**[Dojo: A Differentiable Physics Engine for Robotics](https://sites.google.com/view/dojo-sim/home)** Taylor A. Howell, Simon Le Cleac'h, Jan Brüdigam, J. Zico Kolter, Mac Schwager, Zachary Manchester Arxiv Print  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. Preliminary Knowledge =================================================== Main Equation =================================================== Main Contribution =============================================================== Conclusion =================================================================== Future Ideas ===================================================================