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Model Predictive Path Integral Control using Covariance Variable Importance Sampling

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.

preprint2015arXivOpen access
Grady WilliamsAndrew AldrichEvangelos Theodorou
Systems and ControlRoboticsDistributed, Parallel, and Cluster Computing
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Open access3 authors3 topics
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Grady WilliamsAndrew AldrichEvangelos Theodorou

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