Model Predictive Path Integral Control using Covariance Variable Importance Sampling¶
Authors: Grady Williams, Andrew Aldrich, Evangelos Theodorou
Published: 2015 (Conference Paper)
Algorithm: MPPI
arXiv: 1509.01149
Summary¶
Folds importance sampling into the cost function in order to address the practical issue of infrequent selection of low-cost trajectories under naive sampling of actions on the uncontrolled system, which is a known drawback of MPPI. Also discusses implementation of MPPI on a GPU for massively parallel sampling. Includes a clear description of MPPI in Algorithm 1.
Abstract¶
In this paper we develop a Model Predictive Path Integral (MPPI) control algorithm based on a generalized importance sampling scheme and perform parallel optimization via sampling using a Graphics Processing Unit (GPU). The proposed generalized importance sampling scheme allows for changes in the drift and diffusion terms of stochastic diffusion processes and plays a significant role in the performance of the model predictive control algorithm. We compare the proposed algorithm in simulation with a model predictive control version of differential dynamic programming.