# Robust Model Predictive Path Integral Control: Analysis and Performance Guarantees

arXiv: https://arxiv.org/abs/2102.09027 · HTML: https://mlsanity.com/paper/2102.09027/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Feb 2021.

## Built on by (most cited first, 17 of 17)

- [Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racing](https://mlsanity.com/paper/2202.07008/) (2022)
- [WayFAST: Navigation with Predictive Traversability in the Field](https://mlsanity.com/paper/2203.12071/) (2022)
- [Planning to Practice: Efficient Online Fine-Tuning by Composing Goals in Latent Space](https://mlsanity.com/paper/2205.08129/) (2022)
- [Generalization with Lossy Affordances: Leveraging Broad Offline Data for Learning Visuomotor Tasks](https://mlsanity.com/paper/2210.06601/) (2022)
- [A Super-human Vision-based Reinforcement Learning Agent for Autonomous Racing in Gran Turismo](https://mlsanity.com/paper/2406.12563/) (2024)
- [Expressive Value Learning for Scalable Offline Reinforcement Learning](https://mlsanity.com/paper/2510.08218/) (2025)
- [Pay Attention to How You Drive: Safe and Adaptive Model-Based Reinforcement Learning for Off-Road Driving](https://mlsanity.com/paper/2310.08674/) (2023)
- [CoVO-MPC: Theoretical Analysis of Sampling-based MPC and Optimal Covariance Design](https://mlsanity.com/paper/2401.07369/) (2024)
- [Proactive Emergency Collision Avoidance for Automated Driving in Highway Scenarios](https://mlsanity.com/paper/2310.17381/) (2023)
- [Action Flow Matching for Continual Robot Learning](https://mlsanity.com/paper/2504.18471/) (2025)
- [Bayesian Learning Approach to Model Predictive Control](https://mlsanity.com/paper/2203.02720/) (2022)
- [Safe Beyond the Horizon: Efficient Sampling-based MPC with Neural Control Barrier Functions](https://mlsanity.com/paper/2502.15006/) (2025)
- [Situationally-Aware Dynamics Learning](https://mlsanity.com/paper/2505.19574/) (2025)
- [Learning Causal Structure Distributions for Robust Planning](https://mlsanity.com/paper/2508.06742/) (2025)
- [Discrete fully probabilistic design: towards a control pipeline for the synthesis of policies from examples](https://mlsanity.com/paper/2112.11210/) (2021)
- [Enhancing AUV Autonomy With Model Predictive Path Integral Control](https://mlsanity.com/paper/2308.05547/) (2023)
- [Adapting Generalist Vehicle Models for High-Speed MPC Across Terrains](https://mlsanity.com/paper/2607.13319/) (2026)

## Abstract (arXiv)

In this paper we propose a novel decision making architecture for Robust Model Predictive Path Integral control (RMPPI) and investigate its performance guarantees and applicability to off-road navigation. Key building blocks of the proposed architecture are an augmented state space representation of the system consisting of nominal and actual dynamics, a placeholder for different types of tracking controllers, a safety logic for nominal state propagation, and an importance sampling scheme that takes into account the capabilities of the underlying tracking control. Using these ingredients, we derive a bound on the free energy growth of the dynamical system which is a function of task constraint satisfaction level, the performance of the underlying tracking controller, and the sampling error of the stochastic optimization used within RMPPI. To validate the bound on free energy growth, we perform experiments in simulation using two types of tracking controllers, namely the iterative Linear Quadratic Gaussian and Contraction-Metric based control. We further demonstrate the applicability of RMPPI in real hardware using the GT AutoRally vehicle. Our experiments demonstrate that RMPPI outperforms MPPI and Tube-MPPI by alleviating issues of the aforementioned model predictive controllers related to either lack of robustness or excessive conservatism. RMPPI provides the best of the two worlds in terms of agility and robustness to disturbances.