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

Abstract:

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.

Download:

Bibtex:

  
@INPROCEEDINGS{Williams-RSS-18, 
    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} 
}