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

Provide high-level wrapper for prims that have the Root Articulation API applied.

ClothPrim

Deprecated cloth prim class.

DeformablePrim

Deprecated deformable prim class.

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

Provide high-level functions for prims that have Rigid Body API applied to them.

SdfShapePrim

High level functions to deal with geometry prims that provide their Signed Distance Field (SDF).

XFormPrim

Provide high-level functions for working with Xform prim views and their descendants.

Single Prims

SingleArticulation

High level wrapper to deal with an articulation prim (only one articulation prim) and its attributes/properties.

SingleClothPrim

Deprecated single cloth prim class.

SingleDeformablePrim

Deprecated single deformable prim class.

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#

class Articulation(
prim_paths_expr: str | list[str],
name: str = 'articulation_prim_view',
positions: np.ndarray | torch.Tensor | wp.array | None = None,
translations: np.ndarray | torch.Tensor | wp.array | None = None,
orientations: np.ndarray | torch.Tensor | wp.array | None = None,
scales: np.ndarray | torch.Tensor | wp.array | None = None,
visibilities: np.ndarray | torch.Tensor | wp.array | None = None,
reset_xform_properties: bool = True,
)#

Bases: XFormPrim

Provide high-level wrapper for prims that have the Root Articulation API applied.

Handle attributes and properties of single or multiple articulated prims.

Wrap 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 – 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 – shortname to be used as a key by Scene class. Note: needs to be unique if the object is added to the Scene.

  • positions – default positions in the world frame of the prims. shape is (N, 3).

  • translations – default translations in the local frame of the prims (with respect to its parent prims). shape is (N, 3).

  • orientations – 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 – local scales to be applied to the prim’s dimensions in the view. shape is (N, 3).

  • visibilities – set to false for an invisible prim in the stage while rendering. shape is (N,).

  • reset_xform_properties – 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.

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/FrankaRobotics/FrankaPanda/franka.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>
apply_action(
control_actions: ArticulationActions,
indices: np.ndarray | list | torch.Tensor | wp.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 – actions to be applied for next physics step.

  • indices – 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]))
apply_visual_materials(
visual_materials: 'VisualMaterial' | list['VisualMaterial'],
weaker_than_descendants: bool | list[bool] | None = None,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) None#

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

Parameters:
  • visual_materials – 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 – True if the material shouldn’t override the descendants materials, otherwise 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 – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated 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)
destroy() None#

Clean up and invalidate the prim view by deregistering callbacks and clearing internal state.

get_angular_velocities(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
clone: bool = True,
) np.ndarray | torch.Tensor | wp.indexedarray#

Get the angular velocities of prims in the view.

Parameters:
  • indices – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

  • clone – True to return a clone of the internal buffer. Otherwise False.

Returns:

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

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_applied_actions(
clone: bool = True,
) ArticulationActions#

Get the last applied actions.

Parameters:

clone – 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.]]
get_applied_joint_efforts(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
joint_indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
joint_names: list[str] | None = None,
clone: bool = True,
) np.ndarray | torch.Tensor | wp.indexedarray#

Get the joint efforts of articulations in the view.

This method will return the efforts set by the set_joint_efforts method

Parameters:
  • indices – 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 – joint indices to specify which joints to query. Shape (K,). Where K <= num of dofs. Defaults to None (i.e: all dofs).

  • joint_names – 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 – True to return a clone of the internal buffer. Otherwise False.

Returns:

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

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.]]
get_applied_visual_materials(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) list['VisualMaterial']#

Get the current applied visual materials.

Parameters:

indices – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

Returns:

a list of the current applied visual materials to the prims if its type is currently supported.

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_armatures(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
joint_indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
joint_names: list[str] | None = None,
clone: bool = True,
) np.ndarray | torch.Tensor | wp.indexedarray#

Get armatures for articulation joints in the view.

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

Parameters:
  • indices – 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 – joint indices to specify which joints to query. Shape (K,). Where K <= num of dofs. Defaults to None (i.e: all dofs).

  • joint_names – 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 – True to return a clone of the internal buffer. Otherwise False.

Returns:

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

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

Example:

>>> prims.get_articulation_body_count()
12
get_body_coms(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
body_indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
clone: bool = True,
) np.ndarray | torch.Tensor | wp.indexedarray#

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

Parameters:
  • indices – Indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

  • body_indices – Body indices to specify which bodies to query. Shape (K,). Where K <= num of bodies.

  • clone – True to return a clone of the internal buffer. Otherwise False.

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).

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_body_disable_gravity(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
body_indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
clone: bool = True,
) np.ndarray | torch.Tensor | wp.indexedarray#

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

Parameters:
  • indices – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

  • body_indices – body indices to specify which bodies to query. Shape (K,). Where K <= num of bodies.

  • clone – True to return a clone of the internal buffer. Otherwise False.

Returns:

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

get_body_index(body_name: str) int#

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

Parameters:

body_name – name of the ridig body to query

Returns:

index of the rigid body in the articulation buffers

Example:

>>> # get the index of the left finger: panda_leftfinger
>>> prims.get_body_index("panda_leftfinger")
10
get_body_inertias(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
body_indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
clone: bool = True,
) np.ndarray | torch.Tensor | wp.indexedarray#

Get rigid body inertias of articulations in the view.

Parameters:
  • indices – Indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

  • body_indices – Body indices to specify which bodies to query. Shape (K,). Where K <= num of bodies.

  • clone – True to return a clone of the internal buffer. Otherwise False.

Returns:

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

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_body_inv_inertias(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
body_indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
clone: bool = True,
) np.ndarray | torch.Tensor | wp.indexedarray#

Get rigid body inverse inertias of articulations in the view.

Parameters:
  • indices – Indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

  • body_indices – Body indices to specify which bodies to query. Shape (K,). Where K <= num of bodies.

  • clone – True to return a clone of the internal buffer. Otherwise False.

Returns:

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

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_body_inv_masses(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
body_indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
clone: bool = True,
) np.ndarray | torch.Tensor | wp.indexedarray#

Get rigid body inverse masses of articulations in the view.

Parameters:
  • indices – Indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

  • body_indices – Body indices to specify which bodies to query. Shape (K,). Where K <= num of bodies.

  • clone – True to return a clone of the internal buffer. Otherwise False.

Returns:

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

get_body_masses(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
body_indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
clone: bool = True,
) np.ndarray | torch.Tensor | wp.indexedarray#

Get rigid body masses of articulations in the view.

Parameters:
  • indices – Indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

  • body_indices – Body indices to specify which bodies to query. Shape (K,). Where K <= num of bodies.

  • clone – True to return a clone of the internal buffer. Otherwise False.

Returns:

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

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_coriolis_and_centrifugal_forces(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
joint_indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
joint_names: list[str] | None = None,
clone: bool = True,
) np.ndarray | torch.Tensor | wp.indexedarray#

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 – Indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

  • joint_indices – 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.

  • joint_names – Joint names to specify which joints to manipulate (can’t be sppecified together with joint_indices). Shape (K,). Where K <= num of dofs.

  • clone – True to return a clone of the internal buffer. Otherwise False.

Returns:

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

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_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.

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_dof_index(dof_name: str) int#

Get a DOF index in the joint buffers given its name.

Parameters:

dof_name – name of the joint that corresponds to the degree of freedom to query

Returns:

index of the degree of freedom in the joint buffers

Example:

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

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_dof_types(dof_names: list[str] = None) list[str]#

Get the DOF types given the DOF names.

Parameters:

dof_names – names of the joints that corresponds to the degrees of freedom to query.

Returns:

types of the joints that corresponds to the degrees of freedom. Types can be invalid, translation or rotation.

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_drive_types() ndarray | Tensor#

Get the articulations DOFs drive types.

Returns:

degrees of freedom drive types. Shape is (N, num_dof).

get_effort_modes(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
joint_indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
joint_names: list[str] | None = None,
) list[str]#

Get effort modes for articulations in the view.

Parameters:
  • indices – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

  • joint_indices – joint indices to specify which joints to query. Shape (K,). Where K <= num of dofs.

  • joint_names – joint names to specify which joints to manipulate (can’t be sppecified together with joint_indices). Shape (K,). Where K <= num of dofs.

Returns:

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

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']]
get_enabled_self_collisions(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) np.ndarray | torch.Tensor | wp.indexedarray#

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

Parameters:

indices – Indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

Returns:

Self collisions flags (boolean interpreted as int). shape (M,)

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]
get_fixed_tendon_dampings(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
clone: bool = True,
) np.ndarray | torch.Tensor | wp.indexedarray#

Get the dampings of fixed tendons for articulations in the view.

Search for Fixed Tendon in PhysX docs for more details

Parameters:
  • indices – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

  • clone – True to return a clone of the internal buffer. Otherwise False.

Returns:

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

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_fixed_tendon_limit_stiffnesses(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
clone: bool = True,
) np.ndarray | torch.Tensor | wp.indexedarray#

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

Search for Fixed Tendon in PhysX docs for more details

Parameters:
  • indices – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

  • clone – True to return a clone of the internal buffer. Otherwise False.

Returns:

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

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_fixed_tendon_limits(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
clone: bool = True,
) np.ndarray | torch.Tensor | wp.indexedarray#

Get the limits of fixed tendons for articulations in the view.

Search for Fixed Tendon in PhysX docs for more details

Parameters:
  • indices – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

  • clone – True to return a clone of the internal buffer. Otherwise False.

Returns:

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

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_fixed_tendon_offsets(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
clone: bool = True,
) np.ndarray | torch.Tensor | wp.indexedarray#

Get the offsets of fixed tendons for articulations in the view.

Search for Fixed Tendon in PhysX docs for more details

Parameters:
  • indices – 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 – True to return a clone of the internal buffer. Otherwise False.

Returns:

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

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.]]
get_fixed_tendon_rest_lengths(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
clone: bool = True,
) np.ndarray | torch.Tensor | wp.indexedarray#

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

Search for Fixed Tendon in PhysX docs for more details

Parameters:
  • indices – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

  • clone – True to return a clone of the internal buffer. Otherwise False.

Returns:

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

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_fixed_tendon_stiffnesses(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
clone: bool = True,
) np.ndarray | torch.Tensor | wp.indexedarray#

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

Search for Fixed Tendon in PhysX docs for more details

Parameters:
  • indices – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

  • clone – True to return a clone of the internal buffer. Otherwise False.

Returns:

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

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_friction_coefficients(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
joint_indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
joint_names: list[str] | None = None,
clone: bool = True,
) np.ndarray | torch.Tensor | wp.array#

Get the friction coefficients for the articulation joints in the view.

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

Parameters:
  • indices – 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 – joint indices to specify which joints to query. Shape (K,). Where K <= num of dofs. Defaults to None (i.e: all dofs).

  • joint_names – 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 – True to return a clone of the internal buffer. Otherwise False.

Returns:

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

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.]]
get_gains(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
joint_indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
joint_names: list[str] | None = None,
clone: bool = True,
) tuple[np.ndarray | torch.Tensor, np.ndarray | torch.Tensor, wp.indexedarray | wp.index]#

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

Parameters:
  • indices – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

  • joint_indices – joint indices to specify which joints to query. Shape (K,). Where K <= num of dofs.

  • joint_names – joint names to specify which joints to manipulate (can’t be sppecified together with joint_indices). Shape (K,). Where K <= num of dofs.

  • clone – True to return clones of the internal buffers. Otherwise False.

Returns:

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

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.]]
get_generalized_gravity_forces(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
joint_indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
joint_names: list[str] | None = None,
clone: bool = True,
) np.ndarray | torch.Tensor | wp.indexedarray#

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 – Indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

  • joint_indices – 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.

  • joint_names – Joint names to specify which joints to manipulate (can’t be sppecified together with joint_indices). Shape (K,). Where K <= num of dofs.

  • clone – True to return a clone of the internal buffer. Otherwise False.

Returns:

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

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_jacobian_shape() np.ndarray | torch.Tensor | wp.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.

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_jacobians(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
clone: bool = True,
) np.ndarray | torch.Tensor | wp.indexedarray#

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 – Indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

  • clone – True to return a clone of the internal buffer. Otherwise False.

Returns:

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

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_joint_index(joint_name: str) int#

Get a joint index in the joint buffers given its name.

Parameters:

joint_name – name of the joint that corresponds to the index of the joint in the articulation

Returns:

index of the joint in the joint buffers

get_joint_max_velocities(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
joint_indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
joint_names: list[str] | None = None,
clone: bool = True,
) np.ndarray | torch.Tensor | wp.indexedarray#

Get the maximum joint velocities for articulation dofs in the view.

Parameters:
  • indices – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

  • joint_indices – joint indices to specify which joints to query. Shape (K,). Where K <= num of dofs.

  • joint_names – joint names to specify which joints to manipulate (can’t be sppecified together with joint_indices). Shape (K,). Where K <= num of dofs.

  • clone – True to return a clone of the internal buffer. Otherwise False.

Returns:

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

get_joint_positions(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
joint_indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
joint_names: list[str] | None = None,
clone: bool = True,
) np.ndarray | torch.Tensor | wp.indexedarray#

Get the joint positions of articulations in the view.

Parameters:
  • indices – 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 – joint indices to specify which joints to query. Shape (K,). Where K <= num of dofs. Defaults to None (i.e: all dofs).

  • joint_names – 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 – 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_joint_velocities(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
joint_indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
joint_names: list[str] | None = None,
clone: bool = True,
) np.ndarray | torch.Tensor | wp.indexedarray#

Get the joint velocities of articulations in the view.

Parameters:
  • indices – 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 – joint indices to specify which joints to query. Shape (K,). Where K <= num of dofs. Defaults to None (i.e: all dofs).

  • joint_names – 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 – 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]]
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

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

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_linear_velocities(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
clone: bool = True,
) np.ndarray | torch.Tensor | wp.indexedarray#

Get the linear velocities of prims in the view.

Parameters:
  • indices – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

  • clone – True to return a clone of the internal buffer. Otherwise False.

Returns:

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

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.]]

Get a link index in the link buffers given its name.

Parameters:

link_name – name of the link that corresponds to the index of the link in the articulation

Returns:

index of the link in the link buffers

get_local_poses(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) tuple[np.ndarray, np.ndarray] | tuple[torch.Tensor, torch.Tensor] | tuple[wp.indexedarray, wp.indexedarray]#

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

Parameters:

indices – 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.]]
get_local_scales(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) np.ndarray | torch.Tensor | wp.indexedarray#

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

Parameters:

indices – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

Returns:

scales applied to the prim’s dimensions in the local frame. shape is (M, 3).

