))
>>> velocities[:,3:] = 5.0
>>> prims.set_velocities(velocities, indices=np.array([0, 2, 4]))

Get the linear and angular velocities of prims in the view.

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view)
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

linear and angular velocities of the prims in the view concatenated. shape is (M, 6).

Return type:

Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>> # get all rigid prim velocities. Returned shape is (5, 6) for the example: 5 envs, linear (3) and angular (3)
>>> prims.get_velocities()
[[0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0.]]
>>>
>>> # get only the rigid prim velocities for the first, middle and last of the 5 envs.
>>> # Returned shape is (3, 6) for the example: 3 envs selected, linear (3) and angular (3)
>>> prims.get_velocities(indices=np.array([0, 2, 4]))
[[0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0.]]

Applies forces to prims in the view.

Parameters:

forces (Optional[Union[np.ndarray, torch.Tensor, wp.array]]) – forces to be applied to the prims.
indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
is_global (bool, optional) – True if forces are in the global frame. Otherwise False. Defaults to True.

Example:

>>> # apply an external force to all the rigid bodies to the indicated values.
>>> # Since there are 5 envs, the inertias are repeated 5 times
>>> forces = np.tile(np.array([2e5, 1e5, 0.0]), (num_envs, 1))
>>> prims.apply_forces(forces)
>>>
>>> # apply an external force to the rigid bodies for the first, middle and last of the 5 envs
>>> forces = np.tile(np.array([2e5, 1e5, 0.0]), (3, 1))
>>> prims.apply_forces(forces, indices=np.array([0, 2, 4]))

Applies forces and torques to prims in the view. The forces and/or torques can be in local or global coordinates. The forces can applied at a location given by positions variable.

Parameters:

forces (Optional[Union[np.ndarray, torch.Tensor, wp.array]]) – forces to be applied to the prims. If not specified, no force will be applied. Defaults to None (i.e: no forces will be applied).
torques (Optional[Union[np.ndarray, torch.Tensor, wp.array]]) – torques to be applied to the prims. If not specified, no torque will be applied. Defaults to None (i.e: no torques will be applied).
positions (Optional[Union[np.ndarray, torch.Tensor, wp.array]]) – position of the forces with respect to the body frame. If not specified, the forces are applied at the origin of the body frame. Defaults to None (i.e: applied forces will be at the origin of the body frame).
indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
is_global (bool, optional) – True if forces, torques, and positions are in the global frame. False if forces, torques, and positions are in the local frame. Defaults to True.

Example:

>>> # apply an external force and torque to all the rigid bodies to the indicated values.
>>> # Since there are 5 envs, the inertias are repeated 5 times
>>> forces = np.tile(np.array([2e5, 1e5, 0.0]), (num_envs, 1))
>>> torques = np.tile(np.array([2e5, 1e5, 0.0]), (num_envs, 1))
>>> prims.apply_forces_and_torques_at_pos(forces, torques)
>>>
>>> # apply an external force and torque to the rigid bodies for the first, middle and last of the 5 envs
>>> forces = np.tile(np.array([2e5, 1e5, 0.0]), (3, 1))
>>> torques = np.tile(np.array([2e5, 1e5, 0.0]), (3, 1))
>>> prims.apply_forces_and_torques_at_pos(forces, torques, indices=np.array([0, 2, 4]))

Get rigid body masses of prims in the view.

Parameters:

indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

masses of in kg of prims in the view. shape is (M,).

Return type:

Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>> # get all rigid body masses. Returned shape is (5,) for the example: 5 envs
>>> prims.get_masses()
[999.99994 999.99994 999.99994 999.99994 999.99994]
>>>
>>> # get rigid body masses for the first, middle and last of the 5 envs. Returned shape is (3,)
>>> prims.get_masses(indices=np.array([0, 2, 4]))
[999.99994 999.99994 999.99994]

Get rigid body inverse masses of prims in the view.

Parameters:

indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

rigid body inverse masses of prims in the view. Shape is (M,).

Return type:

Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>> # get all rigid body inverse masses. Returned shape is (5, 1) for the example: 5 envs
>>> prims.get_inv_masses()
[[0.001]
 [0.001]
 [0.001]
 [0.001]
 [0.001]]
>>>
>>> # get rigid body inverse masses for the first, middle and last of the 5 envs. Returned shape is (3, 1)
>>> prims.get_inv_masses(indices=np.array([0, 2, 4]))
[[0.001]
 [0.001]
 [0.001]]

Get rigid body center of mass (COM) of bodies in the view.

Parameters:

indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

rigid body center of mass positions and orientations of prims in the view. position shape is (M, 1, 3), orientation shape is (M, 1, 4).

Return type:

Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>> # get all rigid body center of mass.
>>> # Returned shape is (5, 1, 3) for positions and (5, 1, 4) for orientations for the example: 5 envs
>>> positions, orientations = prims.get_coms()
>>> positions
[[[0. 0. 0.]]
 [[0. 0. 0.]]
 [[0. 0. 0.]]
 [[0. 0. 0.]]
 [[0. 0. 0.]]]
>>> orientations
[[[1. 0. 0. 0.]]
 [[1. 0. 0. 0.]]
 [[1. 0. 0. 0.]]
 [[1. 0. 0. 0.]]
 [[1. 0. 0. 0.]]]
>>>
>>> # get rigid body center of mass for the first, middle and last of the 5 envs.
>>> # Returned shape is (3, 1, 3) for positions and (3, 1, 4) for orientations
>>> positions, orientations = prims.get_coms(indices=np.array([0, 2, 4]))
>>> positions
[[[0. 0. 0.]]
 [[0. 0. 0.]]
 [[0. 0. 0.]]]
>>> orientations
[[[1. 0. 0. 0.]]
 [[1. 0. 0. 0.]]
 [[1. 0. 0. 0.]]]

Get rigid body inertias of prims in the view.

Parameters:

indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

rigid body inertias of prims in the view. Shape is (M, 9).

Return type:

Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>> # get all rigid body inertias. Returned shape is (5, 9) for the example: 5 envs
>>> prims.get_inertias()
[[166.66667  0.  0.  0.  166.66667  0.  0.  0.  166.66667]
 [166.66667  0.  0.  0.  166.66667  0.  0.  0.  166.66667]
 [166.66667  0.  0.  0.  166.66667  0.  0.  0.  166.66667]
 [166.66667  0.  0.  0.  166.66667  0.  0.  0.  166.66667]
 [166.66667  0.  0.  0.  166.66667  0.  0.  0.  166.66667]]
>>>
>>> # get rigid body inertias for the first, middle and last of the 5 envs. Returned shape is (3, 9)
>>> prims.get_inertias(indices=np.array([0, 2, 4]))
[[166.66667  0.  0.  0.  166.66667  0.  0.  0.  166.66667]
 [166.66667  0.  0.  0.  166.66667  0.  0.  0.  166.66667]
 [166.66667  0.  0.  0.  166.66667  0.  0.  0.  166.66667]]

Get rigid body inverse inertias of prims in the view.

Parameters:

indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

rigid body inverse inertias of prims in the view. Shape is (M, 9).

Return type:

Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>> # get all rigid body inverse inertias. Returned shape is (5, 9) for the example: 5 envs
>>> prims.get_inv_inertias()
[[0.006 0.    0.    0.    0.006 0.    0.    0.    0.006]
 [0.006 0.    0.    0.    0.006 0.    0.    0.    0.006]
 [0.006 0.    0.    0.    0.006 0.    0.    0.    0.006]
 [0.006 0.    0.    0.    0.006 0.    0.    0.    0.006]
 [0.006 0.    0.    0.    0.006 0.    0.    0.    0.006]]
>>>
>>> # get rigid body inverse inertias for the first, middle and last of the 5 envs. Returned shape is (3, 9)
>>> prims.get_inv_inertias(indices=np.array([0, 2, 4]))
[[0.006 0.    0.    0.    0.006 0.    0.    0.    0.006]
 [0.006 0.    0.    0.    0.006 0.    0.    0.    0.006]
 [0.006 0.    0.    0.    0.006 0.    0.    0.    0.006]]

Set body masses for prims in the view.

Parameters:

masses (Union[np.ndarray, torch.Tensor, wp.array]) – body masses for prims in kg. shape (M,).
indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Example:

>>> # set the rigid body masses for all the rigid bodies to the indicated values.
>>> prims.set_masses(np.full(num_envs, 10.0))
>>>
>>> # set the rigid body masses for the first, middle and last of the 5 envs
>>> prims.set_masses(np.full(3, 10.0), indices=np.array([0, 2, 4]))

Set rigid body inertias for prims in the view.

Parameters:

values (Union[np.ndarray, torch.Tensor, wp.array]) – body inertias for prims in the view. shape (M, 1, 9).
indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Example:

>>> # set the rigid body inertias for all the rigid bodies to the specified values.
>>> # Since there are 5 envs, the inertias are repeated 5 times
>>> inertias = np.tile(np.array([0.1, 0.0, 0.0, 0.0, 0.1, 0.0, 0.0, 0.0, 0.1]), (num_envs, 1))
>>> prims.set_inertias(inertias)
>>>
>>> # set the rigid body inertias for the first, middle and last of the 5 envs
>>> inertias = np.tile(np.array([0.1, 0.0, 0.0, 0.0, 0.1, 0.0, 0.0, 0.0, 0.1]), (3, 1))
>>> prims.set_inertias(inertias, indices=np.array([0, 2, 4]))

Set body center of mass (COM) positions and orientations for bodies in the view.

Parameters:

positions (Union[np.ndarray, torch.Tensor, wp.array]) – body center of mass positions for bodies in the view. shape (M, 1, 3).
orientations (Union[np.ndarray, torch.Tensor, wp.array]) – body center of mass orientations for bodies in the view. shape (M, 1, 4).
indices (Optional[Union[np.ndarray, List, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Example:

>>> # set the center of mass for all the rigid bodies to the specified values.
>>> # Since there are 5 envs, the inertias are repeated 5 times
>>> positions = np.tile(np.array([0.01, 0.02, 0.03]), (num_envs, 1, 1))
>>> orientations = np.tile(np.array([1.0, 0.0, 0.0, 0.0]), (num_envs, 1, 1))
>>> prims.set_coms(positions, orientations)
>>>
>>> # set the rigid bodies center of mass for the first, middle and last of the 5 envs
>>> positions = np.tile(np.array([0.01, 0.02, 0.03]), (3, 1, 1))
>>> orientations = np.tile(np.array([1.0, 0.0, 0.0, 0.0]), (3, 1, 1))
>>> prims.set_coms(positions, orientations, indices=np.array([0, 2, 4]))

Set densities of prims in the view.

Parameters:

densities (Optional[Union[np.ndarray, torch.Tensor, wp.array]]) – density in kg/m^3 specified for each prim in the view. shape is (M,). Defaults to None.
indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Example:

>>> # set all rigid body densities to the specified values
>>> prims.set_densities(np.full(num_envs, 0.9))
>>>
>>> # set rigid body densities for the first, middle and last of the 5 envs
>>> prims.set_densities(np.full(3, 0.9), indices=np.array([0, 2, 4]))

Get densities of prims in the view.

Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view)
Returns:: densities of prims in the view in kg/m^3. shape (M,).
Return type:: Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>> # get all rigid body densities. Returned shape is (5,) for the example: 5 envs
>>> prims.get_densities()
[0. 0. 0. 0. 0.]
>>>
>>> # get rigid body densities for the first, middle and last of the 5 envs. Returned shape is (3,)
>>> prims.get_densities(indices=np.array([0, 2, 4]))
[0. 0. 0.]

Set sleep thresholds of prims in the view.

Search for Rigid Body Dynamics > Sleeping in PhysX docs for more details

Parameters:

thresholds (Optional[Union[np.ndarray, torch.Tensor, wp.array]]) – Mass-normalized kinetic energy threshold below which an actor may go to sleep. Range: [0, inf) Defaults: 0.00005 * tolerancesSpeed* tolerancesSpeed Units: distance^2 / second^2. shape (M,).
indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Example:

>>> # set all rigid body densities to the specified values
>>> prims.set_sleep_thresholds(np.full(num_envs, 1e-5))
>>>
>>> # set rigid body densities for the first, middle and last of the 5 envs
>>> prims.set_sleep_thresholds(np.full(3, 1e-5), indices=np.array([0, 2, 4]))

Get sleep thresholds of prims in the view.

Search for Rigid Body Dynamics > Sleeping in PhysX docs for more details

Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view)
Returns:: Mass-normalized kinetic energy threshold below which an actor may go to sleep. Range: [0, inf). Defaults: 0.00005 * tolerancesSpeed* tolerancesSpeed Units: distance^2 / second^2. shape (M,).
Return type:: Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>> # get all sleep threshold. Returned shape is (5,) for the example: 5 envs
>>> prims.get_sleep_thresholds()
[5.e-05 5.e-05 5.e-05 5.e-05 5.e-05]
>>>
>>> # get sleep threshold for the first, middle and last of the 5 envs. Returned shape is (3,)
>>> prims.get_sleep_thresholds(indices=np.array([0, 2, 4]))
[5.e-05 5.e-05 5.e-05]

enable_rigid_body_physics( indices: ndarray | list | Tensor | warp.array | None = None, ) → None#

Enable rigid body physics (enabled by default)

When enabled, the objects will be moved by external forces such as gravity and collisions

Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Example:

>>> # enable the rigid body dynamics for all rigid bodies
>>> prims.enable_rigid_body_physics()
>>>
>>> # enable the rigid body dynamics for the first, middle and last of the 5 envs
>>> prims.enable_rigid_body_physics(indices=np.array([0, 2, 4]))

disable_rigid_body_physics( indices: ndarray | list | Tensor | warp.array | None = None, ) → None#

Disable rigid body physics (enabled by default)

When disabled, the objects will not be moved by external forces such as gravity and collisions

Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Example:

>>> # enable the rigid body dynamics for all rigid bodies
>>> prims.disable_rigid_body_physics()
>>>
>>> # enable the rigid body dynamics for the first, middle and last of the 5 envs
>>> prims.disable_rigid_body_physics(indices=np.array([0, 2, 4]))

enable_gravities( indices: ndarray | list | Tensor | warp.array | None = None, ) → None#

Enable gravity on rigid bodies (enabled by default).

Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Example:

>>> # enable the gravity for all rigid bodies
>>> prims.enable_gravities()
>>>
>>> # enable the rigid body gravity for the first, middle and last of the 5 envs
>>> prims.enable_gravities(indices=np.array([0, 2, 4]))

disable_gravities( indices: ndarray | list | Tensor | warp.array | None = None, ) → None#

Disable gravity on rigid bodies (enabled by default).

Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Example:

>>> # disable the gravity for all rigid bodies
>>> prims.disable_gravities()
>>>
>>> # disable the rigid body gravity for the first, middle and last of the 5 envs
>>> prims.disable_gravities(indices=np.array([0, 2, 4]))

Set the default state (position, orientation and linear and angular velocities) of prims in the view, that will be used after each reset.

Note

The default states will be set during post-reset (e.g., calling .post_reset() or world.reset() methods)

Parameters:

positions (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – default positions in the world frame of the prim. shape is (M, 3).
orientations (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – default quaternion orientations in the world frame of the prims. quaternion is scalar-first (w, x, y, z). shape is (M, 4).
linear_velocities (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – default linear velocities of each prim in the view (to be applied in the first frame and on resets). Shape is (M, 3). Defaults to None.
angular_velocities (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – default angular velocities of each prim in the view (to be applied in the first frame and on resets). Shape is (M, 3). Defaults to None.
indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Example:

>>> # configure default states for all prims
>>> positions = np.zeros((num_envs, 3))
>>> positions[:, 0] = np.arange(num_envs)
>>> orientations = np.tile(np.array([1.0, 0.0, 0.0, 0.0]), (num_envs, 1))
>>> linear_velocities = np.zeros((num_envs, 3))
>>> angular_velocities = np.zeros((num_envs, 3))
>>> prims.set_default_state(
...     positions=positions,
...     orientations=orientations,
...     linear_velocities=linear_velocities,
...     angular_velocities=angular_velocities
... )
>>>
>>> # set default states during post-reset
>>> prims.post_reset()

get_default_state() → DynamicsViewState#

Get the default state (position, orientation and linear and angular velocities) of prims in the view, that will be used after each reset.

Returns:: returns the default state of the prims that is used after each reset.
Return type:: DynamicsViewState

Example:

>>> state = prims.get_default_state()
<isaacsim.core.utils.types.DynamicsViewState object at 0x7f184e555480>
>>> state
>>> state.positions
[[ 1.4999989e+00 -7.4999851e-01 -1.5118626e-07]
 [ 1.4999989e+00  7.5000149e-01 -2.5988294e-07]
 [-1.0017333e-06 -7.4999845e-01  7.6070329e-08]
 [-9.5906785e-07  7.5000149e-01  1.0593490e-07]
 [-1.5000011e+00 -7.4999851e-01  1.9655154e-07]]
>>> state.orientations
[[ 9.9999994e-01 -8.8168377e-07 -4.1946004e-07 -1.5067183e-08]
 [ 9.9999994e-01 -8.8691013e-07 -4.2665880e-07 -2.7188951e-09]
 [ 1.0000000e+00 -9.5171310e-07 -2.2615541e-07  5.5922797e-08]
 [ 1.0000000e+00 -8.9923367e-07 -1.4408238e-07  1.3476099e-08]
 [ 1.0000000e+00 -7.9806580e-07 -1.3064776e-07  5.3154917e-08]]
>>> state.linear_velocities
[[0. 0. 0.]
 [0. 0. 0.]
 [0. 0. 0.]
 [0. 0. 0.]
 [0. 0. 0.]]
>>> state.angular_velocities
[[0. 0. 0.]
 [0. 0. 0.]
 [0. 0. 0.]
 [0. 0. 0.]
 [0. 0. 0.]]

get_current_dynamic_state() → DynamicsViewState#

Get the current rigid body states (position, orientation and linear and angular velocities)

Returns:: the dynamic state of the rigid bodies
Return type:: DynamicState

Example:

>>> # for the example the rigid bodies are in free fall
>>> state = prims.get_default_state()
<isaacsim.core.utils.types.DynamicsViewState object at 0x7f182bd72590>
>>> state
>>> state.positions
[[   1.5       -0.75    -207.76808]
 [   1.5        0.75    -207.76808]
 [   0.        -0.75    -207.76808]
 [   0.         0.75    -207.76808]
 [  -1.5       -0.75    -207.76808]]
>>> state.orientations
[[1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]]
>>> state.linear_velocities
[[  0.         0.       -63.765312]
 [  0.         0.       -63.765312]
 [  0.         0.       -63.765312]
 [  0.         0.       -63.765312]
 [  0.         0.       -63.765312]]
>>> state.angular_velocities
[[0. 0. 0.]
 [0. 0. 0.]
 [0. 0. 0.]
 [0. 0. 0.]
 [0. 0. 0.]]

Return the net contact forces on prims

Note

This method requires that the contact forces of the prims in the view be tracked by defining the track_contact_forces argument to True (default to False) during view creation

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.
dt (float) – time step multiplier to convert the underlying impulses to forces. If the default value is used then the forces are in fact contact impulses

Returns:

Net contact forces of the prims with shape (M,3).

Return type:

Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>> # get the net contact force on all rigid bodies. Returned shape is (5, 3).
>>> # For the example the view was instantiated with the extra parameter: track_contact_forces=True
>>> prims.get_net_contact_forces()
[[2.1967362e-05 0.0000000e+00 1.6349771e+02]
 [2.1967124e-05 0.0000000e+00 1.6349591e+02]
 [2.1967891e-05 0.0000000e+00 1.6350165e+02]
 [2.1967257e-05 0.0000000e+00 1.6349693e+02]
 [2.1966895e-05 0.0000000e+00 1.6349425e+02]]
>>>
>>> # get the net contact force on the rigid bodies for the first, middle and last of the 5 envs
>>> prims.get_net_contact_forces(indices=np.array([0, 2, 4]))
[[2.1967362e-05 0.0000000e+00 1.6349771e+02]
 [2.1967891e-05 0.0000000e+00 1.6350165e+02]
 [2.1966895e-05 0.0000000e+00 1.6349425e+02]]

Return the contact forces between the prims in the view and the filter prims

E.g., a matrix of dimension (self.count, _contact_view.num_filters, 3) where _contact_view.num_filters is determined according to the contact_filter_prim_paths_expr parameter

Note

This method requires that the contact forces of the prims in the view be tracked by defining the contact_filter_prim_paths_expr argument to a list of the prim paths from which to generate the information and the max_contact_count argument be greater than 0 during view creation

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.
dt (float) – time step multiplier to convert the underlying impulses to forces. If the default value is used then the forces are in fact contact impulses

Returns:

Net contact forces of the prims with shape (M, self._contact_view.num_filters, 3).

Return type:

Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>> # for the example, the cubes are on top of each other. The view was instantiated with the following
>>> # extra parameters: contact_filter_prim_paths_expr=["/World/envs/env_2/Xform"], max_contact_count=10
>>> # This indicates that only contacts with the middle cube will be reported
>>> prims.get_contact_force_matrix()
[[[ 0.0000000e+00  0.0000000e+00  0.0000000e+00]]
 [[-7.8665102e-03  8.3034458e-03 -4.9063504e+02]]
 [[ 0.0000000e+00  0.0000000e+00  0.0000000e+00]]
 [[ 5.2445102e-03 -5.5358098e-03  3.2710065e+02]]
 [[ 0.0000000e+00  0.0000000e+00  0.0000000e+00]]]

Get more detailed contact information between the prims in the view and the filter prims.

Specifically, this method provides individual contact normals, contact points, contact separations as well as contact forces for each pair (the sum of which equals the forces that the get_contact_force_matrix method provides as the force aggregate of a pair)

Given to the dynamic nature of collision between bodies, this method will provide buffers of contact data which are arranged sequentially for each pair. The starting index and the number of contact data points for each pair in this stream can be realized from pair_contacts_start_indices, and pair_contacts_count tensors. They both have a dimension of (num_shapes, _contact_view.num_filters) where _contact_view.num_filters is determined according to the contact_filter_prim_paths_expr parameter

Note

This method requires that the contact forces of the prims in the view be tracked by defining the contact_filter_prim_paths_expr argument to a list of the prim paths from which to generate the information and the max_contact_count argument be greater than 0 during view creation

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.
dt (float) – time step multiplier to convert the underlying impulses to forces. If the default value is used then the forces are in fact contact impulses

Returns:

Tuple[Union[np.ndarray, torch.Tensor, wp.indexedarray], Union[np.ndarray, torch.Tensor, wp.indexedarray], Union[np.ndarray, torch.Tensor, wp.indexedarray], Union[np.ndarray, torch.Tensor, wp.indexedarray], Union[np.ndarray, torch.Tensor, wp.indexedarray], Union[np.ndarray, torch.Tensor, wp.indexedarray]]: A set of buffers for normal forces with shape (max_contact_count, 1), points with shape (max_contact_count, 3), normals with shape (max_contact_count, 3), and distances with shape (max_contact_count, 1), as well as two tensors with shape (M, self.num_filters) to indicate the starting index and the number of contact data points per pair in the aforementioned buffers.

Example:

>>> # for the example, the cubes are on top of each other. The view was instantiated with the following
>>> # extra parameters: contact_filter_prim_paths_expr=["/World/envs/env_2/Xform"], max_contact_count=10
>>> # This indicates that only contacts with the middle cube will be reported
>>> data = prims.get_contact_force_data()
>>> data[0]  # normal forces
[[-156.449   ]
 [ -81.736336]
 [-169.73076 ]
 [ -82.397804]
 [ 110.11985 ]
 [  59.646057]
 [  98.660545]
 [  58.43006 ]
 [   0.      ]
 [   0.      ]]
>>> data[1]  # points
[[-0.50145745  0.49872556  0.7056795 ]
 [-0.50184476 -0.5010655   0.7057198 ]
 [ 0.49793154 -0.50147027  0.70576656]
 [ 0.4983363   0.49833822  0.70572615]
 [ 0.49818155 -0.5016888   1.7058725 ]
 [ 0.49856627  0.4913648   1.7058672 ]
 [-0.49732465  0.4915302   1.705814  ]
 [-0.49748957 -0.501303    1.7058194 ]
 [ 0.          0.          0.        ]
 [ 0.          0.          0.        ]]
>>> data[2]  # normals
[[ 1.6479074e-05 -1.6995813e-05  1.0000000e+00]
 [ 1.6479074e-05 -1.6995813e-05  1.0000000e+00]
 [ 1.6479074e-05 -1.6995813e-05  1.0000000e+00]
 [ 1.6479074e-05 -1.6995813e-05  1.0000000e+00]
 [ 1.6479074e-05 -1.6995813e-05  1.0000000e+00]
 [ 1.6479074e-05 -1.6995813e-05  1.0000000e+00]
 [ 1.6479074e-05 -1.6995813e-05  1.0000000e+00]
 [ 1.6479074e-05 -1.6995813e-05  1.0000000e+00]
 [ 0.0000000e+00  0.0000000e+00  0.0000000e+00]
 [ 0.0000000e+00  0.0000000e+00  0.0000000e+00]]
>>> data[3]  # distances
[[ 6.3175990e-05]
 [ 5.8271162e-06]
 [-5.7399273e-05]
 [-1.0989098e-08]
 [ 1.6338757e-04]
 [ 1.4112510e-04]
 [ 7.1585178e-05]
 [ 9.3835908e-05]
 [ 0.0000000e+00]
 [ 0.0000000e+00]]
>>> data[4]  # pair contacts count
[[0]
 [4]
 [0]
 [4]
 [0]]
>>> data[5]  # start indices of pair contacts
[[0]
 [0]
 [4]
 [4]
 [8]]

Gets friction data between the prims in the view and the filter prims. Specifically, this method provides frictional contact forces, and points. The data in reported for number of anchor points that includes tangential forces in a single tangent direction to contact normal. Given to the dynamic nature of collision between bodies, this method will provide buffers of friction data arranged sequentially for each pair. The starting index and the number of contact data points for each pair in this stream can be realized from pair_contacts_start_indices, and pair_contacts_count tensors. They both have a dimension of (self.num_shapes, self.num_filters) where filter_count is determined according to the filter_paths_expr parameter.

