PytorchSolver

PytorchSolver#

PytorchSolver solves batched numerical IK using pytorch_kinematics. It supports position-only or full-pose targets, multiple seeds, joint limits, and GPU computation.

Configuration#

The solver is configured using the PytorchSolverCfg class, which allows detailed control over solver parameters and robot model setup.

from embodichain.data import get_data_path
from embodichain.lab.sim.motion.solvers.pytorch_solver import PytorchSolver
from embodichain.lab.sim.motion.solvers.pytorch_solver import PytorchSolverCfg
import torch

cfg = PytorchSolverCfg(
    urdf_path=get_data_path("UniversalRobots/UR5/UR5.urdf"),
    end_link_name="ee_link",
    root_link_name="base_link",
    max_iterations=1000,
    pos_eps=1e-4,
    rot_eps=1e-4,
    dt=0.05,
    damp=1e-6,
    num_samples=30,
    is_only_position_constraint=False,
)

solver = PytorchSolver(cfg)

Dynamic Parameter Adjustment#

Solver parameters can be updated at runtime using set_iteration_params :

solver.set_iteration_params(
    pos_eps=1e-5,
    rot_eps=1e-5,
    max_iterations=500,
    num_samples=50,
    damp=1e-7,
)

Solver-specific behavior#

See shared FK/IK conventions for the common call pattern and return order.

get_ik() returns success flags (B,) and, on success, the selected solution (B, 1, DOF). An entirely failed batch returns joint values with shape (B, DOF). With return_all_solutions=True, the second dimension contains the sampled solutions. Tune num_samples and convergence settings when the initial seed is insufficient.

References#

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