Sampling-Based MPC Using a GPU-parallelizable Physics Simulator as Dynamic Model: an Open Source Implementation with IsaacGym

Corrado Pezzato, Chadi Salmi, Elia Trevisan, Javier Alonso Mora, Carlos Hernandez Corbato

Abstract: We present a method for solving finite horizon optimal control problems using a generic physics simulator as the dynamical model. In particular, we present an open-source implementation of a model predictive path integral controller (MPPI), that uses the GPU-parallelizable IsaacGym simulator as the dynamical model to compute the forward dynamics of the system. This allows one to effortlessly solve complex contact-rich tasks such as for example, non-prehensile manipulation of a variety of objects, or picking with a mobile manipulator. Since there is no explicit dynamic modeling required from a user, the repository is easily extendable to different objects and robots, as we show in the experiments section. This makes this method a powerful and accessible tool to solve a large variety of contact-rich tasks.