不確実性と学習ベース制御のためのパーティクルMPC
Particle MPC for Uncertain and Learning-Based Control
パーティクルで表された不確実性を直接取り込む非線形MPCを提案し、部分的な合意ホライズンを導入して過度な保守性と楽観性を緩和、時変・部分観測ダイナミクスやセンシング不確実性、モデルベース強化学習で性能向上を示した。
著者: Robert Dyro, James Harrison, Apoorva Sharma, Marco Pavone
分類: eess.SY, cs.RO, cs.SY
原文アブストラクト
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.
