rby1_sdk.OptimalControlCommandBuilder

class OptimalControlCommandBuilder

Bases: pybind11_object

Optimal control command builder.

Constructs an optimal control objective composed of Cartesian pose costs, center-of-mass (COM) costs, and joint position costs, with global error and limit scalings.

Notes

  • The overall objective can be viewed as a weighted least-squares form:

    \[J \;=\; \sum_{i} w_t^{(i)} \,\|\, p^{(i)}_d - p^{(i)} \,\|^2 \;+\; \sum_{i} w_r^{(i)} \,\|\, r^{(i)}_d \ominus r^{(i)} \,\|^2 \;+\; w_{\mathrm{com}} \,\|\, c_d - c \,\|^2 \;+\; \sum_j w_q^{(j)} \,(q^{(j)}_d - q^{(j)})^2\]

    where \(p, r\) denote translation and rotation of a link in a reference frame, \(c\) is the COM position, and \(q\) are joint positions.

  • Velocity/acceleration limit scalings multiply internal limits used by the optimizer; they do not redefine hardware limits.

Examples

Set a right-wrist Cartesian target and a joint hint, with moderate limit scalings:

>>> import numpy as np, rby1_sdk as rby
>>> T = np.eye(4); T[0,3] = 0.35; T[2,3] = 0.55
>>> oc = (
...   rby.OptimalControlCommandBuilder()
...   .add_cartesian_target("base_link", "ee_right", T, translation_weight=1.0, rotation_weight=0.5)
...   .add_joint_position_target("right_arm_4", 0.4, weight=0.2)
...   .set_velocity_limit_scaling(0.8)
...   .set_acceleration_limit_scaling(0.6)
...   .set_error_scaling(1.0)
... )
__init__(self: rby1_sdk.OptimalControlCommandBuilder) → None

Methods

__init__(self)

add_cartesian_target(self, ref_link_name, ...)

Add a Cartesian pose target for a link.

add_joint_position_target(self, joint_name, ...)

Add a joint position target.

set_acceleration_limit_scaling(self, ...)

Scale internal joint acceleration limits used by the optimizer.

set_center_of_mass_target(self, ...)

Set a center-of-mass (COM) position target.

set_command_header(self, command_header_builder)

Attach a command header to this builder.

set_error_scaling(self, error_scaling)

Set a global error scaling applied to cost terms.

set_min_delta_cost(self, min_delta_cost)

Set the minimum required improvement in cost between iterations.

set_patience(self, patience)

Set the iteration patience before stopping on small improvements.

set_stop_cost(self, stop_cost)

Set the absolute objective threshold to consider the problem "solved".

set_velocity_limit_scaling(self, ...)

Scale internal joint velocity limits used by the optimizer.