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_mass_matrices(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
clone: bool = True,
) np.ndarray | torch.Tensor | wp.indexedarray#

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 – Indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

  • clone – True to return a clone of the internal buffer. Otherwise False.

Returns:

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

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_mass_matrix_shape() np.ndarray | torch.Tensor | wp.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.

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_max_efforts(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
joint_indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
joint_names: list[str] | None = None,
clone: bool = True,
) np.ndarray | torch.Tensor | wp.indexedarray#

Get the maximum efforts for articulation in the view.

Parameters:
  • indices – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

  • joint_indices – joint indices to specify which joints to query. Shape (K,). Where K <= num of dofs.

  • joint_names – joint names to specify which joints to manipulate (can’t be sppecified together with joint_indices). Shape (K,). Where K <= num of dofs.

  • clone – True to return a clone of the internal buffer. Otherwise False.

Returns:

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

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.]]
get_measured_joint_efforts(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
joint_indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
joint_names: list[str] | None = None,
clone: bool = True,
) np.ndarray | torch.Tensor | wp.indexedarray#

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

Parameters:
  • indices – 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 – joint indices to specify which joints to query. Shape (K,). Where K <= num of dofs. Defaults to None (i.e: all dofs).

  • joint_names – 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 – 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_measured_joint_forces(
indices: ndarray | list | Tensor | None = None,
joint_indices: ndarray | list | Tensor | None = None,
joint_names: list[str] | None = None,
clone: bool = True,
) ndarray | Tensor#

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

Forces and torques are reported in the local body reference frame (child joint frame of the link’s incoming joint).

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 when specifying the joint_indices parameter. For the joint_names parameter, the conversion is done internally.

Parameters:
  • indices – 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 – 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 – 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 – 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_sleep_thresholds(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) np.ndarray | torch.Tensor | wp.indexedarray#

Get the threshold for articulations to enter a sleep state.

Search for Articulations and Sleeping in PhysX docs for more details

Parameters:

indices – Indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

Returns:

Sleep thresholds. shape (M,).

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_solver_position_iteration_counts(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) np.ndarray | torch.Tensor | wp.indexedarray#

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 – Indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

Returns:

Position iteration count. Shape (M,).

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]
get_solver_velocity_iteration_counts(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) np.ndarray | torch.Tensor | wp.indexedarray#

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 – Indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

Returns:

Velocity iteration count. Shape (M,).

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]
get_stabilization_thresholds(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) np.ndarray | torch.Tensor | wp.indexedarray#

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 – Indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

Returns:

Stabilization threshold. Shape (M,).

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]
get_velocities(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
clone: bool = True,
) np.ndarray | torch.Tensor | wp.indexedarray#

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

Parameters:
  • indices – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

  • clone – True to return a clone of the internal buffer. Otherwise False.

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

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.]]
get_visibilities(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) np.ndarray | torch.Tensor | wp.indexedarray#

Return the current visibilities of the prims in stage.

Parameters:

indices – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

Returns:

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

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_world_poses(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
clone: bool = True,
usd: bool = True,
) tuple[np.ndarray, np.ndarray] | tuple[torch.Tensor, torch.Tensor] | tuple[wp.indexedarray, wp.indexedarray]#

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

Parameters:
  • indices – 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 – True to return a clone of the internal buffer. Otherwise False. Defaults to True.

  • usd – 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_world_scales(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) np.ndarray | torch.Tensor | wp.indexedarray#

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

Parameters:

indices – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

Returns:

scales applied to the prim’s dimensions in the world frame. shape is (M, 3).

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.]]
initialize(
physics_sim_view: omni.physics.tensors.SimulationView = 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 – current physics simulation view.

Example:

>>> prims.initialize()
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

Example:

>>> prims.is_physics_handle_valid()
True
is_valid(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) bool#

Check that all prims have a valid USD Prim.

Parameters:

indices – Indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. If None, all prims in the view are queried.

Returns:

True if all prim paths specified in the view correspond to a valid prim in stage. False otherwise.

Example:

>>> prims.is_valid()
True
is_visual_material_applied(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) list[bool]#

Check if there is a visual material applied.

Parameters:

indices – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

Returns:

True if there is a visual material applied is applied to the corresponding prim in the view. False otherwise.

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]
pause_motion() None#

Pause the motion of all articulations wrapped under the Articulation.

post_reset() None#

Reset the prims to its default state.

Example:

>>> prims.post_reset()
resume_motion() None#

Resume the motion of all articulations wrapped under the Articulation using the position and velocity dof targets.

cached when pause_motion was called.

set_angular_velocities(
velocities: np.ndarray | torch.Tensor | wp.array | None = None,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) None#

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 – angular velocities to set the rigid prims to. shape is (M, 3).

  • indices – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated 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]))
set_armatures(
values: np.ndarray | torch.Tensor | wp.array,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
joint_indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
joint_names: list[str] | None = None,
) None#

Set armatures for articulation joints in the view.

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

Parameters:
  • values – armatures for articulation joints in the view. shape (M, K).

  • indices – 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 – joint indices to specify which joints to manipulate. Shape (K,). Where K <= num of dofs. Defaults to None (i.e: all dofs).

  • joint_names – 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]))
set_body_coms(
positions: np.ndarray | torch.Tensor | wp.array = None,
orientations: np.ndarray | torch.Tensor | wp.array = None,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
body_indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) None#

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

Parameters:
  • positions – body center of mass positions for articulations in the view. shape (M, K, 3).

  • orientations – body center of mass orientations for articulations in the view. shape (M, K, 4).

  • indices – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view.

  • body_indices – body indices to specify which bodies to manipulate. Shape (K,). Where K <= num of 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_disable_gravity(
values: np.ndarray | torch.Tensor | wp.array,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
body_indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) None#

Set body gravity activation articulation bodies in the view.

Parameters:
  • values – body gravity activation for articulations in the view. shape (M, K).

  • indices – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view.

  • body_indices – body indices to specify which bodies to manipulate. Shape (K,). Where K <= num of bodies.

set_body_inertias(
values: np.ndarray | torch.Tensor | wp.array,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
body_indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) None#

Set body inertias for articulation bodies in the view.

Parameters:
  • values – body inertias for articulations in the view. shape (M, K, 9).

  • indices – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view.

  • body_indices – body indices to specify which bodies to manipulate. Shape (K,). Where K <= num of 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_masses(
values: np.ndarray | torch.Tensor | wp.array,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
body_indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) None#

Set body masses for articulation bodies in the view.

Parameters:
  • values – body masses for articulations in the view. shape (M, K).

  • indices – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view.

  • body_indices – body indices to specify which bodies to manipulate. Shape (K,). Where K <= num of 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_default_state(
positions: np.ndarray | torch.Tensor | wp.array | None = None,
orientations: np.ndarray | torch.Tensor | wp.array | None = None,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) None#

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 – positions in the world frame of the prim. shape is (M, 3).

  • orientations – quaternion orientations in the world frame of the prim. quaternion is scalar-first (w, x, y, z). shape is (M, 4).

  • indices – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated 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_effort_modes(
mode: str,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
joint_indices: np.ndarray | list | torch.Tensor | None = None,
joint_names: list[str] | None = None,
) None#

Set effort modes for articulations in the view.

Parameters:
  • mode – effort mode to be applied to prims in the view: acceleration or force.

  • indices – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view.

  • joint_indices – joint indices to specify which joints to manipulate. Shape (K,). Where K <= num of dofs.

  • joint_names – joint names to specify which joints to manipulate (can’t be sppecified together with joint_indices). Shape (K,). Where K <= num of 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_enabled_self_collisions(
flags: np.ndarray | torch.Tensor | wp.array,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) None#

Set the enable self collisions flag (physxArticulation:enabledSelfCollisions).

Parameters:
  • flags – True to enable self collision. otherwise false. shape (M,)

  • indices – Indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated 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]))
set_fixed_tendon_properties(
stiffnesses: np.ndarray | torch.Tensor | wp.array = None,
dampings: np.ndarray | torch.Tensor | wp.array = None,
limit_stiffnesses: np.ndarray | torch.Tensor | wp.array = None,
limits: np.ndarray | torch.Tensor | wp.array = None,
rest_lengths: np.ndarray | torch.Tensor | wp.array = None,
offsets: np.ndarray | torch.Tensor | wp.array = None,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) None#

Set fixed tendon properties for articulations in the view.

Search for Fixed Tendon in PhysX docs for more details

Parameters:
  • stiffnesses – fixed tendon stiffnesses for articulations in the view. shape (M, K).

  • dampings – fixed tendon dampings for articulations in the view. shape (M, K).

  • limit_stiffnesses – fixed tendon limit stiffnesses for articulations in the view. shape (M, K).

  • limits – fixed tendon limits for articulations in the view. shape (M, K, 2).

  • rest_lengths – fixed tendon rest lengths for articulations in the view. shape (M, K).

  • offsets – fixed tendon offsets for articulations in the view. shape (M, K).

  • indices – 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)
set_friction_coefficients(
values: np.ndarray | torch.Tensor,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
joint_indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
joint_names: list[str] | None = None,
) None#

Set the friction coefficients for articulation joints in the view.

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

Parameters:
  • values – friction coefficients for articulation joints in the view. shape (M, K).

  • indices – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view.

  • joint_indices – joint indices to specify which joints to manipulate. Shape (K,). Where K <= num of dofs.

  • joint_names – joint names to specify which joints to manipulate (can’t be sppecified together with joint_indices). Shape (K,). Where K <= num of 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]))
set_gains(
kps: np.ndarray | torch.Tensor | wp.array | None = None,
kds: np.ndarray | torch.Tensor | wp.array | None = None,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
joint_indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
joint_names: list[str] | None = None,
save_to_usd: bool = False,
) None#

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

Parameters:
  • kps – stiffness of the drives. shape is (M, K).

  • kds – damping of the drives. shape is (M, K)..

  • indices – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view.

  • joint_indices – joint indices to specify which joints to manipulate. Shape (K,). Where K <= num of dofs.

  • joint_names – joint names to specify which joints to manipulate (can’t be sppecified together with joint_indices). Shape (K,). Where K <= num of dofs.

  • save_to_usd – 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]))
set_joint_efforts(
efforts: np.ndarray | torch.Tensor | wp.array | None,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
joint_indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
joint_names: list[str] | None = None,
) None#

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 – efforts of articulations in the view to be set to in the next frame. shape is (M, K).

  • indices – 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 – joint indices to specify which joints to manipulate. Shape (K,). Where K <= num of dofs. Defaults to None (i.e: all dofs).

  • joint_names – 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]))
set_joint_position_targets(
positions: np.ndarray | torch.Tensor | wp.array | None,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
joint_indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
joint_names: list[str] | None = None,
) None#

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 – joint position targets for the implicit PD controller. shape is (M, K).

  • indices – 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 – joint indices to specify which joints to manipulate. Shape (K,). Where K <= num of dofs. Defaults to None (i.e: all dofs).

  • joint_names – 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_joint_positions(
positions: np.ndarray | torch.Tensor | wp.array | None,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
joint_indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
joint_names: list[str] | None = None,
) None#

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 – joint positions of articulations in the view to be set to in the next frame. shape is (M, K).

  • indices – 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 – joint indices to specify which joints to manipulate. Shape (K,). Where K <= num of dofs. Defaults to None (i.e: all dofs).

  • joint_names – 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_joint_velocities(
velocities: np.ndarray | torch.Tensor | wp.array | None,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
joint_indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
joint_names: list[str] | None = None,
) None#

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 – joint velocities of articulations in the view to be set to in the next frame. shape is (M, K).

  • indices – 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 – joint indices to specify which joints to manipulate. Shape (K,). Where K <= num of dofs. Defaults to None (i.e: all dofs).

  • joint_names – 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_joint_velocity_targets(
velocities: np.ndarray | torch.Tensor | wp.array | None,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
joint_indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
joint_names: list[str] | None = None,
) None#

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 – joint velocity targets for the implicit PD controller. shape is (M, K).

  • indices – 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 – joint indices to specify which joints to manipulate. Shape (K,). Where K <= num of dofs. Defaults to None (i.e: all dofs).

  • joint_names – 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_joints_default_state(
positions: np.ndarray | torch.Tensor | wp.array | None = None,
velocities: np.ndarray | torch.Tensor | wp.array | None = None,
efforts: np.ndarray | torch.Tensor | wp.array | None = None,
) None#

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 – default joint positions. shape is (N, num of dofs).

  • velocities – default joint velocities. shape is (N, num of dofs).

  • efforts – default joint efforts. shape is (N, num of dofs).

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()
set_linear_velocities(
velocities: np.ndarray | torch.Tensor | wp.array | None = None,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) None#

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 – linear velocities to set the rigid prims to. shape is (M, 3).

  • indices – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated 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]))
set_local_poses(
translations: np.ndarray | torch.Tensor | wp.array | None = None,
orientations: np.ndarray | torch.Tensor | wp.array | None = None,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) None#

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 – 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 – 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 – 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_local_scales(
scales: np.ndarray | torch.Tensor | wp.array | None,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) None#

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

Parameters:
  • scales – scales to be applied to the prim’s dimensions in the view. shape is (M, 3).

  • indices – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated 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_max_efforts(
values: np.ndarray | torch.Tensor | wp.array,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
joint_indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
joint_names: list[str] | None = None,
) None#

Set maximum efforts for articulation in the view.

Parameters:
  • values – maximum efforts for articulations in the view. shape (M, K).

  • indices – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view.

  • joint_indices – joint indices to specify which joints to manipulate. Shape (K,). Where K <= num of dofs.

  • joint_names – joint names to specify which joints to manipulate (can’t be sppecified together with joint_indices). Shape (K,). Where K <= num of 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]))
set_max_joint_velocities(
values: np.ndarray | torch.Tensor | wp.array,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
joint_indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
joint_names: list[str] | None = None,
) None#

Set maximum velocities for articulation in the view.

Parameters:
  • values – maximum velocities for articulations in the view. shape (M, K).

  • indices – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view.

  • joint_indices – joint indices to specify which joints to manipulate. Shape (K,). Where K <= num of dofs.

  • joint_names – joint names to specify which joints to manipulate (can’t be sppecified together with joint_indices). Shape (K,). Where K <= num of dofs.

set_sleep_thresholds(
thresholds: np.ndarray | torch.Tensor | wp.array,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) None#

Set the threshold for articulations to enter a sleep state.

