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We present an algorithm which combines recent advances in model based pathintegral control with machine learning approaches to learning forward dynamicsmodels. We take advantage of the parallel computing power of a GPU to quicklytake a massive number of samples from a learned probabilistic dynamics model,which we use to approximate the path integral form of the optimal control. Theresulting algorithm runs in a receding-horizon fashion in realtime, and issubject to no restrictive assumptions about costs, constraints, or dynamics. Asimple change to the path integral control formulation allows the algorithm totake model uncertainty into account during planning, and we demonstrate itsperformance on a quadrotor navigation task. In addition to this noveladaptation of path integral control, this is the first time that areceding-horizon implementation of iterative path integral control has been runon a real system.

GPU Based Path Integral Control with Learned Dynamics