Information Theoretic Model Predictive Control: Theory and Applications to Autonomous Driving

  title={Information Theoretic Model Predictive Control: Theory and Applications to Autonomous Driving},
  author={Grady Williams and Paul Drews and Brian Goldfain and James M. Rehg and Evangelos Theodorou},
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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