[isaacsim.robot_motion.lula] Isaac Sim Lula#

Version: 4.0.4

Extension that provides lula python interface

Enable Extension#

The extension can be enabled (if not already) in one of the following ways:

Command-line interface

Define the next entry as an application argument from a terminal.

APP_SCRIPT.(sh|bat) --enable isaacsim.robot_motion.lula

Experience/extension configuration

Define the next entry under [dependencies] in an experience (.kit) file or an extension configuration (extension.toml) file.

[dependencies]
"isaacsim.robot_motion.lula" = {}

Extension Manager UI

Open the Window > Extensions menu in a running application instance and search for isaacsim.robot_motion.lula. Then, toggle the enable control button if it is not already active.

API#

Python API#

The lula module provides a high-level interface to the Lula library, with classes exposing routines for forward and inverse kinematics, sampling-based global planning, and smooth reactive motion generation via RMPflow and geometric fabrics.

Logging

LogLevel

Logging levels, ordered from least to most verbose.

Rotations and Poses

`Rotation3`	Representation of a rotation (i.e. element of SO(3)) in 3D.
`Pose3`	Representation of a pose (i.e. element of SE(3)) in 3D.

Robot Specification

`RobotDescription`	Robot description, consisting of geometric and kinematic properties of a robot.
`load_robot`	Load a robot description from a YAML file (robot_description_file) and a URDF (robot_urdf).
`load_robot_from_memory`	Load a robot description from the contents of a YAML file (robot_description_file) and the contents of a URDF (robot_urdf).

World Specification

`Obstacle`	Geometric primitive.
`create_obstacle`	Create an obstacle.
`World`	Represents a collection of obstacles.
`create_world`	Create a new, empty world.
`WorldView`	Represents a view of a lula.World.

Kinematics

`CyclicCoordDescentIkConfig`	Configuration parameters for CCD inverse kinematics solver.
`CyclicCoordDescentIkResults`	Results returned by CCD inverse kinematics solver.
`compute_ik_ccd`	Attempt to solve inverse kinematics using cyclic coordinate descent.

Path Specification

`CSpacePathSpec`	Procedurally specify a CSpacePathSpec from a series of configuration space waypoints
`create_c_space_path_spec`	Create a 'CSpacePathSpec' with the specified 'initial_c_space_position'.
`TaskSpacePathSpec`	Procedurally compose a TaskSpacePathSpec from a series of continuous task space segments
`create_task_space_path_spec`	Create a 'TaskSpacePathSpec' with the specified 'initial_pose'.
`CompositePathSpec`	Procedurally compose 'CSpacePathSpec' and 'TaskSpacePathSpec' segments into a single path specification.
`create_composite_path_spec`	Create a 'CompositePathSpec' with the specified 'initial_c_space_position'.
`load_c_space_path_spec_from_file`	Load a 'CSpacePathSpec' from file with absolute path 'c_space_path_spec_file'.
`load_c_space_path_spec_from_memory`	Load a 'CSpacePathSpec' from the contents of a YAML file ('c_space_path_spec_yaml').
`export_c_space_path_spec_to_memory`	Export 'c_space_path_spec' as a string.
`load_task_space_path_spec_from_file`	Load a 'TaskSpacePathSpec' from file with absolute path 'task_space_path_spec_file'.
`load_task_space_path_spec_from_memory`	Load a 'TaskSpacePathSpec' from the contents of a YAML file ('task_space_path_spec_yaml').
`export_task_space_path_spec_to_memory`	Export 'task_space_path_spec' as a string.
`load_composite_path_spec_from_file`	Load a 'CompositePathSpec' from file with absolute path 'composite_path_spec_file'.
`load_composite_path_spec_from_memory`	Load a 'CompositePathSpec' from the contents of a YAML file ('composite_path_spec_yaml').
`export_composite_path_spec_to_memory`	Export 'composite_path_spec' as a string.

Path Generation

`CSpacePath`	Represent a path through configuration space (i.e., c-space or "joint space").
`LinearCSpacePath`	Represent a path linearly interpolated through configuration space (i.e., c-space) waypoints.
`create_linear_c_space_path`	Create a 'LinearCSpacePath' from a c-space path specification.
`TaskSpacePath`	Represent a path through task space (i.e., SE(3) group representing 6-dof poses).
`TaskSpacePathConversionConfig`	Configuration parameters for converting a 'TaskSpacePathSpec' into a series of c-space configurations.
`convert_composite_path_spec_to_c_space`	Convert a composite_path_spec into a linear c-space path.
`convert_task_space_path_spec_to_c_space`	Convert a task_space_path_spec into a linear c-space path.

Trajectory Generation

`Trajectory`	Represent a path through configuration space (i.e., "c-space") parameterized by time.
`CSpaceTrajectoryGenerator`	Configure a trajectory generator that can compute smooth trajectories.
`create_c_space_trajectory_generator`	Overloaded function.

Collision Sphere Generation

`CollisionSphereGenerator`	Configure a generator that can generate spheres to approximate mesh volumes.
`create_collision_sphere_generator`	Create a CollisionSphereGenerator to generate spheres that approximate the volume of a mesh represented by vertices and triangles.

Motion Planning

`MotionPlanner`	Interface for planning collision-free paths for robotic manipulators.
`create_motion_planner`	Overloaded function.

RmpFlow

`RmpFlowConfig`	RMPflow configuration, including parameters and robot description.
`create_rmpflow_config`	Create an RMPflow configuration object.
`create_rmpflow_config_from_memory`
`RmpFlow`	Interface for building and evaluating an RMPflow motion policy.
`create_rmpflow`	Create an instance of the RmpFlow interface from an RMPflow configuration.

Logging#

class LogLevel#

Logging levels, ordered from least to most verbose.

Members:

FATAL

ERROR

WARNING

INFO

VERBOSE

FATAL#: Logging level for nonrecoverable errors (minimum level, so always enabled).

ERROR#: Logging level for recoverable errors.

WARNING#: Logging level for warnings, indicating possible cause for concern.

INFO#: Logging level for informational messages.

VERBOSE#: Logging level for highly verbose informational messages.

set_log_level(level: lula.LogLevel = <LogLevel.ERROR: 1>) → None#: Suppress output for all log messages with associated verbosity higher than log_level.