Search for Articulations and Sleeping in PhysX docs for more details

Parameters:
  • thresholds – Sleep thresholds to be applied. shape (M,).

  • indices – Indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated 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]))
set_solver_position_iteration_counts(
counts: np.ndarray | torch.Tensor | wp.array,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) None#

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 – number of iterations for the solver. Shape (M,).

  • indices – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated 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]))
set_solver_velocity_iteration_counts(
counts: np.ndarray | torch.Tensor | wp.array,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) None#

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 – Number of iterations for the solver. Shape (M,).

  • indices – Indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated 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]))
set_stabilization_thresholds(
thresholds: np.ndarray | torch.Tensor | wp.array,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) 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:
  • thresholds – Stabilization thresholds to be applied. Shape (M,).

  • indices – Indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated 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]))
set_velocities(
velocities: np.ndarray | torch.Tensor | wp.array | None = None,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) None#

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 – linear and angular velocities respectively to set the rigid prims to. shape is (M, 6).

  • indices – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated 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]))
set_visibilities(
visibilities: np.ndarray | torch.Tensor | wp.array,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) None#

Set the visibilities of the prims in stage.

Parameters:
  • visibilities – flag to set the visibilities of the usd prims in stage. Shape (M,). Where M <= size of the encapsulated prims in the view.

  • indices – indices to specify which prims to manipulate. Shape (M,).

Example:

>>> # make all prims not visible in the stage
>>> prims.set_visibilities(visibilities=[False] * num_envs)
set_world_poses(
positions: np.ndarray | torch.Tensor | wp.array | None = None,
orientations: np.ndarray | torch.Tensor | wp.array | None = None,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
usd: bool = True,
) None#

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 – positions in the world frame of the prim. shape is (M, 3). Defaults to None, which means left unchanged.

  • orientations – 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 – 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 – 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]))
switch_control_mode(
mode: str,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
joint_indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
joint_names: list[str] | None = None,
) None#

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 – control mode to switch the articulations specified to. It can be "position", "velocity", or "effort"

  • indices – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view.

  • joint_indices – joint indices to specify which joints to manipulate. Shape (K,). Where K <= num of dofs.

  • joint_names – joint names to specify which joints to manipulate (can’t be sppecified together with joint_indices). Shape (K,). Where K <= num of 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: np.ndarray | list | torch.Tensor | wp.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 – control mode to switch the DOF specified to. It can be "position", "velocity" or "effort"

  • dof_index – dof index to switch the control mode of.

  • indices – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated 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]))
property body_names: list[str] | None#

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 count: int#

Number of prims encapsulated in this view.

Returns:

Number of prims encapsulated in this view.

Example:

>>> prims.count
5
property dof_names: list[str] | None#

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 initialized: bool#

Whether the prim view is initialized.

Returns:

True if the view object was initialized (after the first call of .initialize()). False otherwise.

Example:

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

True if the prim corresponds to a non root link in an articulation.

Returns:

True if the prim corresponds to a non root link in an articulation. Otherwise False.

property joint_names: list[str] | None#

List of prim names for each joint of the articulations.

Returns:

A list of ordered names of joints that corresponds to degrees of freedom for the articulations in the view

property name: str#

Name given to the prims view when instantiating it.

Returns:

Name given to the prims view when instantiating it.

property num_bodies: int | None#

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_dof: int | None#

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_fixed_tendons: int | None#

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 num_joints: int | None#

Number of joints of the articulations.

Returns:

number of joints of the articulations in the view

Return type:

int

property num_shapes: int | None#

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 prim_paths: list[str]#

List of prim paths in the stage encapsulated in this view.

Returns:

List of prim paths in the stage encapsulated in this view.

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]#

List of USD Prim objects encapsulated in this view.

Returns:

List of USD Prim objects encapsulated in this view.

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>)]
class ClothPrim(*args: Any, **kwargs: Any)#

Bases: object

Deprecated cloth prim class. No longer available.

Parameters:
  • *args – Unused positional arguments.

  • **kwargs – Unused keyword arguments.

class DeformablePrim(*args: Any, **kwargs: Any)#

Bases: object

Deprecated deformable prim class. No longer available.

Parameters:
  • *args – Unused positional arguments.

  • **kwargs – Unused keyword arguments.

class GeometryPrim(
prim_paths_expr: str,
name: str = 'geometry_prim_view',
positions: np.ndarray | torch.Tensor | wp.array | None = None,
translations: np.ndarray | torch.Tensor | wp.array | None = None,
orientations: np.ndarray | torch.Tensor | wp.array | None = None,
scales: np.ndarray | torch.Tensor | wp.array | None = None,
visibilities: np.ndarray | torch.Tensor | wp.array | None = None,
reset_xform_properties: bool = True,
collisions: np.ndarray | torch.Tensor | wp.array | None = None,
track_contact_forces: bool = False,
prepare_contact_sensors: bool = False,
disable_stablization: bool = True,
contact_filter_prim_paths_expr: list[str] | None = None,
max_contact_count: int = 0,
)#

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 – 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 – shortname to be used as a key by Scene class. Note: needs to be unique if the object is added to the Scene.

  • positions – default positions in the world frame of the prim. shape is (N, 3).

  • translations – default translations in the local frame of the prims (with respect to its parent prims). shape is (N, 3).

  • orientations – 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).

  • scales – local scales to be applied to the prim’s dimensions. shape is (N, 3).

  • visibilities – set to false for an invisible prim in the stage while rendering. shape is (N,).

  • reset_xform_properties – 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.

  • collisions – Set to True if the geometry already have/ should have a collider (i.e not only a visual geometry). shape is (N,).

  • track_contact_forces – 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.

  • prepare_contact_sensors – applies contact reporter API to the prim if it already does not have one.

  • disable_stablization – disables the contact stabilization parameter in the physics context.

  • contact_filter_prim_paths_expr – 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 – 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>
apply_collision_apis(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) None#

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

Parameters:

indices – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated 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]))
apply_physics_materials(
physics_materials: PhysicsMaterial | list[PhysicsMaterial],
weaker_than_descendants: bool | list[bool] | None = None,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) None#

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

Parameters:
  • physics_materials – 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 – True if the material shouldn’t override the descendants materials, otherwise 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 – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated 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_materials(
visual_materials: 'VisualMaterial' | list['VisualMaterial'],
weaker_than_descendants: bool | list[bool] | None = None,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) None#

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

Parameters:
  • visual_materials – 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 – True if the material shouldn’t override the descendants materials, otherwise 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 – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated 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)
destroy() None#

Clean up and invalidate the prim view by deregistering callbacks and clearing internal state.

disable_collision(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) None#

Disables collision on prims in the view.

Parameters:

indices – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated 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: np.ndarray | list | torch.Tensor | wp.array | None = None,
) None#

Enables collision on prims in the view.

Parameters:

indices – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated 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]))
get_applied_physics_materials(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) list[PhysicsMaterial]#

Get the applied physics material to prims in the view.

Parameters:

indices – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

Returns:

the current applied physics materials for prims in the view.

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: np.ndarray | list | torch.Tensor | wp.array | None = None,
) list['VisualMaterial']#

Get the current applied visual materials.

Parameters:

indices – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

Returns:

a list of the current applied visual materials to the prims if its type is currently supported.

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: np.ndarray | list | torch.Tensor | wp.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 – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

Returns:

approximations used for collision. size == M or size of the view.

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_contact_force_data(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
clone: bool = True,
dt: float = 1.0,
) tuple[np.ndarray | torch.Tensor | wp.indexedarray, np.ndarray | torch.Tensor | wp.indexedarray, np.ndarray | torch.Tensor | wp.indexedarray, np.ndarray | torch.Tensor | wp.indexedarray, np.ndarray | torch.Tensor | wp.indexedarray, np.ndarray | torch.Tensor | wp.indexedarray] | 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 – 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 – True to return a clone of the internal buffer. Otherwise False.

  • dt – 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:

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.

get_contact_force_matrix(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
clone: bool = True,
dt: float = 1.0,
) np.ndarray | torch.Tensor | wp.indexedarray | None#

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 – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

  • clone – True to return a clone of the internal buffer. Otherwise False.

  • dt – 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). None if no contact filter is specified.

get_contact_offsets(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) 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 – Indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

Returns:

Contact offsets of the collision shapes. Shape is (M,).

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.

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_friction_data(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
clone: bool = True,
dt: float = 1.0,
) tuple[np.ndarray | torch.Tensor | wp.indexedarray, np.ndarray | torch.Tensor | wp.indexedarray, np.ndarray | torch.Tensor | wp.indexedarray, np.ndarray | torch.Tensor | wp.indexedarray] | None#

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 – 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 – True to return a clone of the internal buffer. Otherwise False.

  • dt – 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:

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_local_poses(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) tuple[np.ndarray, np.ndarray] | tuple[torch.Tensor, torch.Tensor] | tuple[wp.indexedarray, wp.indexedarray]#

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

Parameters:

indices – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated 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).

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_local_scales(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) np.ndarray | torch.Tensor | wp.indexedarray#

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

Parameters:

indices – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

Returns:

scales applied to the prim’s dimensions in the local frame. shape is (M, 3).

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 – Indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

Returns:

Minimum radius of the contact patch used to apply torsional friction. Shape is (M,).

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.]
get_net_contact_forces(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
clone: bool = True,
dt: float = 1.0,
) np.ndarray | torch.Tensor | wp.indexedarray | None#

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 – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

  • clone – True to return a clone of the internal buffer. Otherwise False.

  • dt – 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). None if contact tracking is not enabled.

get_rest_offsets(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) 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 – Indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

Returns:

Rest offsets of the collision shapes. Shape is (M,).

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(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) np.ndarray | torch.Tensor | wp.indexedarray#

Get torsional patch radii for prims in the view.

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

Parameters:

indices – Indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

Returns:

Radius of the contact patch used to apply torsional friction. Shape is (M,).

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.]
get_visibilities(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) np.ndarray | torch.Tensor | wp.indexedarray#

Return the current visibilities of the prims in stage.

Parameters:

indices – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

Returns:

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

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_world_poses(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
usd: bool = True,
) tuple[np.ndarray, np.ndarray] | tuple[torch.Tensor, torch.Tensor] | tuple[wp.indexedarray, wp.indexedarray]#

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

Parameters:
  • indices – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

  • usd – True to query from usd. Otherwise False to query from Fabric data.

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).

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_world_scales(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) np.ndarray | torch.Tensor | wp.indexedarray#

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

Parameters:

indices – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

Returns:

scales applied to the prim’s dimensions in the world frame. shape is (M, 3).

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.]]
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 – Current physics simulation view.

Example:

>>> prims.initialize()
is_collision_enabled(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) np.ndarray | torch.Tensor | wp.indexedarray#

Queries if collision is enabled on prims in the view.

Parameters:

indices – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

Returns:

True if collision is enabled. Shape is (M,).

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]
is_valid(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) bool#

Check that all prims have a valid USD Prim.

Parameters:

indices – Indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. If None, all prims in the view are queried.

Returns:

True if all prim paths specified in the view correspond to a valid prim in stage. False otherwise.

Example:

>>> prims.is_valid()
True
is_visual_material_applied(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) list[bool]#

Check if there is a visual material applied.

Parameters:

indices – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

Returns:

True if there is a visual material applied is applied to the corresponding prim in the view. False otherwise.

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]
post_reset() None#

Reset the prims to its default state.

Example:

>>> prims.post_reset()
set_collision_approximations(
approximation_types: list[str],
indices: np.ndarray | list | torch.Tensor | wp.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 – approximations used for collision. List size == M or the size of the view.

  • indices – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated 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(
offsets: np.ndarray | torch.Tensor | wp.array,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) None#

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 – 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 – Indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated 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_default_state(
positions: np.ndarray | torch.Tensor | wp.array | None = None,
orientations: np.ndarray | torch.Tensor | wp.array | None = None,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) None#

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 – positions in the world frame of the prim. shape is (M, 3).

  • orientations – quaternion orientations in the world frame of the prim. quaternion is scalar-first (w, x, y, z). shape is (M, 4).

  • indices – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated 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_local_poses(
translations: np.ndarray | torch.Tensor | wp.array | None = None,
orientations: np.ndarray | torch.Tensor | wp.array | None = None,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) None#

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 – translations in the local frame of the prims (with respect to its parent prim). shape is (M, 3).

  • orientations – quaternion orientations in the local frame of the prims. quaternion is scalar-first (w, x, y, z). shape is (M, 4).

  • indices – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated 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_local_scales(
scales: np.ndarray | torch.Tensor | wp.array | None,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) None#

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

Parameters:
  • scales – scales to be applied to the prim’s dimensions in the view. shape is (M, 3).

  • indices – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated 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_min_torsional_patch_radii(
radii: np.ndarray | torch.Tensor | wp.array,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) None#

Set minimum torsional patch radii for prims in the view.

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

Parameters:
  • radii – Minimum radius of the contact patch used to apply torsional friction. Allowed range [0, max_float]. Shape is (M, ).

  • indices – Indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated 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(
offsets: np.ndarray | torch.Tensor | wp.array,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) None#

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 – 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 – Indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated 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(
radii: np.ndarray | torch.Tensor | wp.array,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) None#

Set torsional patch radii for prims in the view.

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

Parameters:
  • radii – Radius of the contact patch used to apply torsional friction. Allowed range [0, max_float]. Shape is (M,).

  • indices – Indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated 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_visibilities(
visibilities: np.ndarray | torch.Tensor | wp.array,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) None#

Set the visibilities of the prims in stage.

Parameters:
  • visibilities – flag to set the visibilities of the usd prims in stage. Shape (M,). Where M <= size of the encapsulated prims in the view.

  • indices – indices to specify which prims to manipulate. Shape (M,).

Example:

>>> # make all prims not visible in the stage
>>> prims.set_visibilities(visibilities=[False] * num_envs)
set_world_poses(
positions: np.ndarray | torch.Tensor | wp.array | None = None,
orientations: np.ndarray | torch.Tensor | wp.array | None = None,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
usd: bool = True,
) None#

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 – positions in the world frame of the prims. shape is (M, 3).

  • orientations – quaternion orientations in the world frame of the prims. quaternion is scalar-first (w, x, y, z). shape is (M, 4).

  • indices – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

  • usd – True to query from usd. Otherwise False to query from Fabric data.

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]))
property count: int#

Number of prims encapsulated in this view.

