TorchRobotics

This library implements differentiable robot tree from URDF or MCJF robot format, and the differentiable planning objects such as obstacle avoidance, self-collision avoidance and via point.

NOTE: torch_robotics is under heavy development and highly experimental.

Installation

Simply activate your conda/Python environment and run

pip install -e .

Examples

To see FK, IK of all available robot kinematics

python examples/forward_kinematics.py

and

python examples/inverse_kinematics.py

Acknowledgements

A part of this implementation is inspired from the library differentiable robot model.

Contact

If you have any questions or find any bugs, please let us know:

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

Citation

If you found this repository useful, please consider citing these references:

@inproceedings{le2023accelerating,
  title={Accelerating Motion Planning via Optimal Transport},
  author={Le, An T. and Chalvatzaki, Georgia and Biess, Armin and Peters, Jan},
  booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
  year={2023}
}

@article{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},
  journal={arXiv preprint arXiv:2308.01557},
  year={2023}
}