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indicies to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.
dt (float) – time step multiplier to convert the underlying impulses to forces. If the default value is used then the forces are in fact contact impulses

Returns:

Tuple[Union[np.ndarray, torch.Tensor, wp.indexedarray], Union[np.ndarray, torch.Tensor, wp.indexedarray],: Union[np.ndarray, torch.Tensor, wp.indexedarray], Union[np.ndarray, torch.Tensor, wp.indexedarray]]: A set of buffers for tangential forces per patch (at number of anchor points, each in a single directions) with shape (max_contact_count, 3), points with shape (max_contact_count, 3), as well as two tensors with shape (M, self.num_filters) to indicate the starting index and the number of contact data points per pair in the aforementioned buffers.

initialize( physics_sim_view: omni.physics.tensors.SimulationView | None = None, ) → None#

Create a physics simulation view if not passed and set other properties using the PhysX tensor API

Note

For this particular class, calling this method will do nothing

Parameters:: physics_sim_view (omni.physics.tensors.SimulationView, optional) – current physics simulation view. Defaults to None.

Example:

>>> prims.initialize()

Apply visual material to the prims and optionally their prim descendants.

Parameters:

visual_materials (Union[VisualMaterial, List[VisualMaterial]]) – visual materials to be applied to the prims. Currently supports PreviewSurface, OmniPBR and OmniGlass. If a list is provided then its size has to be equal the view’s size or indices size. If one material is provided it will be applied to all prims in the view.
weaker_than_descendants (Optional[Union[bool, List[bool]]], optional) – True if the material shouldn’t override the descendants materials, otherwise False. Defaults to False. If a list of visual materials is provided then a list has to be provided with the same size for this arg as well.
indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Raises:

Exception – length of visual materials != length of prims indexed
Exception – length of visual materials != length of weaker descendants bools arg

Example:

>>> from isaacsim.core.api.materials import OmniGlass
>>>
>>> # create a dark-red glass visual material
>>> material = OmniGlass(
...     prim_path="/World/material/glass",  # path to the material prim to create
...     ior=1.25,
...     depth=0.001,
...     thin_walled=False,
...     color=np.array([0.5, 0.0, 0.0])
... )
>>> prims.apply_visual_materials(material)

property count: int#

Returns:: Number of prims encapsulated in this view.
Return type:: int

Example:

>>> prims.count
5

get_applied_visual_materials( indices: ndarray | list | Tensor | warp.array | None = None, ) → List[VisualMaterial]#

Get the current applied visual materials

Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
Returns:: a list of the current applied visual materials to the prims if its type is currently supported.
Return type:: List[VisualMaterial]

Example:

>>> # get all applied visual materials. Returned size is 5 for the example: 5 envs
>>> prims.get_applied_visual_materials()
[<isaacsim.core.api.materials.omni_glass.OmniGlass object at 0x7f829c165de0>,
 <isaacsim.core.api.materials.omni_glass.OmniGlass object at 0x7f829c165de0>,
 <isaacsim.core.api.materials.omni_glass.OmniGlass object at 0x7f829c165de0>,
 <isaacsim.core.api.materials.omni_glass.OmniGlass object at 0x7f829c165de0>,
 <isaacsim.core.api.materials.omni_glass.OmniGlass object at 0x7f829c165de0>]
>>>
>>> # get the applied visual materials for the first, middle and last of the 5 envs. Returned size is 3
>>> prims.get_applied_visual_materials(indices=np.array([0, 2, 4]))
[<isaacsim.core.api.materials.omni_glass.OmniGlass object at 0x7f829c165de0>,
 <isaacsim.core.api.materials.omni_glass.OmniGlass object at 0x7f829c165de0>,
 <isaacsim.core.api.materials.omni_glass.OmniGlass object at 0x7f829c165de0>]

Get prim scales in the view with respect to the local frame (the parent’s frame).

Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
Returns:: scales applied to the prim’s dimensions in the local frame. shape is (M, 3).
Return type:: Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>> # get all prims scales with respect to the local frame.
>>> # Returned shape is (5, 3) for the example: 5 envs
>>> prims.get_local_scales()
[[1. 1. 1.]
 [1. 1. 1.]
 [1. 1. 1.]
 [1. 1. 1.]
 [1. 1. 1.]]
>>>
>>> # get only the prims scales with respect to the local frame for the first, middle and last of the 5 envs.
>>> # Returned shape is (3, 3) for the example: 3 envs selected
>>> prims.get_local_scales(indices=np.array([0, 2, 4]))
[[1. 1. 1.]
 [1. 1. 1.]
 [1. 1. 1.]]

Returns the current visibilities of the prims in stage.

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Returns:

Shape (M,) with type bool, where each item holds True: if the prim is visible in stage. False otherwise.

Return type:

Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>> # get all visibilities. Returned shape is (5,) for the example: 5 envs
>>> prims.get_visibilities()
[ True  True  True  True  True]
>>>
>>> # get the visibilities for the first, middle and last of the 5 envs. Returned shape is (3,)
>>> prims.get_visibilities(indices=np.array([0, 2, 4]))
[ True  True  True]

Get prim scales in the view with respect to the world’s frame

Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
Returns:: scales applied to the prim’s dimensions in the world frame. shape is (M, 3).
Return type:: Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>> # get all prims scales with respect to the world's frame.
>>> # Returned shape is (5, 3) for the example: 5 envs
>>> prims.get_world_scales()
[[1. 1. 1.]
 [1. 1. 1.]
 [1. 1. 1.]
 [1. 1. 1.]
 [1. 1. 1.]]
>>>
>>> # get only the prims scales with respect to the world's frame for the first, middle and last of the 5 envs.
>>> # Returned shape is (3, 3) for the example: 3 envs selected
>>> prims.get_world_scales(indices=np.array([0, 2, 4]))
[[1. 1. 1.]
 [1. 1. 1.]
 [1. 1. 1.]]

property initialized: bool#

Check if prim view is initialized

Returns:: True if the view object was initialized (after the first call of .initialize()). False otherwise.
Return type:: bool

Example:

>>> # given an initialized articulation view
>>> prims.initialized
True

property is_non_root_articulation_link: bool#: Returns: bool: True if the prim corresponds to a non root link in an articulation. Otherwise False.

is_valid( indices: ndarray | list | Tensor | warp.array | None = None, ) → bool#

Check that all prims have a valid USD Prim

Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
Returns:: True if all prim paths specified in the view correspond to a valid prim in stage. False otherwise.
Return type:: bool

Example:

>>> prims.is_valid()
True

is_visual_material_applied( indices: ndarray | list | Tensor | warp.array | None = None, ) → List[bool]#

Check if there is a visual material applied

Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
Returns:: True if there is a visual material applied is applied to the corresponding prim in the view. False otherwise.
Return type:: List[bool]

Example:

>>> # given a visual material that is applied only to the first and the last environment
>>> prims.is_visual_material_applied()
[True, False, False, False, True]
>>>
>>> # check for the first, middle and last of the 5 envs
>>> prims.is_visual_material_applied(indices=np.array([0, 2, 4]))
[True, False, True]

property name: str#: Returns: str: name given to the prims view when instantiating it.

post_reset() → None#

Reset the prims to its default state

Example:

>>> prims.post_reset()

property prim_paths: List[str]#

Returns:: list of prim paths in the stage encapsulated in this view.
Return type:: List[str]

Example:

>>> prims.prim_paths
['/World/envs/env_0', '/World/envs/env_1', '/World/envs/env_2', '/World/envs/env_3', '/World/envs/env_4']

property prims: List[pxr.Usd.Prim]#

Returns:: List of USD Prim objects encapsulated in this view.
Return type:: List[Usd.Prim]

Example:

>>> prims.prims
[Usd.Prim(</World/envs/env_0>), Usd.Prim(</World/envs/env_1>), Usd.Prim(</World/envs/env_2>),
 Usd.Prim(</World/envs/env_3>), Usd.Prim(</World/envs/env_4>)]

Set prim scales in the view with respect to the local frame (the prim’s parent frame)

Parameters:

scales (Optional[Union[np.ndarray, torch.Tensor, wp.array]]) – scales to be applied to the prim’s dimensions in the view. shape is (M, 3).
indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Example:

>>> # set the scale for all prims. Since there are 5 envs, the scale is repeated 5 times
>>> scales = np.tile(np.array([1.0, 0.75, 0.5]), (num_envs, 1))
>>> prims.set_local_scales(scales)
>>>
>>> # set the scale for the first, middle and last of the 5 envs
>>> scales = np.tile(np.array([1.0, 0.75, 0.5]), (3, 1))
>>> prims.set_local_scales(scales, indices=np.array([0, 2, 4]))

Set the visibilities of the prims in stage

Parameters:

visibilities (Union[np.ndarray, torch.Tensor, wp.array]) – flag to set the visibilities of the usd prims in stage. Shape (M,). Where M <= size of the encapsulated prims in the view.
indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Defaults to None (i.e: all prims in the view).

Example:

>>> # make all prims not visible in the stage
>>> prims.set_visibilities(visibilities=[False] * num_envs)

Bases: GeometryPrim

High level functions to deal with geometry prims that provide their Signed Distance Field (SDF).

This object wraps all matching mesh geometry prims found at the regex provided at the prim_paths_expr.

Parameters:

prim_paths_expr (str) – prim paths regex to encapsulate all prims that match it. example: “/World/Env[1-5]/Microwave” will match /World/Env1/Microwave, /World/Env2/Microwave..etc. (a non regex prim path can also be used to encapsulate one XForm).
num_query_points (int) – number of points queried by this view object
prepare_sdf_schemas (bool, optional) – apply PhysxSDFMeshCollisionAPI to prims in prim_paths_expr. Defaults to True.
name (str, optional) – shortname to be used as a key by Scene class. Note: needs to be unique if the object is added to the Scene. Defaults to “sdf_shape_view”.
positions (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – default positions in the world frame of the prim. shape is (N, 3). Defaults to None, which means left unchanged.
translations (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – default translations in the local frame of the prims (with respect to its parent prims). shape is (N, 3). Defaults to None, which means left unchanged.
orientations (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – default quaternion orientations in the world/ local frame of the prim (depends if translation or position is specified). quaternion is scalar-first (w, x, y, z). shape is (N, 4). Defaults to None, which means left unchanged.
scales (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – local scales to be applied to the prim’s dimensions. shape is (N, 3). Defaults to None, which means left unchanged.
visibilities (Optional[Union[np.ndarray, torch.Tensor, wp.array], optional) – set to false for an invisible prim in the stage while rendering. shape is (N,). Defaults to None.
reset_xform_properties (bool, optional) – True if the prims don’t have the right set of xform properties (i.e: translate, orient and scale) ONLY and in that order. Set this parameter to False if the object were cloned using using the cloner api in isaacsim.core.cloner. Defaults to True.
collisions (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – Set to True if the geometry already have/ should have a collider (i.e not only a visual geometry). shape is (N,). Defaults to None.
track_contact_forces (bool, Optional) – if enabled, the view will track the net contact forces on each geometry prim in the view. Note that the collision flag should be set to True to report contact forces. Defaults to False.
prepare_contact_sensors (bool, Optional) – applies contact reporter API to the prim if it already does not have one. Defaults to False.
disable_stablization (bool, optional) – disables the contact stabilization parameter in the physics context. Defaults to True.
contact_filter_prim_paths_expr (Optional[List[str]], Optional) – a list of filter expressions which allows for tracking contact forces between the geometry prim and this subset through get_contact_force_matrix().

property num_query_points: int#: Returns: int: number of points queried by this view object.

is_physics_handle_valid() → bool#

Returns:: True if the physics handle of the view is valid (i.e physics is initialized for the view). Otherwise False.
Return type:: bool

initialize( physics_sim_view: omni.physics.tensors.SimulationView | None = None, ) → None#

Create a physics simulation view if not passed and creates a sdf shape view in physX.

Parameters:: physics_sim_view (omni.physics.tensors.SimulationView, optional) – current physics simulation view. Defaults to None.

get_sdf_and_gradients( points: ndarray | Tensor, indices: ndarray | Tensor | None = None, clone: bool = True, ) → ndarray | Tensor#

Get the SDF values and gradients of the query points

Parameters:

points ([Union[np.ndarray, torch.Tensor]]) – points (represented in the local frames of meshes) to be queried for sdf and gradients. shape is (self.num_shapes, self.num_query_points, 3).
indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

SDF values and gradients of points for prims with shape (self.num_shapes, self.num_query_points, 4). The first component is the SDF value while the last three represent the gradient

Return type:

Union[np.ndarray, torch.Tensor]

get_sdf_margins( indices: ndarray | List | Tensor | None = None, clone: bool = True, ) → ndarray | Tensor#

Gets sdf margin values.

Parameters:

indices (Optional[Union[np.ndarray, List, torch.Tensor]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

margins of the sdf collision apis for prims in the view. shape is (M,).

Return type:

Union[np.ndarray, torch.Tensor]

get_sdf_narrow_band_thickness( indices: ndarray | List | Tensor | None = None, clone: bool = True, ) → ndarray | Tensor#

Gets sdf collision narrow band thickness values.

Parameters:

indices (Optional[Union[np.ndarray, List, torch.Tensor]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

narrow band thickness of the sdf collision apis for prims in the view. shape is (M,).

Return type:

Union[np.ndarray, torch.Tensor]

get_sdf_subgrid_resolution( indices: ndarray | List | Tensor | None = None, clone: bool = True, ) → ndarray | Tensor#

Gets sdf collision subgrid resolution values.

Parameters:

indices (Optional[Union[np.ndarray, List, torch.Tensor]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

subgrid resolutions of the sdf collision apis for prims in the view. shape is (M,).

Return type:

Union[np.ndarray, torch.Tensor]

get_sdf_resolution( indices: ndarray | List | Tensor | None = None, clone: bool = True, ) → ndarray | Tensor#

Gets sdf collision resolution values.

Parameters:

indices (Optional[Union[np.ndarray, List, torch.Tensor]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

Returns:

resolutions of the sdf collision apis for prims in the view. shape is (M,).

Return type:

Union[np.ndarray, torch.Tensor]

set_sdf_margins( values: ndarray | Tensor, indices: ndarray | List | Tensor | None = None, ) → None#

Sets signed distance field margins for prims in the view.

Parameters:

values (Union[np.ndarray, torch.Tensor]) – sdf margins to be set. shape (M,).
indices (Optional[Union[np.ndarray, List, torch.Tensor]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

set_sdf_narrow_band_thickness( values: ndarray | Tensor, indices: ndarray | List | Tensor | None = None, ) → None#

Sets signed distance field narrow band thicknesses for prims in the view.

Parameters:

values (Union[np.ndarray, torch.Tensor]) – sdf margins to be set. shape (M,).
indices (Optional[Union[np.ndarray, List, torch.Tensor]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

set_sdf_subgrid_resolution( values: ndarray | Tensor, indices: ndarray | List | Tensor | None = None, ) → None#

Sets signed distance field subgrid resolutions for prims in the view.

Parameters:

values (Union[np.ndarray, torch.Tensor]) – sdf margins to be set. shape (M,).
indices (Optional[Union[np.ndarray, List, torch.Tensor]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

set_sdf_resolution( values: ndarray | Tensor, indices: ndarray | List | Tensor | None = None, ) → None#

Sets signed distance field subgrid resolutions for prims in the view.

Parameters:

values (Union[np.ndarray, torch.Tensor]) – sdf margins to be set. shape (M,).
indices (Optional[Union[np.ndarray, List, torch.Tensor]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

apply_collision_apis( indices: ndarray | list | Tensor | warp.array | None = None, ) → None#

Apply the collision API to prims in the view and update internal variables

Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Example:

>>> # apply the collision API for all prims
>>> prims.apply_collision_apis()
>>>
>>> # apply the collision API for the first, middle and last of the 5 envs
>>> prims.apply_collision_apis(indices=np.array([0, 2, 4]))

Used to apply physics material to prims in the view and optionally its descendants.

Parameters:

physics_materials (Union[PhysicsMaterial, List[PhysicsMaterial]]) – physics materials to be applied to prims in the view. Physics material can be used to define friction, restitution..etc. Note: if a physics material is not defined, the defaults will be used from PhysX. If a list is provided then its size has to be equal the view’s size or indices size. If one material is provided it will be applied to all prims in the view.
weaker_than_descendants (Optional[Union[bool, List[bool]]], optional) – True if the material shouldn’t override the descendants materials, otherwise False. Defaults to False. If a list of visual materials is provided then a list has to be provided with the same size for this arg as well.
indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Raises:

Exception – length of physics materials != length of prims indexed
Exception – length of physics materials != length of weaker descendants arg

Example:

>>> from isaacsim.core.api.materials import PhysicsMaterial
>>>
>>> # create a rigid body physical material
>>> material = PhysicsMaterial(
...     prim_path="/World/physics_material/aluminum",  # path to the material prim to create
...     dynamic_friction=0.4,
...     static_friction=1.1,
...     restitution=0.1
... )
>>>
>>> # apply the material to all prims
>>> prims.apply_physics_materials(material)  # or [material] * num_envs
>>>
>>> # apply the collision API for the first, middle and last of the 5 envs
>>> prims.apply_physics_materials(material, indices=np.array([0, 2, 4]))

Apply visual material to the prims and optionally their prim descendants.

Parameters:

visual_materials (Union[VisualMaterial, List[VisualMaterial]]) – visual materials to be applied to the prims. Currently supports PreviewSurface, OmniPBR and OmniGlass. If a list is provided then its size has to be equal the view’s size or indices size. If one material is provided it will be applied to all prims in the view.
weaker_than_descendants (Optional[Union[bool, List[bool]]], optional) – True if the material shouldn’t override the descendants materials, otherwise False. Defaults to False. If a list of visual materials is provided then a list has to be provided with the same size for this arg as well.
indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Raises:

Exception – length of visual materials != length of prims indexed
Exception – length of visual materials != length of weaker descendants bools arg

Example:

>>> from isaacsim.core.api.materials import OmniGlass
>>>
>>> # create a dark-red glass visual material
>>> material = OmniGlass(
...     prim_path="/World/material/glass",  # path to the material prim to create
...     ior=1.25,
...     depth=0.001,
...     thin_walled=False,
...     color=np.array([0.5, 0.0, 0.0])
... )
>>> prims.apply_visual_materials(material)

property count: int#

Returns:: Number of prims encapsulated in this view.
Return type:: int

Example:

>>> prims.count
5

disable_collision( indices: ndarray | list | Tensor | warp.array | None = None, ) → None#

Disables collision on prims in the view.

Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Example:

>>> # disable the collision API for all prims
>>> prims.disable_collision()
>>>
>>> # disable the collision API for the prims for the first, middle and last of the 5 envs
>>> prims.disable_collision(indices=np.array([0, 2, 4]))

enable_collision( indices: ndarray | list | Tensor | warp.array | None = None, ) → None#

Enables collision on prims in the view.

Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Example:

>>> # enable the collision API for all prims
>>> prims.enable_collision()
>>>
>>> # enable the collision API for the prims for the first, middle and last of the 5 envs
>>> prims.enable_collision(indices=np.array([0, 2, 4]))

property geoms: List[pxr.UsdGeom.Gprim]#

Returns:: USD geom objects encapsulated.
Return type:: List[UsdGeom.Gprim]

Example:

>>> prims.geoms
[UsdGeom.Gprim(Usd.Prim(</World/envs/env_0/Xform>)), UsdGeom.Gprim(Usd.Prim(</World/envs/env_1/Xform>)),
 UsdGeom.Gprim(Usd.Prim(</World/envs/env_2/Xform>)), UsdGeom.Gprim(Usd.Prim(</World/envs/env_3/Xform>)),
 UsdGeom.Gprim(Usd.Prim(</World/envs/env_4/Xform>))]

get_applied_physics_materials( indices: ndarray | list | Tensor | warp.array | None = None, ) → List[PhysicsMaterial]#

Get the applied physics material to prims in the view.

Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
Returns:: the current applied physics materials for prims in the view.
Return type:: List[PhysicsMaterial]

Example:

>>> # get the applied material for all prims
>>> prims.get_applied_physics_materials()
[<isaacsim.core.api.materials.physics_material.PhysicsMaterial object at 0x7f720859ece0>,
 <isaacsim.core.api.materials.physics_material.PhysicsMaterial object at 0x7f720859ece0>,
 <isaacsim.core.api.materials.physics_material.PhysicsMaterial object at 0x7f720859ece0>,
 <isaacsim.core.api.materials.physics_material.PhysicsMaterial object at 0x7f720859ece0>,
 <isaacsim.core.api.materials.physics_material.PhysicsMaterial object at 0x7f720859ece0>]
>>>
>>> # get the applied material for the first, middle and last of the 5 envs
>>> prims.get_applied_physics_materials(indices=np.array([0, 2, 4]))
[<isaacsim.core.api.materials.physics_material.PhysicsMaterial object at 0x7f720859ece0>,
 <isaacsim.core.api.materials.physics_material.PhysicsMaterial object at 0x7f720859ece0>,
 <isaacsim.core.api.materials.physics_material.PhysicsMaterial object at 0x7f720859ece0>]

get_applied_visual_materials( indices: ndarray | list | Tensor | warp.array | None = None, ) → List[VisualMaterial]#

Get the current applied visual materials

Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
Returns:: a list of the current applied visual materials to the prims if its type is currently supported.
Return type:: List[VisualMaterial]

Example:

>>> # get all applied visual materials. Returned size is 5 for the example: 5 envs
>>> prims.get_applied_visual_materials()
[<isaacsim.core.api.materials.omni_glass.OmniGlass object at 0x7f829c165de0>,
 <isaacsim.core.api.materials.omni_glass.OmniGlass object at 0x7f829c165de0>,
 <isaacsim.core.api.materials.omni_glass.OmniGlass object at 0x7f829c165de0>,
 <isaacsim.core.api.materials.omni_glass.OmniGlass object at 0x7f829c165de0>,
 <isaacsim.core.api.materials.omni_glass.OmniGlass object at 0x7f829c165de0>]
>>>
>>> # get the applied visual materials for the first, middle and last of the 5 envs. Returned size is 3
>>> prims.get_applied_visual_materials(indices=np.array([0, 2, 4]))
[<isaacsim.core.api.materials.omni_glass.OmniGlass object at 0x7f829c165de0>,
 <isaacsim.core.api.materials.omni_glass.OmniGlass object at 0x7f829c165de0>,
 <isaacsim.core.api.materials.omni_glass.OmniGlass object at 0x7f829c165de0>]

get_collision_approximations( indices: ndarray | list | Tensor | warp.array | None = None, ) → List[str]#

Get collision approximation types for prims in the view.

Approximation	Full name	Description
`"none"`	Triangle Mesh	The mesh geometry is used directly as a collider without any approximation
`"convexDecomposition"`	Convex Decomposition	A convex mesh decomposition is performed. This results in a set of convex mesh colliders
`"convexHull"`	Convex Hull	A convex hull of the mesh is generated and used as the collider
`"boundingSphere"`	Bounding Sphere	A bounding sphere is computed around the mesh and used as a collider
`"boundingCube"`	Bounding Cube	An optimally fitting box collider is computed around the mesh
`"meshSimplification"`	Mesh Simplification	A mesh simplification step is performed, resulting in a simplified triangle mesh collider
`"sdf"`	SDF Mesh	SDF (Signed-Distance-Field) use high-detail triangle meshes as collision shape
`"sphereFill"`	Sphere Approximation	A sphere mesh decomposition is performed. This results in a set of sphere colliders

Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
Returns:: approximations used for collision. size == M or size of the view.
Return type:: List[str]

Example:

>>> # get the collision approximation of all prims. Returned size is (5,).
>>> prims.get_collision_approximations()
['none', 'none', 'none', 'none', 'none']
>>>
>>> # get the collision approximation of the prims for the first, middle and last of the 5 envs
>>> prims.get_collision_approximations(indices=np.array([0, 2, 4]))
['none', 'none', 'none']

Get more detailed contact information between the prims in the view and the filter prims. Specifically, this method provides individual contact normals, contact points, contact separations as well as contact forces for each pair (the sum of which equals the forces that the get_contact_force_matrix method provides as the force aggregate of a pair) Given to the dynamic nature of collision between bodies, this method will provide buffers of contact data which are arranged sequentially for each pair. The starting index and the number of contact data points for each pair in this stream can be realized from pair_contacts_start_indices, and pair_contacts_count tensors. They both have a dimension of (self.num_shapes, self.num_filters) where filter_count is determined according to the filter_paths_expr parameter.

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.
dt (float) – time step multiplier to convert the underlying impulses to forces. If the default value is used then the forces are in fact contact impulses

Returns:

Tuple[Union[np.ndarray, torch.Tensor, wp.indexedarray], Union[np.ndarray, torch.Tensor, wp.indexedarray],: Union[np.ndarray, torch.Tensor, wp.indexedarray], Union[np.ndarray, torch.Tensor, wp.indexedarray], Union[np.ndarray, torch.Tensor, wp.indexedarray], Union[np.ndarray, torch.Tensor, wp.indexedarray]]: A set of buffers for normal forces with shape (max_contact_count, 1), points with shape (max_contact_count, 3), normals with shape (max_contact_count, 3), and distances with shape (max_contact_count, 1), as well as two tensors with shape (M, self.num_filters) to indicate the starting index and the number of contact data points per pair in the aforementioned buffers.