Returns:

Number of prims encapsulated in this view.

Example:

>>> prims.count
5
property geoms: list[pxr.UsdGeom.Gprim]#

USD geom objects encapsulated.

Returns:

USD geom objects encapsulated.

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>))]
property initialized: bool#

Whether the prim view is initialized.

Returns:

True if the view object was initialized (after the first call of .initialize()). False otherwise.

Example:

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

True if the prim corresponds to a non root link in an articulation.

Returns:

True if the prim corresponds to a non root link in an articulation. Otherwise False.

property name: str#

Name given to the prims view when instantiating it.

Returns:

Name given to the prims view when instantiating it.

property prim_paths: list[str]#

List of prim paths in the stage encapsulated in this view.

Returns:

List of prim paths in the stage encapsulated in this view.

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]#

List of USD Prim objects encapsulated in this view.

Returns:

List of USD Prim objects encapsulated in this view.

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>)]
class ParticleSystem(
prim_paths_expr: str,
name: str = 'particle_system_view',
particle_systems_enabled: ndarray | Tensor | None = None,
simulation_owners: Sequence[str] | None = None,
contact_offsets: ndarray | Tensor | None = None,
rest_offsets: ndarray | Tensor | None = None,
particle_contact_offsets: ndarray | Tensor | None = None,
solid_rest_offsets: ndarray | Tensor | None = None,
fluid_rest_offsets: ndarray | Tensor | None = None,
enable_ccds: ndarray | Tensor | None = None,
solver_position_iteration_counts: ndarray | Tensor | None = None,
max_depenetration_velocities: ndarray | Tensor | None = None,
winds: ndarray | Tensor | None = None,
max_neighborhoods: int | None = None,
max_velocities: ndarray | Tensor | None = None,
global_self_collisions_enabled: ndarray | Tensor | None = None,
)#

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.

Parameters:
  • prim_paths_expr – Prim paths regex to encapsulate all prims that match it.

  • name – Shortname to be used as a key by Scene class.

  • particle_systems_enabled – Whether to enable or disable the particle system.

  • simulation_owners – Single PhysicsScene that simulates this particle system.

  • contact_offsets – Contact offset used for collisions with non-particle objects such as rigid or deformable bodies.

  • rest_offsets – Rest offset used for collisions with non-particle objects such as rigid or deformable bodies.

  • particle_contact_offsets – Contact offset used for interactions between particles. Must be larger than solid and fluid rest offsets.

  • solid_rest_offsets – Rest offset used for solid-solid or solid-fluid particle interactions. Must be smaller than particle contact offset.

  • fluid_rest_offsets – Rest offset used for fluid-fluid particle interactions. Must be smaller than particle contact offset.

  • enable_ccds – Enable continuous collision detection for particles to help avoid tunneling effects.

  • solver_position_iteration_counts – Number of solver iterations for position.

  • max_depenetration_velocities – The maximum velocity permitted to be introduced by the solver to depenetrate intersecting particles.

  • winds – The wind applied to the current particle system.

  • max_neighborhoods – The particle neighborhood size.

  • max_velocities – Maximum particle velocity.

  • global_self_collisions_enabled – If True, self collisions follow particle-object-specific settings. If False, all particle self collisions are disabled, regardless of any other settings. Improves performance if self collisions are not needed.

apply_particle_materials(
particle_materials: 'ParticleMaterial' | list['ParticleMaterial'],
indices: np.ndarray | list | torch.Tensor | None = None,
) None#

Used to apply particle material to prims in the view.

Parameters:
  • particle_materials – 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 – Indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view.

Raises:

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

get_applied_particle_materials(
indices: np.ndarray | list | torch.Tensor | None = None,
) list[ParticleMaterial]#

Gets the applied particle material to prims in the view.

Parameters:

indices – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

Returns:

The current applied particle materials for prims in the view.

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

The contact offset used for collisions with non-particle objects for each particle system.

Parameters:

indices – Indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

Returns:

The contact offset used for collisions with non-particle objects for each particle system. shape is (M, ).

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

Whether continuous collision detection for particles is enabled or disabled for each particle system.

Parameters:

indices – Indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

Returns:

Whether continuous collision detection for particles is enabled or disabled for each particle system. shape is (M, ).

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

The rest offset used for fluid-fluid particle interactions.

Parameters:
  • indices – 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 – True to return a clone of the internal buffer. Otherwise False.

Returns:

The rest offset used for fluid-fluid particle interactions. shape is (M, ).

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

Whether self collisions to follow particle-object-specific settings is enabled or disabled for each particle system.

Parameters:

indices – Indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

Returns:

Whether self collisions to follow particle-object-specific settings is enabled or disabled. for each particle system. shape is (M, ).

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

The maximum velocity permitted to be introduced by the solver to depenetrate intersecting particles for particle systems for each particle system.

Parameters:

indices – Indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

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, ).

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

The particle neighborhood size for each particle system.

Parameters:

indices – Indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

Returns:

The particle neighborhood size for each particle system. shape is (M, ).

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

The maximum particle velocities for each particle system.

Parameters:

indices – 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 maximum particle velocities for each particle system. shape is (M, ).

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

The contact offset used for interactions between particles in the view concatenated.

Parameters:
  • indices – 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 – True to return a clone of the internal buffer. Otherwise False.

Returns:

The contact offset used for interactions between particles in the view concatenated. shape is (M, ).

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

Whether particle system is enabled or not for each particle system.

Parameters:

indices – Indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

Returns:

Whether particle system is enabled or not for each particle system. shape is (M, ).

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

The rest offset used for collisions with non-particle objects for each particle system.

Parameters:

indices – Indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

Returns:

The rest offset used for collisions with non-particle objects for each particle system. shape is (M, ).

get_simulation_owners(
indices: ndarray | list | Tensor | None = None,
) Sequence[str]#

The physics scene prim path attached to particle system.

Parameters:

indices – Indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

Returns:

The physics scene prim path attached to particle system. shape is (M, ).

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

The rest offset used for solid-solid or solid-fluid particle interactions.

Parameters:
  • indices – 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 – True to return a clone of the internal buffer. Otherwise False.

Returns:

The rest offset used for solid-solid or solid-fluid particle interactions. shape is (M, ).

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

The number of solver iterations for positions for each particle system.

Parameters:

indices – Indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

Returns:

The number of solver iterations for positions for each particle system. shape is (M, ).

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

The winds applied to the current particle system.

Parameters:
  • indices – 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 – True to return a clone of the internal buffer. Otherwise False.

Returns:

The winds applied to the current particle system. shape is (M, 3).

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

Create a physics simulation view if not passed and creates a Particle System View.

Parameters:

physics_sim_view – Current physics simulation view.

is_physics_handle_valid() bool#

Checks whether the physics handle of the view is valid.

Returns:

True if the physics handle of the view is valid (i.e physics is initialized for the view). Otherwise False.

is_valid(
indices: ndarray | list | Tensor | None = None,
) bool#

Checks whether all prim paths in the view correspond to valid prims in the stage.

Parameters:

indices – Indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

Returns:

True if all prim paths specified in the view correspond to a valid prim in stage. False otherwise.

post_reset() None#

Resets the particles to their initial states.

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 – contact offset tensor to set particle systems to. shape is (M, ).

  • indices – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated 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 – Whether to enable continuous collision detection tensor to set particle systems to. shape is (M, ).

  • indices – 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 – fluid rest offset to set particle systems to. shape is (M, ).

  • indices – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated 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 – Whether to enable global self collisions tensor to set particle systems to. shape is (M, ).

  • indices – 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 – maximum particle velocity tensor to set particle systems to. shape is (M, ).

  • indices – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated 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 – Particle neighborhood size tensor to set particle systems to. shape is (M, ).

  • indices – 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 – maximum particle velocity tensor to set particle systems to. shape is (M, ).

  • indices – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view.

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 – The contact offset.

  • indices – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated 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 – Whether to enable particle system tensor to set particle systems to. shape is (M, ).

  • indices – 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 – rest offset tensor to set particle systems to. shape is (M, ).

  • indices – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated 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 – PhysicsScene list to set particle systems to. shape is (M, ).

  • indices – 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 – solid rest offset to set particle systems to. shape is (M, ).

  • indices – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated 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 – solver position iteration count tensor to set particle systems to. shape is (M, ).

  • indices – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated 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 – The wind applied to the current particle system. shape is (M, 3).

  • indices – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view.

property count: int#

Number of particle systems in the view.

Returns:

Number of particle systems for the prims in the view.

property name: str#

Name given to the view when instantiating it.

Returns:

Name given to the view when instantiating it.

class RigidPrim(
prim_paths_expr: str | list[str],
name: str = 'rigid_prim_view',
positions: np.ndarray | torch.Tensor | wp.array | None = None,
translations: np.ndarray | torch.Tensor | wp.array | None = None,
orientations: np.ndarray | torch.Tensor | wp.array | None = None,
scales: np.ndarray | torch.Tensor | wp.array | None = None,
visibilities: np.ndarray | torch.Tensor | wp.array | None = None,
reset_xform_properties: bool = True,
masses: np.ndarray | torch.Tensor | wp.array | None = None,
densities: np.ndarray | torch.Tensor | wp.array | None = None,
linear_velocities: np.ndarray | torch.Tensor | wp.array | None = None,
angular_velocities: np.ndarray | torch.Tensor | wp.array | None = None,
track_contact_forces: bool = False,
prepare_contact_sensors: bool = True,
disable_stablization: bool = True,
contact_filter_prim_paths_expr: list[str] | None = None,
max_contact_count: int = 0,
)#

Bases: XFormPrim

Provide high-level functions for prims that have Rigid Body API applied to them.

Handle attributes and properties of single or multiple rigid body prims.

Wrap 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 – 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 – shortname to be used as a key by Scene class. Note: needs to be unique if the object is added to the Scene.

  • positions – default positions in the world frame of the prims. shape is (N, 3).

  • translations – default translations in the local frame of the prims (with respect to its parent prims). shape is (N, 3).

  • orientations – 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 – local scales to be applied to the prim’s dimensions in the view. shape is (N, 3).

  • visibilities – set to false for an invisible prim in the stage while rendering. shape is (N,).

  • reset_xform_properties – 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.

  • masses – mass in kg specified for each prim in the view. shape is (N,).

  • densities – density in kg/m^3 specified for each prim in the view. shape is (N,).

  • linear_velocities – default linear velocity of each prim in the view (to be applied in the first frame and on resets). Shape is (N, 3).

  • angular_velocities – default angular velocity of each prim in the view (to be applied in the first frame and on resets). Shape is (N, 3).

  • track_contact_forces – if enabled, the view will track the net contact forces on each rigid prim in the view

  • prepare_contact_sensors – 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 – disables the contact stabilization parameter in the physics context

  • contact_filter_prim_paths_expr – a list of filter expressions which allows for tracking contact forces between prims and this subset through get_contact_force_matrix().

  • max_contact_count – 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>
apply_forces(
forces: np.ndarray | torch.Tensor | wp.array | None,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
is_global: bool = True,
) None#

Apply forces to prims in the view.

Parameters:
  • forces – forces to be applied to the prims.

  • indices – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view.

  • is_global – True if forces are in the global frame. Otherwise False.

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]))
apply_forces_and_torques_at_pos(
forces: np.ndarray | torch.Tensor | wp.array | None = None,
torques: np.ndarray | torch.Tensor | wp.array | None = None,
positions: np.ndarray | torch.Tensor | wp.array | None = None,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
is_global: bool = True,
) None#

Apply 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 – forces to be applied to the prims. If not specified, no force will be applied.

  • torques – torques to be applied to the prims. If not specified, no torque will be applied.

  • positions – position of the forces with respect to the body frame. If not specified, the forces are applied at the origin of the body frame.

  • indices – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view.

  • is_global – True if forces, torques, and positions are in the global frame. False if forces, torques, and positions are in the local frame.

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]))
apply_visual_materials(
visual_materials: 'VisualMaterial' | list['VisualMaterial'],
weaker_than_descendants: bool | list[bool] | None = None,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) None#

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

Parameters:
  • visual_materials – 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 – True if the material shouldn’t override the descendants materials, otherwise 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 – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated 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)
destroy() None#

Clean up and invalidate the prim view by deregistering callbacks and clearing internal state.

disable_gravities(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) None#

Disable gravity on rigid bodies (enabled by default).

Parameters:

indices – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated 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]))
disable_rigid_body_physics(
indices: np.ndarray | list | torch.Tensor | wp.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 – 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: np.ndarray | list | torch.Tensor | wp.array | None = None,
) None#

Enable gravity on rigid bodies (enabled by default).

Parameters:

indices – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated 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]))
enable_rigid_body_physics(
indices: np.ndarray | list | torch.Tensor | wp.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 – 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]))
get_angular_velocities(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
clone: bool = True,
) np.ndarray | torch.Tensor | wp.indexedarray#

Get the angular velocities of prims in the view.

Parameters:
  • indices – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

  • clone – True to return a clone of the internal buffer. Otherwise False.

Returns:

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

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.]]
get_applied_visual_materials(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) list['VisualMaterial']#

Get the current applied visual materials.

Parameters:

indices – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

Returns:

a list of the current applied visual materials to the prims if its type is currently supported.

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_coms(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
clone: bool = True,
) np.ndarray | torch.Tensor | wp.indexedarray | None#

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

Parameters:
  • indices – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

  • clone – True to return a clone of the internal buffer. Otherwise False.

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).

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_contact_force_data(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
clone: bool = True,
dt: float = 1.0,
) tuple[np.ndarray | torch.Tensor | wp.indexedarray, np.ndarray | torch.Tensor | wp.indexedarray, np.ndarray | torch.Tensor | wp.indexedarray, np.ndarray | torch.Tensor | wp.indexedarray, np.ndarray | torch.Tensor | wp.indexedarray, 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 (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 – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

  • clone – True to return a clone of the internal buffer. Otherwise False.

  • dt – 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:

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]]
get_contact_force_matrix(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
clone: bool = True,
dt: float = 1.0,
) np.ndarray | torch.Tensor | wp.indexedarray | None#

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 – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

  • clone – True to return a clone of the internal buffer. Otherwise False.

  • dt – 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).

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_current_dynamic_state() DynamicsViewState#

Current rigid body states (position, orientation and linear and angular velocities).

Returns:

The dynamic state of the rigid bodies

Example:

>>> # for the example the rigid bodies are in free fall
>>> state = prims.get_current_dynamic_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.]]
get_default_state() DynamicsViewState#

Default state (position, orientation and linear and angular velocities) of prims in the view,.

that is used after each reset.

