TorchRobotics

Differentiable robot kinematics tree from URDF/MJCF formats, with differentiable planning objectives (obstacle avoidance, self-collision, end-effector tracking).

Requirements

• Python >= 3.9
• PyTorch >= 2.0

Installation

Activate your conda/Python environment and run:

pip install -e .

Examples

Forward kinematics for all available robot models:

python examples/forward_kinematics.py

Inverse kinematics via Adam optimization:

python examples/inverse_kinematics.py

Acknowledgements

Parts of the kinematics tree implementation are based on differentiable-robot-model (Meta AI).

Contact

• An Le — an@robot-learning.de
• Joao Carvalho — joao@robot-learning.de

Citation

If you found this repository useful, please consider citing:

@article{le2023accelerating,
  title={Accelerating motion planning via optimal transport},
  author={Le, An T and Chalvatzaki, Georgia and Biess, Armin and Peters, Jan R},
  journal={Advances in Neural Information Processing Systems},
  volume={36},
  pages={78453--78482},
  year={2023}
}

@inproceedings{carvalho2023motion,
  title={Motion planning diffusion: Learning and planning of robot motions with diffusion models},
  author={Carvalho, Joao and Le, An T and Baierl, Mark and Koert, Dorothea and Peters, Jan},
  booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  pages={1916--1923},
  year={2023},
  organization={IEEE}
}