Rotations and Poses#

class Rotation3#

Bases: pybind11_object

Representation of a rotation (i.e. element of SO(3)) in 3D.

static distance( rotation0: lula.Rotation3, rotation1: lula.Rotation3, ) → float#: Compute the minimum angular distance (in radians) between two rotations.

static from_scaled_axis( scaled_axis: numpy.ndarray[numpy.float64[3, 1]], ) → lula.Rotation3#: Construct a rotation from a scaled axis, where the magnitude of scaled_axis represents the rotation angle in radians.

static identity() → lula.Rotation3#: Create identity rotation.

inverse(self: lula.Rotation3) → lula.Rotation3#: Returns the inverse rotation.

matrix( self: lula.Rotation3, ) → numpy.ndarray[numpy.float64[3, 3]]#: Return matrix representation of the rotation.

scaled_axis( self: lula.Rotation3, ) → numpy.ndarray[numpy.float64[3, 1]]#: Return scaled axis represetation of the rotation where magnitude of the returned vector represents the rotation angle in radians.

static slerp( rotation0: lula.Rotation3, rotation1: lula.Rotation3, t: float, ) → lula.Rotation3#: Smoothly interpolate between two rotations using spherical linear interpolation (“slerp”).

w(self: lula.Rotation3) → float#: Return w component of the quaternion represention of the rotation.

x(self: lula.Rotation3) → float#: Return x component of the quaternion represention of the rotation.

y(self: lula.Rotation3) → float#: Return y component of the quaternion represention of the rotation.

z(self: lula.Rotation3) → float#: Return z component of the quaternion represention of the rotation.

class Pose3#

Bases: pybind11_object

Representation of a pose (i.e. element of SE(3)) in 3D.

static from_rotation(rotation: lula::Rotation3) → lula.Pose3#: Create a pure rotational pose

static from_translation( translation: numpy.ndarray[numpy.float64[3, 1]], ) → lula.Pose3#: Create a pure translational pose.

static identity() → lula.Pose3#: Create identity pose.

inverse(self: lula.Pose3) → lula.Pose3#: Return the inverse pose.

matrix(self: lula.Pose3) → numpy.ndarray[numpy.float64[4, 4]]#: Return matrix representation of the pose.

property rotation#: Rotation component of pose.

property translation#: Translation component of pose.

Robot Specification#

class RobotDescription#

Bases: pybind11_object

Robot description, consisting of geometric and kinematic properties of a robot.

c_space_coord_name( self: lula.RobotDescription, coord_index: int, ) → str#: Return the name of a given joint of the robot.

default_c_space_configuration( self: lula.RobotDescription, ) → numpy.ndarray[numpy.float64[m, 1]]#: Return default joint positions for the robot.

kinematics( self: lula.RobotDescription, ) → lula.Kinematics#: Return the robot kinematics.

num_c_space_coords( self: lula.RobotDescription, ) → int#: Return the number of actuated joints for the robot.

load_robot( robot_description_file: str, robot_urdf: str, ) → lula.RobotDescription#: Load a robot description from a YAML file (robot_description_file) and a URDF (robot_urdf).

load_robot_from_memory( robot_description: str, robot_urdf: str, ) → lula.RobotDescription#: Load a robot description from the contents of a YAML file (robot_description_file) and the contents of a URDF (robot_urdf).

World Specification#

class Obstacle#

Bases: pybind11_object

Geometric primitive.

Note

Currently, the CYLINDER obstacle type is internally represented as a capsule (i.e., a cylinder with two hemispherical end caps) extending beyond the nominal HEIGHT. This will be corrected in a future release.

class Attribute#

Bases: pybind11_object

Members:

HEIGHT

RADIUS

SIDE_LENGTHS

HEIGHT = <Attribute.HEIGHT: 0>#

RADIUS = <Attribute.RADIUS: 1>#

SIDE_LENGTHS = <Attribute.SIDE_LENGTHS: 2>#

property name#

property value#

class AttributeValue#: Bases: pybind11_object

class Type#

Bases: pybind11_object

Members:

CUBE

CYLINDER

SPHERE

CUBE = <Type.CUBE: 0>#

CYLINDER = <Type.CYLINDER: 1>#

SPHERE = <Type.SPHERE: 2>#

property name#

property value#

set_attribute( self: lula.Obstacle, attribute: lula::Obstacle::Attribute, value: lula::Obstacle::AttributeValue, ) → None#: Set attribute for obstacle

type(self: lula.Obstacle) → lula::Obstacle::Type#: Return the type of the obstacle.

create_obstacle(type: lula.Obstacle.Type) → lula.Obstacle#: Create an obstacle.

class World#

Bases: pybind11_object

Represents a collection of obstacles.

class ObstacleHandle#: Bases: pybind11_object

add_obstacle( self: lula.World, obstacle: lula.Obstacle, pose: lula.Pose3 | None = None, ) → lula::World::ObstacleHandle#: Add obstacle to the world.

add_world_view(self: lula.World) → lula::WorldView#: Create a view into the world that can be used for collision checks and distance evaluations.

disable_obstacle( self: lula.World, obstacle: lula::World::ObstacleHandle, ) → None#: Disable an obstacle for the purpose of collision checks and distance evaluations.

enable_obstacle( self: lula.World, obstacle: lula::World::ObstacleHandle, ) → None#: Enable an obstacle for the purpose of collision checks and distance evaluations.

remove_obstacle( self: lula.World, obstacle: lula::World::ObstacleHandle, ) → None#: Permanently remove obstacle, invalidating its handle.

set_pose( self: lula.World, obstacle: lula::World::ObstacleHandle, pose: lula.Pose3, ) → None#: Set the pose of the given obstacle.

create_world() → lula.World#: Create a new, empty world.

class WorldView#

Bases: pybind11_object

Represents a view of a lula.World.

distance_to( self: lula.WorldView, obstacle: lula.World.ObstacleHandle, point: numpy.ndarray[numpy.float64[3, 1]], gradient: numpy.ndarray[numpy.float64[3, 1], flags.writeable] | None = None, ) → float#: Calculate the distance from point to the obstacle specified by obstacle.

distances_to( self: lula.WorldView, point: numpy.ndarray[numpy.float64[3, 1]], compute_distance_gradients: bool = True, ) → Tuple[List[float], List[numpy.ndarray[numpy.float64[3, 1]]] | None]#: Calculate distances from point to all enabled obstacles.

in_collision(*args, **kwargs)#

Overloaded function.

in_collision(self: lula.WorldView, center: numpy.ndarray[numpy.float64[3, 1]], radius: float) -> bool

Test whether a sphere defined by center and radius is in collision with any enabled obstacles in the world.

in_collision(self: lula.WorldView, obstacle: lula.World.ObstacleHandle, center: numpy.ndarray[numpy.float64[3, 1]], radius: float) -> bool

Test whether a sphere defined by center and radius is in collision with the obstacle specified by obstacle.

num_enabled_obstacles(self: lula.WorldView) → int#: Return the number of enabled obstacles in the current view of the world.

update(self: lula.WorldView) → None#: Update WorldView such that any changes to the underlying world are reflected in this view.