Returns:

The default state of the prims that is used after each reset.

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_densities(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) np.ndarray | torch.Tensor | wp.indexedarray#

Get densities of prims in the view.

Parameters:

indices – 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,).

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.]
get_friction_data(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
clone: bool = True,
dt: float = 1.0,
) tuple[np.ndarray | torch.Tensor | wp.indexedarray, np.ndarray | torch.Tensor | wp.indexedarray, np.ndarray | torch.Tensor | wp.indexedarray, np.ndarray | torch.Tensor | wp.indexedarray]#

Get 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 – indicies to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

  • clone – True to return a clone of the internal buffer. Otherwise False.

  • dt – 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:

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_inertias(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
clone: bool = True,
) np.ndarray | torch.Tensor | wp.indexedarray | None#

Get rigid body inertias of prims in the view.

Parameters:
  • indices – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

  • clone – True to return a clone of the internal buffer. Otherwise False.

Returns:

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

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_inv_inertias(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
clone: bool = True,
) np.ndarray | torch.Tensor | wp.indexedarray | None#

Get rigid body inverse inertias of prims in the view.

Parameters:
  • indices – 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 – True to return a clone of the internal buffer. Otherwise False.

Returns:

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

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]]
get_inv_masses(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
clone: bool = True,
) np.ndarray | torch.Tensor | wp.indexedarray | None#

Get rigid body inverse masses of prims in the view.

Parameters:
  • indices – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

  • clone – True to return a clone of the internal buffer. Otherwise False.

Returns:

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

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_linear_velocities(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
clone: bool = True,
) np.ndarray | torch.Tensor | wp.indexedarray#

Get the linear velocities of prims in the view.

Parameters:
  • indices – 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 – True to return a clone of the internal buffer. Otherwise False.

Returns:

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

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.]]
get_local_poses(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) tuple[np.ndarray, np.ndarray] | tuple[torch.Tensor, torch.Tensor] | tuple[wp.indexedarray, wp.indexedarray]#

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

Parameters:

indices – 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).

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]]
get_local_scales(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) np.ndarray | torch.Tensor | wp.indexedarray#

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

Parameters:

indices – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

Returns:

scales applied to the prim’s dimensions in the local frame. shape is (M, 3).

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_masses(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
clone: bool = True,
) np.ndarray | torch.Tensor | wp.indexedarray#

Get rigid body masses of prims in the view.

Parameters:
  • indices – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

  • clone – True to return a clone of the internal buffer. Otherwise False.

Returns:

masses of in kg of prims in the view. shape is (M,).

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_net_contact_forces(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
clone: bool = True,
dt: float = 1.0,
) np.ndarray | torch.Tensor | wp.indexedarray | None#

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 – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

  • clone – True to return a clone of the internal buffer. Otherwise False.

  • dt – 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).

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]]
get_sleep_thresholds(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) np.ndarray | torch.Tensor | wp.indexedarray#

Get sleep thresholds of prims in the view.

Search for Rigid Body Dynamics > Sleeping in PhysX docs for more details

Parameters:

indices – 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,).

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]
get_velocities(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
clone: bool = True,
) np.ndarray | torch.Tensor | wp.indexedarray#

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

Parameters:
  • indices – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

  • clone – True to return a clone of the internal buffer. Otherwise False.

Returns:

linear and angular velocities of the prims in the view concatenated. shape is (M, 6).

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.]]
get_visibilities(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) np.ndarray | torch.Tensor | wp.indexedarray#

Return the current visibilities of the prims in stage.

Parameters:

indices – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

Returns:

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

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_world_poses(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
clone: bool = True,
usd: bool = True,
) tuple[np.ndarray, np.ndarray] | tuple[torch.Tensor, torch.Tensor] | tuple[wp.indexedarray, wp.indexedarray]#

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

Parameters:
  • indices – 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 – True to return a clone of the internal buffer. Otherwise False.

  • usd – True to query from usd. Otherwise False to query from Fabric data.

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).

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_world_scales(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) np.ndarray | torch.Tensor | wp.indexedarray#

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

Parameters:

indices – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

Returns:

scales applied to the prim’s dimensions in the world frame. shape is (M, 3).

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.]]
initialize(
physics_sim_view: omni.physics.tensors.SimulationView = 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 – current physics simulation view.

Example:

>>> prims.initialize()
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
is_valid(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) bool#

Check that all prims have a valid USD Prim.

Parameters:

indices – Indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. If None, all prims in the view are queried.

Returns:

True if all prim paths specified in the view correspond to a valid prim in stage. False otherwise.

Example:

>>> prims.is_valid()
True
is_visual_material_applied(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) list[bool]#

Check if there is a visual material applied.

Parameters:

indices – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

Returns:

True if there is a visual material applied is applied to the corresponding prim in the view. False otherwise.

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]
post_reset() None#

Reset the prims to its default state.

Example:

>>> prims.post_reset()
set_angular_velocities(
velocities: np.ndarray | torch.Tensor | wp.array | None,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) None#

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 – angular velocities to set the rigid prims to. shape is (M, 3).

  • indices – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated 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]))
set_coms(
positions: np.ndarray | torch.Tensor | wp.array = None,
orientations: np.ndarray | torch.Tensor | wp.array = None,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) None#

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

Parameters:
  • positions – body center of mass positions for bodies in the view. shape (M, 1, 3).

  • orientations – body center of mass orientations for bodies in the view. shape (M, 1, 4).

  • indices – 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_default_state(
positions: np.ndarray | torch.Tensor | wp.array | None = None,
orientations: np.ndarray | torch.Tensor | wp.array | None = None,
linear_velocities: np.ndarray | torch.Tensor | wp.array | None = None,
angular_velocities: np.ndarray | torch.Tensor | wp.array | None = None,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) None#

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 – default positions in the world frame of the prim. shape is (M, 3).

  • orientations – default quaternion orientations in the world frame of the prims. quaternion is scalar-first (w, x, y, z). shape is (M, 4).

  • linear_velocities – default linear velocities of each prim in the view (to be applied in the first frame and on resets). Shape is (M, 3).

  • angular_velocities – default angular velocities of each prim in the view (to be applied in the first frame and on resets). Shape is (M, 3).

  • indices – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated 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()
set_densities(
densities: np.ndarray | torch.Tensor | wp.array | None,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) None#

Set densities of prims in the view.

Parameters:
  • densities – density in kg/m^3 specified for each prim in the view. shape is (M,). Defaults to None.

  • indices – 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]))
set_inertias(
values: np.ndarray | torch.Tensor | wp.array,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) None#

Set rigid body inertias for prims in the view.

Parameters:
  • values – body inertias for prims in the view. shape (M, 1, 9).

  • indices – 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_linear_velocities(
velocities: np.ndarray | torch.Tensor | wp.array | None,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) None#

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 – linear velocities to set the rigid prims to. shape is (M, 3).

  • indices – 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]))
set_local_poses(
translations: np.ndarray | torch.Tensor | wp.array | None = None,
orientations: np.ndarray | torch.Tensor | wp.array | None = None,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) None#

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

Parameters:
  • translations – 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 – 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 – 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_local_scales(
scales: np.ndarray | torch.Tensor | wp.array | None,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) None#

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

Parameters:
  • scales – scales to be applied to the prim’s dimensions in the view. shape is (M, 3).

  • indices – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated 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_masses(
masses: np.ndarray | torch.Tensor | wp.array,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) None#

Set body masses for prims in the view.

Parameters:
  • masses – body masses for prims in kg. shape (M,).

  • indices – 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_sleep_thresholds(
thresholds: np.ndarray | torch.Tensor | wp.array | None,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) None#

Set sleep thresholds of prims in the view.

Search for Rigid Body Dynamics > Sleeping in PhysX docs for more details

Parameters:
  • thresholds – 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 – 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]))
set_velocities(
velocities: np.ndarray | torch.Tensor | wp.array,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) None#

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 – linear and angular velocities respectively to set the rigid prims to. shape is (M, 6).

  • indices – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated 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]))
set_visibilities(
visibilities: np.ndarray | torch.Tensor | wp.array,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) None#

Set the visibilities of the prims in stage.

Parameters:
  • visibilities – flag to set the visibilities of the usd prims in stage. Shape (M,). Where M <= size of the encapsulated prims in the view.

  • indices – indices to specify which prims to manipulate. Shape (M,).

Example:

>>> # make all prims not visible in the stage
>>> prims.set_visibilities(visibilities=[False] * num_envs)
set_world_poses(
positions: np.ndarray | torch.Tensor | wp.array | None = None,
orientations: np.ndarray | torch.Tensor | wp.array | None = None,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
usd: bool = True,
) None#

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 – positions in the world frame of the prim. shape is (M, 3). Defaults to None, which means left unchanged.

  • orientations – 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 – 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 – True to query from usd. Otherwise False to query from Fabric data.

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]))
property count: int#

Number of prims encapsulated in this view.

Returns:

Number of prims encapsulated in this view.

Example:

>>> prims.count
5
property initialized: bool#

Whether the prim view is initialized.

Returns:

True if the view object was initialized (after the first call of .initialize()). False otherwise.

Example:

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

True if the prim corresponds to a non root link in an articulation.

Returns:

True if the prim corresponds to a non root link in an articulation. Otherwise False.

property name: str#

Name given to the prims view when instantiating it.

Returns:

Name given to the prims view when instantiating it.

property num_shapes: int#

Number of rigid shapes for the prims in the view.

Returns:

Number of rigid shapes for the prims in the view.

Example:

>>> prims.num_shapes
1
property prim_paths: list[str]#

List of prim paths in the stage encapsulated in this view.

Returns:

List of prim paths in the stage encapsulated in this view.

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]#

List of USD Prim objects encapsulated in this view.

Returns:

List of USD Prim objects encapsulated in this view.

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>)]
class SdfShapePrim(
prim_paths_expr: str,
num_query_points: int,
prepare_sdf_schemas: bool = True,
name: str = 'sdf_shape_view',
positions: np.ndarray | torch.Tensor | wp.array | None = None,
translations: np.ndarray | torch.Tensor | wp.array | None = None,
orientations: np.ndarray | torch.Tensor | wp.array | None = None,
scales: np.ndarray | torch.Tensor | wp.array | None = None,
visibilities: np.ndarray | torch.Tensor | wp.array | None = None,
reset_xform_properties: bool = True,
collisions: np.ndarray | torch.Tensor | wp.array | None = None,
track_contact_forces: bool = False,
prepare_contact_sensors: bool = False,
disable_stablization: bool = True,
contact_filter_prim_paths_expr: list[str] | None = None,
)#

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 – 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 – Number of points queried by this view object.

  • prepare_sdf_schemas – Apply PhysxSDFMeshCollisionAPI to prims in prim_paths_expr.

  • name – Shortname to be used as a key by Scene class. Note: needs to be unique if the object is added to the Scene.

  • positions – Default positions in the world frame of the prim. Shape is (N, 3).

  • translations – Default translations in the local frame of the prims (with respect to its parent prims). Shape is (N, 3).

  • orientations – 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).

  • scales – Local scales to be applied to the prim’s dimensions. Shape is (N, 3).

  • visibilities – Set to false for an invisible prim in the stage while rendering. Shape is (N,).

  • reset_xform_properties – 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.

  • collisions – Set to True if the geometry already have/ should have a collider (i.e not only a visual geometry). Shape is (N,).

  • track_contact_forces – 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.

  • prepare_contact_sensors – Applies contact reporter API to the prim if it already does not have one.

  • disable_stablization – Disables the contact stabilization parameter in the physics context.

  • contact_filter_prim_paths_expr – A list of filter expressions which allows for tracking contact forces between the geometry prim and this subset through get_contact_force_matrix().

apply_collision_apis(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) None#

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

Parameters:

indices – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated 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]))
apply_physics_materials(
physics_materials: PhysicsMaterial | list[PhysicsMaterial],
weaker_than_descendants: bool | list[bool] | None = None,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) None#

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

Parameters:
  • physics_materials – 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 – True if the material shouldn’t override the descendants materials, otherwise 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 – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated 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_materials(
visual_materials: 'VisualMaterial' | list['VisualMaterial'],
weaker_than_descendants: bool | list[bool] | None = None,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) None#

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

Parameters:
  • visual_materials – 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 – True if the material shouldn’t override the descendants materials, otherwise 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 – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated 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)
destroy() None#

Clean up and invalidate the prim view by deregistering callbacks and clearing internal state.

disable_collision(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) None#

Disables collision on prims in the view.

Parameters:

indices – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated 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: np.ndarray | list | torch.Tensor | wp.array | None = None,
) None#

Enables collision on prims in the view.

Parameters:

indices – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated 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]))
get_applied_physics_materials(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) list[PhysicsMaterial]#

Get the applied physics material to prims in the view.

Parameters:

indices – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

Returns:

the current applied physics materials for prims in the view.

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: np.ndarray | list | torch.Tensor | wp.array | None = None,
) list['VisualMaterial']#

Get the current applied visual materials.

Parameters:

indices – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

Returns:

a list of the current applied visual materials to the prims if its type is currently supported.

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: np.ndarray | list | torch.Tensor | wp.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 – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

Returns:

approximations used for collision. size == M or size of the view.

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_contact_force_data(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
clone: bool = True,
dt: float = 1.0,
) tuple[np.ndarray | torch.Tensor | wp.indexedarray, np.ndarray | torch.Tensor | wp.indexedarray, np.ndarray | torch.Tensor | wp.indexedarray, np.ndarray | torch.Tensor | wp.indexedarray, np.ndarray | torch.Tensor | wp.indexedarray, np.ndarray | torch.Tensor | wp.indexedarray] | 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 – 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 – True to return a clone of the internal buffer. Otherwise False.

  • dt – 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:

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.

get_contact_force_matrix(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
clone: bool = True,
dt: float = 1.0,
) np.ndarray | torch.Tensor | wp.indexedarray | None#

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 – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

  • clone – True to return a clone of the internal buffer. Otherwise False.

  • dt – 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). None if no contact filter is specified.

get_contact_offsets(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) 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 – Indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

Returns:

Contact offsets of the collision shapes. Shape is (M,).

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.

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_friction_data(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
clone: bool = True,
dt: float = 1.0,
) tuple[np.ndarray | torch.Tensor | wp.indexedarray, np.ndarray | torch.Tensor | wp.indexedarray, np.ndarray | torch.Tensor | wp.indexedarray, np.ndarray | torch.Tensor | wp.indexedarray] | None#

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 – 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 – True to return a clone of the internal buffer. Otherwise False.

