Model Predictive Path Integral Control using Covariance Variable Importance Sampling

In this paper we present a Model Predictive Path Integral (MPPI) control algorithm that is derived from the path integral control framework and a generalized importance sampling scheme. In order to operate in real time we parallelize the sampling based component of the algorithm and achieve massive speed-up by using a Graphical Processor Unit (GPU). We compare MPPI against traditional model predictive control methods based on Differential Dynamic Programming (MPCDDP) on a benchmark cart-pole swing-up task as well as a navigation tasks for a racing car and a quad-rotor in simulation. Finally, we use MPPI to navigate a team of three (48 states) and nine quad rotors (144 states) through cluttered environments of fixed and moving obstacles in simulation.