If the object is initialized with filter_paths_expr list, this method returns the contact forces between the prims in the view and the filter prims. i.e., a matrix of dimension (self.count, self._contact_view.num_filters, 3) where num_filters is the determined according to the filter_paths_expr parameter.

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.
dt (float) – time step multiplier to convert the underlying impulses to forces. If the default value is used then the forces are in fact contact impulses

Returns:

Net contact forces of the prims with shape (M, self._contact_view.num_filters, 3).

Return type:

Union[np.ndarray, torch.Tensor, wp.indexedarray]

Get contact offsets for prims in the view.

Shapes whose distance is less than the sum of their contact offset values will generate contacts

Search for Advanced Collision Detection in PhysX docs for more details

Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
Returns:: Contact offsets of the collision shapes. Shape is (M,).
Return type:: Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>> # get the contact offsets of all prims. Returned shape is (5,).
>>> prims.get_contact_offsets()
[-inf -inf -inf -inf -inf]
>>>
>>> # get the contact offsets of the prims for the first, middle and last of the 5 envs
>>> prims.get_contact_offsets(indices=np.array([0, 2, 4]))
[-inf -inf -inf]

get_default_state() → XFormPrimViewState#

Get the default states (positions and orientations) defined with the set_default_state method

Returns:: returns the default state of the prims that is used after each reset.
Return type:: XFormPrimViewState

Example:

>>> state = prims.get_default_state()
>>> state
<isaacsim.core.utils.types.XFormPrimViewState object at 0x7f82f73e3070>
>>> state.positions
[[ 1.5  -0.75  0.  ]
 [ 1.5   0.75  0.  ]
 [ 0.   -0.75  0.  ]
 [ 0.    0.75  0.  ]
 [-1.5  -0.75  0.  ]]
>>> state.orientations
[[1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]]

Gets friction data between the prims in the view and the filter prims. Specifically, this method provides frictional contact forces, and points. The data in reported for number of anchor points that includes tangential forces in a single tangent direction to contact normal. Given to the dynamic nature of collision between bodies, this method will provide buffers of friction data arranged sequentially for each pair. The starting index and the number of contact data points for each pair in this stream can be realized from pair_contacts_start_indices, and pair_contacts_count tensors. They both have a dimension of (self.num_shapes, self.num_filters) where filter_count is determined according to the filter_paths_expr parameter.

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indicies to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.
dt (float) – time step multiplier to convert the underlying impulses to forces. If the default value is used then the forces are in fact contact impulses

Returns:

Tuple[Union[np.ndarray, torch.Tensor, wp.indexedarray], Union[np.ndarray, torch.Tensor, wp.indexedarray],: Union[np.ndarray, torch.Tensor, wp.indexedarray], Union[np.ndarray, torch.Tensor, wp.indexedarray]]: A set of buffers for tangential forces per patch (at number of anchor points, each in a single directions) with shape (max_contact_count, 3), points with shape (max_contact_count, 3), as well as two tensors with shape (M, self.num_filters) to indicate the starting index and the number of contact data points per pair in the aforementioned buffers.

Get prim poses in the view with respect to the local frame (the prim’s parent frame)

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Returns:

first index is translations in the local frame of the prims. shape is (M, 3).: second index is quaternion orientations in the local frame of the prims. quaternion is scalar-first (w, x, y, z). shape is (M, 4).

Return type:

Union[Tuple[np.ndarray, np.ndarray], Tuple[torch.Tensor, torch.Tensor], Tuple[wp.indexedarray, wp.indexedarray]]

Example:

>>> # get all prims poses with respect to the local frame.
>>> # Returned shape is position (5, 3) and orientation (5, 4) for the example: 5 envs
>>> positions, orientations = prims.get_local_poses()
>>> positions
[[ 1.5  -0.75  0.  ]
 [ 1.5   0.75  0.  ]
 [ 0.   -0.75  0.  ]
 [ 0.    0.75  0.  ]
 [-1.5  -0.75  0.  ]]
>>> orientations
[[1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]]
>>>
>>> # get only the prims poses with respect to the local frame for the first, middle and last of the 5 envs.
>>> # Returned shape is position (3, 3) and orientation (3, 4) for the example: 3 envs selected
>>> positions, orientations = prims.get_local_poses(indices=np.array([0, 2, 4]))
>>> positions
[[ 1.5  -0.75  0.  ]
 [ 0.   -0.75  0.  ]
 [-1.5  -0.75  0.  ]]
>>> orientations
[[1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]]

Get prim scales in the view with respect to the local frame (the parent’s frame).

Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
Returns:: scales applied to the prim’s dimensions in the local frame. shape is (M, 3).
Return type:: Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>> # get all prims scales with respect to the local frame.
>>> # Returned shape is (5, 3) for the example: 5 envs
>>> prims.get_local_scales()
[[1. 1. 1.]
 [1. 1. 1.]
 [1. 1. 1.]
 [1. 1. 1.]
 [1. 1. 1.]]
>>>
>>> # get only the prims scales with respect to the local frame for the first, middle and last of the 5 envs.
>>> # Returned shape is (3, 3) for the example: 3 envs selected
>>> prims.get_local_scales(indices=np.array([0, 2, 4]))
[[1. 1. 1.]
 [1. 1. 1.]
 [1. 1. 1.]]

get_min_torsional_patch_radii( indices: ndarray | list | Tensor | None = None, ) → ndarray | Tensor#

Get minimum torsional patch radii for prims in the view.

Search for “Torsional Patch Radius” in PhysX docs for more details

Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
Returns:: minimum radius of the contact patch used to apply torsional friction. shape is (M,).
Return type:: Union[np.ndarray, torch.Tensor]

Example:

>>> # get the minimum torsional patch radius of all prims. Returned shape is (5,).
>>> prims.get_min_torsional_patch_radii()
[0. 0. 0. 0. 0.]
>>>
>>> # get the minimum torsional patch radius of the prims for the first, middle and last of the 5 envs
>>> prims.get_min_torsional_patch_radii(indices=np.array([0, 2, 4]))
[0. 0. 0.]

If contact forces of the prims in the view are tracked, this method returns the net contact forces on prims. i.e., a matrix of dimension (self.count, 3)

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
clone (bool, optional) – True to return a clone of the internal buffer. Otherwise False. Defaults to True.
dt (float) – time step multiplier to convert the underlying impulses to forces. If the default value is used then the forces are in fact contact impulses

Returns:

Net contact forces of the prims with shape (M,3).

Return type:

Union[np.ndarray, torch.Tensor, wp.indexedarray]

Get rest offsets for prims in the view.

Two shapes will come to rest at a distance equal to the sum of their rest offset values. If the rest offset is 0, they should converge to touching exactly

Search for Advanced Collision Detection in PhysX docs for more details

Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
Returns:: Rest offsets of the collision shapes. Shape is (M,).
Return type:: Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>> # get the rest offsets of all prims. Returned shape is (5,).
>>> prims.get_rest_offsets()
[-inf -inf -inf -inf -inf]
>>>
>>> # get the rest offsets of the prims for the first, middle and last of the 5 envs
>>> prims.get_rest_offsets(indices=np.array([0, 2, 4]))
[-inf -inf -inf]

Get torsional patch radii for prims in the view.

Search for “Torsional Patch Radius” in PhysX docs for more details

Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
Returns:: radius of the contact patch used to apply torsional friction. shape is (M,).
Return type:: Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>> # get the torsional patch radius of all prims. Returned shape is (5,).
>>> prims.get_torsional_patch_radii()
[0. 0. 0. 0. 0.]
>>>
>>> # get the torsional patch radius of the prims for the first, middle and last of the 5 envs
>>> prims.get_torsional_patch_radii(indices=np.array([0, 2, 4]))
[0. 0. 0.]

Returns the current visibilities of the prims in stage.

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Returns:

Shape (M,) with type bool, where each item holds True: if the prim is visible in stage. False otherwise.

Return type:

Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>> # get all visibilities. Returned shape is (5,) for the example: 5 envs
>>> prims.get_visibilities()
[ True  True  True  True  True]
>>>
>>> # get the visibilities for the first, middle and last of the 5 envs. Returned shape is (3,)
>>> prims.get_visibilities(indices=np.array([0, 2, 4]))
[ True  True  True]

Get the poses of the prims in the view with respect to the world’s frame

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
usd (bool, optional) – True to query from usd. Otherwise False to query from Fabric data. Defaults to True.

Returns:

first index is positions in the world frame of the prims. shape is (M, 3).: second index is quaternion orientations in the world frame of the prims. quaternion is scalar-first (w, x, y, z). shape is (M, 4).

Return type:

Union[Tuple[np.ndarray, np.ndarray], Tuple[torch.Tensor, torch.Tensor], Tuple[wp.indexedarray, wp.indexedarray]]

Example:

>>> # get all prims poses with respect to the world's frame.
>>> # Returned shape is position (5, 3) and orientation (5, 4) for the example: 5 envs
>>> positions, orientations = prims.get_world_poses()
>>> positions
[[ 1.5  -0.75  0.  ]
 [ 1.5   0.75  0.  ]
 [ 0.   -0.75  0.  ]
 [ 0.    0.75  0.  ]
 [-1.5  -0.75  0.  ]]
>>> orientations
[[1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]]
>>>
>>> # get only the prims poses with respect to the world's frame for the first, middle and last of the 5 envs.
>>> # Returned shape is position (3, 3) and orientation (3, 4) for the example: 3 envs selected
>>> positions, orientations = prims.get_world_poses(indices=np.array([0, 2, 4]))
>>> positions
[[ 1.5  -0.75  0.  ]
 [ 0.   -0.75  0.  ]
 [-1.5  -0.75  0.  ]]
>>> orientations
[[1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]]

Get prim scales in the view with respect to the world’s frame

Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
Returns:: scales applied to the prim’s dimensions in the world frame. shape is (M, 3).
Return type:: Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>> # get all prims scales with respect to the world's frame.
>>> # Returned shape is (5, 3) for the example: 5 envs
>>> prims.get_world_scales()
[[1. 1. 1.]
 [1. 1. 1.]
 [1. 1. 1.]
 [1. 1. 1.]
 [1. 1. 1.]]
>>>
>>> # get only the prims scales with respect to the world's frame for the first, middle and last of the 5 envs.
>>> # Returned shape is (3, 3) for the example: 3 envs selected
>>> prims.get_world_scales(indices=np.array([0, 2, 4]))
[[1. 1. 1.]
 [1. 1. 1.]
 [1. 1. 1.]]

property initialized: bool#

Check if prim view is initialized

Returns:: True if the view object was initialized (after the first call of .initialize()). False otherwise.
Return type:: bool

Example:

>>> # given an initialized articulation view
>>> prims.initialized
True

Queries if collision is enabled on prims in the view.

Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
Returns:: True if collision is enabled. Shape is (M,).
Return type:: Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>> # check if the collision is enabled for all prims. Returned size is (5,).
>>> prims.is_collision_enabled()
[ True  True  True  True  True]
>>>
>>> # check if the collision is enabled for the first, middle and last of the 5 envs
>>> prims.is_collision_enabled(indices=np.array([0, 2, 4]))
[ True  True  True]

property is_non_root_articulation_link: bool#: Returns: bool: True if the prim corresponds to a non root link in an articulation. Otherwise False.

is_valid( indices: ndarray | list | Tensor | warp.array | None = None, ) → bool#

Check that all prims have a valid USD Prim

Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
Returns:: True if all prim paths specified in the view correspond to a valid prim in stage. False otherwise.
Return type:: bool

Example:

>>> prims.is_valid()
True

is_visual_material_applied( indices: ndarray | list | Tensor | warp.array | None = None, ) → List[bool]#

Check if there is a visual material applied

Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
Returns:: True if there is a visual material applied is applied to the corresponding prim in the view. False otherwise.
Return type:: List[bool]

Example:

>>> # given a visual material that is applied only to the first and the last environment
>>> prims.is_visual_material_applied()
[True, False, False, False, True]
>>>
>>> # check for the first, middle and last of the 5 envs
>>> prims.is_visual_material_applied(indices=np.array([0, 2, 4]))
[True, False, True]

property name: str#: Returns: str: name given to the prims view when instantiating it.

post_reset() → None#

Reset the prims to its default state

Example:

>>> prims.post_reset()

property prim_paths: List[str]#

Returns:: list of prim paths in the stage encapsulated in this view.
Return type:: List[str]

Example:

>>> prims.prim_paths
['/World/envs/env_0', '/World/envs/env_1', '/World/envs/env_2', '/World/envs/env_3', '/World/envs/env_4']

property prims: List[pxr.Usd.Prim]#

Returns:: List of USD Prim objects encapsulated in this view.
Return type:: List[Usd.Prim]

Example:

>>> prims.prims
[Usd.Prim(</World/envs/env_0>), Usd.Prim(</World/envs/env_1>), Usd.Prim(</World/envs/env_2>),
 Usd.Prim(</World/envs/env_3>), Usd.Prim(</World/envs/env_4>)]

set_collision_approximations( approximation_types: List[str], indices: ndarray | list | Tensor | warp.array | None = None, ) → None#

Set collision approximation types for prims in the view.

Approximation	Full name	Description
`"none"`	Triangle Mesh	The mesh geometry is used directly as a collider without any approximation
`"convexDecomposition"`	Convex Decomposition	A convex mesh decomposition is performed. This results in a set of convex mesh colliders
`"convexHull"`	Convex Hull	A convex hull of the mesh is generated and used as the collider
`"boundingSphere"`	Bounding Sphere	A bounding sphere is computed around the mesh and used as a collider
`"boundingCube"`	Bounding Cube	An optimally fitting box collider is computed around the mesh
`"meshSimplification"`	Mesh Simplification	A mesh simplification step is performed, resulting in a simplified triangle mesh collider
`"sdf"`	SDF Mesh	SDF (Signed-Distance-Field) use high-detail triangle meshes as collision shape
`"sphereFill"`	Sphere Approximation	A sphere mesh decomposition is performed. This results in a set of sphere colliders

Parameters:

approximation_types (List[str]) – approximations used for collision. List size == M or the size of the view.
indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Example:

>>> # set the collision approximations for all the prims to the specified values.
>>> prims.set_collision_approximations(["convexDecomposition"] * num_envs)
>>>
>>> # set the collision approximations for the first, middle and last of the 5 envs
>>> types = ["convexDecomposition", "convexHull", "meshSimplification"]
>>> prims.set_collision_approximations(types, indices=np.array([0, 2, 4]))

Set contact offsets for prims in the view.

Shapes whose distance is less than the sum of their contact offset values will generate contacts

Search for Advanced Collision Detection in PhysX docs for more details

Parameters:

offsets (Union[np.ndarray, torch.Tensor, wp.array]) – Contact offsets of the collision shapes. Allowed range [maximum(0, rest_offset), 0]. Default value is -inf, means default is picked by simulation based on the shape extent. Shape (M,).
indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Example:

>>> # set the contact offset for all the prims to the specified values.
>>> prims.set_contact_offsets(np.full(num_envs, 0.02))
>>>
>>> # set the contact offset for the first, middle and last of the 5 envs
>>> prims.set_contact_offsets(np.full(3, 0.02), indices=np.array([0, 2, 4]))

Set the default state of the prims (positions and orientations), that will be used after each reset.

Note

The default states will be set during post-reset (e.g., calling .post_reset() or world.reset() methods)

Parameters:

positions (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – positions in the world frame of the prim. shape is (M, 3). Defaults to None, which means left unchanged.
orientations (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – quaternion orientations in the world frame of the prim. quaternion is scalar-first (w, x, y, z). shape is (M, 4). Defaults to None, which means left unchanged.
indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Example:

>>> # configure default states for all prims
>>> positions = np.zeros((num_envs, 3))
>>> positions[:, 0] = np.arange(num_envs)
>>> orientations = np.tile(np.array([1.0, 0.0, 0.0, 0.0]), (num_envs, 1))
>>> prims.set_default_state(positions=positions, orientations=orientations)
>>>
>>> # set default states during post-reset
>>> prims.post_reset()

Set prim poses in the view with respect to the local frame (the prim’s parent frame)

Warning

This method will change (teleport) the prim poses immediately to the indicated value

Parameters:

translations (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – translations in the local frame of the prims (with respect to its parent prim). shape is (M, 3). Defaults to None, which means left unchanged.
orientations (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – quaternion orientations in the local frame of the prims. quaternion is scalar-first (w, x, y, z). shape is (M, 4). Defaults to None, which means left unchanged.
indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Hint

This method belongs to the methods used to set the prim state

Example:

>>> # reposition all prims
>>> positions = np.zeros((num_envs, 3))
>>> positions[:,0] = np.arange(num_envs)
>>> orientations = np.tile(np.array([1.0, 0.0, 0.0, 0.0]), (num_envs, 1))
>>> prims.set_local_poses(positions, orientations)
>>>
>>> # reposition only the prims for the first, middle and last of the 5 envs
>>> positions = np.zeros((3, 3))
>>> positions[:,1] = np.arange(3)
>>> orientations = np.tile(np.array([1.0, 0.0, 0.0, 0.0]), (3, 1))
>>> prims.set_local_poses(positions, orientations, indices=np.array([0, 2, 4]))

Set prim scales in the view with respect to the local frame (the prim’s parent frame)

Parameters:

scales (Optional[Union[np.ndarray, torch.Tensor, wp.array]]) – scales to be applied to the prim’s dimensions in the view. shape is (M, 3).
indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Example:

>>> # set the scale for all prims. Since there are 5 envs, the scale is repeated 5 times
>>> scales = np.tile(np.array([1.0, 0.75, 0.5]), (num_envs, 1))
>>> prims.set_local_scales(scales)
>>>
>>> # set the scale for the first, middle and last of the 5 envs
>>> scales = np.tile(np.array([1.0, 0.75, 0.5]), (3, 1))
>>> prims.set_local_scales(scales, indices=np.array([0, 2, 4]))

Set minimum torsional patch radii for prims in the view.

Search for “Torsional Patch Radius” in PhysX docs for more details

Parameters:

radii (Union[np.ndarray, torch.Tensor, wp.array]) – minimum radius of the contact patch used to apply torsional friction. Allowed range [0, max_float]. shape is (M, ).
indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Example:

>>> # set the minimum torsional patch radius for all the prims to the specified values.
>>> prims.set_min_torsional_patch_radii(np.full(num_envs, 0.05))
>>>
>>> # set the minimum torsional patch radius for the first, middle and last of the 5 envs
>>> prims.set_min_torsional_patch_radii(np.full(3, 0.05), indices=np.array([0, 2, 4]))

Set rest offsets for prims in the view.

Two shapes will come to rest at a distance equal to the sum of their rest offset values. If the rest offset is 0, they should converge to touching exactly

Search for Advanced Collision Detection in PhysX docs for more details

Warning

The contact offset must be positive and greater than the rest offset

Parameters:

offsets (Union[np.ndarray, torch.Tensor, wp.array]) – Rest offset of a collision shape. Allowed range [-max_float, contact_offset]. Default value is -inf, means default is picked by simulation. For rigid bodies its zero. Shape (M,).
indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Example:

>>> # set the rest offset for all the prims to the specified values.
>>> prims.set_rest_offsets(np.full(num_envs, 0.01))
>>>
>>> # set the rest offset for the first, middle and last of the 5 envs
>>> prims.set_rest_offsets(np.full(3, 0.01), indices=np.array([0, 2, 4]))

Set torsional patch radii for prims in the view.

Search for “Torsional Patch Radius” in PhysX docs for more details

Parameters:

radii (Union[np.ndarray, torch.Tensor, wp.array]) – radius of the contact patch used to apply torsional friction. Allowed range [0, max_float]. shape is (M,).
indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Example:

>>> # set the torsional patch radius for all the prims to the specified values.
>>> prims.set_torsional_patch_radii(np.full(num_envs, 0.1))
>>>
>>> # set the torsional patch radius for the first, middle and last of the 5 envs
>>> prims.set_torsional_patch_radii(np.full(3, 0.1), indices=np.array([0, 2, 4]))

Set the visibilities of the prims in stage

Parameters:

visibilities (Union[np.ndarray, torch.Tensor, wp.array]) – flag to set the visibilities of the usd prims in stage. Shape (M,). Where M <= size of the encapsulated prims in the view.
indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Defaults to None (i.e: all prims in the view).

Example:

>>> # make all prims not visible in the stage
>>> prims.set_visibilities(visibilities=[False] * num_envs)

Set prim poses in the view with respect to the world’s frame

Warning

This method will change (teleport) the prim poses immediately to the indicated value

Parameters:

positions (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – positions in the world frame of the prims. shape is (M, 3). Defaults to None, which means left unchanged.
orientations (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – quaternion orientations in the world frame of the prims. quaternion is scalar-first (w, x, y, z). shape is (M, 4). Defaults to None, which means left unchanged.
indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
usd (bool, optional) – True to query from usd. Otherwise False to query from Fabric data. Defaults to True.

Hint

This method belongs to the methods used to set the prim state

Example:

>>> # reposition all prims in row (x-axis)
>>> positions = np.zeros((num_envs, 3))
>>> positions[:,0] = np.arange(num_envs)
>>> orientations = np.tile(np.array([1.0, 0.0, 0.0, 0.0]), (num_envs, 1))
>>> prims.set_world_poses(positions, orientations)
>>>
>>> # reposition only the prims for the first, middle and last of the 5 envs in column (y-axis)
>>> positions = np.zeros((3, 3))
>>> positions[:,1] = np.arange(3)
>>> orientations = np.tile(np.array([1.0, 0.0, 0.0, 0.0]), (3, 1))
>>> prims.set_world_poses(positions, orientations, indices=np.array([0, 2, 4]))

Bases: Prim

Provides high level functions to deal with a Xform prim view (one or many) and its descendants as well as its attributes/properties.

This class wraps all matching Xforms found at the regex provided at the prim_paths_expr argument

Note

Each prim will have xformOp:orient, xformOp:translate and xformOp:scale only post-init, unless it is a non-root articulation link.

Parameters:

prim_paths_expr (Union[str, List[str]]) – prim paths regex to encapsulate all prims that match it. example: “/World/Env[1-5]/Franka” will match /World/Env1/Franka, /World/Env2/Franka..etc. (a non regex prim path can also be used to encapsulate one Xform). Additionally a list of regex can be provided. example [“/World/Env[1-5]/Franka”, “/World/Env[10-19]/Franka”].
name (str, optional) – shortname to be used as a key by Scene class. Note: needs to be unique if the object is added to the Scene. Defaults to “xform_prim_view”.
positions (Optional[Union[np.ndarray, torch.Tensor]], optional) – default positions in the world frame of the prim. shape is (N, 3). Defaults to None, which means left unchanged.
translations (Optional[Union[np.ndarray, torch.Tensor]], optional) – default translations in the local frame of the prims (with respect to its parent prims). shape is (N, 3). Defaults to None, which means left unchanged.
orientations (Optional[Union[np.ndarray, torch.Tensor]], optional) – default quaternion orientations in the world/ local frame of the prim (depends if translation or position is specified). quaternion is scalar-first (w, x, y, z). shape is (N, 4). Defaults to None, which means left unchanged.
scales (Optional[Union[np.ndarray, torch.Tensor]], optional) – local scales to be applied to the prim’s dimensions. shape is (N, 3). Defaults to None, which means left unchanged.
visibilities (Optional[Union[np.ndarray, torch.Tensor]], optional) – set to false for an invisible prim in the stage while rendering. shape is (N,). Defaults to None.
reset_xform_properties (bool, optional) – True if the prims don’t have the right set of xform properties (i.e: translate, orient and scale) ONLY and in that order. Set this parameter to False if the object were cloned using using the cloner api in isaacsim.core.cloner. Defaults to True.
usd (bool, optional) – True to strictly read/ write from usd. Otherwise False to allow read/ write from Fabric during initialization. Defaults to True.

Raises:

Exception – if translations and positions defined at the same time.
Exception – No prim was matched using the prim_paths_expr provided.

Example:

>>> import isaacsim.core.utils.stage as stage_utils
>>> from isaacsim.core.cloner import GridCloner
>>> from isaacsim.core.prims import XFormPrim
>>> from pxr import UsdGeom
>>>
>>> env_zero_path = "/World/envs/env_0"
>>> num_envs = 5
>>>
>>> # load the Franka Panda robot USD file
>>> stage_utils.add_reference_to_stage(usd_path, prim_path=f"{env_zero_path}/panda")  # /World/envs/env_0/panda
>>>
>>> # clone the environment (num_envs)
>>> cloner = GridCloner(spacing=1.5)
>>> cloner.define_base_env(env_zero_path)
>>> UsdGeom.Xform.Define(stage_utils.get_current_stage(), env_zero_path)
>>> env_pos = cloner.clone(
...     source_prim_path=env_zero_path,
...     prim_paths=cloner.generate_paths("/World/envs/env", num_envs),
...     copy_from_source=True
... )
>>>
>>> # wrap all Xforms
>>> prims = XFormPrim(prim_paths_expr="/World/envs/env.*", name="xform_view")
>>> prims
<isaacsim.core.prims.xform_prim.XFormPrim object at 0x7f8ffd22ebc0>

property is_non_root_articulation_link: bool#: Returns: bool: True if the prim corresponds to a non root link in an articulation. Otherwise False.

Set the visibilities of the prims in stage

Parameters:

visibilities (Union[np.ndarray, torch.Tensor, wp.array]) – flag to set the visibilities of the usd prims in stage. Shape (M,). Where M <= size of the encapsulated prims in the view.
indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Defaults to None (i.e: all prims in the view).

