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}
}