NeuralIKSolver

NeuralIKSolver#

Experimental

NeuralIKSolver is an experimental feature. The API, checkpoint format, and default parameters may change without a deprecation cycle. It is currently only validated on the Franka Panda robot.

NeuralIKSolver is a learning-based inverse kinematics (IK) solver that uses a trained neural network policy to iteratively solve IK queries. It requires a pre-trained checkpoint and supports batch processing.

Key Features#

  • Iterative neural policy inference for IK solving

  • Batch processing for multiple target poses simultaneously

  • Multi-seed sampling: generate several random initial guesses and return the best solution

  • Joint limit enforcement at every iteration

  • PyTorch-based — supports both CPU and CUDA devices

Configuration#

The solver is configured using the NeuralIKSolverCfg class. Pre-trained checkpoints are hosted on HuggingFace and can be downloaded with download_neural_ik_checkpoint() (requires HF_TOKEN environment variable).

from embodichain.data.assets.solver_assets import download_neural_ik_checkpoint
from embodichain.lab.sim.solvers.neural_ik_solver import NeuralIKSolverCfg

checkpoint_path = download_neural_ik_checkpoint()

cfg = NeuralIKSolverCfg(
    checkpoint_path=checkpoint_path,
    num_arm_joints=7,
    max_steps=30,
    action_scale=0.2,
    hidden_dims=[256, 256],
    pos_eps=0.01,
    rot_eps=0.1,
    num_samples=1,
)

Main Methods#

  • get_ik(self, target_xpos, qpos_seed=None, num_samples=None, **kwargs) Solve IK for the given target end-effector pose(s).

    Parameters:

    • target_xpos (torch.Tensor): Target pose(s) as 4x4 matrix, shape (4, 4) or (B, 4, 4).

    • qpos_seed (torch.Tensor, optional): Initial joint positions, shape (dof,) or (B, dof).

    • num_samples (int, optional): Override cfg.num_samples for this call.

    • return_all_solutions (bool): If True, return all sampled solutions with shape (B, num_samples, dof).

    Returns:

    • Tuple[torch.Tensor, torch.Tensor]:

      • Success flags, shape (B,).

      • Joint positions, shape (B, 1, dof) or (B, num_samples, dof).

    Example:

  import torch
  success, ik_qpos = solver.get_ik(target_xpos=target_pose, qpos_seed=qpos_seed)
  print("Success:", success)
  print("IK solution:", ik_qpos)
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