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Preference-Based Learning for User-Guided HZD Gait Generation on Bipedal Walking Robots

Tucker, Maegan and Csomay-Shanklin, Noel and Ma, Wen-Loong and Ames, Aaron D. (2020) Preference-Based Learning for User-Guided HZD Gait Generation on Bipedal Walking Robots. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20210120-165245372

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Abstract

This paper presents a framework that unifies control theory and machine learning in the setting of bipedal locomotion. Traditionally, gaits are generated through trajectory optimization methods and then realized experimentally -- a process that often requires extensive tuning due to differences between the models and hardware. In this work, the process of gait realization via hybrid zero dynamics (HZD) based optimization problems is formally combined with preference-based learning to systematically realize dynamically stable walking. Importantly, this learning approach does not require a carefully constructed reward function, but instead utilizes human pairwise preferences. The power of the proposed approach is demonstrated through two experiments on a planar biped AMBER-3M: the first with rigid point feet, and the second with induced model uncertainty through the addition of springs where the added compliance was not accounted for in the gait generation or in the controller. In both experiments, the framework achieves stable, robust, efficient, and natural walking in fewer than 50 iterations with no reliance on a simulation environment. These results demonstrate a promising step in the unification of control theory and learning.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
http://arxiv.org/abs/2011.05424arXivDiscussion Paper
ORCID:
AuthorORCID
Tucker, Maegan0000-0001-7363-6809
Csomay-Shanklin, Noel0000-0002-2361-1694
Ma, Wen-Loong0000-0002-0115-5632
Ames, Aaron D.0000-0003-0848-3177
Additional Information:This research was supported by NSF NRI award 1924526 and CMMI award 1923239, NSF Graduate Research Fellowship No. DGE-1745301, and the Caltech Big Ideas and ZEITLIN Funds.
Funders:
Funding AgencyGrant Number
NSFECCS-1924526
NSFCMMI-1923239
NSF Graduate Research FellowshipDGE-1745301
Caltech Big Ideas FundUNSPECIFIED
Zeitlin Family Discovery FundUNSPECIFIED
Record Number:CaltechAUTHORS:20210120-165245372
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20210120-165245372
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:107614
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:21 Jan 2021 15:06
Last Modified:21 Jan 2021 15:06

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