Robust Model Predictive Path Integral Control: Analysis and Performance Guarantees

TL;DR

This paper introduces a robust decision-making architecture for Model Predictive Path Integral control, providing theoretical guarantees and demonstrating improved performance in off-road navigation tasks through simulation and real-world experiments.

Contribution

The paper presents a novel RMPPI framework with performance guarantees, integrating safety, tracking control, and importance sampling for enhanced robustness and applicability.

Findings

RMPPI outperforms MPPI and Tube-MPPI in robustness and agility.

Theoretical bound on free energy growth derived and validated.

Successful real-world implementation on GT AutoRally vehicle.

Abstract

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.