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arXiv · 1909.12316

Preference-Based Learning for Exoskeleton Gait Optimization

Maegan Tucker·Ellen Novoseller·Claudia Kann·Yanan Sui·Yisong Yue·Joel Burdick·Aaron D. Ames
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Abstract

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.

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Maegan Tucker, Ellen Novoseller, Claudia Kann, Yanan Sui, Yisong Yue, Joel Burdick, Aaron D. Ames. 2019-09-26. Preference-Based Learning for Exoskeleton Gait Optimization. https://arxiv.org/abs/1909.12316

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