Kinematics#

class Kinematics#

Bases: pybind11_object

Kinematics interface class.

class Limits#

Bases: pybind11_object

Simple class consisting of upper and lower limits for a given c-space coordinate.

property lower#

property upper#

base_frame_name(self: lula.Kinematics) → str#: Return the name of the base frame of the kinematic structure.

c_space_coord_acceleration_limit( self: lula.Kinematics, coord_index: int, ) → float#: Return the acceleration limit of a given configuration space coordinate of the kinematic structure.

c_space_coord_jerk_limit( self: lula.Kinematics, coord_index: int, ) → float#: Return the jerk limit of a given configuration space coordinate of the kinematic structure.

c_space_coord_limits( self: lula.Kinematics, coord_index: int, ) → lula.Kinematics.Limits#: Return the limits of a given configuration space coordinate of the kinematic structure.

c_space_coord_name( self: lula.Kinematics, coord_index: int, ) → str#: Return the name of a given configuration space coordinate of the kinematic structure.

c_space_coord_velocity_limit( self: lula.Kinematics, coord_index: int, ) → float#: Return the velocity limit of a given configuration space coordinate of the kinematic structure.

frame_names(self: lula.Kinematics) → List[str]#: Return all of the frame names in the kinematic structure.

has_c_space_acceleration_limit( self: lula.Kinematics, coord_index: int, ) → bool#: Return true if the given configuration space coordinate has an associated acceleration limit.

has_c_space_jerk_limit( self: lula.Kinematics, coord_index: int, ) → bool#: Return true if the given configuration space coordinate has an associated jerk limit.

jacobian( self: lula.Kinematics, cspace_position: numpy.ndarray[numpy.float64[m, 1]], frame: str, ) → numpy.ndarray[numpy.float64[6, n]]#

Return the Jacobian of the origin of the given frame with respect to the base frame (i.e., base_frame_name()) at the given cspace_position.

The returned Jacobian is a 6 x N matrix where N is the c-space dimension (i.e., num_c_space_coords()). Column i of the returned Jacobian corresponds to the derivative of the origin of frame with respect to the ith c-space coordinate in the coordinates of the base frame. For each column, the first three elements correspond to the translational portion, and the last three elements correspond to the rotational portion.

num_c_space_coords(self: lula.Kinematics) → int#: Return the number of configuration space coordinates for the kinematic structure.

orientation(*args, **kwargs)#

Overloaded function.

orientation(self: lula.Kinematics, cspace_position: numpy.ndarray[numpy.float64[m, 1]], frame: str, reference_frame: str) -> lula::Rotation3

Return the orientation of the given frame with respect to reference_frame at the given cspace_position.

orientation(self: lula.Kinematics, cspace_position: numpy.ndarray[numpy.float64[m, 1]], frame: str) -> lula::Rotation3

Return the orientation of the given frame with respect to the base frame (i.e., base_frame_name()) at the given cspace_position.

orientation_jacobian( self: lula.Kinematics, cspace_position: numpy.ndarray[numpy.float64[m, 1]], frame: str, ) → numpy.ndarray[numpy.float64[3, n]]#

Return the Jacobian of the orientation of the given frame with respect to the base frame (i.e., base_frame_name()) at the given cspace_position.

The returned Jacobian is a 3 x N matrix where N is the c-space dimension (i.e., num_c_space_coords()). Column i of the returned Jacobian corresponds to the derivative of the orientation of frame with respect to the ith c-space coordinate in the coordinates of the base frame.

pose(*args, **kwargs)#

Overloaded function.

pose(self: lula.Kinematics, cspace_position: numpy.ndarray[numpy.float64[m, 1]], frame: str, reference_frame: str) -> lula::Pose3

Return the pose of the given frame with respect to reference_frame at the given cspace_position.

pose(self: lula.Kinematics, cspace_position: numpy.ndarray[numpy.float64[m, 1]], frame: str) -> lula::Pose3

Return the pose of the given frame with respect to the base frame (i.e., base_frame_name()) at the given cspace_position.

position(*args, **kwargs)#

Overloaded function.

position(self: lula.Kinematics, cspace_position: numpy.ndarray[numpy.float64[m, 1]], frame: str, reference_frame: str) -> numpy.ndarray[numpy.float64[3, 1]]

Return the position of the origin of the given frame with respect to reference_frame at the given cspace_position.

position(self: lula.Kinematics, cspace_position: numpy.ndarray[numpy.float64[m, 1]], frame: str) -> numpy.ndarray[numpy.float64[3, 1]]

Return the position of the origin of the given frame with respect to the base frame (i.e., base_frame_name()) at the given cspace_position.

position_jacobian( self: lula.Kinematics, cspace_position: numpy.ndarray[numpy.float64[m, 1]], frame: str, ) → numpy.ndarray[numpy.float64[3, n]]#

Return the Jacobian of the position of the origin of the given frame with respect to the base frame (i.e., base_frame_name()) at the given cspace_position.

The returned Jacobian is a 3 x N matrix where N is the c-space dimension (i.e., num_c_space_coords()). Column i of the returned Jacobian corresponds to the derivative of the position of the origin of frame with respect to the ith c-space coordinate in the coordinates of the base frame.

within_cspace_limits( self: lula.Kinematics, cspace_position: numpy.ndarray[numpy.float64[m, 1]], log_warnings: bool, ) → bool#: Determine whether a specified configuration space position is within limits.

Inverse Kinematics#

class CyclicCoordDescentIkConfig#

Bases: pybind11_object

Configuration parameters for CCD inverse kinematics solver.

class CSpaceLimitBiasing#

Bases: pybind11_object

Members:

AUTO

ENABLE

DISABLE

AUTO = <CSpaceLimitBiasing.AUTO: 0>#

DISABLE = <CSpaceLimitBiasing.DISABLE: 2>#

ENABLE = <CSpaceLimitBiasing.ENABLE: 1>#

property name#

property value#

property bfgs_cspace_limit_biasing#: Indicate whether c-space limit avoidance is active for BFGS descent

property bfgs_cspace_limit_biasing_weight#: Relative weight applied to c-space limit error (if active).

property bfgs_cspace_limit_penalty_region#: Region near c-space limits which will induce penalties when c-space limit biasing is active.

property bfgs_gradient_norm_termination#: Minimal change in L2-norm of the error function gradient for early descent termination from BFGS descent.

property bfgs_gradient_norm_termination_coarse_scale_factor#: Ratio between ‘bfgs_gradient_norm_termination’ for coarse and fine stagesof BFGS descent.

property bfgs_max_iterations#: Number of iterations used for each BFGS descent.

property bfgs_orientation_weight#: Weight for the relative importance of orientation error during BFGS descent.

property bfgs_position_weight#: Weight for the relative importance of position error during BFGS descent.

property ccd_bracket_search_num_uniform_samples#: Number of samples used to identify valid three-point bracket for numerical optimization of c-space updates.

property ccd_descent_termination_delta#: Minimal change in c-space coordinates for early descent termination.

property ccd_max_iterations#: Number of iterations used for each cyclic coordinate descent.

