制御リアプノフ関数を用いた不確実なロボットシステムのエピソード学習
Episodic Learning with Control Lyapunov Functions for Uncertain Robotic Systems
制御リアプノフ関数(CLF)を中心とした機械学習フレームワークを提案し、パラメータ不確実性や未モデル化動態に適応しながら反復的に制御器を改善する。平面セグウェイのシミュレーションで有効性を示した。
著者: Andrew J. Taylor, Victor D. Dorobantu, Hoang M. Le, Yisong Yue, Aaron D. Ames
分類: cs.RO, cs.LG, cs.SY, eess.SY
原文アブストラクト
Many modern nonlinear control methods aim to endow systems with guaranteed properties, such as stability or safety, and have been successfully applied to the domain of robotics. However, model uncertainty remains a persistent challenge, weakening theoretical guarantees and causing implementation failures on physical systems. This paper develops a machine learning framework centered around Control Lyapunov Functions (CLFs) to adapt to parametric uncertainty and unmodeled dynamics in general robotic systems. Our proposed method proceeds by iteratively updating estimates of Lyapunov function derivatives and improving controllers, ultimately yielding a stabilizing quadratic program model-based controller. We validate our approach on a planar Segway simulation, demonstrating substantial performance improvements by iteratively refining on a base model-free controller.