Example:

>>> # make all prims not visible in the stage
>>> prims.set_visibilities(visibilities=[False] * num_envs)

Returns the current visibilities of the prims in stage.

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Returns:

Shape (M,) with type bool, where each item holds True: if the prim is visible in stage. False otherwise.

Return type:

Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>> # get all visibilities. Returned shape is (5,) for the example: 5 envs
>>> prims.get_visibilities()
[ True  True  True  True  True]
>>>
>>> # get the visibilities for the first, middle and last of the 5 envs. Returned shape is (3,)
>>> prims.get_visibilities(indices=np.array([0, 2, 4]))
[ True  True  True]

get_default_state() → XFormPrimViewState#

Get the default states (positions and orientations) defined with the set_default_state method

Returns:: returns the default state of the prims that is used after each reset.
Return type:: XFormPrimViewState

Example:

>>> state = prims.get_default_state()
>>> state
<isaacsim.core.utils.types.XFormPrimViewState object at 0x7f82f73e3070>
>>> state.positions
[[ 1.5  -0.75  0.  ]
 [ 1.5   0.75  0.  ]
 [ 0.   -0.75  0.  ]
 [ 0.    0.75  0.  ]
 [-1.5  -0.75  0.  ]]
>>> state.orientations
[[1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]]

Set the default state of the prims (positions and orientations), that will be used after each reset.

Note

The default states will be set during post-reset (e.g., calling .post_reset() or world.reset() methods)

Parameters:

positions (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – positions in the world frame of the prim. shape is (M, 3). Defaults to None, which means left unchanged.
orientations (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – quaternion orientations in the world frame of the prim. quaternion is scalar-first (w, x, y, z). shape is (M, 4). Defaults to None, which means left unchanged.
indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Example:

>>> # configure default states for all prims
>>> positions = np.zeros((num_envs, 3))
>>> positions[:, 0] = np.arange(num_envs)
>>> orientations = np.tile(np.array([1.0, 0.0, 0.0, 0.0]), (num_envs, 1))
>>> prims.set_default_state(positions=positions, orientations=orientations)
>>>
>>> # set default states during post-reset
>>> prims.post_reset()

Apply visual material to the prims and optionally their prim descendants.

Parameters:

visual_materials (Union[VisualMaterial, List[VisualMaterial]]) – visual materials to be applied to the prims. Currently supports PreviewSurface, OmniPBR and OmniGlass. If a list is provided then its size has to be equal the view’s size or indices size. If one material is provided it will be applied to all prims in the view.
weaker_than_descendants (Optional[Union[bool, List[bool]]], optional) – True if the material shouldn’t override the descendants materials, otherwise False. Defaults to False. If a list of visual materials is provided then a list has to be provided with the same size for this arg as well.
indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Raises:

Exception – length of visual materials != length of prims indexed
Exception – length of visual materials != length of weaker descendants bools arg

Example:

>>> from isaacsim.core.api.materials import OmniGlass
>>>
>>> # create a dark-red glass visual material
>>> material = OmniGlass(
...     prim_path="/World/material/glass",  # path to the material prim to create
...     ior=1.25,
...     depth=0.001,
...     thin_walled=False,
...     color=np.array([0.5, 0.0, 0.0])
... )
>>> prims.apply_visual_materials(material)

get_applied_visual_materials( indices: ndarray | list | Tensor | warp.array | None = None, ) → List[VisualMaterial]#

Get the current applied visual materials

Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
Returns:: a list of the current applied visual materials to the prims if its type is currently supported.
Return type:: List[VisualMaterial]

Example:

>>> # get all applied visual materials. Returned size is 5 for the example: 5 envs
>>> prims.get_applied_visual_materials()
[<isaacsim.core.api.materials.omni_glass.OmniGlass object at 0x7f829c165de0>,
 <isaacsim.core.api.materials.omni_glass.OmniGlass object at 0x7f829c165de0>,
 <isaacsim.core.api.materials.omni_glass.OmniGlass object at 0x7f829c165de0>,
 <isaacsim.core.api.materials.omni_glass.OmniGlass object at 0x7f829c165de0>,
 <isaacsim.core.api.materials.omni_glass.OmniGlass object at 0x7f829c165de0>]
>>>
>>> # get the applied visual materials for the first, middle and last of the 5 envs. Returned size is 3
>>> prims.get_applied_visual_materials(indices=np.array([0, 2, 4]))
[<isaacsim.core.api.materials.omni_glass.OmniGlass object at 0x7f829c165de0>,
 <isaacsim.core.api.materials.omni_glass.OmniGlass object at 0x7f829c165de0>,
 <isaacsim.core.api.materials.omni_glass.OmniGlass object at 0x7f829c165de0>]

is_visual_material_applied( indices: ndarray | list | Tensor | warp.array | None = None, ) → List[bool]#

Check if there is a visual material applied

Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
Returns:: True if there is a visual material applied is applied to the corresponding prim in the view. False otherwise.
Return type:: List[bool]

Example:

>>> # given a visual material that is applied only to the first and the last environment
>>> prims.is_visual_material_applied()
[True, False, False, False, True]
>>>
>>> # check for the first, middle and last of the 5 envs
>>> prims.is_visual_material_applied(indices=np.array([0, 2, 4]))
[True, False, True]

Get the poses of the prims in the view with respect to the world’s frame

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
usd (bool, optional) – True to query from usd. Otherwise False to query from Fabric data. Defaults to True.

Returns:

first index is positions in the world frame of the prims. shape is (M, 3).: second index is quaternion orientations in the world frame of the prims. quaternion is scalar-first (w, x, y, z). shape is (M, 4).

Return type:

Union[Tuple[np.ndarray, np.ndarray], Tuple[torch.Tensor, torch.Tensor], Tuple[wp.indexedarray, wp.indexedarray]]

Example:

>>> # get all prims poses with respect to the world's frame.
>>> # Returned shape is position (5, 3) and orientation (5, 4) for the example: 5 envs
>>> positions, orientations = prims.get_world_poses()
>>> positions
[[ 1.5  -0.75  0.  ]
 [ 1.5   0.75  0.  ]
 [ 0.   -0.75  0.  ]
 [ 0.    0.75  0.  ]
 [-1.5  -0.75  0.  ]]
>>> orientations
[[1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]]
>>>
>>> # get only the prims poses with respect to the world's frame for the first, middle and last of the 5 envs.
>>> # Returned shape is position (3, 3) and orientation (3, 4) for the example: 3 envs selected
>>> positions, orientations = prims.get_world_poses(indices=np.array([0, 2, 4]))
>>> positions
[[ 1.5  -0.75  0.  ]
 [ 0.   -0.75  0.  ]
 [-1.5  -0.75  0.  ]]
>>> orientations
[[1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]]

Set prim poses in the view with respect to the world’s frame

Warning

This method will change (teleport) the prim poses immediately to the indicated value

Parameters:

positions (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – positions in the world frame of the prims. shape is (M, 3). Defaults to None, which means left unchanged.
orientations (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – quaternion orientations in the world frame of the prims. quaternion is scalar-first (w, x, y, z). shape is (M, 4). Defaults to None, which means left unchanged.
indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
usd (bool, optional) – True to query from usd. Otherwise False to query from Fabric data. Defaults to True.

Hint

This method belongs to the methods used to set the prim state

Example:

>>> # reposition all prims in row (x-axis)
>>> positions = np.zeros((num_envs, 3))
>>> positions[:,0] = np.arange(num_envs)
>>> orientations = np.tile(np.array([1.0, 0.0, 0.0, 0.0]), (num_envs, 1))
>>> prims.set_world_poses(positions, orientations)
>>>
>>> # reposition only the prims for the first, middle and last of the 5 envs in column (y-axis)
>>> positions = np.zeros((3, 3))
>>> positions[:,1] = np.arange(3)
>>> orientations = np.tile(np.array([1.0, 0.0, 0.0, 0.0]), (3, 1))
>>> prims.set_world_poses(positions, orientations, indices=np.array([0, 2, 4]))

Get prim poses in the view with respect to the local frame (the prim’s parent frame)

Parameters:

indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Returns:

first index is translations in the local frame of the prims. shape is (M, 3).: second index is quaternion orientations in the local frame of the prims. quaternion is scalar-first (w, x, y, z). shape is (M, 4).

Return type:

Union[Tuple[np.ndarray, np.ndarray], Tuple[torch.Tensor, torch.Tensor], Tuple[wp.indexedarray, wp.indexedarray]]

Example:

>>> # get all prims poses with respect to the local frame.
>>> # Returned shape is position (5, 3) and orientation (5, 4) for the example: 5 envs
>>> positions, orientations = prims.get_local_poses()
>>> positions
[[ 1.5  -0.75  0.  ]
 [ 1.5   0.75  0.  ]
 [ 0.   -0.75  0.  ]
 [ 0.    0.75  0.  ]
 [-1.5  -0.75  0.  ]]
>>> orientations
[[1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]]
>>>
>>> # get only the prims poses with respect to the local frame for the first, middle and last of the 5 envs.
>>> # Returned shape is position (3, 3) and orientation (3, 4) for the example: 3 envs selected
>>> positions, orientations = prims.get_local_poses(indices=np.array([0, 2, 4]))
>>> positions
[[ 1.5  -0.75  0.  ]
 [ 0.   -0.75  0.  ]
 [-1.5  -0.75  0.  ]]
>>> orientations
[[1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]]

Set prim poses in the view with respect to the local frame (the prim’s parent frame)

Warning

This method will change (teleport) the prim poses immediately to the indicated value

Parameters:

translations (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – translations in the local frame of the prims (with respect to its parent prim). shape is (M, 3). Defaults to None, which means left unchanged.
orientations (Optional[Union[np.ndarray, torch.Tensor, wp.array]], optional) – quaternion orientations in the local frame of the prims. quaternion is scalar-first (w, x, y, z). shape is (M, 4). Defaults to None, which means left unchanged.
indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Hint

This method belongs to the methods used to set the prim state

Example:

>>> # reposition all prims
>>> positions = np.zeros((num_envs, 3))
>>> positions[:,0] = np.arange(num_envs)
>>> orientations = np.tile(np.array([1.0, 0.0, 0.0, 0.0]), (num_envs, 1))
>>> prims.set_local_poses(positions, orientations)
>>>
>>> # reposition only the prims for the first, middle and last of the 5 envs
>>> positions = np.zeros((3, 3))
>>> positions[:,1] = np.arange(3)
>>> orientations = np.tile(np.array([1.0, 0.0, 0.0, 0.0]), (3, 1))
>>> prims.set_local_poses(positions, orientations, indices=np.array([0, 2, 4]))

Get prim scales in the view with respect to the world’s frame

Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
Returns:: scales applied to the prim’s dimensions in the world frame. shape is (M, 3).
Return type:: Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>> # get all prims scales with respect to the world's frame.
>>> # Returned shape is (5, 3) for the example: 5 envs
>>> prims.get_world_scales()
[[1. 1. 1.]
 [1. 1. 1.]
 [1. 1. 1.]
 [1. 1. 1.]
 [1. 1. 1.]]
>>>
>>> # get only the prims scales with respect to the world's frame for the first, middle and last of the 5 envs.
>>> # Returned shape is (3, 3) for the example: 3 envs selected
>>> prims.get_world_scales(indices=np.array([0, 2, 4]))
[[1. 1. 1.]
 [1. 1. 1.]
 [1. 1. 1.]]

Set prim scales in the view with respect to the local frame (the prim’s parent frame)

Parameters:

scales (Optional[Union[np.ndarray, torch.Tensor, wp.array]]) – scales to be applied to the prim’s dimensions in the view. shape is (M, 3).
indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).

Example:

>>> # set the scale for all prims. Since there are 5 envs, the scale is repeated 5 times
>>> scales = np.tile(np.array([1.0, 0.75, 0.5]), (num_envs, 1))
>>> prims.set_local_scales(scales)
>>>
>>> # set the scale for the first, middle and last of the 5 envs
>>> scales = np.tile(np.array([1.0, 0.75, 0.5]), (3, 1))
>>> prims.set_local_scales(scales, indices=np.array([0, 2, 4]))

Get prim scales in the view with respect to the local frame (the parent’s frame).

Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
Returns:: scales applied to the prim’s dimensions in the local frame. shape is (M, 3).
Return type:: Union[np.ndarray, torch.Tensor, wp.indexedarray]

Example:

>>> # get all prims scales with respect to the local frame.
>>> # Returned shape is (5, 3) for the example: 5 envs
>>> prims.get_local_scales()
[[1. 1. 1.]
 [1. 1. 1.]
 [1. 1. 1.]
 [1. 1. 1.]
 [1. 1. 1.]]
>>>
>>> # get only the prims scales with respect to the local frame for the first, middle and last of the 5 envs.
>>> # Returned shape is (3, 3) for the example: 3 envs selected
>>> prims.get_local_scales(indices=np.array([0, 2, 4]))
[[1. 1. 1.]
 [1. 1. 1.]
 [1. 1. 1.]]

property count: int#

Returns:: Number of prims encapsulated in this view.
Return type:: int

Example:

>>> prims.count
5

initialize( physics_sim_view: omni.physics.tensors.SimulationView | None = None, ) → None#

Create a physics simulation view if not passed and set other properties using the PhysX tensor API

Note

For this particular class, calling this method will do nothing

Parameters:: physics_sim_view (omni.physics.tensors.SimulationView, optional) – current physics simulation view. Defaults to None.

Example:

>>> prims.initialize()

property initialized: bool#

Check if prim view is initialized

Returns:: True if the view object was initialized (after the first call of .initialize()). False otherwise.
Return type:: bool

Example:

>>> # given an initialized articulation view
>>> prims.initialized
True

is_valid( indices: ndarray | list | Tensor | warp.array | None = None, ) → bool#

Check that all prims have a valid USD Prim

Parameters:: indices (Optional[Union[np.ndarray, list, torch.Tensor, wp.array]], optional) – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. Defaults to None (i.e: all prims in the view).
Returns:: True if all prim paths specified in the view correspond to a valid prim in stage. False otherwise.
Return type:: bool

Example:

>>> prims.is_valid()
True

property name: str#: Returns: str: name given to the prims view when instantiating it.

post_reset() → None#

Reset the prims to its default state

Example:

>>> prims.post_reset()

property prim_paths: List[str]#

Returns:: list of prim paths in the stage encapsulated in this view.
Return type:: List[str]

Example:

>>> prims.prim_paths
['/World/envs/env_0', '/World/envs/env_1', '/World/envs/env_2', '/World/envs/env_3', '/World/envs/env_4']

property prims: List[pxr.Usd.Prim]#

Returns:: List of USD Prim objects encapsulated in this view.
Return type:: List[Usd.Prim]

Example:

>>> prims.prims
[Usd.Prim(</World/envs/env_0>), Usd.Prim(</World/envs/env_1>), Usd.Prim(</World/envs/env_2>),
 Usd.Prim(</World/envs/env_3>), Usd.Prim(</World/envs/env_4>)]

Single Prims#

Warning

The use of Single Prim classes (a particular case of the Prims classes for a single prim) is discouraged as they will be removed in future versions. Use Prims classes (formerly Prim Views) instead.

class SingleArticulation( prim_path: str, name: str = 'articulation', position: Sequence[float] | None = None, translation: Sequence[float] | None = None, orientation: Sequence[float] | None = None, scale: Sequence[float] | None = None, visible: bool | None = None, articulation_controller: ArticulationController | None = None, enable_residual_reports: bool = False, )#

Bases: _SinglePrimWrapper

High level wrapper to deal with an articulation prim (only one articulation prim) and its attributes/properties.

Warning

The articulation object must be initialized in order to be able to operate on it. See the initialize method for more details.

Parameters:

prim_path (str) – prim path of the Prim to encapsulate or create.
name (str, optional) – shortname to be used as a key by Scene class. Note: needs to be unique if the object is added to the Scene. Defaults to “articulation”.
position (Optional[Sequence[float]], optional) – position in the world frame of the prim. Shape is (3, ). Defaults to None, which means left unchanged.
translation (Optional[Sequence[float]], optional) – translation in the local frame of the prim (with respect to its parent prim). Shape is (3, ). Defaults to None, which means left unchanged.
orientation (Optional[Sequence[float]], optional) – quaternion orientation in the world/ local frame of the prim (depends if translation or position is specified). quaternion is scalar-first (w, x, y, z). Shape is (4, ). Defaults to None, which means left unchanged.
scale (Optional[Sequence[float]], optional) – local scale to be applied to the prim’s dimensions. Shape is (3, ). Defaults to None, which means left unchanged.
visible (bool, optional) – set to false for an invisible prim in the stage while rendering. Defaults to True.
articulation_controller (Optional[ArticulationController], optional) – a custom ArticulationController which inherits from it. Defaults to creating the basic ArticulationController.

Example:

>>> import isaacsim.core.utils.stage as stage_utils
>>> from isaacsim.core.prims import SingleArticulation
>>>
>>> usd_path = "/home/<user>/Documents/Assets/Robots/Franka/franka_alt_fingers.usd"
>>> prim_path = "/World/envs/env_0/panda"
>>>
>>> # load the Franka Panda robot USD file
>>> stage_utils.add_reference_to_stage(usd_path, prim_path)
>>>
>>> # wrap the prim as an articulation
>>> prim = SingleArticulation(prim_path=prim_path, name="franka_panda")
>>> prim
<isaacsim.core.prims.single_articulation.SingleArticulation object at 0x7fdd165bf520>

property handles_initialized: bool#

Check if articulation handler is initialized

Returns:: whether the handler was initialized
Return type:: bool

Example:

>>> prim.handles_initialized
True

property num_dof: int#

Number of DOF of the articulation

Returns:: amount of DOFs
Return type:: int

Example:

>>> prim.num_dof
9

property num_bodies: int#

Number of articulation links

Returns:: number of links
Return type:: int

Example:

>>> prim.num_bodies
9

property dof_properties: ndarray#

Articulation DOF properties

DOF properties#
Index	Property name	Description
0	`type`	DOF type: invalid/unknown/uninitialized (0), rotation (1), translation (2)
1	`hasLimits`	Whether the DOF has limits
2	`lower`	Lower DOF limit (in radians or meters)
3	`upper`	Upper DOF limit (in radians or meters)
4	`driveMode`	Drive mode for the DOF: force (1), acceleration (2)
5	`maxVelocity`	Maximum DOF velocity. In radians/s, or stage_units/s
6	`maxEffort`	Maximum DOF effort. In N or N*stage_units
7	`stiffness`	DOF stiffness
8	`damping`	DOF damping

Returns:: named NumPy array of shape (num_dof, 9)
Return type:: np.ndarray

Example:

>>> # get properties for all DOFs
>>> prim.dof_properties
[(1,  True, -2.8973,  2.8973, 1, 1.0000000e+01, 5220., 60000., 3000.)
 (1,  True, -1.7628,  1.7628, 1, 1.0000000e+01, 5220., 60000., 3000.)
 (1,  True, -2.8973,  2.8973, 1, 5.9390470e+36, 5220., 60000., 3000.)
 (1,  True, -3.0718, -0.0698, 1, 5.9390470e+36, 5220., 60000., 3000.)
 (1,  True, -2.8973,  2.8973, 1, 5.9390470e+36,  720., 25000., 3000.)
 (1,  True, -0.0175,  3.7525, 1, 5.9390470e+36,  720., 15000., 3000.)
 (1,  True, -2.8973,  2.8973, 1, 1.0000000e+01,  720.,  5000., 3000.)
 (2,  True,  0.    ,  0.04  , 1, 3.4028235e+38,  720.,  6000., 1000.)
 (2,  True,  0.    ,  0.04  , 1, 3.4028235e+38,  720.,  6000., 1000.)]
>>>
>>> # property names
>>> prim.dof_properties.dtype.names
('type', 'hasLimits', 'lower', 'upper', 'driveMode', 'maxVelocity', 'maxEffort', 'stiffness', 'damping')
>>>
>>> # get DOF upper limits
>>> prim.dof_properties["upper"]
[ 2.8973  1.7628  2.8973 -0.0698  2.8973  3.7525  2.8973  0.04    0.04  ]
>>>
>>> # get the last DOF (panda_finger_joint2) upper limit
>>> prim.dof_properties["upper"][8]  # or prim.dof_properties[8][3]
0.04

property dof_names: List[str]#

List of prim names for each DOF.

Returns:: prim names
Return type:: list(string)

Example:

>>> prim.dof_names
['panda_joint1', 'panda_joint2', 'panda_joint3', 'panda_joint4', 'panda_joint5',
 'panda_joint6', 'panda_joint7', 'panda_finger_joint1', 'panda_finger_joint2']

initialize( physics_sim_view: omni.physics.tensors.SimulationView | None = None, ) → None#

Create a physics simulation view if not passed and an articulation view using PhysX tensor API

Note

If the articulation has been added to the world scene (e.g., world.scene.add(prim)), it will be automatically initialized when the world is reset (e.g., world.reset()).

Warning

This method needs to be called after each hard reset (e.g., Stop + Play on the timeline) before interacting with any other class method.

Parameters:: physics_sim_view (omni.physics.tensors.SimulationView, optional) – current physics simulation view. Defaults to None.

Example:

>>> prim.initialize()

get_dof_index(dof_name: str) → int#

Get a DOF index given its name

Parameters:: dof_name (str) – name of the DOF
Returns:: DOF index
Return type:: int

Example:

>>> prim.get_dof_index("panda_finger_joint2")
8

get_articulation_body_count() → int#

Get the number of bodies (links) that make up the articulation

Returns:: amount of bodies
Return type:: int

Example:

>>> prim.get_articulation_body_count()
12

disable_gravity() → None#

Keep gravity from affecting the robot

Example:

>>> prim.disable_gravity()

enable_gravity() → None#

Gravity will affect the robot

Example:

>>> prim.enable_gravity()

set_world_velocity(velocity: ndarray)#

Set the articulation root velocity

Parameters:: velocity (np.ndarray) – linear and angular velocity to set the root prim to. Shape (6,).

get_world_velocity() → ndarray#

Get the articulation root velocity

Returns:: current velocity of the the root prim. Shape (3,).
Return type:: np.ndarray

get_position_residual( report_max: bool | None = True, ) → float#

Get physics solver position residuals for articulations. This is the residual across all joints that are part of articulations.

The solver residuals are computed according to impulse variation normalized by the effective mass.

Parameters:: report_max (Optional[bool]) – whether to report max or RMS residual. Defaults to True, i.e. max criteria
Returns:: solver position/velocity max/rms residual.
Return type:: float

get_velocity_residual( report_max: bool | None = True, ) → float#

Get physics solver velocity residuals for articulations. This is the residual across all joints that are part of articulations.

The solver residuals are computed according to impulse variation normalized by the effective mass.

Parameters:: report_max (Optional[bool]) – whether to report max or RMS residual. Defaults to True, i.e. max criteria
Returns:: solver velocity max/rms residual.
Return type:: float

set_joint_positions( positions: ndarray, joint_indices: List | ndarray | None = None, ) → None#

Set the articulation joint positions

Warning

This method will immediately set (teleport) the affected joints to the indicated value. Use the apply_action method to control robot joints.

Parameters:

positions (np.ndarray) – articulation joint positions
joint_indices (Optional[Union[list, np.ndarray]], optional) – indices to specify which joints to manipulate. Defaults to None (all joints)

Hint

This method belongs to the methods used to set the articulation kinematic state:

set_linear_velocity, set_angular_velocity, set_joint_positions, set_joint_velocities, set_joint_efforts

Example:

>>> # set all the robot joints
>>> prim.set_joint_positions(np.array([0.0, -1.0, 0.0, -2.2, 0.0, 2.4, 0.8, 0.04, 0.04]))
>>>
>>> # set only the fingers in closed position: panda_finger_joint1 (7) and panda_finger_joint2 (8) to 0.0
>>> prim.set_joint_positions(np.array([0.04, 0.04]), joint_indices=np.array([7, 8]))

get_joint_positions( joint_indices: List | ndarray | None = None, ) → ndarray#

Get the articulation joint positions

Parameters:: joint_indices (Optional[Union[List, np.ndarray]], optional) – indices to specify which joints to read. Defaults to None (all joints)
Returns:: all or selected articulation joint positions
Return type:: np.ndarray

Example:

>>> # get all joint positions
>>> prim.get_joint_positions()
[ 1.1999920e-02 -5.6962633e-01  1.3480479e-08 -2.8105433e+00  6.8284894e-06
  3.0301569e+00  7.3234749e-01  3.9912373e-02  3.9999999e-02]
>>>
>>> # get finger positions: panda_finger_joint1 (7) and panda_finger_joint2 (8)
>>> prim.get_joint_positions(joint_indices=np.array([7, 8]))
[0.03991237  3.9999999e-02]

set_joint_velocities( velocities: ndarray, joint_indices: List | ndarray | None = None, ) → None#

Set the articulation joint velocities

Warning

This method will immediately set the affected joints to the indicated value. Use the apply_action method to control robot joints.

Parameters:

velocities (np.ndarray) – articulation joint velocities
joint_indices (Optional[Union[list, np.ndarray]], optional) – indices to specify which joints to manipulate. Defaults to None (all joints)

Hint

This method belongs to the methods used to set the articulation kinematic state:

set_linear_velocity, set_angular_velocity, set_joint_positions, set_joint_velocities, set_joint_efforts

Example:

>>> # set all the robot joint velocities to 0.0
>>> prim.set_joint_velocities(np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]))
>>>
>>> # set only the fingers velocities: panda_finger_joint1 (7) and panda_finger_joint2 (8) to -0.01
>>> prim.set_joint_velocities(np.array([-0.01, -0.01]), joint_indices=np.array([7, 8]))

set_joint_efforts( efforts: ndarray, joint_indices: List | ndarray | None = None, ) → None#

Set the articulation joint efforts

Note

This method can be used for effort control. For this purpose, there must be no joint drive or the stiffness and damping must be set to zero.

