  This work addresses the problem of robot interaction in complex environments
where online control and adaptation is necessary. By expanding the sample space
in the free energy formulation of path integral control, we derive a natural
extension to the path integral control that embeds uncertainty into action and
provides robustness for model-based robot planning. Our algorithm is applied to
a diverse set of tasks using different robots and validate our results in
simulation and real-world experiments. We further show that our method is
capable of running in real-time without loss of performance. Videos of the
experiments as well as additional implementation details can be found at
https://sites.google.com/view/emppi.
