ICRA 2016 Paper Abstract


Paper TuCbT2.11

Williams, Grady (Georgia Institute of Technology), Drews, Paul (Georgia Institute of Technology), Goldfain, Brian (Georgia Institute of Technology), Rehg, James (Georgia Institute of Technology), Theodorou, Evangelos (Georgia Institute of Technology)

Aggressive Driving with Model Predictive Path Integral Control

Scheduled for presentation during the Regular Session "Optimization and Optimal Control I" (TuCbT2), Tuesday, May 17, 2016, 14:41−14:44, Rm. A3

2016 IEEE International Conference on Robotics and Automation, May 16-21, 2016, Stockholm, Sweden

This information is tentative and subject to change. Compiled on July 14, 2020

Keywords Optimization and Optimal Control, Wheeled Robots, Control Architectures and Programming


In this paper we present a model predictive control algorithm designed for optimizing non-linear systems subject to complex cost criteria. The algorithm is based on a stochastic optimal control framework using a fundamental relationship between the information theoretic notions of free energy and relative entropy. The optimal controls in this setting take the form of a path integral, which we approximate using an efficient importance sampling scheme. We experimentally verify the algorithm by implementing it on a Graphics Processing Unit (GPU) and apply it to the problem of controlling a fifth-scale Auto-Rally vehicle in an aggressive driving task.



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