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A Control Lyapunov Perspective on Episodic Learning via Projection to State Stability

Taylor, Andrew J. and Dorobantu, Victor D. and Krishnamoorthy, Meera and Le, Hoang M. and Yue, Yisong and Ames, Aaron D. (2019) A Control Lyapunov Perspective on Episodic Learning via Projection to State Stability. . (Unpublished)

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The goal of this paper is to understand the impact of learning on control synthesis from a Lyapunov function perspective. In particular, rather than consider uncertainties in the full system dynamics, we employ Control Lyapunov Functions (CLFs) as low-dimensional projections. To understand and characterize the uncertainty that these projected dynamics introduce in the system, we introduce a new notion: Projection to State Stability (PSS). PSS can be viewed as a variant of Input to State Stability defined on projected dynamics, and enables characterizing robustness of a CLF with respect to the data used to learn system uncertainties. We use PSS to bound uncertainty in affine control, and demonstrate that a practical episodic learning approach can use PSS to characterize uncertainty in the CLF for robust control synthesis.

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:20190327-085842025
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:94190
Deposited By: George Porter
Deposited On:27 Mar 2019 22:22
Last Modified:03 Oct 2019 21:01

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