  We present a method for sampling-based model predictive control that makes
use of a generic physics simulator as the dynamical model. In particular, we
propose a Model Predictive Path Integral controller (MPPI), that uses the
GPU-parallelizable IsaacGym simulator to compute the forward dynamics of a
problem. By doing so, we eliminate the need for explicit encoding of robot
dynamics and contacts with objects for MPPI. Since no explicit dynamic modeling
is required, our method is easily extendable to different objects and robots
and allows one to solve complex navigation and contact-rich tasks. We
demonstrate the effectiveness of this method in several simulated and
real-world settings, among which mobile navigation with collision avoidance,
non-prehensile manipulation, and whole-body control for high-dimensional
configuration spaces. This method is a powerful and accessible open-source tool
to solve a large variety of contact-rich motion planning tasks.