Parameters:

efforts (np.ndarray) – articulation joint efforts
joint_indices (Optional[Union[list, np.ndarray]], optional) – indices to specify which joints to manipulate. Defaults to None (all joints)

Hint

This method belongs to the methods used to set the articulation kinematic state:

set_linear_velocity, set_angular_velocity, set_joint_positions, set_joint_velocities, set_joint_efforts

Example:

>>> # set all the robot joint efforts to 0.0
>>> prim.set_joint_efforts(np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]))
>>>
>>> # set only the fingers efforts: panda_finger_joint1 (7) and panda_finger_joint2 (8) to 10
>>> prim.set_joint_efforts(np.array([10, 10]), joint_indices=np.array([7, 8]))

get_joint_velocities( joint_indices: List | ndarray | None = None, ) → ndarray#

Get the articulation joint velocities

Parameters:: joint_indices (Optional[Union[List, np.ndarray]], optional) – indices to specify which joints to read. Defaults to None (all joints)
Returns:: all or selected articulation joint velocities
Return type:: np.ndarray

Example:

>>> # get all joint velocities
>>> prim.get_joint_velocities()
[ 1.91603772e-06 -7.67638255e-03 -2.19138826e-07  1.10636465e-02 -4.63412944e-05
  3.48245539e-02  8.84692147e-02  5.40335372e-04 1.02849208e-05]
>>>
>>> # get finger velocities: panda_finger_joint1 (7) and panda_finger_joint2 (8)
>>> prim.get_joint_velocities(joint_indices=np.array([7, 8]))
[5.4033537e-04 1.0284921e-05]

get_measured_joint_efforts( joint_indices: List | ndarray | None = None, ) → ndarray#

Returns the efforts computed/measured by the physics solver of the joint forces in the DOF motion direction

Parameters:: joint_indices (Optional[Union[List, np.ndarray]], optional) – indices to specify which joints to read. Defaults to None (all joints)
Raises:: Exception – If the handlers are not initialized
Returns:: all or selected articulation joint measured efforts
Return type:: np.ndarray

Example:

>>> # get all joint efforts
>>> prim.get_measured_joint_efforts()
[ 2.7897308e-06 -6.9083519e+00 -3.6398471e-06  1.9158335e+01 -4.3552645e-06
  1.1866090e+00 -4.7079347e-06  3.2339853e-04 -3.2044132e-04]
>>>
>>> # get finger efforts: panda_finger_joint1 (7) and panda_finger_joint2 (8)
>>> prim.get_measured_joint_efforts(joint_indices=np.array([7, 8]))
[ 0.0003234  -0.00032044]

get_applied_joint_efforts( joint_indices: List | ndarray | None = None, ) → ndarray#

Get the efforts applied to the joints set by the set_joint_efforts method

Parameters:: joint_indices (Optional[Union[List, np.ndarray]], optional) – indices to specify which joints to read. Defaults to None (all joints)
Raises:: Exception – If the handlers are not initialized
Returns:: all or selected articulation joint applied efforts
Return type:: np.ndarray

Example:

>>> # get all applied joint efforts
>>> prim.get_applied_joint_efforts()
[ 0.  0.  0.  0.  0.  0.  0.  0.  0.]
>>>
>>> # get finger applied efforts: panda_finger_joint1 (7) and panda_finger_joint2 (8)
>>> prim.get_applied_joint_efforts(joint_indices=np.array([7, 8]))
[0.  0.]

get_measured_joint_forces( joint_indices: List | ndarray | None = None, ) → ndarray#

Get the measured joint reaction forces and torques (link incoming joint forces and torques) to external loads

Note

Since the name->index map for joints has not been exposed yet, it is possible to access the joint names and their indices through the articulation metadata.

prim._articulation_view._metadata.joint_names  # list of names
prim._articulation_view._metadata.joint_indices  # dict of name: index

To retrieve a specific row for the link incoming joint force/torque use joint_index + 1

Parameters:: joint_indices (Optional[Union[List, np.ndarray]], optional) – indices to specify which joints to read. Defaults to None (all joints)
Raises:: Exception – If the handlers are not initialized
Returns:: measured joint forces and torques. Shape is (num_joint + 1, 6). Row index 0 is the incoming joint of the base link. For the last dimension the first 3 values are for forces and the last 3 for torques
Return type:: np.ndarray

Example:

>>> # get all measured joint forces and torques
>>> prim.get_measured_joint_forces()
[[ 0.0000000e+00  0.0000000e+00  0.0000000e+00  0.0000000e+00  0.0000000e+00  0.0000000e+00]
 [ 1.4995076e+02  4.2574748e-06  5.6364370e-04  4.8701895e-05 -6.9072924e+00  3.1881387e-05]
 [-2.8971717e-05 -1.0677823e+02 -6.8384506e+01 -6.9072924e+00 -5.4927128e-05  6.1222494e-07]
 [ 8.7120995e+01 -4.3871860e-05 -5.5795174e+01  5.3687054e-05 -2.4538563e+01  1.3333466e-05]
 [ 5.3519474e-05 -4.8109909e+01  6.0709282e+01  1.9157074e+01 -5.9258469e-05  8.2744418e-07]
 [-3.1691040e+01  2.3313689e-04  3.9990173e+01 -5.8968733e-05 -1.1863431e+00  2.2335558e-05]
 [-1.0809851e-04  1.5340537e+01 -1.5458489e+01  1.1863426e+00  6.1094368e-05 -1.5940281e-05]
 [-7.5418940e+00 -5.0814648e+00 -5.6512990e+00 -5.6385466e-05  3.8859999e-01 -3.4943256e-01]
 [ 4.7421460e+00 -3.1945827e+00  3.5528181e+00  5.5852943e-05  8.4794536e-03  7.6405057e-03]
 [ 4.0760727e+00  2.1640673e-01 -4.0513167e+00 -5.9565349e-04  1.1407082e-02  2.1432268e-06]
 [ 5.1680198e-03 -9.7754575e-02 -9.7093947e-02 -8.4155556e-12 -1.2910691e-12 -1.9347857e-11]
 [-5.1910793e-03  9.7588278e-02 -9.7106412e-02  8.4155573e-12  1.2910637e-12 -1.9347855e-11]]
>>>
>>> # get measured joint force and torque for the fingers
>>> metadata = prim._articulation_view._metadata
>>> joint_indices = 1 + np.array([
...     metadata.joint_indices["panda_finger_joint1"],
...     metadata.joint_indices["panda_finger_joint2"],
... ])
>>> joint_indices
[10 11]
>>> prim.get_measured_joint_forces(joint_indices)
[[ 5.1680198e-03 -9.7754575e-02 -9.7093947e-02 -8.4155556e-12 -1.2910691e-12 -1.9347857e-11]
 [-5.1910793e-03  9.7588278e-02 -9.7106412e-02  8.4155573e-12  1.2910637e-12 -1.9347855e-11]]

get_joints_default_state() → JointsState#

Get the default joint states (positions and velocities).

Returns:: an object that contains the default joint positions and velocities
Return type:: JointsState

Example:

>>> state = prim.get_joints_default_state()
>>> state
<isaacsim.core.utils.types.JointsState object at 0x7f04a0061240>
>>>
>>> state.positions
[ 0.012  -0.57000005  0.  -2.81  0.  3.037  0.785398  0.04  0.04 ]
>>> state.velocities
[0. 0. 0. 0. 0. 0. 0. 0. 0.]

set_joints_default_state( positions: ndarray | None = None, velocities: ndarray | None = None, efforts: ndarray | None = None, ) → None#

Set the joint default states (positions, velocities and/or efforts) to be applied after each reset.

Note

The default states will be set during post-reset (e.g., calling .post_reset() or world.reset() methods)

Parameters:

positions (Optional[np.ndarray], optional) – joint positions. Defaults to None.
velocities (Optional[np.ndarray], optional) – joint velocities. Defaults to None.
efforts (Optional[np.ndarray], optional) – joint efforts. Defaults to None.

Example:

>>> # configure default joint states
>>> prim.set_joints_default_state(
...     positions=np.array([0.0, -1.0, 0.0, -2.2, 0.0, 2.4, 0.8, 0.04, 0.04]),
...     velocities=np.zeros(shape=(prim.num_dof,)),
...     efforts=np.zeros(shape=(prim.num_dof,))
... )
>>>
>>> # set default states during post-reset
>>> prim.post_reset()

get_joints_state() → JointsState#

Get the current joint states (positions and velocities)

Returns:: an object that contains the current joint positions and velocities
Return type:: JointsState

Example:

>>> state = prim.get_joints_state()
>>> state
<isaacsim.core.utils.types.JointsState object at 0x7f02f6df57b0>
>>>
>>> state.positions
[ 1.1999920e-02 -5.6962633e-01  1.3480479e-08 -2.8105433e+00 6.8284894e-06
  3.0301569e+00  7.3234749e-01  3.9912373e-02  3.9999999e-02]
>>> state.velocities
[ 1.91603772e-06 -7.67638255e-03 -2.19138826e-07  1.10636465e-02 -4.63412944e-05
  245539e-02  8.84692147e-02  5.40335372e-04  1.02849208e-05]

get_articulation_controller() → ArticulationController#

Get the articulation controller

Note

If no articulation_controller was passed during class instantiation, a default controller of type ArticulationController (a Proportional-Derivative controller that can apply position targets, velocity targets and efforts) will be used

Returns:: articulation controller
Return type:: ArticulationController

Example:

>>> prim.get_articulation_controller()
<isaacsim.core.api.controllers.articulation_controller.ArticulationController object at 0x7f04a0060190>

set_linear_velocity( velocity: ndarray, ) → None#

Set the linear velocity of the root articulation prim

Warning

This method will immediately set the articulation state

Parameters:: velocity (np.ndarray) – 3D linear velocity vector. Shape (3,).

Hint

This method belongs to the methods used to set the articulation kinematic state:

set_linear_velocity, set_angular_velocity, set_joint_positions, set_joint_velocities, set_joint_efforts

Example:

>>> prim.set_linear_velocity(np.array([0.1, 0.0, 0.0]))

get_linear_velocity() → ndarray#

Get the linear velocity of the root articulation prim

Returns:: 3D linear velocity vector. Shape (3,)
Return type:: np.ndarray

Example:

>>> prim.get_linear_velocity()
[0. 0. 0.]

set_angular_velocity( velocity: ndarray, ) → None#

Set the angular velocity of the root articulation prim

Warning

This method will immediately set the articulation state

Parameters:: velocity (np.ndarray) – 3D angular velocity vector. Shape (3,)

Hint

This method belongs to the methods used to set the articulation kinematic state:

set_linear_velocity, set_angular_velocity, set_joint_positions, set_joint_velocities, set_joint_efforts

Example:

>>> prim.set_angular_velocity(np.array([0.1, 0.0, 0.0]))

get_angular_velocity() → ndarray#

Get the angular velocity of the root articulation prim

Returns:: 3D angular velocity vector. Shape (3,)
Return type:: np.ndarray

Example:

>>> prim.get_angular_velocity()
[0. 0. 0.]

apply_action( control_actions: ArticulationAction, ) → None#

Apply joint positions, velocities and/or efforts to control an articulation

Parameters:

control_actions (ArticulationAction) – actions to be applied for next physics step.
indices (Optional[Union[list, np.ndarray]], optional) – degree of freedom indices to apply actions to. Defaults to all degrees of freedom.

Hint

High stiffness makes the joints snap faster and harder to the desired target, and higher damping smoothes but also slows down the joint’s movement to target

For position control, set relatively high stiffness and low damping (to reduce vibrations)
For velocity control, stiffness must be set to zero with a non-zero damping
For effort control, stiffness and damping must be set to zero

Example:

>>> from isaacsim.core.utils.types import ArticulationAction
>>>
>>> # move all the robot joints to the indicated position
>>> action = ArticulationAction(joint_positions=np.array([0.0, -1.0, 0.0, -2.2, 0.0, 2.4, 0.8, 0.04, 0.04]))
>>> prim.apply_action(action)
>>>
>>> # close the robot fingers: panda_finger_joint1 (7) and panda_finger_joint2 (8) to 0.0
>>> action = ArticulationAction(joint_positions=np.array([0.0, 0.0]), joint_indices=np.array([7, 8]))
>>> prim.apply_action(action)

get_applied_action() → ArticulationAction#

Get the last applied action

Returns:: last applied action. Note: a dictionary is used as the object’s string representation
Return type:: ArticulationAction

Example:

>>> # last applied action: joint_positions -> [0.0, -1.0, 0.0, -2.2, 0.0, 2.4, 0.8, 0.04, 0.04]
>>> prim.get_applied_action()
{'joint_positions': [0.0, -1.0, 0.0, -2.200000047683716, 0.0, 2.4000000953674316,
                     0.800000011920929, 0.03999999910593033, 0.03999999910593033],
 'joint_velocities': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
 'joint_efforts': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}

set_solver_position_iteration_count( count: int, ) → None#

Set the solver (position) iteration count for the articulation

The solver iteration count determines how accurately contacts, drives, and limits are resolved. Search for Solver Iteration Count in PhysX docs for more details.

Warning

Setting a higher number of iterations may improve the fidelity of the simulation, although it may affect its performance.

Parameters:: count (int) – position iteration count

Example:

>>> prim.set_solver_position_iteration_count(64)

get_solver_position_iteration_count() → int#

Get the solver (position) iteration count for the articulation

The solver iteration count determines how accurately contacts, drives, and limits are resolved. Search for Solver Iteration Count in PhysX docs for more details.

Returns:: position iteration count
Return type:: int

Example:

>>> prim.get_solver_position_iteration_count()
32

set_solver_velocity_iteration_count(count: int)#

Set the solver (velocity) iteration count for the articulation

The solver iteration count determines how accurately contacts, drives, and limits are resolved. Search for Solver Iteration Count in PhysX docs for more details.

Warning

Setting a higher number of iterations may improve the fidelity of the simulation, although it may affect its performance.

Parameters:: count (int) – velocity iteration count

Example:

>>> prim.set_solver_velocity_iteration_count(64)

get_solver_velocity_iteration_count() → int#

Get the solver (velocity) iteration count for the articulation

The solver iteration count determines how accurately contacts, drives, and limits are resolved. Search for Solver Iteration Count in PhysX docs for more details.

Returns:: velocity iteration count
Return type:: int

Example:

>>> prim.get_solver_velocity_iteration_count()
32

set_stabilization_threshold( threshold: float, ) → None#

Set the mass-normalized kinetic energy below which the articulation may participate in stabilization

Search for Stabilization Threshold in PhysX docs for more details

Parameters:: threshold (float) – stabilization threshold

Example:

>>> prim.set_stabilization_threshold(0.005)

get_stabilization_threshold() → float#

Get the mass-normalized kinetic energy below which the articulation may participate in stabilization

Search for Stabilization Threshold in PhysX docs for more details

Returns:: stabilization threshold
Return type:: float

Example:

>>> prim.get_stabilization_threshold()
0.0009999999

set_enabled_self_collisions(flag: bool) → None#

Set the enable self collisions flag (physxArticulation:enabledSelfCollisions)

Parameters:: flag (bool) – whether to enable self collisions

Example:

>>> prim.set_enabled_self_collisions(True)

get_enabled_self_collisions() → int#

Get the enable self collisions flag (physxArticulation:enabledSelfCollisions)

Returns:: self collisions flag (boolean interpreted as int)
Return type:: int

Example:

>>> prim.get_enabled_self_collisions()
0

set_sleep_threshold(threshold: float) → None#

Set the threshold for articulations to enter a sleep state

Search for Articulations and Sleeping in PhysX docs for more details

Parameters:: threshold (float) – sleep threshold

Example:

>>> prim.set_sleep_threshold(0.01)

apply_visual_material( visual_material: VisualMaterial, weaker_than_descendants: bool = False, ) → None#

Apply visual material to the held prim and optionally its descendants.

Parameters:

visual_material (VisualMaterial) – visual material to be applied to the held prim. Currently supports PreviewSurface, OmniPBR and OmniGlass.
weaker_than_descendants (bool, optional) – True if the material shouldn’t override the descendants materials, otherwise False. Defaults to False.

Example:

>>> from isaacsim.core.api.materials import OmniGlass
>>>
>>> # create a dark-red glass visual material
>>> material = OmniGlass(
...     prim_path="/World/material/glass",  # path to the material prim to create
...     ior=1.25,
...     depth=0.001,
...     thin_walled=False,
...     color=np.array([0.5, 0.0, 0.0])
... )
>>> prim.apply_visual_material(material)

get_applied_visual_material() → VisualMaterial#

Return the current applied visual material in case it was applied using apply_visual_material or it’s one of the following materials that was already applied before: PreviewSurface, OmniPBR and OmniGlass.

Returns:: the current applied visual material if its type is currently supported.
Return type:: VisualMaterial

Example:

>>> # given a visual material applied
>>> prim.get_applied_visual_material()
<isaacsim.core.api.materials.omni_glass.OmniGlass object at 0x7f36263106a0>

get_default_state() → XFormPrimState#

Get the default prim states (spatial position and orientation).

Returns:: an object that contains the default state of the prim (position and orientation)
Return type:: XFormPrimState

Example:

>>> state = prim.get_default_state()
>>> state
<isaacsim.core.utils.types.XFormPrimState object at 0x7f33addda650>
>>>
>>> state.position
[-4.5299529e-08 -1.8347054e-09 -2.8610229e-08]
>>> state.orientation
[1. 0. 0. 0.]

get_local_pose() → Tuple[ndarray, ndarray]#

Get prim’s pose with respect to the local frame (the prim’s parent frame)

Returns:: first index is the position in the local frame (with shape (3, )). Second index is quaternion orientation (with shape (4, )) in the local frame
Return type:: Tuple[np.ndarray, np.ndarray]

Example:

>>> # if the prim is in position (1.0, 0.5, 0.0) with respect to the world frame
>>> position, orientation = prim.get_local_pose()
>>> position
[0. 0. 0.]
>>> orientation
[0. 0. 0.]

get_local_scale() → ndarray#

Get prim’s scale with respect to the local frame (the parent’s frame)

Returns:: scale applied to the prim’s dimensions in the local frame. shape is (3, ).
Return type:: np.ndarray

Example:

>>> prim.get_local_scale()
[1. 1. 1.]

get_sleep_threshold() → float#

Get the threshold for articulations to enter a sleep state

Search for Articulations and Sleeping in PhysX docs for more details

Returns:: sleep threshold
Return type:: float

Example:

>>> prim.get_sleep_threshold()
0.005

get_visibility() → bool#

Returns:: true if the prim is visible in stage. false otherwise.
Return type:: bool

Example:

>>> # get the visible state of an visible prim on the stage
>>> prim.get_visibility()
True

get_world_pose() → Tuple[ndarray, ndarray]#

Get prim’s pose with respect to the world’s frame

Returns:: first index is the position in the world frame (with shape (3, )). Second index is quaternion orientation (with shape (4, )) in the world frame
Return type:: Tuple[np.ndarray, np.ndarray]

Example:

>>> # if the prim is in position (1.0, 0.5, 0.0) with respect to the world frame
>>> position, orientation = prim.get_world_pose()
>>> position
[1.  0.5 0. ]
>>> orientation
[1. 0. 0. 0.]

get_world_scale() → ndarray#

Get prim’s scale with respect to the world’s frame

Returns:: scale applied to the prim’s dimensions in the world frame. shape is (3, ).
Return type:: np.ndarray

Example:

>>> prim.get_world_scale()
[1. 1. 1.]

is_valid() → bool#

Check if the prim path has a valid USD Prim at it

Returns:: True is the current prim path corresponds to a valid prim in stage. False otherwise.
Return type:: bool

Example:

>>> # given an existing and valid prim
>>> prims.is_valid()
True

is_visual_material_applied() → bool#

Check if there is a visual material applied

Returns:: True if there is a visual material applied. False otherwise.
Return type:: bool

Example:

>>> # given a visual material applied
>>> prim.is_visual_material_applied()
True

property name: str | None#: Returns: str: name given to the prim when instantiating it. Otherwise None.

property non_root_articulation_link: bool#

Used to query if the prim is a non root articulation link

Returns:: True if the prim itself is a non root link
Return type:: bool

Example:

>>> # for a wrapped articulation (where the root prim has the Physics Articulation Root property applied)
>>> prim.non_root_articulation_link
False

post_reset() → None#

Reset the prim to its default state (position and orientation).

Note

For an articulation, in addition to configuring the root prim’s default position and spatial orientation (defined via the set_default_state method), the joint’s positions, velocities, and efforts (defined via the set_joints_default_state method) are imposed

Example:

>>> prim.post_reset()

property prim: pxr.Usd.Prim#: Returns: Usd.Prim: USD Prim object that this object holds.

property prim_path: str#: Returns: str: prim path in the stage

set_default_state( position: Sequence[float] | None = None, orientation: Sequence[float] | None = None, ) → None#

Set the default state of the prim (position and orientation), that will be used after each reset.

Parameters:

position (Optional[Sequence[float]], optional) – position in the world frame of the prim. shape is (3, ). Defaults to None, which means left unchanged.
orientation (Optional[Sequence[float]], optional) – quaternion orientation in the world frame of the prim. quaternion is scalar-first (w, x, y, z). shape is (4, ). Defaults to None, which means left unchanged.

Example:

>>> # configure default state
>>> prim.set_default_state(position=np.array([1.0, 0.5, 0.0]), orientation=np.array([1, 0, 0, 0]))
>>>
>>> # set default states during post-reset
>>> prim.post_reset()

set_local_pose( translation: Sequence[float] | None = None, orientation: Sequence[float] | None = None, ) → None#

Set prim’s pose with respect to the local frame (the prim’s parent frame).

Warning

This method will change (teleport) the prim pose immediately to the indicated value

Parameters:

translation (Optional[Sequence[float]], optional) – translation in the local frame of the prim (with respect to its parent prim). shape is (3, ). Defaults to None, which means left unchanged.
orientation (Optional[Sequence[float]], optional) – quaternion orientation in the local frame of the prim. quaternion is scalar-first (w, x, y, z). shape is (4, ). Defaults to None, which means left unchanged.

Hint

This method belongs to the methods used to set the prim state

Example:

>>> prim.set_local_pose(translation=np.array([1.0, 0.5, 0.0]), orientation=np.array([1., 0., 0., 0.]))

set_local_scale( scale: Sequence[float] | None, ) → None#

Set prim’s scale with respect to the local frame (the prim’s parent frame).

Parameters:: scale (Optional[Sequence[float]]) – scale to be applied to the prim’s dimensions. shape is (3, ). Defaults to None, which means left unchanged.

Example:

>>> # scale prim 10 times smaller
>>> prim.set_local_scale(np.array([0.1, 0.1, 0.1]))

set_visibility(visible: bool) → None#

Set the visibility of the prim in stage

Parameters:: visible (bool) – flag to set the visibility of the usd prim in stage.

Example:

>>> # make prim not visible in the stage
>>> prim.set_visibility(visible=False)

set_world_pose( position: Sequence[float] | None = None, orientation: Sequence[float] | None = None, ) → None#

Ses prim’s pose with respect to the world’s frame

Warning

This method will change (teleport) the prim pose immediately to the indicated value

Parameters:

position (Optional[Sequence[float]], optional) – position in the world frame of the prim. shape is (3, ). Defaults to None, which means left unchanged.
orientation (Optional[Sequence[float]], optional) – quaternion orientation in the world frame of the prim. quaternion is scalar-first (w, x, y, z). shape is (4, ). Defaults to None, which means left unchanged.

Hint

This method belongs to the methods used to set the prim state

Example:

>>> prim.set_world_pose(position=np.array([1.0, 0.5, 0.0]), orientation=np.array([1., 0., 0., 0.]))

Bases: _SinglePrimWrapper

Cloth primitive object provide functionalities to create and control cloth parameters

property mesh: pxr.UsdGeom.Mesh#: Returns: Usd.Prim: USD Prim object that this object tracks.

get_current_dynamic_state() → DynamicState#

Return the DynamicState that contains the position and orientation of the cloth prim

Returns:

position (np.ndarray, optional):: position in the world frame of the prim. shape is (3, ). Defaults to None, which means left unchanged.
orientation (np.ndarray, optional):: quaternion orientation in the world frame of the prim. quaternion is scalar-first (w, x, y, z). shape is (4, ). Defaults to None, which means left unchanged.

Return type:

DynamicState

set_stretch_stiffness( stiffness: ndarray | Tensor, ) → None#

Sets stretch stiffness values of spring constraints in the cloth It represents a stiffness for linear springs placed between particles to counteract stretching.

