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Episodic Learning with Control Lyapunov Functions for Uncertain Robotic Systems

Taylor, Andrew J. and Dorobantu, Victor D. and Le, Hoang M. and Yue, Yisong and Ames, Aaron D. (2019) Episodic Learning with Control Lyapunov Functions for Uncertain Robotic Systems. . (Unpublished)

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Many modern nonlinear control methods aim to endow systems with guaranteed properties, such as stability or safety, and have been successfully applied to the domain of robotics. However, model uncertainty remains a persistent challenge, weakening theoretical guarantees and causing implementation failures on physical systems. This paper develops a machine learning framework centered around Control Lyapunov Functions (CLFs) to adapt to parametric uncertainty and unmodeled dynamics in general robotic systems. Our proposed method proceeds by iteratively updating estimates of Lyapunov function derivatives and improving controllers, ultimately yielding a stabilizing quadratic program model-based controller. We validate our approach on a planar Segway simulation, demonstrating substantial performance improvements by iteratively refining on a base model-free controller.

Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription Paper
Yue, Yisong0000-0001-9127-1989
Ames, Aaron D.0000-0003-0848-3177
Additional Information:This work was supported by Google Brain Robotics and DARPA Award HR00111890035.
Funding AgencyGrant Number
Google Brain RoboticsUNSPECIFIED
Defense Advanced Research Projects Agency (DARPA)HR00111890035
Record Number:CaltechAUTHORS:20190327-085838590
Persistent URL:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:94189
Deposited By: George Porter
Deposited On:27 Mar 2019 22:24
Last Modified:03 Oct 2019 21:01

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