Preference-Based Learning for Exoskeleton Gait Optimization

TL;DR

This paper introduces a preference-based learning framework using the CoSpar algorithm to personalize exoskeleton gait by directly learning user preferences, improving comfort and adaptability over traditional objective-based methods.

Contribution

It presents a novel preference-based learning approach with the CoSpar algorithm for exoskeleton gait optimization, demonstrated through simulation and real-world experiments.

Findings

CoSpar effectively learns user preferences in simulation.

Prototype implementation successfully personalized exoskeleton gait.

User-preferred parameters were consistently identified.

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.