property ccd_orientation_weight#: Weight for the relative importance of orientation error during CCD.

property ccd_position_weight#: Weight for the relative importance of position error during CCD.

property cspace_seeds#: Optional parameter to set the initial c-space configurations for descent.

property irwin_hall_sampling_order#: Sampling distribution for initial c-space positions.

property max_num_descents#: Maximum number of c-space positions that will be used as seeds.

property orientation_tolerance#: Maximum orientation error (per-axis L2-norm) for a successful IK solution

property position_tolerance#: Maximum position error (L2-norm) for a successful IK solution

property sampling_seed#: Seed for Irwin-Hall sampling of initial c-space positions

class CyclicCoordDescentIkResults#

Bases: pybind11_object

Results returned by CCD inverse kinematics solver.

property cspace_position#: If success is true, joint configuration corresponding to target_pose.

property num_descents#: Number of unique c-sapce positions used for CCD prior to termination.

property position_error#: Position error (L2-norm) of returned c-space position

property success#: Indicate whether a cspace_position within the tolerances has been found.

property x_axis_orientation_error#: X-axis orientation error (L2-norm) of returned c-space position

property y_axis_orientation_error#: Y-axis orientation error (L2-norm) of returned c-space position

property z_axis_orientation_error#: Z-axis orientation error (L2-norm) of returned c-space position

compute_ik_ccd( kinematics: lula::Kinematics, target_pose: lula::Pose3, target_frame: str, config: lula.CyclicCoordDescentIkConfig, ) → lula.CyclicCoordDescentIkResults#: Attempt to solve inverse kinematics using cyclic coordinate descent.

Path Specification#

class CSpacePathSpec#

Bases: pybind11_object

Procedurally specify a CSpacePathSpec from a series of configuration space waypoints

add_c_space_waypoint( self: lula.CSpacePathSpec, waypoint: numpy.ndarray[numpy.float64[m, 1]], ) → bool#: Add a path to connect the previous configuration to the waypoint.

num_c_space_coords(self: lula.CSpacePathSpec) → int#: Return the number of configuration space coordinates for the path specification.

create_c_space_path_spec( initial_c_space_position: numpy.ndarray[numpy.float64[m, 1]], ) → lula.CSpacePathSpec#: Create a ‘CSpacePathSpec’ with the specified ‘initial_c_space_position’.

class TaskSpacePathSpec#

Bases: pybind11_object

Procedurally compose a TaskSpacePathSpec from a series of continuous task space segments

add_linear_path( self: lula.TaskSpacePathSpec, target_pose: lula.Pose3, blend_radius: float = 0.0, ) → bool#: Add a path to linearly connect the previous pose to the ‘target_pose’.

add_rotation( self: lula.TaskSpacePathSpec, target_rotation: lula.Rotation3, ) → bool#: Add a rotation-only path connecting the previous pose to the target_rotation.

add_tangent_arc( self: lula.TaskSpacePathSpec, target_position: numpy.ndarray[numpy.float64[3, 1]], constant_orientation: bool = True, ) → bool#: Add a path to connect the previous pose to the ‘target_position’ along a circular arc that is tangent to the previous segment.

add_tangent_arc_with_orientation_target( self: lula.TaskSpacePathSpec, target_pose: lula.Pose3, ) → bool#: Add a path to connect the previous pose to the ‘target_pose’ along a circular arc that is tangent to the previous segment.

add_three_point_arc( self: lula.TaskSpacePathSpec, target_position: numpy.ndarray[numpy.float64[3, 1]], intermediate_position: numpy.ndarray[numpy.float64[3, 1]], constant_orientation: bool = True, ) → bool#: Add a path to connect the previous pose to the ‘target_position’ along a circular arc that passes through ‘intermediate_position’.

add_three_point_arc_with_orientation_target( self: lula.TaskSpacePathSpec, target_pose: lula.Pose3, intermediate_position: numpy.ndarray[numpy.float64[3, 1]], ) → bool#: Add a path to connect the previous pose to the ‘target_pose’ along a circular arc that passes through ‘intermediate_position’.

add_translation( self: lula.TaskSpacePathSpec, target_position: numpy.ndarray[numpy.float64[3, 1]], blend_radius: float = 0.0, ) → bool#: Add a translation-only path to linearly connect the previous pose to the ‘target_position’

generate_path( self: lula.TaskSpacePathSpec, ) → lula.TaskSpacePath#: Generate a continuous path between all of the procedurally added task space path segments.

create_task_space_path_spec( initial_pose: lula.Pose3, ) → lula.TaskSpacePathSpec#: Create a ‘TaskSpacePathSpec’ with the specified ‘initial_pose’.

class CompositePathSpec#

Bases: pybind11_object

Procedurally compose ‘CSpacePathSpec’ and ‘TaskSpacePathSpec’ segments into a single path specification.

class PathSpecType#

Bases: pybind11_object

Members:

TASK_SPACE

CSPACE

CSPACE = <PathSpecType.CSPACE: 1>#

TASK_SPACE = <PathSpecType.TASK_SPACE: 0>#

property name#

property value#

class TransitionMode#

Bases: pybind11_object

Members:

SKIP

FREE

LINEAR_TASK_SPACE

FREE = <TransitionMode.FREE: 1>#

LINEAR_TASK_SPACE = <TransitionMode.LINEAR_TASK_SPACE: 2>#

SKIP = <TransitionMode.SKIP: 0>#

property name#

property value#

add_c_space_path_spec( self: lula.CompositePathSpec, path_spec: lula.CSpacePathSpec, transition_mode: lula.CompositePathSpec.TransitionMode, ) → bool#: Add a c-space ‘path_spec’ to the ‘CompositePathSpec’ with the specified ‘transition_mode’.

add_task_space_path_spec( self: lula.CompositePathSpec, path_spec: lula::TaskSpacePathSpec, transition_mode: lula.CompositePathSpec.TransitionMode, ) → bool#: Add a task space ‘path_spec’ to the ‘CompositePathSpec’ with the specified ‘transition_mode’.

c_space_path_spec( self: lula.CompositePathSpec, path_spec_index: int, ) → lula.CSpacePathSpec#: Return the ‘CSpacePathSpec’ at the given ‘path_spec_index’.

num_c_space_coords( self: lula.CompositePathSpec, ) → int#: Return the number of configuration space coordinates for the path specification.

num_path_specs(self: lula.CompositePathSpec) → int#: Return the number of path specifications contained in the ‘CompositePathSpec’.

path_spec_type( self: lula.CompositePathSpec, path_spec_index: int, ) → lula.CompositePathSpec.PathSpecType#: Given a ‘path_spec_index’ in range [0, ‘num_path_specs()’), return the type of the corresponding path specification.

task_space_path_spec( self: lula.CompositePathSpec, path_spec_index: int, ) → lula::TaskSpacePathSpec#: Return the ‘TaskSpacePathSpec’ at the given ‘path_spec_index’.

