Particle MPC for Uncertain and Learning-Based Control

TL;DR

This paper introduces a nonlinear particle model predictive control method that effectively manages uncertainty in robotic systems by leveraging scenario-based optimization with partial consensus horizons, improving robustness and performance.

Contribution

It presents a novel particle MPC approach with partial consensus horizons, enabling efficient control under uncertainty in nonlinear robotic systems.

Findings

Improved control performance in uncertain, partially observed, and learning-based robotic systems.

Efficient optimization via sequential convex methods tailored to information gain.

Enhanced robustness compared to baseline control strategies.

Abstract

As robotic systems move from highly structured environments to open worlds, incorporating uncertainty from dynamics learning or state estimation into the control pipeline is essential for robust performance. In this paper we present a nonlinear particle model predictive control (PMPC) approach to control under uncertainty, which directly incorporates any particle-based uncertainty representation, such as those common in robotics. Our approach builds on scenario methods for MPC, but in contrast to existing approaches, which either constrain all or only the first timestep to share actions across scenarios, we investigate the impact of a \textit{partial consensus horizon}. Implementing this optimization for nonlinear dynamics by leveraging sequential convex optimization, our approach yields an efficient framework that can be tuned to the particular information gain dynamics of a system to mitigate both over-conservatism and over-optimism. We investigate our approach for two robotic systems across three problem settings: time-varying, partially observed dynamics; sensing uncertainty; and model-based reinforcement learning, and show that our approach improves performance over baselines in all settings.