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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 W. and Ames, Aaron D. (2020) Preference-Based Learning for Exoskeleton Gait Optimization. In: 2020 IEEE International Conference on Robotics and Automation (ICRA). IEEE , Piscataway, NJ, pp. 2351-2357. ISBN 978-1-7281-7395-5.

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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:Book Section
Related URLs:
URLURL TypeDescription Paper
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:© 2020 IEEE. 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.
Funding AgencyGrant Number
NSF Graduate Research FellowshipDGE-1745301
Record Number:CaltechAUTHORS:20200109-095946819
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Official Citation:M. Tucker et al., "Preference-Based Learning for Exoskeleton Gait Optimization," 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France, 2020, pp. 2351-2357, doi: 10.1109/ICRA40945.2020.9196661
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:100589
Deposited By: Tony Diaz
Deposited On:09 Jan 2020 18:04
Last Modified:16 Nov 2021 17:55

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