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Information Theoretic Model Predictive Control: Theory and Applications to Autonomous Driving
July 07, 2017 Β· Declared Dead Β· π IEEE Transactions on robotics
"No code URL or promise found in abstract"
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Authors
Grady Williams, Paul Drews, Brian Goldfain, James M. Rehg, Evangelos A. Theodorou
arXiv ID
1707.02342
Category
cs.RO: Robotics
Citations
349
Venue
IEEE Transactions on robotics
Last Checked
3 months ago
Abstract
We present an information theoretic approach to stochastic optimal control problems that can be used to derive general sampling based optimization schemes. This new mathematical method is used to develop a sampling based model predictive control algorithm. We apply this information theoretic model predictive control (IT-MPC) scheme to the task of aggressive autonomous driving around a dirt test track, and compare its performance to a model predictive control version of the cross-entropy method.
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