Parameters:: stiffness (Union[np.ndarray, torch.Tensor]) – The stretch stiffnesses. Range: [0 , inf), Units: force/distance = mass/second/second

set_spring_damping( damping: ndarray | Tensor, ) → None#

Sets damping values of spring constraints in the cloth

Parameters:: damping (List[float]) – The damping values of springs. Range: [0 , inf), Units: force/distance = mass/second

set_cloth_stretch_stiffness( stiffness: ndarray | Tensor, ) → None#

Sets a single stretch stiffness value to all springs constraints in the cloth

Parameters:: stiffness (Union[np.ndarray, torch.Tensor]) – The cloth springs stretch stiffness value. Range: [0 , inf), Units: force/distance = mass/second/second

set_cloth_bend_stiffness(stiffness: float) → None#

Sets a single bend stiffness value to all springs constraints in the cloth

Parameters:: stiffness (float) – The cloth springs bend stiffness value. Range: [0 , inf), Units: force/distance = mass/second/second

set_cloth_shear_stiffness(stiffness: float) → None#

Sets a single shear stiffness value to all springs constraints in the cloth

Parameters:: stiffness (float) – The cloth springs shear stiffness value. Range: [0 , inf), Units: force/distance = mass/second/second

set_cloth_damping(damping: float) → None#

Sets a single damping value to all springs constraints in the cloth

Parameters:: damping (float) – The cloth springs damping value. Range: [0 , inf), Units: force/velocity = mass/second

set_pressure(pressure: float) → None#

Parameters:: pressure (float) – pressure value.

set_self_collision_filter( self_collision_filter: bool, ) → None#

Parameters:: self_collision_filter (bool) – self collision filter.

set_self_collision(self_collision: bool) → None#

Parameters:: self_collision (bool) – self collision.

set_particle_group(particle_group: int) → None#

Parameters:: particle_group (int) – particle group.

get_stretch_stiffness() → ndarray | Tensor#

Gets stretch stiffness values of spring constraints

Returns:: The stretch stiffness.
Return type:: float

get_spring_damping() → ndarray | Tensor#

Gets damping values of spring constraints

Returns:: The spring damping.
Return type:: Union[np.ndarray, torch.Tensor]

get_cloth_stretch_stiffness() → float#

Reports a single value that would be used to generate the stiffnesses. This API does not report the actually created stiffnesses.

Returns:: The stretch stiffness.
Return type:: float

get_cloth_bend_stiffness() → float#

Reports a single value that would be used to generate the stiffnesses. This API does not report the actually created stiffnesses.

Returns:: The bend stiffness.
Return type:: float

get_cloth_shear_stiffness() → float#

Reports a single value that would be used to generate the stiffnesses. This API does not report the actually created stiffnesses.

Returns:: The shear stiffness.
Return type:: float

get_cloth_damping() → ndarray | Tensor#

Reports a single value that would be used to generate the dampings. This API does not report the actually created dampings.

Returns:: The spring damping.
Return type:: float

get_pressure() → float#

Returns:: pressure value.
Return type:: float

get_self_collision_filter() → bool#

Returns:: self collision filter.
Return type:: bool

get_self_collision() → bool#

Returns:: self collision.
Return type:: bool

get_particle_group() → int#

Returns:: self collision.
Return type:: bool

apply_visual_material( visual_material: VisualMaterial, weaker_than_descendants: bool = False, ) → None#

Apply visual material to the held prim and optionally its descendants.

Parameters:

visual_material (VisualMaterial) – visual material to be applied to the held prim. Currently supports PreviewSurface, OmniPBR and OmniGlass.
weaker_than_descendants (bool, optional) – True if the material shouldn’t override the descendants materials, otherwise False. Defaults to False.

Example:

>>> from isaacsim.core.api.materials import OmniGlass
>>>
>>> # create a dark-red glass visual material
>>> material = OmniGlass(
...     prim_path="/World/material/glass",  # path to the material prim to create
...     ior=1.25,
...     depth=0.001,
...     thin_walled=False,
...     color=np.array([0.5, 0.0, 0.0])
... )
>>> prim.apply_visual_material(material)

get_applied_visual_material() → VisualMaterial#

Return the current applied visual material in case it was applied using apply_visual_material or it’s one of the following materials that was already applied before: PreviewSurface, OmniPBR and OmniGlass.

Returns:: the current applied visual material if its type is currently supported.
Return type:: VisualMaterial

Example:

>>> # given a visual material applied
>>> prim.get_applied_visual_material()
<isaacsim.core.api.materials.omni_glass.OmniGlass object at 0x7f36263106a0>

get_default_state() → XFormPrimState#

Get the default prim states (spatial position and orientation).

Returns:: an object that contains the default state of the prim (position and orientation)
Return type:: XFormPrimState

Example:

>>> state = prim.get_default_state()
>>> state
<isaacsim.core.utils.types.XFormPrimState object at 0x7f33addda650>
>>>
>>> state.position
[-4.5299529e-08 -1.8347054e-09 -2.8610229e-08]
>>> state.orientation
[1. 0. 0. 0.]

get_local_pose() → Tuple[ndarray, ndarray]#

Get prim’s pose with respect to the local frame (the prim’s parent frame)

Returns:: first index is the position in the local frame (with shape (3, )). Second index is quaternion orientation (with shape (4, )) in the local frame
Return type:: Tuple[np.ndarray, np.ndarray]

Example:

>>> # if the prim is in position (1.0, 0.5, 0.0) with respect to the world frame
>>> position, orientation = prim.get_local_pose()
>>> position
[0. 0. 0.]
>>> orientation
[0. 0. 0.]

get_local_scale() → ndarray#

Get prim’s scale with respect to the local frame (the parent’s frame)

Returns:: scale applied to the prim’s dimensions in the local frame. shape is (3, ).
Return type:: np.ndarray

Example:

>>> prim.get_local_scale()
[1. 1. 1.]

get_visibility() → bool#

Returns:: true if the prim is visible in stage. false otherwise.
Return type:: bool

Example:

>>> # get the visible state of an visible prim on the stage
>>> prim.get_visibility()
True

get_world_pose() → Tuple[ndarray, ndarray]#

Get prim’s pose with respect to the world’s frame

Returns:: first index is the position in the world frame (with shape (3, )). Second index is quaternion orientation (with shape (4, )) in the world frame
Return type:: Tuple[np.ndarray, np.ndarray]

Example:

>>> # if the prim is in position (1.0, 0.5, 0.0) with respect to the world frame
>>> position, orientation = prim.get_world_pose()
>>> position
[1.  0.5 0. ]
>>> orientation
[1. 0. 0. 0.]

get_world_scale() → ndarray#

Get prim’s scale with respect to the world’s frame

Returns:: scale applied to the prim’s dimensions in the world frame. shape is (3, ).
Return type:: np.ndarray

Example:

>>> prim.get_world_scale()
[1. 1. 1.]

initialize(physics_sim_view=None) → None#

Create a physics simulation view if not passed and using PhysX tensor API

Note

If the prim has been added to the world scene (e.g., world.scene.add(prim)), it will be automatically initialized when the world is reset (e.g., world.reset()).

Parameters:: physics_sim_view (omni.physics.tensors.SimulationView, optional) – current physics simulation view. Defaults to None.

Example:

>>> prim.initialize()

is_valid() → bool#

Check if the prim path has a valid USD Prim at it

Returns:: True is the current prim path corresponds to a valid prim in stage. False otherwise.
Return type:: bool

Example:

>>> # given an existing and valid prim
>>> prims.is_valid()
True

is_visual_material_applied() → bool#

Check if there is a visual material applied

Returns:: True if there is a visual material applied. False otherwise.
Return type:: bool

Example:

>>> # given a visual material applied
>>> prim.is_visual_material_applied()
True

property name: str | None#: Returns: str: name given to the prim when instantiating it. Otherwise None.

property non_root_articulation_link: bool#

Used to query if the prim is a non root articulation link

Returns:: True if the prim itself is a non root link
Return type:: bool

Example:

>>> # for a wrapped articulation (where the root prim has the Physics Articulation Root property applied)
>>> prim.non_root_articulation_link
False

post_reset() → None#

Reset the prim to its default state (position and orientation).

Note

For an articulation, in addition to configuring the root prim’s default position and spatial orientation (defined via the set_default_state method), the joint’s positions, velocities, and efforts (defined via the set_joints_default_state method) are imposed

Example:

>>> prim.post_reset()

property prim: pxr.Usd.Prim#: Returns: Usd.Prim: USD Prim object that this object holds.

property prim_path: str#: Returns: str: prim path in the stage

set_default_state( position: Sequence[float] | None = None, orientation: Sequence[float] | None = None, ) → None#

Set the default state of the prim (position and orientation), that will be used after each reset.

Parameters:

position (Optional[Sequence[float]], optional) – position in the world frame of the prim. shape is (3, ). Defaults to None, which means left unchanged.
orientation (Optional[Sequence[float]], optional) – quaternion orientation in the world frame of the prim. quaternion is scalar-first (w, x, y, z). shape is (4, ). Defaults to None, which means left unchanged.

Example:

>>> # configure default state
>>> prim.set_default_state(position=np.array([1.0, 0.5, 0.0]), orientation=np.array([1, 0, 0, 0]))
>>>
>>> # set default states during post-reset
>>> prim.post_reset()

set_local_pose( translation: Sequence[float] | None = None, orientation: Sequence[float] | None = None, ) → None#

Set prim’s pose with respect to the local frame (the prim’s parent frame).

Warning

This method will change (teleport) the prim pose immediately to the indicated value

Parameters:

translation (Optional[Sequence[float]], optional) – translation in the local frame of the prim (with respect to its parent prim). shape is (3, ). Defaults to None, which means left unchanged.
orientation (Optional[Sequence[float]], optional) – quaternion orientation in the local frame of the prim. quaternion is scalar-first (w, x, y, z). shape is (4, ). Defaults to None, which means left unchanged.

Hint

This method belongs to the methods used to set the prim state

Example:

>>> prim.set_local_pose(translation=np.array([1.0, 0.5, 0.0]), orientation=np.array([1., 0., 0., 0.]))

set_local_scale( scale: Sequence[float] | None, ) → None#

Set prim’s scale with respect to the local frame (the prim’s parent frame).

Parameters:: scale (Optional[Sequence[float]]) – scale to be applied to the prim’s dimensions. shape is (3, ). Defaults to None, which means left unchanged.

Example:

>>> # scale prim 10 times smaller
>>> prim.set_local_scale(np.array([0.1, 0.1, 0.1]))

set_visibility(visible: bool) → None#

Set the visibility of the prim in stage

Parameters:: visible (bool) – flag to set the visibility of the usd prim in stage.

Example:

>>> # make prim not visible in the stage
>>> prim.set_visibility(visible=False)

set_world_pose( position: Sequence[float] | None = None, orientation: Sequence[float] | None = None, ) → None#

Ses prim’s pose with respect to the world’s frame

Warning

This method will change (teleport) the prim pose immediately to the indicated value

Parameters:

position (Optional[Sequence[float]], optional) – position in the world frame of the prim. shape is (3, ). Defaults to None, which means left unchanged.
orientation (Optional[Sequence[float]], optional) – quaternion orientation in the world frame of the prim. quaternion is scalar-first (w, x, y, z). shape is (4, ). Defaults to None, which means left unchanged.

Hint

This method belongs to the methods used to set the prim state

Example:

>>> prim.set_world_pose(position=np.array([1.0, 0.5, 0.0]), orientation=np.array([1., 0., 0., 0.]))

class SingleDeformablePrim( prim_path: str, deformable_material: DeformableMaterial | None = None, name: str | None = 'deformable', position: Sequence[float] | None = None, orientation: Sequence[float] | None = None, scale: Sequence[float] | None = None, visible: bool | None = None, vertex_velocity_damping: float | None = None, sleep_damping: float | None = None, sleep_threshold: float | None = None, settling_threshold: float | None = None, self_collision: bool | None = True, self_collision_filter_distance: float | None = None, solver_position_iteration_count: int | None = None, kinematic_enabled: bool | None = False, simulation_rest_points: Sequence[float] | None = None, simulation_indices: Sequence[int] | None = None, simulation_hexahedral_resolution: int | None = 10, collision_rest_points: Sequence[float] | None = None, collision_indices: Sequence[int] | None = None, collision_simplification: bool | None = True, collision_simplification_remeshing: bool | None = True, collision_simplification_remeshing_resolution: int | None = 0, collision_simplification_target_triangle_count: int | None = 0, collision_simplification_force_conforming: bool | None = False, embedding: Sequence[int] | None = None, )#

Bases: _SinglePrimWrapper

Deformable primitive object provide functionalities to create and control deformable objects

property mesh: pxr.UsdGeom.Mesh#: Returns: Usd.Prim: USD Prim object that this object tracks.

get_current_dynamic_state() → DynamicState#

Return the DynamicState that contains the position and orientation of the underlying xform prim

Returns:

position (np.ndarray, optional):: position in the world frame of the prim. shape is (3, ). Defaults to None, which means left unchanged.
orientation (np.ndarray, optional):: quaternion orientation in the world frame of the prim. quaternion is scalar-first (w, x, y, z). shape is (4, ). Defaults to None, which means left unchanged.

Return type:

DynamicState

apply_deformable_material( deformable_materials: DeformableMaterial, ) → None#

get_applied_deformable_material() → DeformableMaterial#

set_vertex_velocity_damping( vertex_velocity_damping: float, ) → None#

Parameters:: vertex_velocity_damping (float) – deformable body vertex velocity damping parameter.

set_sleep_damping(sleep_damping: float) → None#

Parameters:: sleep_damping (float) – deformable body sleep damping parameter.

set_sleep_threshold( sleep_threshold: float, ) → None#

Parameters:: sleep_threshold (float) – deformable body sleep threshold parameter.

set_settling_threshold( settling_threshold: float, ) → None#

Parameters:: settling_threshold (float) – deformable body settling threshold parameter.

set_self_collision_filter_distance( self_collision_filter_distance: float, ) → None#

Parameters:: self_collision_filter_distance (float) – deformable body self collision filter distance parameter.

set_self_collision(self_collision: bool) → None#

Parameters:: self_collision (bool) – deformable body self collision parameter.

set_solver_position_iteration_count( iterations: int, ) → None#

Parameters:: iterations (float) – solver position iteration count

get_simulation_mesh_rest_points() → ndarray | Tensor#

Gets the simulation mesh rest positions

Returns:: simulation mesh vertices rest positions
Return type:: Union[np.ndarray, torch.Tensor]

get_simulation_mesh_indices() → ndarray | Tensor#

Gets the simulation mesh element indices

Returns:: simulation mesh element indices
Return type:: Union[np.ndarray, torch.Tensor]

get_collision_mesh_indices() → ndarray | Tensor#

Gets the collision mesh element indices

Returns:: collision mesh element indices
Return type:: Union[np.ndarray, torch.Tensor]

get_solver_position_iteration_count() → int#

Gets the solver’s positional iteration count

Returns:: positional iteration count
Return type:: int

get_vertex_velocity_damping() → float#

Gets the deformable body vertex velocity damping parameter

Returns:: vertex velocity damping
Return type:: float

get_sleep_damping() → float#

Gets the deformable body sleep damping parameter

Returns:: sleep damping
Return type:: float

get_sleep_threshold() → float#

Gets the deformable body sleep threshold

Returns:: sleep threshold
Return type:: float

get_settling_threshold() → float#

Gets the deformable body settling threshold

Returns:: settling threshold
Return type:: float

get_self_collision_filter_distance() → float#

Gets the deformable body self collision filter distance

Returns:: self collision filter distance
Return type:: float

get_self_collision() → bool#

Gets the deformable body self collision

Returns:: self collision
Return type:: float

apply_visual_material( visual_material: VisualMaterial, weaker_than_descendants: bool = False, ) → None#

Apply visual material to the held prim and optionally its descendants.

Parameters:

visual_material (VisualMaterial) – visual material to be applied to the held prim. Currently supports PreviewSurface, OmniPBR and OmniGlass.
weaker_than_descendants (bool, optional) – True if the material shouldn’t override the descendants materials, otherwise False. Defaults to False.

Example:

>>> from isaacsim.core.api.materials import OmniGlass
>>>
>>> # create a dark-red glass visual material
>>> material = OmniGlass(
...     prim_path="/World/material/glass",  # path to the material prim to create
...     ior=1.25,
...     depth=0.001,
...     thin_walled=False,
...     color=np.array([0.5, 0.0, 0.0])
... )
>>> prim.apply_visual_material(material)

get_applied_visual_material() → VisualMaterial#

Return the current applied visual material in case it was applied using apply_visual_material or it’s one of the following materials that was already applied before: PreviewSurface, OmniPBR and OmniGlass.

Returns:: the current applied visual material if its type is currently supported.
Return type:: VisualMaterial

Example:

>>> # given a visual material applied
>>> prim.get_applied_visual_material()
<isaacsim.core.api.materials.omni_glass.OmniGlass object at 0x7f36263106a0>

get_default_state() → XFormPrimState#

Get the default prim states (spatial position and orientation).

Returns:: an object that contains the default state of the prim (position and orientation)
Return type:: XFormPrimState

Example:

>>> state = prim.get_default_state()
>>> state
<isaacsim.core.utils.types.XFormPrimState object at 0x7f33addda650>
>>>
>>> state.position
[-4.5299529e-08 -1.8347054e-09 -2.8610229e-08]
>>> state.orientation
[1. 0. 0. 0.]

get_local_pose() → Tuple[ndarray, ndarray]#

Get prim’s pose with respect to the local frame (the prim’s parent frame)

Returns:: first index is the position in the local frame (with shape (3, )). Second index is quaternion orientation (with shape (4, )) in the local frame
Return type:: Tuple[np.ndarray, np.ndarray]

Example:

>>> # if the prim is in position (1.0, 0.5, 0.0) with respect to the world frame
>>> position, orientation = prim.get_local_pose()
>>> position
[0. 0. 0.]
>>> orientation
[0. 0. 0.]

get_local_scale() → ndarray#

Get prim’s scale with respect to the local frame (the parent’s frame)

Returns:: scale applied to the prim’s dimensions in the local frame. shape is (3, ).
Return type:: np.ndarray

Example:

>>> prim.get_local_scale()
[1. 1. 1.]

get_visibility() → bool#

Returns:: true if the prim is visible in stage. false otherwise.
Return type:: bool

Example:

>>> # get the visible state of an visible prim on the stage
>>> prim.get_visibility()
True

get_world_pose() → Tuple[ndarray, ndarray]#

Get prim’s pose with respect to the world’s frame

Returns:: first index is the position in the world frame (with shape (3, )). Second index is quaternion orientation (with shape (4, )) in the world frame
Return type:: Tuple[np.ndarray, np.ndarray]

Example:

>>> # if the prim is in position (1.0, 0.5, 0.0) with respect to the world frame
>>> position, orientation = prim.get_world_pose()
>>> position
[1.  0.5 0. ]
>>> orientation
[1. 0. 0. 0.]

get_world_scale() → ndarray#

Get prim’s scale with respect to the world’s frame

Returns:: scale applied to the prim’s dimensions in the world frame. shape is (3, ).
Return type:: np.ndarray

Example:

>>> prim.get_world_scale()
[1. 1. 1.]

initialize(physics_sim_view=None) → None#

Create a physics simulation view if not passed and using PhysX tensor API

Note

If the prim has been added to the world scene (e.g., world.scene.add(prim)), it will be automatically initialized when the world is reset (e.g., world.reset()).

Parameters:: physics_sim_view (omni.physics.tensors.SimulationView, optional) – current physics simulation view. Defaults to None.

Example:

>>> prim.initialize()

is_valid() → bool#

Check if the prim path has a valid USD Prim at it

Returns:: True is the current prim path corresponds to a valid prim in stage. False otherwise.
Return type:: bool

Example:

>>> # given an existing and valid prim
>>> prims.is_valid()
True

is_visual_material_applied() → bool#

Check if there is a visual material applied

Returns:: True if there is a visual material applied. False otherwise.
Return type:: bool

Example:

>>> # given a visual material applied
>>> prim.is_visual_material_applied()
True

property name: str | None#: Returns: str: name given to the prim when instantiating it. Otherwise None.

property non_root_articulation_link: bool#

Used to query if the prim is a non root articulation link

Returns:: True if the prim itself is a non root link
Return type:: bool

Example:

>>> # for a wrapped articulation (where the root prim has the Physics Articulation Root property applied)
>>> prim.non_root_articulation_link
False

post_reset() → None#

Reset the prim to its default state (position and orientation).

Note

For an articulation, in addition to configuring the root prim’s default position and spatial orientation (defined via the set_default_state method), the joint’s positions, velocities, and efforts (defined via the set_joints_default_state method) are imposed

Example:

>>> prim.post_reset()

property prim: pxr.Usd.Prim#: Returns: Usd.Prim: USD Prim object that this object holds.

property prim_path: str#: Returns: str: prim path in the stage

set_default_state( position: Sequence[float] | None = None, orientation: Sequence[float] | None = None, ) → None#

Set the default state of the prim (position and orientation), that will be used after each reset.

Parameters:

position (Optional[Sequence[float]], optional) – position in the world frame of the prim. shape is (3, ). Defaults to None, which means left unchanged.
orientation (Optional[Sequence[float]], optional) – quaternion orientation in the world frame of the prim. quaternion is scalar-first (w, x, y, z). shape is (4, ). Defaults to None, which means left unchanged.

Example:

>>> # configure default state
>>> prim.set_default_state(position=np.array([1.0, 0.5, 0.0]), orientation=np.array([1, 0, 0, 0]))
>>>
>>> # set default states during post-reset
>>> prim.post_reset()

set_local_pose( translation: Sequence[float] | None = None, orientation: Sequence[float] | None = None, ) → None#

Set prim’s pose with respect to the local frame (the prim’s parent frame).

Warning

This method will change (teleport) the prim pose immediately to the indicated value

Parameters:

translation (Optional[Sequence[float]], optional) – translation in the local frame of the prim (with respect to its parent prim). shape is (3, ). Defaults to None, which means left unchanged.
orientation (Optional[Sequence[float]], optional) – quaternion orientation in the local frame of the prim. quaternion is scalar-first (w, x, y, z). shape is (4, ). Defaults to None, which means left unchanged.

Hint

This method belongs to the methods used to set the prim state

Example:

>>> prim.set_local_pose(translation=np.array([1.0, 0.5, 0.0]), orientation=np.array([1., 0., 0., 0.]))

set_local_scale( scale: Sequence[float] | None, ) → None#

Set prim’s scale with respect to the local frame (the prim’s parent frame).

Parameters:: scale (Optional[Sequence[float]]) – scale to be applied to the prim’s dimensions. shape is (3, ). Defaults to None, which means left unchanged.

Example:

>>> # scale prim 10 times smaller
>>> prim.set_local_scale(np.array([0.1, 0.1, 0.1]))

set_visibility(visible: bool) → None#

Set the visibility of the prim in stage

Parameters:: visible (bool) – flag to set the visibility of the usd prim in stage.

Example:

>>> # make prim not visible in the stage
>>> prim.set_visibility(visible=False)

set_world_pose( position: Sequence[float] | None = None, orientation: Sequence[float] | None = None, ) → None#

Ses prim’s pose with respect to the world’s frame

Warning

This method will change (teleport) the prim pose immediately to the indicated value

Parameters:

position (Optional[Sequence[float]], optional) – position in the world frame of the prim. shape is (3, ). Defaults to None, which means left unchanged.
orientation (Optional[Sequence[float]], optional) – quaternion orientation in the world frame of the prim. quaternion is scalar-first (w, x, y, z). shape is (4, ). Defaults to None, which means left unchanged.

Hint

This method belongs to the methods used to set the prim state

Example:

>>> prim.set_world_pose(position=np.array([1.0, 0.5, 0.0]), orientation=np.array([1., 0., 0., 0.]))

class SingleGeometryPrim( prim_path: str, name: str = 'geometry_prim', position: Sequence[float] | None = None, translation: Sequence[float] | None = None, orientation: Sequence[float] | None = None, scale: Sequence[float] | None = None, visible: bool | None = None, collision: bool = False, track_contact_forces: bool = False, prepare_contact_sensor: bool = False, disable_stablization: bool = True, contact_filter_prim_paths_expr: List[str] | None = [], )#

Bases: _SinglePrimWrapper

High level wrapper to deal with a Geom prim (only one geometry prim) and its attributes/properties.

The prim_path should correspond to type UsdGeom.Cube, UsdGeom.Capsule, UsdGeom.Cone, UsdGeom.Cylinder, UsdGeom.Sphere or UsdGeom.Mesh.

Warning

The geometry object must be initialized in order to be able to operate on it. See the initialize method for more details.

Warning

Some methods require the prim to have the Physx Collision API. Instantiate the class with the collision parameter to True to apply the collision API.

Parameters:

prim_path (str) – prim path of the Prim to encapsulate or create.
name (str, optional) – shortname to be used as a key by Scene class. Note: needs to be unique if the object is added to the Scene. Defaults to “xform_prim”.
position (Optional[Sequence[float]], optional) – position in the world frame of the prim. shape is (3, ). Defaults to None, which means left unchanged.
translation (Optional[Sequence[float]], optional) – translation in the local frame of the prim (with respect to its parent prim). shape is (3, ). Defaults to None, which means left unchanged.
orientation (Optional[Sequence[float]], optional) – quaternion orientation in the world/ local frame of the prim (depends if translation or position is specified). quaternion is scalar-first (w, x, y, z). shape is (4, ). Defaults to None, which means left unchanged.
scale (Optional[Sequence[float]], optional) – local scale to be applied to the prim’s dimensions. shape is (3, ). Defaults to None, which means left unchanged.
visible (bool, optional) – set to false for an invisible prim in the stage while rendering. Defaults to True.
collision (bool, optional) – Set to True if the geometry should have a collider (i.e not only a visual geometry). Defaults to False.
track_contact_forces (bool, Optional) – if enabled, the view will track the net contact forces on each geometry prim in the view. Note that the collision flag should be set to True to report contact forces. Defaults to False.
prepare_contact_sensors (bool, Optional) – applies contact reporter API to the prim if it already does not have one. Defaults to False.
disable_stablization (bool, optional) – disables the contact stabilization parameter in the physics context. Defaults to True.
contact_filter_prim_paths_expr (Optional[List[str]], Optional) – a list of filter expressions which allows for tracking contact forces between the geometry prim and this subset through get_contact_force_matrix().

