roboplan.roboplan_ext.optimal_ik

Classes

`Task`	Abstract base class for IK tasks.
`FrameTaskOptions`	Parameters for FrameTask.
`FrameTask`	Task to reach a target pose for a specified frame.
`ConfigurationTaskOptions`	Parameters for ConfigurationTask.
`ConfigurationTask`	Task to reach a target joint configuration.
`Constraints`	Abstract base class for IK constraints.
`PositionLimit`	Constraint to enforce joint position limits.
`VelocityLimit`	Constraint to enforce joint velocity limits.
`Oink`	Optimal Inverse Kinematics solver.

Module Contents

class roboplan.roboplan_ext.optimal_ik.Task

Abstract base class for IK tasks.

property gain: float

Task gain for low-pass filtering.

Return type:: float

property weight: Annotated[numpy.typing.NDArray[numpy.float64], dict(shape=None, None, order='F')]

Weight matrix for cost normalization.

Return type:: Annotated[numpy.typing.NDArray[numpy.float64], dict(shape=(None, None), order=’F’)]

property lm_damping: float

Levenberg-Marquardt damping.

Return type:: float

property num_variables: int

Number of optimization variables.

Return type:: int

class roboplan.roboplan_ext.optimal_ik.FrameTaskOptions(position_cost=1.0, orientation_cost=1.0, task_gain=1.0, lm_damping=0.0)

Parameters for FrameTask.

Parameters:

position_cost (float)
orientation_cost (float)
task_gain (float)
lm_damping (float)

property position_cost: float

Position cost weight.

Return type:: float

property orientation_cost: float

Orientation cost weight.

Return type:: float

property task_gain: float

Task gain for low-pass filtering.

Return type:: float

property lm_damping: float

Levenberg-Marquardt damping.

Return type:: float

class roboplan.roboplan_ext.optimal_ik.FrameTask(target_pose, num_variables, options=...)

Bases: Task

Task to reach a target pose for a specified frame.

Parameters:

target_pose (roboplan_ext.core.CartesianConfiguration)
num_variables (int)
options (FrameTaskOptions)

property frame_name: str

Name of the frame to control.

Return type:: str

property frame_id: std::optional<unsigned long>

Index of the frame in the scene’s Pinocchio model.

Return type:: std::optional<unsigned long>

property target_pose: roboplan_ext.core.CartesianConfiguration

Target pose for the frame.

Return type:: roboplan_ext.core.CartesianConfiguration

setTargetFrameTransform(arg, /)

Sets the target transform for this frame task.

Parameters:: arg (Annotated[numpy.typing.NDArray[numpy.float64], dict(shape=(4, 4), order='F')])
Return type:: None

class roboplan.roboplan_ext.optimal_ik.ConfigurationTaskOptions(task_gain=1.0, lm_damping=0.0)

Parameters for ConfigurationTask.

Parameters:

task_gain (float)
lm_damping (float)

property task_gain: float

Task gain for low-pass filtering.

Return type:: float

property lm_damping: float

Levenberg-Marquardt damping.

Return type:: float

class roboplan.roboplan_ext.optimal_ik.ConfigurationTask(target_q, joint_weights, options=...)

Bases: Task

Task to reach a target joint configuration.

Parameters:

target_q (Annotated[numpy.typing.NDArray[numpy.float64], dict(shape=(None, ), order='C')])
joint_weights (Annotated[numpy.typing.NDArray[numpy.float64], dict(shape=(None, ), order='C')])
options (ConfigurationTaskOptions)

property target_q: Annotated[numpy.typing.NDArray[numpy.float64], dict(shape=None, order='C')]

Target joint configuration.

Return type:: Annotated[numpy.typing.NDArray[numpy.float64], dict(shape=(None, ), order=’C’)]

property joint_weights: Annotated[numpy.typing.NDArray[numpy.float64], dict(shape=None, order='C')]

Weights for each joint in the configuration task.

Return type:: Annotated[numpy.typing.NDArray[numpy.float64], dict(shape=(None, ), order=’C’)]

class roboplan.roboplan_ext.optimal_ik.Constraints: Abstract base class for IK constraints.

class roboplan.roboplan_ext.optimal_ik.PositionLimit(num_variables, gain=1.0)

Bases: Constraints

Constraint to enforce joint position limits.

Parameters:

num_variables (int)
gain (float)

property config_limit_gain: float

Gain for position limit enforcement.

Return type:: float

class roboplan.roboplan_ext.optimal_ik.VelocityLimit(num_variables, dt, v_max)

Bases: Constraints

Constraint to enforce joint velocity limits.

Parameters:

num_variables (int)
dt (float)
v_max (Annotated[numpy.typing.NDArray[numpy.float64], dict(shape=(None, ), order='C')])

property dt: float

Time step for velocity calculation.

Return type:: float

property v_max: Annotated[numpy.typing.NDArray[numpy.float64], dict(shape=None, order='C')]

Maximum joint velocities.

Return type:: Annotated[numpy.typing.NDArray[numpy.float64], dict(shape=(None, ), order=’C’)]

class roboplan.roboplan_ext.optimal_ik.Oink(num_variables)

Optimal Inverse Kinematics solver.

Parameters:: num_variables (int)

solveIk(tasks, constraints, scene, delta_q, regularization=1e-12)

Solve inverse kinematics for given tasks and constraints.

Solves a QP optimization problem to compute the joint velocity that minimizes weighted task errors while satisfying all constraints. The result is written directly into the provided delta_q buffer.

Parameters:

tasks (collections.abc.Sequence[Task]) – List of weighted tasks to optimize for.
constraints (collections.abc.Sequence[Constraints]) – List of constraints to satisfy.
scene (roboplan_ext.core.Scene) – Scene containing robot model and state.
delta_q (Annotated[numpy.typing.NDArray[numpy.float64], dict(shape=(None, ))]) – Pre-allocated numpy array for output (size = num_variables). Must be a contiguous float64 array. Modified in-place.
regularization (float) – Tikhonov regularization weight added to the Hessian diagonal. Provides numerical stability. Higher values increase regularization but may reduce task tracking accuracy. Default is 1e-12.

Raises:

RuntimeError – If the QP solver fails to find a solution.

Return type:

None

Example

delta_q = np.zeros(oink.num_variables) oink.solveIk(tasks, constraints, scene, delta_q) # Or with custom regularization: oink.solveIk(tasks, constraints, scene, delta_q, regularization=1e-6)