create_composite_path_spec( initial_c_space_position: numpy.ndarray[numpy.float64[m, 1]], ) → lula.CompositePathSpec#: Create a ‘CompositePathSpec’ with the specified ‘initial_c_space_position’.

load_c_space_path_spec_from_file( c_space_path_spec_file: str, ) → lula.CSpacePathSpec#: Load a ‘CSpacePathSpec’ from file with absolute path ‘c_space_path_spec_file’.

load_c_space_path_spec_from_memory( c_space_path_spec_yaml: str, ) → lula.CSpacePathSpec#: Load a ‘CSpacePathSpec’ from the contents of a YAML file (‘c_space_path_spec_yaml’).

export_c_space_path_spec_to_memory( c_space_path_spec: lula.CSpacePathSpec, ) → str#: Export ‘c_space_path_spec’ as a string.

load_task_space_path_spec_from_file( task_space_path_spec_file: str, ) → lula::TaskSpacePathSpec#: Load a ‘TaskSpacePathSpec’ from file with absolute path ‘task_space_path_spec_file’.

load_task_space_path_spec_from_memory( task_space_path_spec_yaml: str, ) → lula::TaskSpacePathSpec#: Load a ‘TaskSpacePathSpec’ from the contents of a YAML file (‘task_space_path_spec_yaml’).

export_task_space_path_spec_to_memory( task_space_path_spec: lula::TaskSpacePathSpec, ) → str#: Export ‘task_space_path_spec’ as a string.

load_composite_path_spec_from_file( composite_path_spec_file: str, ) → lula.CompositePathSpec#: Load a ‘CompositePathSpec’ from file with absolute path ‘composite_path_spec_file’.

load_composite_path_spec_from_memory( composite_path_spec_yaml: str, ) → lula.CompositePathSpec#: Load a ‘CompositePathSpec’ from the contents of a YAML file (‘composite_path_spec_yaml’).

export_composite_path_spec_to_memory( composite_path_spec: lula.CompositePathSpec, ) → str#: Export ‘composite_path_spec’ as a string.

Path Generation#

class CSpacePath#

Bases: pybind11_object

Represent a path through configuration space (i.e., c-space or “joint space”).

class Domain#

Bases: pybind11_object

Indicates the continuous range of independent values, ‘s’, for which the path is defined.

property lower#

span(self: lula.CSpacePath.Domain) → float#: Convenience function to return the span of ‘s’ values included in domain.

property upper#

domain(self: lula.CSpacePath) → lula.CSpacePath.Domain#: Return the domain for the path.

eval( self: lula.CSpacePath, s: float, ) → numpy.ndarray[numpy.float64[m, 1]]#: Evaluate the path at the given ‘s’.

max_position( self: lula.CSpacePath, ) → numpy.ndarray[numpy.float64[m, 1]]#: Return the maximum x, y, and z positions reached by the path (not necessarily simultaneously) within the defined ‘domain()’.

min_position( self: lula.CSpacePath, ) → numpy.ndarray[numpy.float64[m, 1]]#: Return the minimum x, y, and z positions reached by the path (not necessarily simultaneously) within the defined ‘domain()’.

num_c_space_coords(self: lula.CSpacePath) → int#: Return the number of configuration space coordinates for the path.

path_length(self: lula.CSpacePath) → float#: Return the total translation distance accumulated along the path, where distance is computed using the L2-norm.

class LinearCSpacePath#

Bases: pybind11_object

Represent a path linearly interpolated through configuration space (i.e., c-space) waypoints.

domain( self: lula.LinearCSpacePath, ) → lula.CSpacePath.Domain#: Return the domain for the path.

eval( self: lula.LinearCSpacePath, s: float, ) → numpy.ndarray[numpy.float64[m, 1]]#: Evaluate the path at the given ‘s’.

max_position( self: lula.LinearCSpacePath, ) → numpy.ndarray[numpy.float64[m, 1]]#: Return the maximum x, y, and z positions reached by the path (not necessarily simultaneously) within the defined ‘domain()’.

min_position( self: lula.LinearCSpacePath, ) → numpy.ndarray[numpy.float64[m, 1]]#: Return the minimum x, y, and z positions reached by the path (not necessarily simultaneously) within the defined ‘domain()’.

num_c_space_coords( self: lula.LinearCSpacePath, ) → int#: Return the number of configuration space coordinates for the path.

path_length(self: lula.LinearCSpacePath) → float#: Return the total translation distance accumulated along the path, where distance is computed using the L2-norm.

waypoints( self: lula.LinearCSpacePath, ) → List[numpy.ndarray[numpy.float64[m, 1]]]#: Return all of the waypoints through which the path is linearly interpolated (including the initial and final c-space configurations).

create_linear_c_space_path( c_space_path_spec: lula.CSpacePathSpec, ) → lula.LinearCSpacePath#: Create a ‘LinearCSpacePath’ from a c-space path specification.

class TaskSpacePath#

Bases: pybind11_object

Represent a path through task space (i.e., SE(3) group representing 6-dof poses).

class Domain#

Bases: pybind11_object

Indicates the continuous range of independent values, ‘s’, for which the path is defined.

property lower#

span(self: lula.TaskSpacePath.Domain) → float#: Convenience function to return the span of ‘s’ values included in domain.

property upper#

accumulated_rotation(self: lula.TaskSpacePath) → float#: Return the total angular distance (in radians) accumulated throughout the path.

domain( self: lula.TaskSpacePath, ) → lula.TaskSpacePath.Domain#: Return the domain for the path.

eval(self: lula.TaskSpacePath, s: float) → lula.Pose3#: Evaluate the path at the given ‘s’.

max_position( self: lula.TaskSpacePath, ) → numpy.ndarray[numpy.float64[3, 1]]#: Return the maximum x, y, and z positions reached by the path (not necessarily simultaneously) within the defined ‘domain()’.

min_position( self: lula.TaskSpacePath, ) → numpy.ndarray[numpy.float64[3, 1]]#: Return the minimum x, y, and z positions reached by the path (not necessarily simultaneously) within the defined ‘domain()’.

path_length(self: lula.TaskSpacePath) → float#: Return the total translation distance accumulated along the path.

class TaskSpacePathConversionConfig#

Bases: pybind11_object

Configuration parameters for converting a ‘TaskSpacePathSpec’ into a series of c-space configurations.

property alpha#: “alpha” is used to exponentially scale the ‘s’ step size “delta” to speed convergence when the ‘s’ step size is being successively increased or successively decreased. When an increase is followed by a decrease, or vice versa, “alpha” is used to decrease the ‘s’ step size “delta” to reduce overshoot. The default value is generally recommended. ‘alpha’ must be greater than 1. Default value is 1.4.

