A differentiable physics engine for robotics
- arXiv preprint: https://arxiv.org/abs/2203.00806
- Python interface: https://github.com/dojo-sim/dojopy
- site: https://sites.google.com/view/dojo-sim
- video presentation: https://youtu.be/TRtOESXJxJQ
- We are no longer actively developing Dojo, but pull requests are always welcome.
- We have updated or removed examples to account for changes since the initial version of Dojo.
- Additional developments on differentiable simulation:
- Differentiable collision detection (Kevin Tracy): capsules, convex primitives
- Single-level contact dynamics + collision detection (Simon Le Cleac'h): Silico
ReinforcementLearning.jl: DQN | ControlSystems.jl: LQR |
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Dojo
can be added via the Julia package manager (type ]
):
pkg> add Dojo
For convenience mechanisms and environments, add DojoEnvironments
additionally:
pkg> add DojoEnvironments
@article{howelllecleach2022,
title={Dojo: A Differentiable Physics Engine for Robotics},
author={Howell, Taylor and Le Cleac'h, Simon and Bruedigam, Jan and Kolter, Zico and Schwager, Mac and Manchester, Zachary},
journal={arXiv preprint arXiv:2203.00806},
url={https://arxiv.org/abs/2203.00806},
year={2022}
}
Please submit a pull request or open an issue. See the docs for contribution ideas.