Robotics: Science and Systems XIV

Robust Sampling Based Model Predictive Control with Sparse Objective Information

Grady Williams, Brian Goldfain, Paul Drews, Kamil Saigol, James Rehg, Evangelos Theodorou


We present an algorithmic framework for stochastic model predictive control that is able to optimize non-linear systems with cost functions that have sparse, discontinuous gradient information. The proposed framework combines the benefits of sampling-based model predictive control with linearization-based trajectory optimization methods. The resulting algorithm consists of a novel utilization of Tube-based model predictive control. We demonstrate robust algorithmic performance on a variety of simulated tasks, and on a real-world fast autonomous driving task.



    AUTHOR    = {Grady Williams AND Brian Goldfain AND Paul Drews AND Kamil Saigol AND James Rehg AND Evangelos Theodorou}, 
    TITLE     = {Robust Sampling Based Model Predictive Control with Sparse Objective Information}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2018}, 
    ADDRESS   = {Pittsburgh, Pennsylvania}, 
    MONTH     = {June}, 
    DOI       = {10.15607/RSS.2018.XIV.042}