property initial_s_step_size#: Initial step size in the domain value ‘s’ used to sample poses from the task space path to be converted to c-space waypoints. The ‘s’ step size will be adaptively updated throughout the path conversion and the default value for initialization is generally recommended. ‘initial_s_step_size’ must be positive. Default value is 0.05.

property initial_s_step_size_delta#: Initial step size “delta” that is used to adaptively adjust the ‘s’ step size; ‘s’ is the domain value ‘s’ used to sample poses from the task space path to be converted to c-space waypoints. The ‘s’ step size “delta” will be adaptively updated throughout the path conversion and the default value for initialization is generally recommended. ‘initial_s_step_size_delta’ must be positive. Default value is 0.005.

property max_iterations#: Maximum number of iterations to search for each c-space waypoint. If ‘max_iterations’ is reached for any c-space waypoint, then path conversion will fail. ‘max_iterations’ must be positive. Default value is 50.

property max_position_deviation#: For each c-space waypoint that is generated, the position deviation between the desired task space path and a task space mapping of a straight-line interpolation in c-space is approximated. The maximum position deviation places an upper bound of deviation allowed to add a c-space waypoint. ‘max_position_deviation’ must be positive and greater than ‘min_position_deviation’. Default value is 0.003.

property min_position_deviation#: For each c-space waypoint that is generated, the position deviation between the desired task space path and a task space mapping of a straight-line interpolation in c-space is approximated. The minimum position deviation places a lower bound of deviation required to add a c-space waypoint. While it is somewhat unintuitive that the deviation could be too small, this minimum deviation is used to control the minimum spacing between c-space configurations along the task space path domain. A (relatively) sparse series of c-space waypoints is desirable for trajectory generation; if the minimum deviation is arbitrarily small then the c-space points will be (in general) too close together to generate a time-optimal trajectory. Generation of excessive c-space waypoints will also be computationally expensive and is, in general, best avoided. ‘min_position_deviation’ must be positive and less than ‘max_position_deviation’. Default value is 0.001.

property min_s_step_size#: Minimum allowable interval in domain value ‘s’ that can separate poses from the task space path to be converted to c-space waypoints. The minimum ‘s’ step size serves to limit the number of c-space configurations that can be returned in the converted path. Specifically, the upper bound for the number of returned c-space configurations is (“span of the task space path domain” / min_s_step_size) + 1. ‘min_s_step_size’ must be positive. Default value is 1e-5.

property min_s_step_size_delta#: Minimum allowable ‘s’ step size “delta” used to adaptively update the ‘s’ step size. The ‘min_s_step_size_delta’ serves to limit wasted iterations when (minimal) progress is being made towards path conversion. If ‘min_s_step_size_delta’ is reached during the search for any c-space waypoint, then path conversion will fail. The default value is generally recommended. ‘min_s_step_size_delta` must be positive’. Default value is 1e-5.

convert_composite_path_spec_to_c_space( composite_path_spec: lula.CompositePathSpec, kinematics: lula.Kinematics, control_frame: str, task_space_path_conversion_config: lula.TaskSpacePathConversionConfig = <lula.TaskSpacePathConversionConfig object at 0x7fe003f803b0>, ik_config: lula.CyclicCoordDescentIkConfig = <lula.CyclicCoordDescentIkConfig object at 0x7fe0040b8370>, ) → lula.LinearCSpacePath#: Convert a composite_path_spec into a linear c-space path.

convert_task_space_path_spec_to_c_space( task_space_path_spec: lula::TaskSpacePathSpec, kinematics: lula.Kinematics, control_frame: str, task_space_path_conversion_config: lula.TaskSpacePathConversionConfig = <lula.TaskSpacePathConversionConfig object at 0x7fe003f80430>, ik_config: lula.CyclicCoordDescentIkConfig = <lula.CyclicCoordDescentIkConfig object at 0x7fe202c5f870>, ) → lula.LinearCSpacePath#: Convert a task_space_path_spec into a linear c-space path.

Trajectory Generation#

class Trajectory#

Bases: pybind11_object

Represent a path through configuration space (i.e., “c-space”) parameterized by time.

class Domain#

Bases: pybind11_object

Indicates the continuous range of time values, ‘t’, for which the trajectory and its derivatives are defined.

property lower#

span(self: lula.Trajectory.Domain) → float#: Convenience function to return the span of time values included in domain.

property upper#

domain(self: lula.Trajectory) → lula.Trajectory.Domain#: Return the domain for the trajectory.

eval( self: lula.Trajectory, time: float, derivative_order: int = 0, ) → numpy.ndarray[numpy.float64[m, 1]]#: Evaluate specified trajectory derivative at the given ‘time’ and return value.

eval_all( self: lula.Trajectory, arg0: float, ) → Tuple[numpy.ndarray[numpy.float64[m, 1]], numpy.ndarray[numpy.float64[m, 1]], numpy.ndarray[numpy.float64[m, 1]], numpy.ndarray[numpy.float64[m, 1]]]#: Evaluate the trajectory at the given ‘time’, returning position, velocity, acceleration, and jerk.

max_acceleration_magnitude( self: lula.Trajectory, ) → numpy.ndarray[numpy.float64[m, 1]]#: Return the maximum magnitude of acceleration for each c-space coordinate within the defined ‘domain()’.

max_jerk_magnitude( self: lula.Trajectory, ) → numpy.ndarray[numpy.float64[m, 1]]#: Return the maximum magnitude of jerk for each c-space coordinate within the defined ‘domain()’.

max_position( self: lula.Trajectory, ) → numpy.ndarray[numpy.float64[m, 1]]#: Return the maximum position for each c-space coordinate within the defined ‘domain()’.

max_velocity_magnitude( self: lula.Trajectory, ) → numpy.ndarray[numpy.float64[m, 1]]#: Return the maximum magnitude of velocity for each c-space coordinate within the defined ‘domain()’.

min_position( self: lula.Trajectory, ) → numpy.ndarray[numpy.float64[m, 1]]#: Return the minimum position for each c-space coordinate within the defined ‘domain()’.

num_c_space_coords(self: lula.Trajectory) → int#: Return the number of configuration space coordinates for the trajectory.

class CSpaceTrajectoryGenerator#

Bases: pybind11_object

Configure a trajectory generator that can compute smooth trajectories.

class InterpolationMode#

Bases: pybind11_object

Members:

LINEAR

CUBIC_SPLINE

CUBIC_SPLINE = <InterpolationMode.CUBIC_SPLINE: 1>#

LINEAR = <InterpolationMode.LINEAR: 0>#

property name#

property value#

class SolverParamValue#

Bases: pybind11_object

Value for a given parameter.

