ROIAL：外骨格歩行の選好ランドスケープを推定する関心領域能動学習
ROIAL: Region of Interest Active Learning for Characterizing Exoskeleton Gait Preference Landscapes
外骨格歩行の各ユーザーの効用関数を、安全性と快適性を保証する関心領域内で順序・選好フィードバックから能動的に学習するROIALを提案し、シミュレーションと3名の被験者実験で4つの歩行パラメータに対する選好ランドスケープの推定可能性を示した。
著者: Kejun Li, Maegan Tucker, Erdem Bıyık, Ellen Novoseller, Joel W. Burdick, Yanan Sui, Dorsa Sadigh, Yisong Yue, Aaron D. Ames
分類: cs.RO, cs.HC, cs.LG
原文アブストラクト
Characterizing what types of exoskeleton gaits are comfortable for users, and understanding the science of walking more generally, require recovering a user's utility landscape. Learning these landscapes is challenging, as walking trajectories are defined by numerous gait parameters, data collection from human trials is expensive, and user safety and comfort must be ensured. This work proposes the Region of Interest Active Learning (ROIAL) framework, which actively learns each user's underlying utility function over a region of interest that ensures safety and comfort. ROIAL learns from ordinal and preference feedback, which are more reliable feedback mechanisms than absolute numerical scores. The algorithm's performance is evaluated both in simulation and experimentally for three non-disabled subjects walking inside of a lower-body exoskeleton. ROIAL learns Bayesian posteriors that predict each exoskeleton user's utility landscape across four exoskeleton gait parameters. The algorithm discovers both commonalities and discrepancies across users' gait preferences and identifies the gait parameters that most influenced user feedback. These results demonstrate the feasibility of recovering gait utility landscapes from limited human trials.