  • dt – 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:

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_local_poses(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) tuple[np.ndarray, np.ndarray] | tuple[torch.Tensor, torch.Tensor] | tuple[wp.indexedarray, wp.indexedarray]#

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

Parameters:

indices – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated 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).

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_local_scales(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) np.ndarray | torch.Tensor | wp.indexedarray#

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

Parameters:

indices – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

Returns:

scales applied to the prim’s dimensions in the local frame. shape is (M, 3).

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 – Indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

Returns:

Minimum radius of the contact patch used to apply torsional friction. Shape is (M,).

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.]
get_net_contact_forces(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
clone: bool = True,
dt: float = 1.0,
) np.ndarray | torch.Tensor | wp.indexedarray | None#

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 – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

  • clone – True to return a clone of the internal buffer. Otherwise False.

  • dt – 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). None if contact tracking is not enabled.

get_rest_offsets(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) 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 – Indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

Returns:

Rest offsets of the collision shapes. Shape is (M,).

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_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 – 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 – Indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

  • clone – True to return a clone of the internal buffer. Otherwise False.

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

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

Gets sdf margin values.

Parameters:
  • indices – Indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

  • clone – True to return a clone of the internal buffer. Otherwise False.

Returns:

Margins of the sdf collision apis for prims in the view. shape is (M,).

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 – Indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

  • clone – True to return a clone of the internal buffer. Otherwise False.

Returns:

Narrow band thickness of the sdf collision apis for prims in the view. shape is (M,).

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

Gets sdf collision resolution values.

Parameters:
  • indices – Indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

  • clone – True to return a clone of the internal buffer. Otherwise False.

Returns:

Resolutions of the sdf collision apis for prims in the view. shape is (M,).

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

Gets sdf collision subgrid resolution values.

Parameters:
  • indices – Indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

  • clone – True to return a clone of the internal buffer. Otherwise False.

Returns:

Subgrid resolutions of the sdf collision apis for prims in the view. shape is (M,).

get_torsional_patch_radii(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) np.ndarray | torch.Tensor | wp.indexedarray#

Get torsional patch radii for prims in the view.

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

Parameters:

indices – Indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

Returns:

Radius of the contact patch used to apply torsional friction. Shape is (M,).

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.]
get_visibilities(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) np.ndarray | torch.Tensor | wp.indexedarray#

Return the current visibilities of the prims in stage.

Parameters:

indices – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

Returns:

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

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_world_poses(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
usd: bool = True,
) tuple[np.ndarray, np.ndarray] | tuple[torch.Tensor, torch.Tensor] | tuple[wp.indexedarray, wp.indexedarray]#

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

Parameters:
  • indices – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

  • usd – True to query from usd. Otherwise False to query from Fabric data.

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).

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_world_scales(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) np.ndarray | torch.Tensor | wp.indexedarray#

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

Parameters:

indices – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

Returns:

scales applied to the prim’s dimensions in the world frame. shape is (M, 3).

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

Create a physics simulation view if not passed and creates a sdf shape view in physX.

Parameters:

physics_sim_view – Current physics simulation view.

is_collision_enabled(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) np.ndarray | torch.Tensor | wp.indexedarray#

Queries if collision is enabled on prims in the view.

Parameters:

indices – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

Returns:

True if collision is enabled. Shape is (M,).

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]
is_physics_handle_valid() bool#

Whether the physics handle of the view is valid.

Returns:

True if the physics handle of the view is valid (i.e physics is initialized for the view). Otherwise False.

is_valid(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) bool#

Check that all prims have a valid USD Prim.

Parameters:

indices – Indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. If None, all prims in the view are queried.

Returns:

True if all prim paths specified in the view correspond to a valid prim in stage. False otherwise.

Example:

>>> prims.is_valid()
True
is_visual_material_applied(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) list[bool]#

Check if there is a visual material applied.

Parameters:

indices – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

Returns:

True if there is a visual material applied is applied to the corresponding prim in the view. False otherwise.

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]
post_reset() None#

Reset the prims to its default state.

Example:

>>> prims.post_reset()
set_collision_approximations(
approximation_types: list[str],
indices: np.ndarray | list | torch.Tensor | wp.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 – approximations used for collision. List size == M or the size of the view.

  • indices – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated 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(
offsets: np.ndarray | torch.Tensor | wp.array,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) None#

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 – 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 – Indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated 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_default_state(
positions: np.ndarray | torch.Tensor | wp.array | None = None,
orientations: np.ndarray | torch.Tensor | wp.array | None = None,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) None#

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 – positions in the world frame of the prim. shape is (M, 3).

  • orientations – quaternion orientations in the world frame of the prim. quaternion is scalar-first (w, x, y, z). shape is (M, 4).

  • indices – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated 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_local_poses(
translations: np.ndarray | torch.Tensor | wp.array | None = None,
orientations: np.ndarray | torch.Tensor | wp.array | None = None,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) None#

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 – translations in the local frame of the prims (with respect to its parent prim). shape is (M, 3).

  • orientations – quaternion orientations in the local frame of the prims. quaternion is scalar-first (w, x, y, z). shape is (M, 4).

  • indices – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated 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_local_scales(
scales: np.ndarray | torch.Tensor | wp.array | None,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) None#

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

Parameters:
  • scales – scales to be applied to the prim’s dimensions in the view. shape is (M, 3).

  • indices – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated 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_min_torsional_patch_radii(
radii: np.ndarray | torch.Tensor | wp.array,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) None#

Set minimum torsional patch radii for prims in the view.

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

Parameters:
  • radii – Minimum radius of the contact patch used to apply torsional friction. Allowed range [0, max_float]. Shape is (M, ).

  • indices – Indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated 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(
offsets: np.ndarray | torch.Tensor | wp.array,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) None#

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 – 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 – Indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated 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_sdf_margins(
values: ndarray | Tensor,
indices: ndarray | list | Tensor | None = None,
) None#

Sets signed distance field margins for prims in the view.

Parameters:
  • values – Sdf margins to be set. shape (M,).

  • indices – Indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated 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 – Sdf narrow band thicknesses to be set. shape (M,).

  • indices – Indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view.

set_sdf_resolution(
values: ndarray | Tensor,
indices: ndarray | list | Tensor | None = None,
) None#

Sets signed distance field resolutions for prims in the view.

Parameters:
  • values – Sdf resolutions to be set. shape (M,).

  • indices – Indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated 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 – Sdf subgrid resolutions to be set. shape (M,).

  • indices – Indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated prims in the view.

set_torsional_patch_radii(
radii: np.ndarray | torch.Tensor | wp.array,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) None#

Set torsional patch radii for prims in the view.

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

Parameters:
  • radii – Radius of the contact patch used to apply torsional friction. Allowed range [0, max_float]. Shape is (M,).

  • indices – Indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated 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_visibilities(
visibilities: np.ndarray | torch.Tensor | wp.array,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) None#

Set the visibilities of the prims in stage.

Parameters:
  • visibilities – flag to set the visibilities of the usd prims in stage. Shape (M,). Where M <= size of the encapsulated prims in the view.

  • indices – indices to specify which prims to manipulate. Shape (M,).

Example:

>>> # make all prims not visible in the stage
>>> prims.set_visibilities(visibilities=[False] * num_envs)
set_world_poses(
positions: np.ndarray | torch.Tensor | wp.array | None = None,
orientations: np.ndarray | torch.Tensor | wp.array | None = None,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
usd: bool = True,
) None#

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 – positions in the world frame of the prims. shape is (M, 3).

  • orientations – quaternion orientations in the world frame of the prims. quaternion is scalar-first (w, x, y, z). shape is (M, 4).

  • indices – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

  • usd – True to query from usd. Otherwise False to query from Fabric data.

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]))
property count: int#

Number of prims encapsulated in this view.

Returns:

Number of prims encapsulated in this view.

Example:

>>> prims.count
5
property geoms: list[pxr.UsdGeom.Gprim]#

USD geom objects encapsulated.

Returns:

USD geom objects encapsulated.

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>))]
property initialized: bool#

Whether the prim view is initialized.

Returns:

True if the view object was initialized (after the first call of .initialize()). False otherwise.

Example:

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

True if the prim corresponds to a non root link in an articulation.

Returns:

True if the prim corresponds to a non root link in an articulation. Otherwise False.

property name: str#

Name given to the prims view when instantiating it.

Returns:

Name given to the prims view when instantiating it.

property num_query_points: int#

Number of points queried by this view object.

Returns:

Number of points queried by this view object.

property prim_paths: list[str]#

List of prim paths in the stage encapsulated in this view.

Returns:

List of prim paths in the stage encapsulated in this view.

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]#

List of USD Prim objects encapsulated in this view.

Returns:

List of USD Prim objects encapsulated in this view.

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>)]
class XFormPrim(
prim_paths_expr: str | list[str],
name: str = 'xform_prim_view',
positions: ndarray | Tensor | None = None,
translations: ndarray | Tensor | None = None,
orientations: ndarray | Tensor | None = None,
scales: ndarray | Tensor | None = None,
visibilities: ndarray | Tensor | None = None,
reset_xform_properties: bool = True,
usd: bool = True,
)#

Bases: Prim

Provide high-level functions for working with Xform prim views and their descendants.

Handle attributes and properties of single or multiple Xform prims.

Wrap 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 – 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 – shortname to be used as a key by Scene class. Note: needs to be unique if the object is added to the Scene.

  • positions – default positions in the world frame of the prim. shape is (N, 3).

  • translations – default translations in the local frame of the prims (with respect to its parent prims). shape is (N, 3).

  • orientations – 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).

  • scales – local scales to be applied to the prim’s dimensions. shape is (N, 3).

  • visibilities – set to false for an invisible prim in the stage while rendering. shape is (N,).

  • reset_xform_properties – 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.

  • usd – True to strictly read/ write from usd. Otherwise False to allow read/ write from Fabric during initialization.

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>
apply_visual_materials(
visual_materials: 'VisualMaterial' | list['VisualMaterial'],
weaker_than_descendants: bool | list[bool] | None = None,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) None#

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

Parameters:
  • visual_materials – 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 – True if the material shouldn’t override the descendants materials, otherwise 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 – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated 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)
destroy() None#

Clean up and invalidate the prim view by deregistering callbacks and clearing internal state.

get_applied_visual_materials(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) list['VisualMaterial']#

Get the current applied visual materials.

Parameters:

indices – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

Returns:

a list of the current applied visual materials to the prims if its type is currently supported.

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.

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_local_poses(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) tuple[np.ndarray, np.ndarray] | tuple[torch.Tensor, torch.Tensor] | tuple[wp.indexedarray, wp.indexedarray]#

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

Parameters:

indices – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated 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).

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_local_scales(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) np.ndarray | torch.Tensor | wp.indexedarray#

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

Parameters:

indices – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

Returns:

scales applied to the prim’s dimensions in the local frame. shape is (M, 3).

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_visibilities(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) np.ndarray | torch.Tensor | wp.indexedarray#

Return the current visibilities of the prims in stage.

Parameters:

indices – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

Returns:

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

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_world_poses(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
usd: bool = True,
) tuple[np.ndarray, np.ndarray] | tuple[torch.Tensor, torch.Tensor] | tuple[wp.indexedarray, wp.indexedarray]#

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

Parameters:
  • indices – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

  • usd – True to query from usd. Otherwise False to query from Fabric data.

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).

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_world_scales(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) np.ndarray | torch.Tensor | wp.indexedarray#

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

Parameters:

indices – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

Returns:

scales applied to the prim’s dimensions in the world frame. shape is (M, 3).

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.]]
initialize(
physics_sim_view: omni.physics.tensors.SimulationView = 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 – Current physics simulation view.

Example:

>>> prims.initialize()
is_valid(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) bool#

Check that all prims have a valid USD Prim.

Parameters:

indices – Indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view. If None, all prims in the view are queried.

Returns:

True if all prim paths specified in the view correspond to a valid prim in stage. False otherwise.

Example:

>>> prims.is_valid()
True
is_visual_material_applied(
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) list[bool]#

Check if there is a visual material applied.

Parameters:

indices – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

Returns:

True if there is a visual material applied is applied to the corresponding prim in the view. False otherwise.

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]
post_reset() None#

Reset the prims to its default state.

Example:

>>> prims.post_reset()
set_default_state(
positions: np.ndarray | torch.Tensor | wp.array | None = None,
orientations: np.ndarray | torch.Tensor | wp.array | None = None,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) None#

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 – positions in the world frame of the prim. shape is (M, 3).

  • orientations – quaternion orientations in the world frame of the prim. quaternion is scalar-first (w, x, y, z). shape is (M, 4).

  • indices – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated 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_local_poses(
translations: np.ndarray | torch.Tensor | wp.array | None = None,
orientations: np.ndarray | torch.Tensor | wp.array | None = None,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) None#

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 – translations in the local frame of the prims (with respect to its parent prim). shape is (M, 3).

  • orientations – quaternion orientations in the local frame of the prims. quaternion is scalar-first (w, x, y, z). shape is (M, 4).

  • indices – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated 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_local_scales(
scales: np.ndarray | torch.Tensor | wp.array | None,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) None#

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

Parameters:
  • scales – scales to be applied to the prim’s dimensions in the view. shape is (M, 3).

  • indices – indices to specify which prims to manipulate. Shape (M,). Where M <= size of the encapsulated 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_visibilities(
visibilities: np.ndarray | torch.Tensor | wp.array,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
) None#

Set the visibilities of the prims in stage.

Parameters:
  • visibilities – flag to set the visibilities of the usd prims in stage. Shape (M,). Where M <= size of the encapsulated prims in the view.

  • indices – indices to specify which prims to manipulate. Shape (M,).

Example:

>>> # make all prims not visible in the stage
>>> prims.set_visibilities(visibilities=[False] * num_envs)
set_world_poses(
positions: np.ndarray | torch.Tensor | wp.array | None = None,
orientations: np.ndarray | torch.Tensor | wp.array | None = None,
indices: np.ndarray | list | torch.Tensor | wp.array | None = None,
usd: bool = True,
) None#

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 – positions in the world frame of the prims. shape is (M, 3).

  • orientations – quaternion orientations in the world frame of the prims. quaternion is scalar-first (w, x, y, z). shape is (M, 4).

  • indices – indices to specify which prims to query. Shape (M,). Where M <= size of the encapsulated prims in the view.