generate_time_stamped_trajectory(self: lula.CSpaceTrajectoryGenerator, waypoints: List[numpy.ndarray[numpy.float64[m, 1]]], times: List[float], interpolation_mode: lula.CSpaceTrajectoryGenerator.InterpolationMode = <InterpolationMode.CUBIC_SPLINE: 1>) → lula.Trajectory#: Attempt to interpolate a trajectory passing through the specified ‘waypoints’ at the specified ‘times’

generate_trajectory( self: lula.CSpaceTrajectoryGenerator, waypoints: List[numpy.ndarray[numpy.float64[m, 1]]], ) → lula.Trajectory#: Attempt to generate a time-optimal trajectory with the specified constraints.

num_c_space_coords( self: lula.CSpaceTrajectoryGenerator, ) → int#: Return the number of configuration space coordinates for the trajectory generator

set_acceleration_limits( self: lula.CSpaceTrajectoryGenerator, max_acceleration: numpy.ndarray[numpy.float64[m, 1]], ) → None#: Set acceleration limits.

set_jerk_limits( self: lula.CSpaceTrajectoryGenerator, max_jerk: numpy.ndarray[numpy.float64[m, 1]], ) → None#: Set jerk limits.

set_position_limits( self: lula.CSpaceTrajectoryGenerator, min_position: numpy.ndarray[numpy.float64[m, 1]], max_position: numpy.ndarray[numpy.float64[m, 1]], ) → None#: Set position limits.

set_solver_param( self: lula.CSpaceTrajectoryGenerator, param_name: str, value: lula.CSpaceTrajectoryGenerator.SolverParamValue, ) → bool#: Set the value of the solver parameter.

set_velocity_limits( self: lula.CSpaceTrajectoryGenerator, max_velocity: numpy.ndarray[numpy.float64[m, 1]], ) → None#: Set velocity limits.

create_c_space_trajectory_generator(*args, **kwargs)#

Overloaded function.

create_c_space_trajectory_generator(num_c_space_coords: int) -> lula.CSpaceTrajectoryGenerator

Create a CreateCSpaceTrajectoryGenerator with the specified number of configuration space coordinates.

create_c_space_trajectory_generator(kinematics: lula.Kinematics) -> lula.CSpaceTrajectoryGenerator

Create a CreateCSpaceTrajectoryGenerator with the specified ‘kinematics’.

Collision Sphere Generation#

class CollisionSphereGenerator#

Bases: pybind11_object

Configure a generator that can generate spheres to approximate mesh volumes.

class ParamValue#

Bases: pybind11_object

Value for a given parameter.

class Sphere#

Bases: pybind11_object

Simple representation of a sphere.

property center#

property radius#

generate_spheres( self: lula.CollisionSphereGenerator, num_spheres: int, radius_offset: float, ) → List[lula.CollisionSphereGenerator.Sphere]#: Generate a set of num_spheres that approximate the volume of the specified mesh

get_sampled_spheres( self: lula.CollisionSphereGenerator, ) → List[lula.CollisionSphereGenerator.Sphere]#: Return all of the medial axis spheres used to approximate the volume of the mesh. The spheres returned by generateSpheres() are selected from this set.

num_triangles( self: lula.CollisionSphereGenerator, ) → int#: Return the number of validated triangles that have been included in the mesh used for sampling spheres to approximate volume.

set_param( self: lula.CollisionSphereGenerator, param_name: str, value: lula.CollisionSphereGenerator.ParamValue, ) → bool#: Set the value of the parameter.

create_collision_sphere_generator( vertices: List[numpy.ndarray[numpy.float64[3, 1]]], triangles: List[numpy.ndarray[numpy.int32[3, 1]]], ) → lula.CollisionSphereGenerator#: Create a CollisionSphereGenerator to generate spheres that approximate the volume of a mesh represented by vertices and triangles.

Motion Planning#

class MotionPlanner#

Bases: pybind11_object

Interface for planning collision-free paths for robotic manipulators.

class Limit#

Bases: pybind11_object

Simple class consisting of upper and lower limits for a variable.

property lower#

property upper#

class ParamValue#

Bases: pybind11_object

Value for a given parameter.

class Results#

Bases: pybind11_object

Results from a path search.

property interpolated_path#

property path#

property path_found#

plan_to_cspace_target( self: lula.MotionPlanner, q_initial: numpy.ndarray[numpy.float64[m, 1]], q_target: numpy.ndarray[numpy.float64[m, 1]], generate_interpolated_path: bool = False, ) → lula::MotionPlanner::Results#: Attempt to find path to configuration space target

plan_to_pose_target(self: lula.MotionPlanner, q_initial: numpy.ndarray[numpy.float64[m, 1]], pose_target: lula::Pose3, generate_interpolated_path: bool = False) → lula::MotionPlanner::Results#: Attempt to find path to task space pose target using JtRRT.

plan_to_task_space_target( self: lula.MotionPlanner, q_initial: numpy.ndarray[numpy.float64[m, 1]], x_target: numpy.ndarray[numpy.float64[3, 1]], generate_interpolated_path: bool = False, ) → lula::MotionPlanner::Results#

Attempt to find path to task space translation target. .. warning:

This function supports legacy code from before JtRRT supported full-pose
targets. This function has been deprecated and slated for removal in a
near-future release.  Equivalent functionality is available in the
`plan_to_translation_target()` function.

plan_to_translation_target( self: lula.MotionPlanner, q_initial: numpy.ndarray[numpy.float64[m, 1]], translation_target: numpy.ndarray[numpy.float64[3, 1]], generate_interpolated_path: bool = False, ) → lula::MotionPlanner::Results#: Attempt to find path to task space translation target using JtRRT.

set_param(*args, **kwargs)#

Overloaded function.

set_param(self: lula.MotionPlanner, param_name: str, value: List[float]) -> bool

Set parameter for motion planner.

set_param(self: lula.MotionPlanner, param_name: str, value: List[lula::MotionPlanner::Limit]) -> bool

Set parameter for motion planner.

set_param(self: lula.MotionPlanner, param_name: str, value: lula::MotionPlanner::ParamValue) -> bool

Set parameter for motion planner.

set_parame( self: lula.MotionPlanner, param_name: str, value: numpy.ndarray[numpy.float64[3, 1]], ) → bool#: Set parameter for motion planner.

update_world_view(self: lula.MotionPlanner) → None#: Update the internal WorldView to latest state of its underlying World.

create_motion_planner(*args, **kwargs)#

Overloaded function.

create_motion_planner(planning_config_file: str, robot_description: lula::RobotDescription, world_view: lula::WorldView) -> lula.MotionPlanner

Create an MotionPlanner interface from configuration parameters, robot description and world view.

create_motion_planner(robot_description: lula::RobotDescription, world_view: lula::WorldView) -> lula.MotionPlanner

Create an MotionPlanner interface from robot description and world view, using default configuration parameters.

RmpFlow#

class RmpFlowConfig#

Bases: pybind11_object

RMPflow configuration, including parameters and robot description.

get_all_params( self: lula.RmpFlowConfig, names: List[str], values: List[float], ) → None#

Get the names and values of all parameters.