Example:

>>> import isaacsim.core.utils.stage as stage_utils
>>> from isaacsim.core.prims import SingleGeometryPrim
>>>
>>> # create a Cube at the given path
>>> stage_utils.get_current_stage().DefinePrim("/World/Xform", "Xform")
>>> stage_utils.get_current_stage().DefinePrim("/World/Xform/Cube", "Cube")
>>>
>>> # wrap the prim as geometry prim
>>> prim = SingleGeometryPrim("/World/Xform", collision=True)
>>> prim
<isaacsim.core.prims.single_geometry_prim.SingleGeometryPrim object at 0x7fe960247400>

property geom: pxr.UsdGeom.Gprim#: Returns: UsdGeom.Gprim: USD geometry object encapsulated.

set_contact_offset(offset: float) → None#

Set the contact offset

Shapes whose distance is less than the sum of their contact offset values will generate contacts

Search for Advanced Collision Detection in PhysX docs for more details

Warning

The contact offset must be positive and greater than the rest offset

Parameters:: offset (float) – Contact offset of a collision shape. Allowed range [maximum(0, rest_offset), 0]. Default value is -inf, means default is picked by simulation based on the shape extent.

Example:

>>> prim.set_contact_offset(0.02)

get_contact_offset() → float#

Get the contact offset

Shapes whose distance is less than the sum of their contact offset values will generate contacts

Search for Advanced Collision Detection in PhysX docs for more details

Returns:: contact offset of the collision shape. Default value is -inf, means default is picked by simulation.
Return type:: float

Example:

>>> prim.get_contact_offset()
-inf

set_rest_offset(offset: float) → None#

Set the rest offset

Two shapes will come to rest at a distance equal to the sum of their rest offset values. If the rest offset is 0, they should converge to touching exactly

Search for Advanced Collision Detection in PhysX docs for more details

Warning

The contact offset must be positive and greater than the rest offset

Parameters:: offset (float) – Rest offset of a collision shape. Allowed range [-max_float, contact_offset. Default value is -inf, means default is picked by simulation. For rigid bodies its zero.

Example:

>>> prim.set_rest_offset(0.01)

get_rest_offset() → float#

Get the rest offset

Two shapes will come to rest at a distance equal to the sum of their rest offset values. If the rest offset is 0, they should converge to touching exactly

Search for Advanced Collision Detection in PhysX docs for more details

Returns:: rest offset of the collision shape.
Return type:: float

Example:

>>> prim.get_rest_offset()
-inf

set_torsional_patch_radius(radius: float) → None#

Set the radius of the contact patch used to apply torsional friction

Search for “Torsional Patch Radius” in PhysX docs for more details

Parameters:: radius (float) – radius of the contact patch used to apply torsional friction. Allowed range [0, max_float].

Example:

>>> prim.set_torsional_patch_radius(0.1)

get_torsional_patch_radius() → float#

Get the radius of the contact patch used to apply torsional friction

Search for “Torsional Patch Radius” in PhysX docs for more details

Returns:: radius of the contact patch used to apply torsional friction. Allowed range [0, max_float].
Return type:: float

Example:

>>> prim.get_torsional_patch_radius()
0.0

set_min_torsional_patch_radius( radius: float, ) → None#

Set the minimum radius of the contact patch used to apply torsional friction

Search for “Torsional Patch Radius” in PhysX docs for more details

Parameters:: radius (float) – minimum radius of the contact patch used to apply torsional friction. Allowed range [0, max_float].

Example:

>>> prim.set_min_torsional_patch_radius(0.05)

get_min_torsional_patch_radius() → float#

Get the minimum radius of the contact patch used to apply torsional friction

Search for “Torsional Patch Radius” in PhysX docs for more details

Returns:: minimum radius of the contact patch used to apply torsional friction. Allowed range [0, max_float].
Return type:: float

Example:

>>> prim.get_min_torsional_patch_radius()
0.0

set_collision_approximation( approximation_type: str, ) → None#

Set the collision approximation

Approximation	Full name	Description
`"none"`	Triangle Mesh	The mesh geometry is used directly as a collider without any approximation
`"convexDecomposition"`	Convex Decomposition	A convex mesh decomposition is performed. This results in a set of convex mesh colliders
`"convexHull"`	Convex Hull	A convex hull of the mesh is generated and used as the collider
`"boundingSphere"`	Bounding Sphere	A bounding sphere is computed around the mesh and used as a collider
`"boundingCube"`	Bounding Cube	An optimally fitting box collider is computed around the mesh
`"meshSimplification"`	Mesh Simplification	A mesh simplification step is performed, resulting in a simplified triangle mesh collider
`"sdf"`	SDF Mesh	SDF (Signed-Distance-Field) use high-detail triangle meshes as collision shape
`"sphereFill"`	Sphere Approximation	A sphere mesh decomposition is performed. This results in a set of sphere colliders

Note

Use Convex Decomposition or SDF (Signed-Distance-Field) tri-meshes to capture details better

Warning

Switching to Convex Decomposition or SDF (Signed-Distance-Field) will have a simulation performance impact due to higher computational cost

Parameters:: approximation_type (str) – approximation used for collision

Example:

>>> prim.set_collision_approximation("convexDecomposition")

get_collision_approximation() → str#

Get the collision approximation

Approximation	Full name	Description
`"none"`	Triangle Mesh	The mesh geometry is used directly as a collider without any approximation
`"convexDecomposition"`	Convex Decomposition	A convex mesh decomposition is performed. This results in a set of convex mesh colliders
`"convexHull"`	Convex Hull	A convex hull of the mesh is generated and used as the collider
`"boundingSphere"`	Bounding Sphere	A bounding sphere is computed around the mesh and used as a collider
`"boundingCube"`	Bounding Cube	An optimally fitting box collider is computed around the mesh
`"meshSimplification"`	Mesh Simplification	A mesh simplification step is performed, resulting in a simplified triangle mesh collider
`"sdf"`	SDF Mesh	SDF (Signed-Distance-Field) use high-detail triangle meshes as collision shape
`"sphereFill"`	Sphere Approximation	A sphere mesh decomposition is performed. This results in a set of sphere colliders

Returns:: approximation used for collision
Return type:: str

Example:

>>> prim.get_collision_approximation()
none

set_collision_enabled(enabled: bool) → None#

Enable/disable the Collision API

Parameters:: enabled (bool) – Whether to enable or disable the Collision API

Example:

>>> # disable collisions
>>> prim.set_collision_enabled(False)

get_collision_enabled() → bool#

Check if the Collision API is enabled

Returns:: True if the Collision API is enabled. Otherwise False
Return type:: bool

Example:

>>> prim.get_collision_enabled()
True

apply_physics_material( physics_material: PhysicsMaterial, weaker_than_descendants: bool = False, )#

Used to apply physics material to the held prim and optionally its descendants.

Parameters:

physics_material (PhysicsMaterial) – physics material to be applied to the held prim. This where you want to define friction, restitution..etc. Note: if a physics material is not defined, the defaults will be used from PhysX.
weaker_than_descendants (bool, optional) – True if the material shouldn’t override the descendants materials, otherwise False. Defaults to False.

Example:

>>> from isaacsim.core.api.materials import PhysicsMaterial
>>>
>>> # create a rigid body physical material
>>> material = PhysicsMaterial(
...     prim_path="/World/physics_material/aluminum",  # path to the material prim to create
...     dynamic_friction=0.4,
...     static_friction=1.1,
...     restitution=0.1
... )
>>> prim.apply_physics_material(material)

get_applied_physics_material() → PhysicsMaterial#

Return the current applied physics material in case it was applied using apply_physics_material or not.

Returns:: the current applied physics material.
Return type:: PhysicsMaterial

Example:

>>> # given a physics material applied
>>> prim.get_applied_physics_material()
<isaacsim.core.api.materials.physics_material.PhysicsMaterial object at 0x7fb66c30cd30>

get_net_contact_forces( dt: float = 1.0, ) → ndarray | Tensor#

If contact forces of the prims in the view are tracked, this method returns the net contact forces on prims. i.e., a matrix of dimension (1, 3)

Parameters:: dt (float) – time step multiplier to convert the underlying impulses to forces. If the default value is used then the forces are in fact contact impulses
Returns:: Net contact forces of the prims with shape (3).
Return type:: Union[np.ndarray, torch.Tensor]

get_contact_force_matrix( dt: float = 1.0, ) → ndarray | Tensor#

If the object is initialized with filter_paths_expr list, this method returns the contact forces between the prims in the view and the filter prims. i.e., a matrix of dimension (self._contact_view.num_filters, 3) where num_filters is the determined according to the filter_paths_expr parameter.

Parameters:: dt (float) – time step multiplier to convert the underlying impulses to forces. If the default value is used then the forces are in fact contact impulses
Returns:: Net contact forces of the prims with shape (self._geometry_prim_view._contact_view.num_filters, 3).
Return type:: Union[np.ndarray, torch.Tensor]

get_contact_force_data( dt: float = 1.0, ) → ndarray | Tensor#

If the object is initialized with filter_paths_expr list, this method returns the detailed contact forces between the prims in the view and the filter prims including the normal contact forces, normal directions, contact points, separations. The number of contacts per pair is determined from a static tensor of dimension (self._contact_view.num_filters) while the starting index of the associated contact in the above tensors is determined from another static tensor of dimension (self._contact_view.num_filters).

Parameters:: dt (float) – time step multiplier to convert the underlying impulses to forces. If the default value is used then the forces are in fact contact impulses
Returns:: Tuple[Union[np.ndarray, torch.Tensor, wp.indexedarray], Union[np.ndarray, torch.Tensor, wp.indexedarray], Union[np.ndarray, torch.Tensor, wp.indexedarray], Union[np.ndarray, torch.Tensor, wp.indexedarray], Union[np.ndarray, torch.Tensor, wp.indexedarray], Union[np.ndarray, torch.Tensor, wp.indexedarray]]: A set of buffers for normal forces with shape (max_contact_count, 1), points with shape (max_contact_count, 3), normals with shape (max_contact_count, 3), and distances with shape (max_contact_count, 1), as well as two tensors with shape (self.num_filters) to indicate the starting index and the number of contact data points per pair in the aforementioned buffers.

get_friction_data( dt: float = 1.0, ) → ndarray | Tensor#

If the object is initialized with filter_paths_expr list, this method returns the detailed friction forces between the prims in the view and the filter prims including the tangential forces, and points. The number of points per pair is determined from a static tensor of dimension (self._contact_view.num_filters) while the starting index of the associated contact in the above tensors is determined from another static tensor of dimension (self._contact_view.num_filters).

Parameters:: dt (float) – time step multiplier to convert the underlying impulses to forces. If the default value is used then the forces are in fact contact impulses
Returns:: Tuple[Union[np.ndarray, torch.Tensor, wp.indexedarray], Union[np.ndarray, torch.Tensor, wp.indexedarray], Union[np.ndarray, torch.Tensor, wp.indexedarray], Union[np.ndarray, torch.Tensor, wp.indexedarray]]: A set of buffers for normal forces with shape (max_contact_count, 1), points with shape (max_contact_count, 3), as well as two tensors with shape (self.num_filters) to indicate the starting index and the number of contact data points per pair in the aforementioned buffers.

apply_visual_material( visual_material: VisualMaterial, weaker_than_descendants: bool = False, ) → None#

Apply visual material to the held prim and optionally its descendants.

Parameters:

visual_material (VisualMaterial) – visual material to be applied to the held prim. Currently supports PreviewSurface, OmniPBR and OmniGlass.
weaker_than_descendants (bool, optional) – True if the material shouldn’t override the descendants materials, otherwise False. Defaults to False.

Example:

>>> from isaacsim.core.api.materials import OmniGlass
>>>
>>> # create a dark-red glass visual material
>>> material = OmniGlass(
...     prim_path="/World/material/glass",  # path to the material prim to create
...     ior=1.25,
...     depth=0.001,
...     thin_walled=False,
...     color=np.array([0.5, 0.0, 0.0])
... )
>>> prim.apply_visual_material(material)

get_applied_visual_material() → VisualMaterial#

Return the current applied visual material in case it was applied using apply_visual_material or it’s one of the following materials that was already applied before: PreviewSurface, OmniPBR and OmniGlass.

Returns:: the current applied visual material if its type is currently supported.
Return type:: VisualMaterial

Example:

>>> # given a visual material applied
>>> prim.get_applied_visual_material()
<isaacsim.core.api.materials.omni_glass.OmniGlass object at 0x7f36263106a0>

get_default_state() → XFormPrimState#

Get the default prim states (spatial position and orientation).

Returns:: an object that contains the default state of the prim (position and orientation)
Return type:: XFormPrimState

Example:

>>> state = prim.get_default_state()
>>> state
<isaacsim.core.utils.types.XFormPrimState object at 0x7f33addda650>
>>>
>>> state.position
[-4.5299529e-08 -1.8347054e-09 -2.8610229e-08]
>>> state.orientation
[1. 0. 0. 0.]

get_local_pose() → Tuple[ndarray, ndarray]#

Get prim’s pose with respect to the local frame (the prim’s parent frame)

Returns:: first index is the position in the local frame (with shape (3, )). Second index is quaternion orientation (with shape (4, )) in the local frame
Return type:: Tuple[np.ndarray, np.ndarray]

Example:

>>> # if the prim is in position (1.0, 0.5, 0.0) with respect to the world frame
>>> position, orientation = prim.get_local_pose()
>>> position
[0. 0. 0.]
>>> orientation
[0. 0. 0.]

get_local_scale() → ndarray#

Get prim’s scale with respect to the local frame (the parent’s frame)

Returns:: scale applied to the prim’s dimensions in the local frame. shape is (3, ).
Return type:: np.ndarray

Example:

>>> prim.get_local_scale()
[1. 1. 1.]

get_visibility() → bool#

Returns:: true if the prim is visible in stage. false otherwise.
Return type:: bool

Example:

>>> # get the visible state of an visible prim on the stage
>>> prim.get_visibility()
True

get_world_pose() → Tuple[ndarray, ndarray]#

Get prim’s pose with respect to the world’s frame

Returns:: first index is the position in the world frame (with shape (3, )). Second index is quaternion orientation (with shape (4, )) in the world frame
Return type:: Tuple[np.ndarray, np.ndarray]

Example:

>>> # if the prim is in position (1.0, 0.5, 0.0) with respect to the world frame
>>> position, orientation = prim.get_world_pose()
>>> position
[1.  0.5 0. ]
>>> orientation
[1. 0. 0. 0.]

get_world_scale() → ndarray#

Get prim’s scale with respect to the world’s frame

Returns:: scale applied to the prim’s dimensions in the world frame. shape is (3, ).
Return type:: np.ndarray

Example:

>>> prim.get_world_scale()
[1. 1. 1.]

initialize(physics_sim_view=None) → None#

Create a physics simulation view if not passed and using PhysX tensor API

Note

If the prim has been added to the world scene (e.g., world.scene.add(prim)), it will be automatically initialized when the world is reset (e.g., world.reset()).

Parameters:: physics_sim_view (omni.physics.tensors.SimulationView, optional) – current physics simulation view. Defaults to None.

Example:

>>> prim.initialize()

is_valid() → bool#

Check if the prim path has a valid USD Prim at it

Returns:: True is the current prim path corresponds to a valid prim in stage. False otherwise.
Return type:: bool

Example:

>>> # given an existing and valid prim
>>> prims.is_valid()
True

is_visual_material_applied() → bool#

Check if there is a visual material applied

Returns:: True if there is a visual material applied. False otherwise.
Return type:: bool

Example:

>>> # given a visual material applied
>>> prim.is_visual_material_applied()
True

property name: str | None#: Returns: str: name given to the prim when instantiating it. Otherwise None.

property non_root_articulation_link: bool#

Used to query if the prim is a non root articulation link

Returns:: True if the prim itself is a non root link
Return type:: bool

Example:

>>> # for a wrapped articulation (where the root prim has the Physics Articulation Root property applied)
>>> prim.non_root_articulation_link
False

post_reset() → None#

Reset the prim to its default state (position and orientation).

Note

For an articulation, in addition to configuring the root prim’s default position and spatial orientation (defined via the set_default_state method), the joint’s positions, velocities, and efforts (defined via the set_joints_default_state method) are imposed

Example:

>>> prim.post_reset()

property prim: pxr.Usd.Prim#: Returns: Usd.Prim: USD Prim object that this object holds.

property prim_path: str#: Returns: str: prim path in the stage

set_default_state( position: Sequence[float] | None = None, orientation: Sequence[float] | None = None, ) → None#

Set the default state of the prim (position and orientation), that will be used after each reset.

Parameters:

position (Optional[Sequence[float]], optional) – position in the world frame of the prim. shape is (3, ). Defaults to None, which means left unchanged.
orientation (Optional[Sequence[float]], optional) – quaternion orientation in the world frame of the prim. quaternion is scalar-first (w, x, y, z). shape is (4, ). Defaults to None, which means left unchanged.

Example:

>>> # configure default state
>>> prim.set_default_state(position=np.array([1.0, 0.5, 0.0]), orientation=np.array([1, 0, 0, 0]))
>>>
>>> # set default states during post-reset
>>> prim.post_reset()

set_local_pose( translation: Sequence[float] | None = None, orientation: Sequence[float] | None = None, ) → None#

Set prim’s pose with respect to the local frame (the prim’s parent frame).

Warning

This method will change (teleport) the prim pose immediately to the indicated value

Parameters:

translation (Optional[Sequence[float]], optional) – translation in the local frame of the prim (with respect to its parent prim). shape is (3, ). Defaults to None, which means left unchanged.
orientation (Optional[Sequence[float]], optional) – quaternion orientation in the local frame of the prim. quaternion is scalar-first (w, x, y, z). shape is (4, ). Defaults to None, which means left unchanged.

Hint

This method belongs to the methods used to set the prim state

Example:

>>> prim.set_local_pose(translation=np.array([1.0, 0.5, 0.0]), orientation=np.array([1., 0., 0., 0.]))

set_local_scale( scale: Sequence[float] | None, ) → None#

Set prim’s scale with respect to the local frame (the prim’s parent frame).

Parameters:: scale (Optional[Sequence[float]]) – scale to be applied to the prim’s dimensions. shape is (3, ). Defaults to None, which means left unchanged.

Example:

>>> # scale prim 10 times smaller
>>> prim.set_local_scale(np.array([0.1, 0.1, 0.1]))

set_visibility(visible: bool) → None#

Set the visibility of the prim in stage

Parameters:: visible (bool) – flag to set the visibility of the usd prim in stage.

Example:

>>> # make prim not visible in the stage
>>> prim.set_visibility(visible=False)

set_world_pose( position: Sequence[float] | None = None, orientation: Sequence[float] | None = None, ) → None#

Ses prim’s pose with respect to the world’s frame

Warning

This method will change (teleport) the prim pose immediately to the indicated value

Parameters:

position (Optional[Sequence[float]], optional) – position in the world frame of the prim. shape is (3, ). Defaults to None, which means left unchanged.
orientation (Optional[Sequence[float]], optional) – quaternion orientation in the world frame of the prim. quaternion is scalar-first (w, x, y, z). shape is (4, ). Defaults to None, which means left unchanged.

Hint

This method belongs to the methods used to set the prim state

Example:

>>> prim.set_world_pose(position=np.array([1.0, 0.5, 0.0]), orientation=np.array([1., 0., 0., 0.]))

class SingleParticleSystem( prim_path: str, name: str | None = 'particle_system', particle_system_enabled: bool | None = None, simulation_owner: str | None = None, contact_offset: float | None = None, rest_offset: float | None = None, particle_contact_offset: float | None = None, solid_rest_offset: float | None = None, fluid_rest_offset: float | None = None, enable_ccd: bool | None = None, solver_position_iteration_count: float | None = None, max_depenetration_velocity: float | None = None, wind: Sequence[float] | None = None, max_neighborhood: int | None = None, max_velocity: float | None = None, global_self_collision_enabled: bool | None = None, non_particle_collision_enabled: bool | None = None, )#

Bases: object

A wrapper around PhysX particle system.

PhysX uses GPU-accelerated position-based-dynamics (PBD) particle simulation [1]. The particle system can be used to simulate fluids, cloth and inflatables [2].

The wrapper is useful for creating and setting solver parameters common to the particle objects associated with the system. The particle system’s solver parameters cannot be changed once the scene is playing.

Note

CPU simulation of particles is not supported. PhysX must be simulated with GPU enabled.

Reference:: [1] https://mmacklin.com/pbf_sig_preprint.pdf [2] https://docs.omniverse.nvidia.com/prod_extensions/prod_extensions/ext_physics.html#particle-simulation

property prim_path: str#: Returns: str: The stage path to the particle system.

property prim: pxr.Usd.Prim#: Returns: Usd.Prim: The USD prim present.

property particle_system: pxr.PhysxSchema.PhysxParticleSystem#: Returns: PhysxSchema.PhysxParticleSystem: The particle system.

property name: str | None#: Returns: str: name given to the prim when instantiating it. Otherwise None.

initialize(physics_sim_view=None) → None#

is_valid() → bool#

Returns:: True is the current prim path corresponds to a valid prim in stage. False otherwise.
Return type:: bool

post_reset() → None#

apply_particle_material( particle_materials: ParticleMaterial, ) → None#

get_applied_particle_material() → ParticleMaterial#

set_particle_system_enabled(value: bool) → None#

Set enabling of the particle system.

Parameters:: value (bool) – Whether to enable or disable.

set_simulation_owner(value: str) → None#

Set the PhysicsScene that simulates this particle system.

Parameters:: value (str) – The prim path to the physics scene.

set_contact_offset(value: float) → None#

Set the contact offset used for collisions with non-particle objects such as rigid or deformable bodies.

Parameters:: value (float) – The contact offset.

set_rest_offset(value: float) → None#

Set the rest offset used for collisions with non-particle objects such as rigid or deformable bodies.

Parameters:: value (float) – The rest offset.

set_particle_contact_offset(value: float) → None#

Set the contact offset used for interactions between particles.

Note: Must be larger than solid and fluid rest offsets.

Parameters:: value (float) – The contact offset.

set_solid_rest_offset(value: float) → None#

Set the rest offset used for solid-solid or solid-fluid particle interactions.

Note: Must be smaller than particle contact offset.

Parameters:: value (float) – The rest offset.

set_fluid_rest_offset(value: float) → None#

Set the rest offset used for fluid-fluid particle interactions.

Note: Must be smaller than particle contact offset.

Parameters:: value (float) – The rest offset.

set_enable_ccd(value: bool) → None#

Enable continuous collision detection for particles.

Parameters:: value (bool) – Whether to enable or disable.

set_solver_position_iteration_count( value: int, ) → None#

Set the number of solver iterations for position.

Parameters:: value (int) – Number of solver iterations.

set_max_depenetration_velocity( value: float, ) → None#

Set the maximum velocity permitted to be introduced by the solver to depenetrate intersecting particles.

Parameters:: value (float) – The maximum depenetration velocity.

set_wind(value: Sequence[float]) → None#

Set the wind velocity applied to the current particle system.

Parameters:: value (Sequence[float]) – The wind applied to the current particle system.

set_max_neighborhood(value: int) → None#

Set the particle neighborhood size.

Parameters:: value (int) – The neighborhood size.

set_max_velocity(value: float) → None#

Set the maximum particle velocity.

Parameters:: value (float) – The maximum velocity.

set_global_self_collision_enabled( value: bool, ) → None#

Enable self collisions to follow particle-object-specific settings.

If True, self collisions follow particle-object-specific settings. If False, all particle self collisions are disabled, regardless of any other settings.

Note: Improves performance if self collisions are not needed.

Parameters:: value (bool) – Whether to enable or disable.

get_particle_system_enabled() → bool#

Returns:: Whether particle system is enabled or not.
Return type:: bool

get_simulation_owner() → pxr.Usd.Prim#

Returns:: The physics scene prim attached to particle system.
Return type:: Usd.Prim

get_contact_offset() → float#

Returns:: The contact offset used for collisions with non-particle objects.
Return type:: float

get_rest_offset() → float#

Returns:: The rest offset used for collisions with non-particle objects.
Return type:: float

get_particle_contact_offset() → float#

Returns:: The contact offset used for interactions between particles.
Return type:: float

get_solid_rest_offset() → float#

Returns:: The rest offset used for solid-solid or solid-fluid particle interactions.
Return type:: float

get_fluid_rest_offset() → float#

Returns:: The rest offset used for fluid-fluid particle interactions.
Return type:: float

get_enable_ccd() → bool#

Returns:: Whether continuous collision detection for particles is enabled or disabled.
Return type:: bool

get_solver_position_iteration_count() → int#

Returns:: The number of solver iterations for positions.
Return type:: int

get_max_depenetration_velocity() → None#

Returns:: The maximum velocity permitted between intersecting particles.
Return type:: float

get_wind() → Sequence[float]#

Returns:: The wind applied to the current particle system.
Return type:: Sequence[float]

get_max_neighborhood() → int#

Returns:: The particle neighborhood size.
Return type:: int

get_max_velocity() → float#

Returns:: The maximum particle velocity.
Return type:: float

get_global_self_collision_enabled() → bool#

Returns:

Whether self collisions to follow particle-object-specific settings: is enabled or disabled.

Return type:

bool

apply_particle_anisotropy() → pxr.PhysxSchema.PhysxParticleAnisotropyAPI#

Applies anisotropy to the particle system.

This is used to compute anisotropic scaling of particles in a post-processing step. It only affects the rendering output including iso-surface generation.

apply_particle_smoothing() → pxr.PhysxSchema.PhysxParticleSmoothingAPI#

Applies smoothing to the simulated particle system.

This is used to control smoothing of particles in a post-processing step. It only affects the rendering output including iso-surface generation.

apply_particle_isotropy() → pxr.PhysxSchema.PhysxParticleAnisotropyAPI#

Applies iso-surface extraction to the particle system.

