# Model-Based Generalization Under Parameter Uncertainty Using Path Integral Control

arXiv: https://arxiv.org/abs/2006.03106 · HTML: https://mlsanity.com/paper/2006.03106/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jun 2020.

## Built on by (most cited first, 7 of 7)

- [Dropout Q-Functions for Doubly Efficient Reinforcement Learning](https://mlsanity.com/paper/2110.02034/) (2021)
- [Robust Policy Learning over Multiple Uncertainty Sets](https://mlsanity.com/paper/2202.07013/) (2022)
- [Dual Online Stein Variational Inference for Control and Dynamics](https://mlsanity.com/paper/2103.12890/) (2021)
- [Learning Control Barrier Functions and their application in Reinforcement Learning: A Survey](https://mlsanity.com/paper/2404.16879/) (2024)
- [Mind the Uncertainty: Risk-Aware and Actively Exploring Model-Based Reinforcement Learning](https://mlsanity.com/paper/2309.05582/) (2023)
- [Robust Reinforcement Learning through Efficient Adversarial Herding](https://mlsanity.com/paper/2306.07408/) (2023)
- [Incremental Few-Shot Adaptation for Non-Prehensile Object Manipulation using Parallelizable Physics Simulators](https://mlsanity.com/paper/2409.13228/) (2024)

## Abstract (arXiv)

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.