  • usd – True to query from usd. Otherwise False to query from Fabric data.

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]))
property count: int#

Number of prims encapsulated in this view.

Returns:

Number of prims encapsulated in this view.

Example:

>>> prims.count
5
property initialized: bool#

Whether the prim view is initialized.

Returns:

True if the view object was initialized (after the first call of .initialize()). False otherwise.

Example:

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

True if the prim corresponds to a non root link in an articulation.

Returns:

True if the prim corresponds to a non root link in an articulation. Otherwise False.

property name: str#

Name given to the prims view when instantiating it.

Returns:

Name given to the prims view when instantiating it.

property prim_paths: list[str]#

List of prim paths in the stage encapsulated in this view.

Returns:

List of prim paths in the stage encapsulated in this view.

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]#

List of USD Prim objects encapsulated in this view.

Returns:

List of USD Prim objects encapsulated in this view.

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,
reset_xform_properties: bool = True,
articulation_controller: 'ArticulationController' | None = None,
)#

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 – prim path of the Prim to encapsulate or create.

  • name – shortname to be used as a key by Scene class. Note: needs to be unique if the object is added to the Scene.

  • position – position in the world frame of the prim. Shape is (3, ).

  • translation – translation in the local frame of the prim (with respect to its parent prim). Shape is (3, ).

  • orientation – 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, ).

  • scale – local scale to be applied to the prim’s dimensions. Shape is (3, ).

  • visible – set to false for an invisible prim in the stage while rendering.

  • reset_xform_properties – 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.

  • articulation_controller – 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/FrankaRobotics/FrankaPanda/franka.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>
apply_action(
control_actions: ArticulationAction,
) None#

Apply joint positions, velocities and/or efforts to control an articulation.

Parameters:

control_actions – Actions to be applied for next physics step.

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)
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 – Visual material to be applied to the held prim. Currently supports PreviewSurface, OmniPBR and OmniGlass.

  • weaker_than_descendants – True if the material shouldn’t override the descendants materials, otherwise 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)
disable_gravity() None#

Keep gravity from affecting the robot.

Example:

>>> prim.disable_gravity()
enable_gravity() None#

Allow gravity to affect the robot.

Example:

>>> prim.enable_gravity()
get_angular_velocity() ndarray#

Angular velocity of the root articulation prim.

Returns:

3D angular velocity vector. Shape (3,).

Example:

>>> prim.get_angular_velocity()
[0. 0. 0.]
get_applied_action() ArticulationAction#

Last applied action.

Returns:

Last applied action. Note that a dictionary is used as the object’s string representation.

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]}
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 – 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

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_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.

Example:

>>> # given a visual material applied
>>> prim.get_applied_visual_material()
<isaacsim.core.api.materials.omni_glass.OmniGlass object at 0x7f36263106a0>
get_articulation_body_count() int#

Get the number of bodies (links) that make up the articulation.

Returns:

Amount of bodies.

Example:

>>> prim.get_articulation_body_count()
12
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.

Example:

>>> prim.get_articulation_controller()
<isaacsim.core.api.controllers.articulation_controller.ArticulationController object at 0x7f04a0060190>
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)

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_dof_index(dof_name: str) int#

Get a DOF index given its name.

Parameters:

dof_name – Name of the DOF.

Returns:

DOF index.

Example:

>>> prim.get_dof_index("panda_finger_joint2")
8
get_enabled_self_collisions() uint8#

Get the enable self collisions flag (physxArticulation:enabledSelfCollisions).

Returns:

self collisions flag (boolean interpreted as int)

Example:

>>> prim.get_enabled_self_collisions()
0
get_joint_positions(
joint_indices: list | ndarray | None = None,
) ndarray#

Get the articulation joint positions.

Parameters:

joint_indices – indices to specify which joints to read. Defaults to None (all joints)

Returns:

all or selected articulation joint positions

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]
get_joint_velocities(
joint_indices: list | ndarray | None = None,
) ndarray#

Get the articulation joint velocities.

Parameters:

joint_indices – indices to specify which joints to read. Defaults to None (all joints)

Returns:

all or selected articulation joint velocities

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_joints_default_state() JointsState#

Default joint states (positions and velocities).

Returns:

An object that contains the default joint positions and velocities.

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.]
get_joints_state() JointsState#

Current joint states (positions and velocities).

Returns:

An object that contains the current joint positions and velocities.

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_linear_velocity() ndarray#

Linear velocity of the root articulation prim.

Returns:

3D linear velocity vector. Shape (3,).

Example:

>>> prim.get_linear_velocity()
[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

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, ).

Example:

>>> prim.get_local_scale()
[1. 1. 1.]
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 – 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

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_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.

Forces and torques are reported in the local body reference frame (child joint frame of the link’s incoming joint).

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 – 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

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_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

Example:

>>> prim.get_sleep_threshold()
0.005
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

Example:

>>> prim.get_solver_position_iteration_count()
32
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

Example:

>>> prim.get_solver_velocity_iteration_count()
32
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

Example:

>>> prim.get_stabilization_threshold()
0.0009999999
get_visibility() bool#

Get the visibility of the prim in stage.

Returns:

True if the prim is visible in stage. False otherwise.

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

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, ).

Example:

>>> prim.get_world_scale()
[1. 1. 1.]
get_world_velocity() ndarray#

Get the articulation root velocity.

Returns:

current velocity of the root prim. Shape (6,).

initialize(
physics_sim_view: omni.physics.tensors.SimulationView = 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 – Current physics simulation view.

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.

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.

Example:

>>> # given a visual material applied
>>> prim.is_visual_material_applied()
True
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()
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 – 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]))
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 – Position in the world frame of the prim. shape is (3, ). Which means left unchanged.

  • orientation – Quaternion orientation in the world frame of the prim. Quaternion is scalar-first (w, x, y, z). shape is (4, ). 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_enabled_self_collisions(flag: bool) None#

Set the enable self collisions flag (physxArticulation:enabledSelfCollisions).

Parameters:

flag – whether to enable self collisions

Example:

>>> prim.set_enabled_self_collisions(True)
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 – articulation joint efforts

  • joint_indices – 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]))
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 – articulation joint positions

  • joint_indices – 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]))
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 – articulation joint velocities

  • joint_indices – 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_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 – Joint positions.

  • velocities – Joint velocities.

  • efforts – Joint efforts.

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()
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 – 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]))
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 – Translation in the local frame of the prim (with respect to its parent prim). shape is (3, ).

  • orientation – Quaternion orientation in the local frame of the prim. quaternion is scalar-first (w, x, y, z). shape is (4, ).

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 – Scale to be applied to the prim’s dimensions. shape is (3, ).

Example:

>>> # scale prim 10 times smaller
>>> prim.set_local_scale(np.array([0.1, 0.1, 0.1]))
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 – sleep threshold

Example:

>>> prim.set_sleep_threshold(0.01)
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 – position iteration count

Example:

>>> prim.set_solver_position_iteration_count(64)
set_solver_velocity_iteration_count(
count: int,
) None#

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 – velocity iteration count

Example:

>>> prim.set_solver_velocity_iteration_count(64)
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 – stabilization threshold

Example:

>>> prim.set_stabilization_threshold(0.005)
set_visibility(visible: bool) None#

Set the visibility of the prim in stage.

Parameters:

visible – 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#

Set 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 – Position in the world frame of the prim. shape is (3, ).

  • orientation – Quaternion orientation in the world frame of the prim. quaternion is scalar-first (w, x, y, z). shape is (4, ).

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.]))
set_world_velocity(
velocity: ndarray,
) None#

Set the articulation root velocity.

Parameters:

velocity – linear and angular velocity to set the root prim to. Shape (6,).

property dof_names: list[str]#

Prim names for each DOF.

Returns:

Prim names.

Example:

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

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 handles_initialized: bool#

Whether the articulation handler is initialized.

Returns:

Whether the handler was initialized.

Example:

>>> prim.handles_initialized
True
property name: str | None#

Name given to the prim when instantiating it.

Returns:

Name given to the prim when instantiating it. Otherwise None.

Used to query if the prim is a non root articulation link.

Returns:

True if the prim itself is a non root link

Example:

>>> # for a wrapped articulation (where the root prim has the Physics Articulation Root property applied)
>>> prim.non_root_articulation_link
False
property num_bodies: int#

Number of articulation links.

Returns:

Number of links.

Example:

>>> prim.num_bodies
9
property num_dof: int#

Number of degrees of freedom of the articulation.

Returns:

Amount of DOFs.

Example:

>>> prim.num_dof
9
property prim: pxr.Usd.Prim#

USD Prim object that this object holds.

Returns:

USD Prim object that this object holds.

property prim_path: str#

Prim path in the stage.

Returns:

Prim path in the stage.

class SingleClothPrim(*args: Any, **kwargs: Any)#

Bases: object

Deprecated single cloth prim class. No longer available.

Parameters:
  • *args – Unused positional arguments.

  • **kwargs – Unused keyword arguments.

class SingleDeformablePrim(*args: Any, **kwargs: Any)#

Bases: object

Deprecated single deformable prim class. No longer available.

Parameters:
  • *args – Unused positional arguments.

  • **kwargs – Unused keyword arguments.

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,
reset_xform_properties: bool = True,
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 = 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 – Prim path of the Prim to encapsulate or create.

  • name – Shortname to be used as a key by Scene class. Note: needs to be unique if the object is added to the Scene.

  • position – Position in the world frame of the prim. Shape is (3, ).

  • translation – Translation in the local frame of the prim (with respect to its parent prim). Shape is (3, ).

  • orientation – 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, ).

  • scale – Local scale to be applied to the prim’s dimensions. Shape is (3, ).

  • visible – Set to false for an invisible prim in the stage while rendering.

  • reset_xform_properties – 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 the cloner api in isaacsim.core.cloner.

  • collision – Set to True if the geometry should have a collider (i.e not only a visual geometry).

  • track_contact_forces – 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.

  • prepare_contact_sensor – Applies contact reporter API to the prim if it already does not have one.

  • disable_stablization – Disables the contact stabilization parameter in the physics context.

  • contact_filter_prim_paths_expr – 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>
apply_physics_material(
physics_material: PhysicsMaterial,
weaker_than_descendants: bool = False,
) None#

Used to apply physics material to the held prim and optionally its descendants.

Parameters:
  • physics_material – 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 – True if the material shouldn’t override the descendants materials, otherwise 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)
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 – Visual material to be applied to the held prim. Currently supports PreviewSurface, OmniPBR and OmniGlass.

  • weaker_than_descendants – True if the material shouldn’t override the descendants materials, otherwise 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_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.

Example:

>>> # given a physics material applied
>>> prim.get_applied_physics_material()
<isaacsim.core.api.materials.physics_material.PhysicsMaterial object at 0x7fb66c30cd30>
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.

Example:

>>> # given a visual material applied
>>> prim.get_applied_visual_material()
<isaacsim.core.api.materials.omni_glass.OmniGlass object at 0x7f36263106a0>
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

Example:

>>> prim.get_collision_approximation()
none
get_collision_enabled() bool#

Check if the Collision API is enabled.

Returns:

True if the Collision API is enabled. Otherwise False

Example:

>>> prim.get_collision_enabled()
True
get_contact_force_data(
dt: float = 1.0,
) ndarray | Tensor#

Return detailed contact forces between the prim and filter prims if the object is initialized with.

filter_paths_expr. This includes 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 – 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:

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_contact_force_matrix(
dt: float = 1.0,
) ndarray | Tensor#

Return contact forces between the prim and filter prims if the object is initialized with filter_paths_expr.

i.e., a matrix of dimension (self._contact_view.num_filters, 3) where num_filters is determined according to the filter_paths_expr parameter.

Parameters:

dt – 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 prim with shape (self._geometry_prim_view._contact_view.num_filters, 3).

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.

Example:

>>> prim.get_contact_offset()
-inf
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)

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_friction_data(
dt: float = 1.0,
) ndarray | Tensor#

Return detailed friction forces between the prim and filter prims if the object is initialized with.

filter_paths_expr. This includes 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 – 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:

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.

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

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, ).

Example:

>>> prim.get_local_scale()
[1. 1. 1.]
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].

Example:

>>> prim.get_min_torsional_patch_radius()
0.0
get_net_contact_forces(
dt: float = 1.0,
) ndarray | Tensor#

Return the net contact forces on the prim if contact forces are tracked.

i.e., a matrix of dimension (1, 3).

Parameters:

dt – 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 prim with shape (3).

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.

Example:

>>> prim.get_rest_offset()
-inf
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].

Example:

>>> prim.get_torsional_patch_radius()
0.0
get_visibility() bool#

Get the visibility of the prim in stage.

Returns:

True if the prim is visible in stage. False otherwise.

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

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, ).

Example:

>>> prim.get_world_scale()
[1. 1. 1.]
initialize(physics_sim_view: object = 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 – Current physics simulation view.

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.

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.

Example:

>>> # given a visual material applied
>>> prim.is_visual_material_applied()
True
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()
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 – Approximation used for collision

Example:

>>> prim.set_collision_approximation("convexDecomposition")
set_collision_enabled(enabled: bool) None#

Enable/disable the Collision API.

Parameters:

enabled – Whether to enable or disable the Collision API

Example:

>>> # disable collisions
>>> prim.set_collision_enabled(False)
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 – 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)
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 – Position in the world frame of the prim. shape is (3, ). Which means left unchanged.

  • orientation – Quaternion orientation in the world frame of the prim. Quaternion is scalar-first (w, x, y, z). shape is (4, ). 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 – Translation in the local frame of the prim (with respect to its parent prim). shape is (3, ).

  • orientation – Quaternion orientation in the local frame of the prim. quaternion is scalar-first (w, x, y, z). shape is (4, ).

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 – Scale to be applied to the prim’s dimensions. shape is (3, ).

Example:

>>> # scale prim 10 times smaller
>>> prim.set_local_scale(np.array([0.1, 0.1, 0.1]))
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 – 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)
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 – 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)
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 – Radius of the contact patch used to apply torsional friction. Allowed range [0, max_float].

Example:

>>> prim.set_torsional_patch_radius(0.1)
set_visibility(visible: bool) None#

Set the visibility of the prim in stage.

Parameters:

visible – 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#

Set 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 – Position in the world frame of the prim. shape is (3, ).

  • orientation – Quaternion orientation in the world frame of the prim. quaternion is scalar-first (w, x, y, z). shape is (4, ).

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.]))
property geom: pxr.UsdGeom.Gprim#

USD geometry object encapsulated.