This is used to define settings to extract an iso-surface from the particles in a post-processing step. It only affects the rendering output including iso-surface generation.

Bases: _SinglePrimWrapper

High level wrapper to deal with a rigid body prim (only one rigid body prim) and its attributes/properties.

Warning

The rigid body object must be initialized in order to be able to operate on it. See the initialize method for more details.

Note

If the prim does not already have the Rigid Body API applied to it before init, it will apply it.

Parameters:

prim_path (str) – prim path of the Prim to encapsulate or create.
name (str, optional) – shortname to be used as a key by Scene class. Note: needs to be unique if the object is added to the Scene. Defaults to “rigid_prim”.
position (Optional[Sequence[float]], optional) – position in the world frame of the prim. shape is (3, ). Defaults to None, which means left unchanged.
translation (Optional[Sequence[float]], optional) – translation in the local frame of the prim (with respect to its parent prim). shape is (3, ). Defaults to None, which means left unchanged.
orientation (Optional[Sequence[float]], optional) – quaternion orientation in the world/ local frame of the prim (depends if translation or position is specified). quaternion is scalar-first (w, x, y, z). shape is (4, ). Defaults to None, which means left unchanged.
scale (Optional[Sequence[float]], optional) – local scale to be applied to the prim’s dimensions. shape is (3, ). Defaults to None, which means left unchanged.
visible (bool, optional) – set to false for an invisible prim in the stage while rendering. Defaults to True.
mass (Optional[float], optional) – mass in kg. Defaults to None.
density (Optional[float], optional) – density. Defaults to None.
linear_velocity (Optional[np.ndarray], optional) – linear velocity in the world frame. Defaults to None.
angular_velocity (Optional[np.ndarray], optional) – angular velocity in the world frame. Defaults to None.

Example:

>>> import isaacsim.core.utils.stage as stage_utils
>>> from isaacsim.core.prims import SingleRigidPrim
>>>
>>> # create a Cube at the given path
>>> stage_utils.get_current_stage().DefinePrim("/World/Xform", "Xform")
>>> stage_utils.get_current_stage().DefinePrim("/World/Xform/Cube", "Cube")
>>>
>>> # wrap the prim as rigid prim
>>> prim = SingleRigidPrim("/World/Xform")
>>> prim
<isaacsim.core.prims.single_rigid_prim.SingleRigidPrim object at 0x7fc4a7f56e90>

set_linear_velocity(velocity: ndarray)#

Set the linear velocity of the rigid body in stage

Warning

This method will immediately set the rigid prim state

Parameters:: velocity (np.ndarray) – linear velocity to set the rigid prim to. Shape (3,).

get_linear_velocity() → ndarray#

Get the linear velocity of the rigid body

Returns:: current linear velocity of the the rigid prim. Shape (3,).
Return type:: np.ndarray

Example:

>>> prim.get_linear_velocity()
[ 1.0812164e-04  6.1415871e-05 -2.1341663e-04]

set_angular_velocity(velocity: ndarray) → None#

Set the angular velocity of the rigid body in stage

Warning

This method will immediately set the articulation state

Parameters:: velocity (np.ndarray) – angular velocity to set the rigid prim to. Shape (3,).

get_angular_velocity()#

Get the angular velocity of the rigid body

Returns:: current angular velocity of the the rigid prim. Shape (3,).
Return type:: np.ndarray

set_com( position: ndarray, orientation: ndarray, ) → None#

Set the center of mass pose of the rigid body

Parameters:

position (np.ndarray) – center of mass position. Shape (3,).
orientation (np.ndarray) – center of mass orientation. Shape (4,).

get_com() → float#

Get the center of mass pose of the rigid body

Returns:: position of the center of mass of the rigid body. np.ndarray: orientation of the center of mass of the rigid body.
Return type:: np.ndarray

set_mass(mass: float) → None#

Set the mass of the rigid body

Parameters:: mass (float) – mass of the rigid body in kg.

Example:

>>> prim.set_mass(1.0)

get_mass() → float#

Get the mass of the rigid body

Returns:: mass of the rigid body in kg.
Return type:: float

Example:

>>> prim.get_mass()
0

set_density(density: float) → None#

Set the density of the rigid body

Parameters:: mass (float) – density of the rigid body.

Example:

>>> prim.set_density(0.9)

get_density() → float#

Get the density of the rigid body

Returns:: density of the rigid body.
Return type:: float

Example:

>>> prim.get_density()
0

set_sleep_threshold(threshold: float) → None#

Set the threshold for the rigid body to enter a sleep state

Search for Rigid Body Dynamics > Sleeping in PhysX docs for more details

Parameters:: threshold (float) – Mass-normalized kinetic energy threshold below which an actor may go to sleep. Range: [0, inf) Defaults: 0.00005 * tolerancesSpeed* tolerancesSpeed Units: distance^2 / second^2.

Example:

>>> prim.set_sleep_threshold(1e-5)

get_sleep_threshold() → float#

Get the threshold for the rigid body to enter a sleep state

Search for Rigid Body Dynamics > Sleeping in PhysX docs for more details

Returns:

Mass-normalized kinetic energy threshold below which: an actor may go to sleep. Range: [0, inf) Defaults: 0.00005 * tolerancesSpeed* tolerancesSpeed Units: distance^2 / second^2.

Return type:

float

Example:

>>> prim.get_sleep_threshold()
5e-05

enable_rigid_body_physics() → None#

Enable the rigid body physics

When enabled, the object will be moved by external forces such as gravity and collisions

Example:

>>> prim.enable_rigid_body_physics()

disable_rigid_body_physics() → None#

Disable the rigid body physics

When disabled, the object will not be moved by external forces such as gravity and collisions

Example:

>>> prim.disable_rigid_body_physics()

set_default_state( position: Sequence[float] | None = None, orientation: Sequence[float] | None = None, linear_velocity: ndarray | None = None, angular_velocity: ndarray | None = None, ) → None#

Set the default state of the prim (position, orientation and linear and angular velocities), that will be used after each reset

Note

The default states will be set during post-reset (e.g., calling .post_reset() or world.reset() methods)

Parameters:

position (Optional[Sequence[float]], optional) – position in the world frame of the prim. shape is (3, ). Defaults to None, which means left unchanged.
orientation (Optional[Sequence[float]], optional) – quaternion orientation in the world frame of the prim. quaternion is scalar-first (w, x, y, z). shape is (4, ). Defaults to None, which means left unchanged.
linear_velocity (np.ndarray) – linear velocity to set the rigid prim to. Shape (3,).
angular_velocity (np.ndarray) – angular velocity to set the rigid prim to. Shape (3,).

Example:

>>> prim.set_default_state(
...     position=np.array([1.0, 2.0, 3.0]),
...     orientation=np.array([1.0, 0.0, 0.0, 0.0]),
...     linear_velocity=np.array([0.0, 0.0, 0.0]),
...     angular_velocity=np.array([0.0, 0.0, 0.0])
... )
>>>
>>> prim.post_reset()

get_default_state() → DynamicState#

Get the default rigid body state (position, orientation and linear and angular velocities)

Returns:: returns the default state of the prim that is used after each reset
Return type:: DynamicState

Example:

>>> state = prim.get_default_state()
>>> state
<isaacsim.core.utils.types.DynamicState object at 0x7f7411fcbe20>
>>> state.position
[-7.8622378e-07  1.4450421e-06  1.6135601e-07]
>>> state.orientation
[ 9.9999994e-01 -2.7194994e-07  2.9607077e-07  2.7016510e-08]
>>> state.linear_velocity
[0. 0. 0.]
>>> state.angular_velocity
[0. 0. 0.]

get_current_dynamic_state() → DynamicState#

Get the current rigid body state (position, orientation and linear and angular velocities)

Returns:: the dynamic state of the rigid body prim
Return type:: DynamicState

Example:

>>> # for the example the rigid body is in free fall
>>> state = prim.get_current_dynamic_state()
>>> state
<isaacsim.core.utils.types.DynamicState object at 0x7f740b36f670>
>>> state.position
[  0.99999857   2.0000017  -74.2862    ]
>>> state.orientation
[ 1.0000000e+00 -2.3961178e-07 -4.9891562e-09  4.9388258e-09]
>>> state.linear_velocity
[  0.        0.      -38.09554]
>>> state.angular_velocity
[0. 0. 0.]

apply_visual_material( visual_material: VisualMaterial, weaker_than_descendants: bool = False, ) → None#

Apply visual material to the held prim and optionally its descendants.

Parameters:

visual_material (VisualMaterial) – visual material to be applied to the held prim. Currently supports PreviewSurface, OmniPBR and OmniGlass.
weaker_than_descendants (bool, optional) – True if the material shouldn’t override the descendants materials, otherwise False. Defaults to False.

Example:

>>> from isaacsim.core.api.materials import OmniGlass
>>>
>>> # create a dark-red glass visual material
>>> material = OmniGlass(
...     prim_path="/World/material/glass",  # path to the material prim to create
...     ior=1.25,
...     depth=0.001,
...     thin_walled=False,
...     color=np.array([0.5, 0.0, 0.0])
... )
>>> prim.apply_visual_material(material)

get_applied_visual_material() → VisualMaterial#

Return the current applied visual material in case it was applied using apply_visual_material or it’s one of the following materials that was already applied before: PreviewSurface, OmniPBR and OmniGlass.

Returns:: the current applied visual material if its type is currently supported.
Return type:: VisualMaterial

Example:

>>> # given a visual material applied
>>> prim.get_applied_visual_material()
<isaacsim.core.api.materials.omni_glass.OmniGlass object at 0x7f36263106a0>

get_local_pose() → Tuple[ndarray, ndarray]#

Get prim’s pose with respect to the local frame (the prim’s parent frame)

Returns:: first index is the position in the local frame (with shape (3, )). Second index is quaternion orientation (with shape (4, )) in the local frame
Return type:: Tuple[np.ndarray, np.ndarray]

Example:

>>> # if the prim is in position (1.0, 0.5, 0.0) with respect to the world frame
>>> position, orientation = prim.get_local_pose()
>>> position
[0. 0. 0.]
>>> orientation
[0. 0. 0.]

get_local_scale() → ndarray#

Get prim’s scale with respect to the local frame (the parent’s frame)

Returns:: scale applied to the prim’s dimensions in the local frame. shape is (3, ).
Return type:: np.ndarray

Example:

>>> prim.get_local_scale()
[1. 1. 1.]

get_visibility() → bool#

Returns:: true if the prim is visible in stage. false otherwise.
Return type:: bool

Example:

>>> # get the visible state of an visible prim on the stage
>>> prim.get_visibility()
True

get_world_pose() → Tuple[ndarray, ndarray]#

Get prim’s pose with respect to the world’s frame

Returns:: first index is the position in the world frame (with shape (3, )). Second index is quaternion orientation (with shape (4, )) in the world frame
Return type:: Tuple[np.ndarray, np.ndarray]

Example:

>>> # if the prim is in position (1.0, 0.5, 0.0) with respect to the world frame
>>> position, orientation = prim.get_world_pose()
>>> position
[1.  0.5 0. ]
>>> orientation
[1. 0. 0. 0.]

get_world_scale() → ndarray#

Get prim’s scale with respect to the world’s frame

Returns:: scale applied to the prim’s dimensions in the world frame. shape is (3, ).
Return type:: np.ndarray

Example:

>>> prim.get_world_scale()
[1. 1. 1.]

initialize(physics_sim_view=None) → None#

Create a physics simulation view if not passed and using PhysX tensor API

Note

If the prim has been added to the world scene (e.g., world.scene.add(prim)), it will be automatically initialized when the world is reset (e.g., world.reset()).

Parameters:: physics_sim_view (omni.physics.tensors.SimulationView, optional) – current physics simulation view. Defaults to None.

Example:

>>> prim.initialize()

is_valid() → bool#

Check if the prim path has a valid USD Prim at it

Returns:: True is the current prim path corresponds to a valid prim in stage. False otherwise.
Return type:: bool

Example:

>>> # given an existing and valid prim
>>> prims.is_valid()
True

is_visual_material_applied() → bool#

Check if there is a visual material applied

Returns:: True if there is a visual material applied. False otherwise.
Return type:: bool

Example:

>>> # given a visual material applied
>>> prim.is_visual_material_applied()
True

property name: str | None#: Returns: str: name given to the prim when instantiating it. Otherwise None.

property non_root_articulation_link: bool#

Used to query if the prim is a non root articulation link

Returns:: True if the prim itself is a non root link
Return type:: bool

Example:

>>> # for a wrapped articulation (where the root prim has the Physics Articulation Root property applied)
>>> prim.non_root_articulation_link
False

post_reset() → None#

Reset the prim to its default state (position and orientation).

Note

For an articulation, in addition to configuring the root prim’s default position and spatial orientation (defined via the set_default_state method), the joint’s positions, velocities, and efforts (defined via the set_joints_default_state method) are imposed

Example:

>>> prim.post_reset()

property prim: pxr.Usd.Prim#: Returns: Usd.Prim: USD Prim object that this object holds.

property prim_path: str#: Returns: str: prim path in the stage

set_local_pose( translation: Sequence[float] | None = None, orientation: Sequence[float] | None = None, ) → None#

Set prim’s pose with respect to the local frame (the prim’s parent frame).

Warning

This method will change (teleport) the prim pose immediately to the indicated value

Parameters:

translation (Optional[Sequence[float]], optional) – translation in the local frame of the prim (with respect to its parent prim). shape is (3, ). Defaults to None, which means left unchanged.
orientation (Optional[Sequence[float]], optional) – quaternion orientation in the local frame of the prim. quaternion is scalar-first (w, x, y, z). shape is (4, ). Defaults to None, which means left unchanged.

Hint

This method belongs to the methods used to set the prim state

Example:

>>> prim.set_local_pose(translation=np.array([1.0, 0.5, 0.0]), orientation=np.array([1., 0., 0., 0.]))

set_local_scale( scale: Sequence[float] | None, ) → None#

Set prim’s scale with respect to the local frame (the prim’s parent frame).

Parameters:: scale (Optional[Sequence[float]]) – scale to be applied to the prim’s dimensions. shape is (3, ). Defaults to None, which means left unchanged.

Example:

>>> # scale prim 10 times smaller
>>> prim.set_local_scale(np.array([0.1, 0.1, 0.1]))

set_visibility(visible: bool) → None#

Set the visibility of the prim in stage

Parameters:: visible (bool) – flag to set the visibility of the usd prim in stage.

Example:

>>> # make prim not visible in the stage
>>> prim.set_visibility(visible=False)

set_world_pose( position: Sequence[float] | None = None, orientation: Sequence[float] | None = None, ) → None#

Ses prim’s pose with respect to the world’s frame

Warning

This method will change (teleport) the prim pose immediately to the indicated value

Parameters:

position (Optional[Sequence[float]], optional) – position in the world frame of the prim. shape is (3, ). Defaults to None, which means left unchanged.
orientation (Optional[Sequence[float]], optional) – quaternion orientation in the world frame of the prim. quaternion is scalar-first (w, x, y, z). shape is (4, ). Defaults to None, which means left unchanged.

Hint

This method belongs to the methods used to set the prim state

Example:

>>> prim.set_world_pose(position=np.array([1.0, 0.5, 0.0]), orientation=np.array([1., 0., 0., 0.]))

class SingleXFormPrim( prim_path: str, name: str = 'xform_prim', position: Sequence[float] | None = None, translation: Sequence[float] | None = None, orientation: Sequence[float] | None = None, scale: Sequence[float] | None = None, visible: bool | None = None, )#

Bases: _SinglePrimWrapper

Provides high level functions to deal with an Xform prim (only one Xform prim) and its attributes/properties

If there is an Xform prim present at the path, it will use it. Otherwise, a new XForm prim at the specified prim path will be created

Note

The prim will have xformOp:orient, xformOp:translate and xformOp:scale only post-init, unless it is a non-root articulation link.

Parameters:

prim_path (str) – prim path of the Prim to encapsulate or create.
name (str, optional) – shortname to be used as a key by Scene class. Note: needs to be unique if the object is added to the Scene. Defaults to “xform_prim”.
position (Optional[Sequence[float]], optional) – position in the world frame of the prim. shape is (3, ). Defaults to None, which means left unchanged.
translation (Optional[Sequence[float]], optional) – translation in the local frame of the prim (with respect to its parent prim). shape is (3, ). Defaults to None, which means left unchanged.
orientation (Optional[Sequence[float]], optional) – quaternion orientation in the world/ local frame of the prim (depends if translation or position is specified). quaternion is scalar-first (w, x, y, z). shape is (4, ). Defaults to None, which means left unchanged.
scale (Optional[Sequence[float]], optional) – local scale to be applied to the prim’s dimensions. shape is (3, ). Defaults to None, which means left unchanged.
visible (bool, optional) – set to false for an invisible prim in the stage while rendering. Defaults to True.

Raises:

Exception – if translation and position defined at the same time

Example:

>>> from isaacsim.core.prims import SingleXFormPrim
>>>
>>> # given the stage: /World. Get the Xform prim at /World
>>> prim = SingleXFormPrim("/World")
>>> prim
<isaacsim.core.prims.single_xform_prim.SingleXFormPrim object at 0x7f52381547c0>
>>>
>>> # create a new Xform prim at path: /World/Objects
>>> prim = SingleXFormPrim("/World/Objects", name="objects")
>>> prim
<isaacsim.core.prims.single_xform_prim.SingleXFormPrim object at 0x7f525c11d420>

apply_visual_material( visual_material: VisualMaterial, weaker_than_descendants: bool = False, ) → None#

Apply visual material to the held prim and optionally its descendants.

Parameters:

visual_material (VisualMaterial) – visual material to be applied to the held prim. Currently supports PreviewSurface, OmniPBR and OmniGlass.
weaker_than_descendants (bool, optional) – True if the material shouldn’t override the descendants materials, otherwise False. Defaults to False.

Example:

>>> from isaacsim.core.api.materials import OmniGlass
>>>
>>> # create a dark-red glass visual material
>>> material = OmniGlass(
...     prim_path="/World/material/glass",  # path to the material prim to create
...     ior=1.25,
...     depth=0.001,
...     thin_walled=False,
...     color=np.array([0.5, 0.0, 0.0])
... )
>>> prim.apply_visual_material(material)

get_applied_visual_material() → VisualMaterial#

Return the current applied visual material in case it was applied using apply_visual_material or it’s one of the following materials that was already applied before: PreviewSurface, OmniPBR and OmniGlass.

Returns:: the current applied visual material if its type is currently supported.
Return type:: VisualMaterial

Example:

>>> # given a visual material applied
>>> prim.get_applied_visual_material()
<isaacsim.core.api.materials.omni_glass.OmniGlass object at 0x7f36263106a0>

get_default_state() → XFormPrimState#

Get the default prim states (spatial position and orientation).

Returns:: an object that contains the default state of the prim (position and orientation)
Return type:: XFormPrimState

Example:

>>> state = prim.get_default_state()
>>> state
<isaacsim.core.utils.types.XFormPrimState object at 0x7f33addda650>
>>>
>>> state.position
[-4.5299529e-08 -1.8347054e-09 -2.8610229e-08]
>>> state.orientation
[1. 0. 0. 0.]

get_local_pose() → Tuple[ndarray, ndarray]#

Get prim’s pose with respect to the local frame (the prim’s parent frame)

Returns:: first index is the position in the local frame (with shape (3, )). Second index is quaternion orientation (with shape (4, )) in the local frame
Return type:: Tuple[np.ndarray, np.ndarray]

Example:

>>> # if the prim is in position (1.0, 0.5, 0.0) with respect to the world frame
>>> position, orientation = prim.get_local_pose()
>>> position
[0. 0. 0.]
>>> orientation
[0. 0. 0.]

get_local_scale() → ndarray#

Get prim’s scale with respect to the local frame (the parent’s frame)

Returns:: scale applied to the prim’s dimensions in the local frame. shape is (3, ).
Return type:: np.ndarray

Example:

>>> prim.get_local_scale()
[1. 1. 1.]

get_visibility() → bool#

Returns:: true if the prim is visible in stage. false otherwise.
Return type:: bool

Example:

>>> # get the visible state of an visible prim on the stage
>>> prim.get_visibility()
True

get_world_pose() → Tuple[ndarray, ndarray]#

Get prim’s pose with respect to the world’s frame

Returns:: first index is the position in the world frame (with shape (3, )). Second index is quaternion orientation (with shape (4, )) in the world frame
Return type:: Tuple[np.ndarray, np.ndarray]

Example:

>>> # if the prim is in position (1.0, 0.5, 0.0) with respect to the world frame
>>> position, orientation = prim.get_world_pose()
>>> position
[1.  0.5 0. ]
>>> orientation
[1. 0. 0. 0.]

get_world_scale() → ndarray#

Get prim’s scale with respect to the world’s frame

Returns:: scale applied to the prim’s dimensions in the world frame. shape is (3, ).
Return type:: np.ndarray

Example:

>>> prim.get_world_scale()
[1. 1. 1.]

initialize(physics_sim_view=None) → None#

Create a physics simulation view if not passed and using PhysX tensor API

Note

If the prim has been added to the world scene (e.g., world.scene.add(prim)), it will be automatically initialized when the world is reset (e.g., world.reset()).

Parameters:: physics_sim_view (omni.physics.tensors.SimulationView, optional) – current physics simulation view. Defaults to None.

Example:

>>> prim.initialize()

is_valid() → bool#

Check if the prim path has a valid USD Prim at it

Returns:: True is the current prim path corresponds to a valid prim in stage. False otherwise.
Return type:: bool

Example:

>>> # given an existing and valid prim
>>> prims.is_valid()
True

is_visual_material_applied() → bool#

Check if there is a visual material applied

Returns:: True if there is a visual material applied. False otherwise.
Return type:: bool

Example:

>>> # given a visual material applied
>>> prim.is_visual_material_applied()
True

property name: str | None#: Returns: str: name given to the prim when instantiating it. Otherwise None.

property non_root_articulation_link: bool#

Used to query if the prim is a non root articulation link

Returns:: True if the prim itself is a non root link
Return type:: bool

Example:

>>> # for a wrapped articulation (where the root prim has the Physics Articulation Root property applied)
>>> prim.non_root_articulation_link
False

post_reset() → None#

Reset the prim to its default state (position and orientation).

Note

For an articulation, in addition to configuring the root prim’s default position and spatial orientation (defined via the set_default_state method), the joint’s positions, velocities, and efforts (defined via the set_joints_default_state method) are imposed

Example:

>>> prim.post_reset()

property prim: pxr.Usd.Prim#: Returns: Usd.Prim: USD Prim object that this object holds.

property prim_path: str#: Returns: str: prim path in the stage

set_default_state( position: Sequence[float] | None = None, orientation: Sequence[float] | None = None, ) → None#

Set the default state of the prim (position and orientation), that will be used after each reset.

Parameters:

position (Optional[Sequence[float]], optional) – position in the world frame of the prim. shape is (3, ). Defaults to None, which means left unchanged.
orientation (Optional[Sequence[float]], optional) – quaternion orientation in the world frame of the prim. quaternion is scalar-first (w, x, y, z). shape is (4, ). Defaults to None, which means left unchanged.

Example:

>>> # configure default state
>>> prim.set_default_state(position=np.array([1.0, 0.5, 0.0]), orientation=np.array([1, 0, 0, 0]))
>>>
>>> # set default states during post-reset
>>> prim.post_reset()

set_local_pose( translation: Sequence[float] | None = None, orientation: Sequence[float] | None = None, ) → None#

Set prim’s pose with respect to the local frame (the prim’s parent frame).

Warning

This method will change (teleport) the prim pose immediately to the indicated value

Parameters:

translation (Optional[Sequence[float]], optional) – translation in the local frame of the prim (with respect to its parent prim). shape is (3, ). Defaults to None, which means left unchanged.
orientation (Optional[Sequence[float]], optional) – quaternion orientation in the local frame of the prim. quaternion is scalar-first (w, x, y, z). shape is (4, ). Defaults to None, which means left unchanged.

Hint

This method belongs to the methods used to set the prim state

Example:

>>> prim.set_local_pose(translation=np.array([1.0, 0.5, 0.0]), orientation=np.array([1., 0., 0., 0.]))

set_local_scale( scale: Sequence[float] | None, ) → None#

Set prim’s scale with respect to the local frame (the prim’s parent frame).

Parameters:: scale (Optional[Sequence[float]]) – scale to be applied to the prim’s dimensions. shape is (3, ). Defaults to None, which means left unchanged.

Example:

>>> # scale prim 10 times smaller
>>> prim.set_local_scale(np.array([0.1, 0.1, 0.1]))

set_visibility(visible: bool) → None#

Set the visibility of the prim in stage

Parameters:: visible (bool) – flag to set the visibility of the usd prim in stage.

Example:

>>> # make prim not visible in the stage
>>> prim.set_visibility(visible=False)

set_world_pose( position: Sequence[float] | None = None, orientation: Sequence[float] | None = None, ) → None#

Ses prim’s pose with respect to the world’s frame

Warning

This method will change (teleport) the prim pose immediately to the indicated value

Parameters:

position (Optional[Sequence[float]], optional) – position in the world frame of the prim. shape is (3, ). Defaults to None, which means left unchanged.
orientation (Optional[Sequence[float]], optional) – quaternion orientation in the world frame of the prim. quaternion is scalar-first (w, x, y, z). shape is (4, ). Defaults to None, which means left unchanged.

Hint

This method belongs to the methods used to set the prim state

Example:

>>> prim.set_world_pose(position=np.array([1.0, 0.5, 0.0]), orientation=np.array([1., 0., 0., 0.]))