人間の選好に基づく外骨格歩行パターンの高次元最適化
Human Preference-Based Learning for High-dimensional Optimization of Exoskeleton Walking Gaits
ユーザーの好みを反復的に1次元部分空間で探索するLineCoSparを提案し、高次元の歩行パラメータ最適化を実現した。実験から好みと動的特性の対応や個人差を明らかにした。
著者: Maegan Tucker, Myra Cheng, Ellen Novoseller, Richard Cheng, Yisong Yue, Joel W. Burdick, Aaron D. Ames
分類: cs.RO, cs.HC, cs.LG
原文アブストラクト
Optimizing lower-body exoskeleton walking gaits for user comfort requires understanding users' preferences over a high-dimensional gait parameter space. However, existing preference-based learning methods have only explored low-dimensional domains due to computational limitations. To learn user preferences in high dimensions, this work presents LineCoSpar, a human-in-the-loop preference-based framework that enables optimization over many parameters by iteratively exploring one-dimensional subspaces. Additionally, this work identifies gait attributes that characterize broader preferences across users. In simulations and human trials, we empirically verify that LineCoSpar is a sample-efficient approach for high-dimensional preference optimization. Our analysis of the experimental data reveals a correspondence between human preferences and objective measures of dynamicity, while also highlighting differences in the utility functions underlying individual users' gait preferences. This result has implications for exoskeleton gait synthesis, an active field with applications to clinical use and patient rehabilitation.