Returns:

USD geometry object encapsulated.

Return type:

UsdGeom.Gprim

property name: str | None#

Name given to the prim when instantiating it.

Returns:

Name given to the prim when instantiating it. Otherwise None.

Used to query if the prim is a non root articulation link.

Returns:

True if the prim itself is a non root link

Example:

>>> # for a wrapped articulation (where the root prim has the Physics Articulation Root property applied)
>>> prim.non_root_articulation_link
False
property prim: pxr.Usd.Prim#

USD Prim object that this object holds.

Returns:

USD Prim object that this object holds.

property prim_path: str#

Prim path in the stage.

Returns:

Prim path in the stage.

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,
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.

Initializes and Applies PhysxSchema.PhysxParticleSystem to the prim at prim_path.

All arguments are accepted as None. In this case, they either have the default values from PhysxParticleSystem schema (in case a new particle system is created), or the values present in the existing particle system.

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

Parameters:
  • prim_path – The path to the particle system.

  • name – Name given to the prim when instantiating it.

  • particle_system_enabled – Whether to enable or disable the particle system.

  • simulation_owner – Single PhysicsScene that simulates this particle system.

  • contact_offset – Contact offset used for collisions with non-particle objects such as rigid or deformable bodies.

  • rest_offset – Rest offset used for collisions with non-particle objects such as rigid or deformable bodies.

  • particle_contact_offset – Contact offset used for interactions between particles. Must be larger than solid and fluid rest offsets.

  • solid_rest_offset – Rest offset used for solid-solid or solid-fluid particle interactions. Must be smaller than particle contact offset.

  • fluid_rest_offset – Rest offset used for fluid-fluid particle interactions. Must be smaller than particle contact offset.

  • enable_ccd – Enable continuous collision detection for particles to help avoid tunneling effects.

  • solver_position_iteration_count – Number of solver iterations for position.

  • max_depenetration_velocity – The maximum velocity permitted to be introduced by the solver to depenetrate intersecting particles.

  • wind – The wind applied to the current particle system.

  • max_neighborhood – The particle neighborhood size.

  • max_velocity – Maximum particle velocity.

  • global_self_collision_enabled – If True, self collisions follow particle-object-specific settings. If False, all particle self collisions are disabled, regardless of any other settings. Improves performance if self collisions are not needed.

  • non_particle_collision_enabled – Enable or disable particle collision with non-particle objects for all particles in the system. Improves performance if non-particle collisions are not needed.

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.

Returns:

The applied anisotropy API schema.

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.

Returns:

The applied anisotropy API schema.

apply_particle_material(
particle_materials: ParticleMaterial,
) None#

Applies particle material to the particle system.

Parameters:

particle_materials – The particle material to apply.

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.

Returns:

The applied smoothing API schema.

get_applied_particle_material() ParticleMaterial#

Gets the applied particle material from the particle system.

Returns:

The applied particle material.

get_contact_offset() float#

The contact offset used for collisions with non-particle objects.

Returns:

The contact offset used for collisions with non-particle objects.

get_enable_ccd() bool#

Whether continuous collision detection for particles is enabled or disabled.

Returns:

Whether continuous collision detection for particles is enabled or disabled.

get_fluid_rest_offset() float#

The rest offset used for fluid-fluid particle interactions.

Returns:

The rest offset used for fluid-fluid particle interactions.

get_global_self_collision_enabled() bool#

Whether self collisions to follow particle-object-specific settings is enabled or disabled.

Returns:

Whether self collisions to follow particle-object-specific settings

is enabled or disabled.

get_max_depenetration_velocity() float#

The maximum velocity permitted between intersecting particles.

Returns:

The maximum velocity permitted between intersecting particles.

get_max_neighborhood() int#

The particle neighborhood size.

Returns:

The particle neighborhood size.

get_max_velocity() float#

The maximum particle velocity.

Returns:

The maximum particle velocity.

get_particle_contact_offset() float#

The contact offset used for interactions between particles.

Returns:

The contact offset used for interactions between particles.

get_particle_system_enabled() bool#

Whether particle system is enabled.

Returns:

Whether particle system is enabled or not.

get_rest_offset() float#

The rest offset used for collisions with non-particle objects.

Returns:

The rest offset used for collisions with non-particle objects.

get_simulation_owner() pxr.Usd.Prim#

The physics scene prim attached to particle system.

Returns:

The physics scene prim attached to particle system.

get_solid_rest_offset() float#

The rest offset used for solid-solid or solid-fluid particle interactions.

Returns:

The rest offset used for solid-solid or solid-fluid particle interactions.

get_solver_position_iteration_count() int#

The number of solver iterations for positions.

Returns:

The number of solver iterations for positions.

get_wind() Sequence[float]#

The wind applied to the current particle system.

Returns:

The wind applied to the current particle system.

initialize(
physics_sim_view: object = None,
) None#

Initializes the particle system.

Parameters:

physics_sim_view – Physics simulation view to initialize with.

is_valid() bool#

Checks if the particle system prim is valid.

Returns:

True if the current prim path corresponds to a valid prim in stage. False otherwise.

post_reset() None#

Resets the particle system to its initial state.

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 – The contact offset.

set_enable_ccd(value: bool) None#

Enable continuous collision detection for particles.

Parameters:

value – Whether to enable or disable.

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 – The rest offset.

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 – Whether to enable or disable.

set_max_depenetration_velocity(
value: float,
) None#

Set the maximum velocity permitted to be introduced by the solver to.

depenetrate intersecting particles.

Parameters:

value – The maximum depenetration velocity.

set_max_neighborhood(value: int) None#

Set the particle neighborhood size.

Parameters:

value – The neighborhood size.

set_max_velocity(value: float) None#

Set the maximum particle velocity.

Parameters:

value – The maximum velocity.

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 – The contact offset.

set_particle_system_enabled(value: bool) None#

Set enabling of the particle system.

Parameters:

value – Whether to enable or disable.

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 – The rest offset.

set_simulation_owner(value: str) None#

Set the PhysicsScene that simulates this particle system.

Parameters:

value – The prim path to the physics scene.

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 – The rest offset.

set_solver_position_iteration_count(
value: int,
) None#

Set the number of solver iterations for position.

Parameters:

value – Number of solver iterations.

set_wind(
value: Sequence[float],
) None#

Set the wind velocity applied to the current particle system.

Parameters:

value – The wind applied to the current particle system.

property name: str | None#

Name given to the prim when instantiating it.

Returns:

Name given to the prim when instantiating it. Otherwise None.

property particle_system: pxr.PhysxSchema.PhysxParticleSystem#

PhysX particle system schema.

Returns:

The particle system.

property prim: pxr.Usd.Prim#

USD prim of the particle system.

Returns:

The USD prim present.

property prim_path: str#

Stage path to the particle system.

Returns:

The stage path to the particle system.

class SingleRigidPrim(
prim_path: str,
name: str = 'rigid_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,
reset_xform_properties: bool = True,
mass: float | None = None,
density: float | None = None,
linear_velocity: ndarray | None = None,
angular_velocity: ndarray | None = None,
)#

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 – prim path of the Prim to encapsulate or create.

  • name – shortname to be used as a key by Scene class. Note: needs to be unique if the object is added to the Scene.

  • position – position in the world frame of the prim. shape is (3, ).

  • translation – translation in the local frame of the prim (with respect to its parent prim). shape is (3, ).

  • orientation – 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, ).

  • scale – local scale to be applied to the prim’s dimensions. shape is (3, ).

  • visible – set to false for an invisible prim in the stage while rendering.

  • reset_xform_properties – 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.

  • mass – mass in kg.

  • density – density.

  • linear_velocity – linear velocity in the world frame.

  • angular_velocity – angular velocity in the world frame.

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>
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 – Visual material to be applied to the held prim. Currently supports PreviewSurface, OmniPBR and OmniGlass.

  • weaker_than_descendants – True if the material shouldn’t override the descendants materials, otherwise 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)
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()
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()
get_angular_velocity() ndarray#

Get the angular velocity of the rigid body.

Returns:

current angular velocity of the the rigid prim. Shape (3,).

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.

Example:

>>> # given a visual material applied
>>> prim.get_applied_visual_material()
<isaacsim.core.api.materials.omni_glass.OmniGlass object at 0x7f36263106a0>
get_com() tuple[ndarray, ndarray]#

Get the center of mass pose of the rigid body.

Returns:

A tuple of (position, orientation) where position is the center of mass position and orientation is the center of mass orientation.

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

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.]
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

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_density() float#

Get the density of the rigid body.

Returns:

density of the rigid body.

Example:

>>> prim.get_density()
0
get_linear_velocity() ndarray#

Get the linear velocity of the rigid body.

Returns:

current linear velocity of the the rigid prim. Shape (3,).

Example:

>>> prim.get_linear_velocity()
[ 1.0812164e-04  6.1415871e-05 -2.1341663e-04]
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

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, ).

Example:

>>> prim.get_local_scale()
[1. 1. 1.]
get_mass() float#

Get the mass of the rigid body.

Returns:

mass of the rigid body in kg.

Example:

>>> prim.get_mass()
0
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.

Example:

>>> prim.get_sleep_threshold()
5e-05
get_visibility() bool#

Get the visibility of the prim in stage.

Returns:

True if the prim is visible in stage. False otherwise.

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

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, ).

Example:

>>> prim.get_world_scale()
[1. 1. 1.]
initialize(physics_sim_view: object = 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 – Current physics simulation view.

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.

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.

Example:

>>> # given a visual material applied
>>> prim.is_visual_material_applied()
True
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()
set_angular_velocity(velocity: ndarray) None#

Set the angular velocity of the rigid body in stage.

Warning

This method will immediately set the rigid body state

Parameters:

velocity – angular velocity to set the rigid prim to. Shape (3,).

set_com(
position: ndarray,
orientation: ndarray,
) None#

Set the center of mass pose of the rigid body.

Parameters:
  • position – center of mass position. Shape (3,).

  • orientation – center of mass orientation. Shape (4,).

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 – position in the world frame of the prim. shape is (3, ). Defaults to None, which means left unchanged.

  • orientation – 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 – linear velocity to set the rigid prim to. Shape (3,).

  • angular_velocity – 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()
set_density(density: float) None#

Set the density of the rigid body.

Parameters:

density – density of the rigid body.

Example:

>>> prim.set_density(0.9)
set_linear_velocity(velocity: ndarray) None#

Set the linear velocity of the rigid body in stage.

Warning

This method will immediately set the rigid prim state

Parameters:

velocity – linear velocity to set the rigid prim to. Shape (3,).

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 – Translation in the local frame of the prim (with respect to its parent prim). shape is (3, ).

  • orientation – Quaternion orientation in the local frame of the prim. quaternion is scalar-first (w, x, y, z). shape is (4, ).

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 – Scale to be applied to the prim’s dimensions. shape is (3, ).

Example:

>>> # scale prim 10 times smaller
>>> prim.set_local_scale(np.array([0.1, 0.1, 0.1]))
set_mass(mass: float) None#

Set the mass of the rigid body.

Parameters:

mass – mass of the rigid body in kg.

Example:

>>> prim.set_mass(1.0)
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 – 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)
set_visibility(visible: bool) None#

Set the visibility of the prim in stage.

Parameters:

visible – 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#

Set 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 – Position in the world frame of the prim. shape is (3, ).

  • orientation – Quaternion orientation in the world frame of the prim. quaternion is scalar-first (w, x, y, z). shape is (4, ).

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.]))
property name: str | None#

Name given to the prim when instantiating it.

Returns:

Name given to the prim when instantiating it. Otherwise None.

Used to query if the prim is a non root articulation link.

Returns:

True if the prim itself is a non root link

Example:

>>> # for a wrapped articulation (where the root prim has the Physics Articulation Root property applied)
>>> prim.non_root_articulation_link
False
property prim: pxr.Usd.Prim#

USD Prim object that this object holds.

Returns:

USD Prim object that this object holds.

property prim_path: str#

Prim path in the stage.

Returns:

Prim path in the stage.

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,
reset_xform_properties: bool = True,
)#

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 – prim path of the Prim to encapsulate or create.

  • name – shortname to be used as a key by Scene class. Note: needs to be unique if the object is added to the Scene.

  • position – position in the world frame of the prim. shape is (3, ).

  • translation – translation in the local frame of the prim (with respect to its parent prim). shape is (3, ).

  • orientation – 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, ).

  • scale – local scale to be applied to the prim’s dimensions. shape is (3, ).

  • visible – set to false for an invisible prim in the stage while rendering.

  • reset_xform_properties – 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.

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 – Visual material to be applied to the held prim. Currently supports PreviewSurface, OmniPBR and OmniGlass.

  • weaker_than_descendants – True if the material shouldn’t override the descendants materials, otherwise 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.

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)

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

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, ).

Example:

>>> prim.get_local_scale()
[1. 1. 1.]
get_visibility() bool#

Get the visibility of the prim in stage.

Returns:

True if the prim is visible in stage. False otherwise.

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

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, ).

Example:

>>> prim.get_world_scale()
[1. 1. 1.]
initialize(physics_sim_view: object = 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 – Current physics simulation view.

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.

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.

Example:

>>> # given a visual material applied
>>> prim.is_visual_material_applied()
True
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()
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 – Position in the world frame of the prim. shape is (3, ). Which means left unchanged.

  • orientation – Quaternion orientation in the world frame of the prim. Quaternion is scalar-first (w, x, y, z). shape is (4, ). 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 – Translation in the local frame of the prim (with respect to its parent prim). shape is (3, ).

  • orientation – Quaternion orientation in the local frame of the prim. quaternion is scalar-first (w, x, y, z). shape is (4, ).

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 – Scale to be applied to the prim’s dimensions. shape is (3, ).

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 – 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#

Set 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 – Position in the world frame of the prim. shape is (3, ).

  • orientation – Quaternion orientation in the world frame of the prim. quaternion is scalar-first (w, x, y, z). shape is (4, ).

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.]))
property name: str | None#

Name given to the prim when instantiating it.

Returns:

Name given to the prim when instantiating it. Otherwise None.

Used to query if the prim is a non root articulation link.

Returns:

True if the prim itself is a non root link

Example:

>>> # for a wrapped articulation (where the root prim has the Physics Articulation Root property applied)
>>> prim.non_root_articulation_link
False
property prim: pxr.Usd.Prim#

USD Prim object that this object holds.

Returns:

USD Prim object that this object holds.

property prim_path: str#

Prim path in the stage.

Returns:

Prim path in the stage.