Abstract
We present a differentiable rigid-body-dynamics simulator for robotics that prioritizes physical accuracy and differentiability: Dojo. The simulator utilizes an expressive maximal-coordinates representation, achieves stable simulation at low sample rates, and conserves energy and momentum by employing a variational integrator. A nonlinear complementarity problem, with nonlinear friction cones, models hard contact and is reliably solved using a custom primal-dual interior-point method. The implicit-function theorem enables efficient differentiation of an intermediate relaxed problem and computes smooth gradients from the contact model. We demonstrate the usefulness of the simulator and it’s gradients through a number of examples including: simulation, trajectory optimization, reinforcement learning, and system identification.
Links
arXiv preprint: https://arxiv.org/abs/2203.00806
Python interface: https://github.com/dojo-sim/dojopy
video presentation: https://youtu.be/TRtOESXJxJQ
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@article{howelllecleach2022,
title={Dojo: A Differentiable Simulator for Robotics},
author={Taylor, A. Howell and Le Cleac'h, Simon and Kolter, Zico and Schwager, Mac and Manchester, Zachary},
journal={arXiv preprint arXiv:2203.00806},
year={2022}
}
Features
Application domains
Real hardware
Gym
Classic control
Physics Simulation
Acknowledgments
The authors would like to thank Jan BrĂ¼digam for his contributions to the open-source libraries ConstrainedDynamics.jl and GraphBasedSystems.jl which served as a foundation for Dojo, as well as early technical discussions and support; and Suvansh Sanjeev for assistance with the Python interface. Toyota Research Institute provided funds to support this work.
Authors