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Learning-based Robust Motion Planning With Guaranteed Stability: A Contraction Theory Approach

Tsukamoto, Hiroyasu and Chung, Soon-Jo (2021) Learning-based Robust Motion Planning With Guaranteed Stability: A Contraction Theory Approach. IEEE Robotics and Automation Letters, 6 (4). pp. 6164-6171. ISSN 2377-3766. doi:10.1109/LRA.2021.3091019. https://resolver.caltech.edu/CaltechAUTHORS:20210304-094303690

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Abstract

This letter presents Learning-based Autonomous Guidance with RObustness and Stability guarantees (LAG-ROS), which provides machine learning-based nonlinear motion planners with formal robustness and stability guarantees, by designing a differential Lyapunov function using contraction theory. LAG-ROS utilizes a neural network to model a robust tracking controller independently of a target trajectory, for which we show that the Euclidean distance between the target and controlled trajectories is exponentially bounded linearly in the learning error, even under the existence of bounded external disturbances. We also present a convex optimization approach that minimizes the steady-state bound of the tracking error to construct the robust control law for neural network training. In numerical simulations, it is demonstrated that the proposed method indeed possesses superior properties of robustness and nonlinear stability resulting from contraction theory, whilst retaining the computational efficiency of existing learning-based motion planners.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1109/LRA.2021.3091019DOIArticle
http://arxiv.org/abs/2102.12668arXivDiscussion Paper
ORCID:
AuthorORCID
Tsukamoto, Hiroyasu0000-0002-6337-2667
Chung, Soon-Jo0000-0002-6657-3907
Alternate Title:Imitation Learning for Robust and Safe Real-time Motion Planning: A Contraction Theory Approach
Additional Information:© 2021 IEEE. Manuscript received February 24, 2021; accepted June 1, 2021. Date of publication June 21, 2021; date of current version July 6, 2021. This letter was recommended for publication by Associate Editor L. Peternel and Editor D. Kulic upon evaluation of the reviewers’ comments. This work was supported by the Jet Propulsion Laboratory, California Institute of Technology, and benefited from discussions with J. Castillo-Rogez, M. D. Ingham, and J.-J. E. Slotine.
Group:GALCIT
Funders:
Funding AgencyGrant Number
JPL/CaltechUNSPECIFIED
Subject Keywords:Machine learning for robot control, robust/adaptive control, and optimization & optimal control
Issue or Number:4
DOI:10.1109/LRA.2021.3091019
Record Number:CaltechAUTHORS:20210304-094303690
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20210304-094303690
Official Citation:H. Tsukamoto and S. -J. Chung, "Learning-based Robust Motion Planning With Guaranteed Stability: A Contraction Theory Approach," in IEEE Robotics and Automation Letters, vol. 6, no. 4, pp. 6164-6171, Oct. 2021, doi: 10.1109/LRA.2021.3091019
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:108308
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:04 Mar 2021 21:35
Last Modified:12 Jul 2021 20:04

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