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.
Simply activate your conda/Python environment and run
pip install -e .
To see FK, IK of all available robot kinematics
python examples/forward_kinematics.py
and
python examples/inverse_kinematics.py
A part of this implementation is inspired from the library differentiable robot model.
If you have any questions or find any bugs, please let us know:
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}
}