  We present an algorithm which combines recent advances in model based path
integral control with machine learning approaches to learning forward dynamics
models. We take advantage of the parallel computing power of a GPU to quickly
take a massive number of samples from a learned probabilistic dynamics model,
which we use to approximate the path integral form of the optimal control. The
resulting algorithm runs in a receding-horizon fashion in realtime, and is
subject to no restrictive assumptions about costs, constraints, or dynamics. A
simple change to the path integral control formulation allows the algorithm to
take model uncertainty into account during planning, and we demonstrate its
performance on a quadrotor navigation task. In addition to this novel
adaptation of path integral control, this is the first time that a
receding-horizon implementation of iterative path integral control has been run
on a real system.
