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Preference-Based Learning for Exoskeleton Gait Optimization

Tucker, Maegan and Novoseller, Ellen and Kann, Claudia and Sui, Yanan and Yue, Yisong and Burdick, Joel and Ames, Aaron D. (2019) Preference-Based Learning for Exoskeleton Gait Optimization. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20200109-095946819

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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.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
http://arxiv.org/abs/1909.12316v1arXivDiscussion Paper
ORCID:
AuthorORCID
Tucker, Maegan0000-0001-7363-6809
Novoseller, Ellen0000-0001-5263-0598
Kann, Claudia0000-0002-8318-4890
Sui, Yanan0000-0002-9480-627X
Yue, Yisong0000-0001-9127-1989
Ames, Aaron D.0000-0003-0848-3177
Additional Information:This research was supported by NIH grant EB007615, NSF NRI award 1724464, NSF Graduate Research Fellowship No. DGE1745301, and the Caltech Big Ideas and ZEITLIN Funds. This work was conducted under IRB No. 16-0693. The authors would like to thank the volunteers who participated in the experiments, as well as the entire Wandercraft team that designed Atalante and continues to provide technical support for this project.
Funders:
Funding AgencyGrant Number
NIHEB007615
NSFIIS-1724464
NSF Graduate Research FellowshipDGE-1745301
CaltechUNSPECIFIED
Record Number:CaltechAUTHORS:20200109-095946819
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20200109-095946819
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:100589
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:09 Jan 2020 18:04
Last Modified:09 Jan 2020 18:04

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