ユーザーの好みから学ぶ外骨格歩行の最適化
Preference-Based Learning for Exoskeleton Gait Optimization
外骨格の歩行を、数値的な効率ではなくユーザーの好み（快適さなど）をペア比較で学習して個人に合わせて最適化するCoSparを提案し、実機で好まれる歩行パラメータを安定して見つけられることを示した。
著者: Maegan Tucker, Ellen Novoseller, Claudia Kann, Yanan Sui, Yisong Yue, Joel Burdick, Aaron D. Ames
分類: cs.RO
原文アブストラクト
This paper presents a personalized gait optimization framework for lower-body exoskeletons. Rather than optimizing numerical objectives such as the mechanical cost of transport, our approach directly learns from user preferences, e.g., for comfort. Building upon work in preference-based interactive learning, we present the CoSpar algorithm. CoSpar prompts the user to give pairwise preferences between trials and suggest improvements; as exoskeleton walking is a non-intuitive behavior, users can provide preferences more easily and reliably than numerical feedback. We show that CoSpar performs competitively in simulation and demonstrate a prototype implementation of CoSpar on a lower-body exoskeleton to optimize human walking trajectory features. In the experiments, CoSpar consistently found user-preferred parameters of the exoskeleton's walking gait, which suggests that it is a promising starting point for adapting and personalizing exoskeletons (or other assistive devices) to individual users.
