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 TheodorouAbstract:
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.
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} }