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.
@article{arxiv.2006.03106,
title = {Model-Based Generalization Under Parameter Uncertainty Using Path Integral Control},
author = {Ian Abraham and Ankur Handa and Nathan Ratliff and Kendall Lowrey and Todd D. Murphey and Dieter Fox},
journal= {arXiv preprint arXiv:2006.03106},
year = {2020}
}