The two lists will be overwritten if not empty.

Parameters:

names (List[str]) – List of all parameter names.
values (List[float]) – Values of all parameters, in same order as names.

Returns:

None

get_param( self: lula.RmpFlowConfig, param_name: str, ) → float#

Get the value of an RMPflow parameter.

Parameters:: param_name (str) – Fully-qualified parameter name of the form <rmp_name>/<parameter_name>.
Returns:: Current value of the parameter.
Return type:: float

set_all_params( self: lula.RmpFlowConfig, names: List[str], values: List[float], ) → None#

Set all parameters at once.

The lists names and values must have the same size. The parameter corresponding to names[i] will be set to the value given by values[i].

Parameters:

names (List[str]) – List of all parameter names.
values (List[float]) – Desired values for all parameters, in same order as names.

Returns:

None

set_param( self: lula.RmpFlowConfig, param_name: str, value: float, ) → None#

Set the value of an RMPflow parameter.

Parameters:

param_name (str) – Fully-qualified parameter name of the form <rmp_name>/<parameter_name>.
value (float) – New value for the parameter.

Returns:

None

set_world_view( self: lula.RmpFlowConfig, world_view: lula::WorldView, ) → None#

Set the world view that will be used for obstacle avoidance.

All enabled obstacles in world_view will be avoided by the RMPflow policy.

Parameters:: world_view (lula.WorldView) – Input world view.
Returns:: None

create_rmpflow_config( rmpflow_config_file: str, robot_description: lula::RobotDescription, end_effector_frame: str, world_view: lula::WorldView, ) → lula.RmpFlowConfig#

Create an RMPflow configuration object.

This function loads a set of RMPflow parameters from a file and combines them with a robot description to create a configuration object for consumption by lula.create_rmpflow().

Parameters:

rmpflow_config_file (str) – Path to RMPflow configuration file.
robot_description (lula.RobotDescription) – RobotDescription object, e.g. created by lula.load_robot().
end_effector_frame (str) – This control frame name must correspond to a link name as specified in the original URDF used to create the robot description.
world_view (lula.WorldView) – World view that will be used for obstacle avoidance.

Returns:

RMPflow motion policy interface object.

Return type:

lula.RmpFlow

create_rmpflow_config_from_memory( rmpflow_config: str, robot_description: lula::RobotDescription, end_effector_frame: str, world_view: lula::WorldView, ) → lula.RmpFlowConfig#

Parameters:

rmpflow_config (str) – RMPflow configuration as a single string (unparsed YAML).
robot_description (lula.RobotDescription) – RobotDescription object, e.g. created by lula.load_robot().
end_effector_frame (str) – This control frame name must correspond to a link name as specified in the original URDF used to create the robot description.
world_view (lula.WorldView) – World view that will be used for obstacle avoidance.

Returns:

RMPflow motion policy interface object.

Return type:

lula.RmpFlow

class RmpFlow#

Bases: pybind11_object

Interface for building and evaluating an RMPflow motion policy.

clear_end_effector_orientation_attractor( self: lula.RmpFlow, ) → None#: Clear end-effector orientation attractor.

clear_end_effector_position_attractor( self: lula.RmpFlow, ) → None#: Clear end-effector position attractor.

collision_sphere_positions( self: lula.RmpFlow, cspace_position: numpy.ndarray[numpy.float64[m, 1]], ) → List[numpy.ndarray[numpy.float64[3, 1]]]#

Compute positions of all collision spheres on the robot at the specified cspace_position .. warning:

This function is part of a legacy interface for spatial queries that is deprecated
and slated for removal in a near-future release. It is recommended that this
function *NOT* be used. Equivalent functionality is available from
'lula.RobotWorldInspector'.

collision_sphere_radii(self: lula.RmpFlow) → List[float]#

Return the radii of each collision sphere on the robot. .. warning:

This function is part of a legacy interface for spatial queries that is deprecated
and slated for removal in a near-future release. It is recommended that this
function *NOT* be used. Equivalent functionality is available from
'lula.RobotWorldInspector'.

distance_to_obstacle(self: lula.RmpFlow, obstacle: lula::World::ObstacleHandle, collision_sphere_index: int, cspace_position: numpy.ndarray[numpy.float64[m, 1]]) → float#

Compute the distance between a given obstacle and collision sphere for the provided position in configuration space. .. warning:

This function is part of a legacy interface for spatial queries that is deprecated
and slated for removal in a near-future release. It is recommended that this
function *NOT* be used. Equivalent functionality is available from
'lula.RobotWorldInspector'.

eval_accel( self: lula.RmpFlow, cspace_position: numpy.ndarray[numpy.float64[m, 1]], cspace_velocity: numpy.ndarray[numpy.float64[m, 1]], cspace_accel: numpy.ndarray[numpy.float64[m, 1], flags.writeable], ) → None#: Compute configuration-space acceleration from motion policy, given input state.

eval_force_and_metric( self: lula.RmpFlow, cspace_position: numpy.ndarray[numpy.float64[m, 1]], cspace_velocity: numpy.ndarray[numpy.float64[m, 1]], ) → Tuple[numpy.ndarray[numpy.float64[m, 1]], numpy.ndarray[numpy.float64[m, n]]]#: Compute configuration-space force and metric from motion policy, given input state.

in_collision_with_obstacle( self: lula.RmpFlow, cspace_position: numpy.ndarray[numpy.float64[m, 1]], ) → bool#

Determine whether a specified joint configuration puts the robot into collision with an obstacle. .. warning:

This function is part of a legacy interface for spatial queries that is deprecated
and slated for removal in a near-future release. It is recommended that this
function *NOT* be used. Equivalent functionality is available from
'lula.RobotWorldInspector'.

num_collision_spheres(self: lula.RmpFlow) → int#

Return the number of collision spheres in the robot representation. .. warning:

This function is part of a legacy interface for spatial queries that is deprecated
and slated for removal in a near-future release. It is recommended that this
function *NOT* be used. Equivalent functionality is available from
'lula.RobotWorldInspector'.

set_cspace_attractor( self: lula.RmpFlow, cspace_position: numpy.ndarray[numpy.float64[m, 1]], ) → None#: Set an attractor in generalized coordinates (configuration space).

set_end_effector_orientation_attractor( self: lula.RmpFlow, orientation: lula::Rotation3, ) → None#: Set an end-effector orientation attractor.

set_end_effector_position_attractor( self: lula.RmpFlow, position: numpy.ndarray[numpy.float64[3, 1]], ) → None#: Set an end-effector position attractor.

update_world_view(self: lula.RmpFlow) → None#: Update the internal WorldView to latest state of its underlying World.

create_rmpflow(config: lula.RmpFlowConfig) → lula.RmpFlow#: Create an instance of the RmpFlow interface from an RMPflow configuration.