Dojo: A Differentiable Simulator for Robotics

@article{howell_dojo_2022,
  title = {Dojo: {A} {Differentiable} {Simulator} for {Robotics}},
  shorttitle = {Dojo},
  url = {http://arxiv.org/abs/2203.00806},
  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 interiorpoint 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 its gradients through a number of examples including: simulation, trajectory optimization, reinforcement learning, and system identification.},
  language = {en},
  urldate = {2022-03-07},
  journal = {Robotics: Science and Systems 2022},
  author = {Howell, Taylor A. and Le Cleac'h, Simon and Kolter, J. Zico and Schwager, Mac and Manchester, Zachary},
  month = mar,
  year = {2022},
  note = {under review},
  keywords = {optimal\_control},
  month_numeric = {3}
}