Sample-Efficient Safe Learning for Online Nonlinear Control with Control Barrier Functions

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Sample-Efficient Safe Learning for Online Nonlinear Control with Control Barrier Functions

Luo, Wenhao / Sun, Wen / Kapoor, Ashish

Reinforcement Learning (RL) and continuous nonlinear control have been successfully deployed in multiple domains of complicated sequential decision-making tasks. However, given the exploration nature of the learning process and the presence of model uncertainty, it is challenging to apply them to safety-critical control tasks due to the lack of safety guarantee. On the other hand, while combining control-theoretical approaches with learning algorithms has shown promise in safe RL applications, the sample efficiency of safe data collection process for control is not well addressed. In this paper, we propose a provably sample efficient episodic safe learning framework for online control tasks that leverages safe exploration and exploitation in an unknown, nonlinear dynamical system. In particular, the framework (1) extends control barrier functions (CBFs) in a stochastic setting to achieve provable high-probability safety under uncertainty during model learning and (2) integrates an optimism-based exploration strategy to efficiently guide the safe exploration process with learned dynamics for near optimal control performance. We provide formal analysis on the episodic regret bound against the optimal controller and probabilistic safety with theoretical guarantees. Simulation results are provided to demonstrate the effectiveness and efficiency of the proposed algorithm.

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Dokumentinformationen

Titel :

Sample-Efficient Safe Learning for Online Nonlinear Control with Control Barrier Functions

Weitere Titelangaben:

Springer Proceedings in Advanced Robotics

Beteiligte:

LaValle, Steven M. (Herausgeber:in) / O’Kane, Jason M. (Herausgeber:in) / Otte, Michael (Herausgeber:in) / Sadigh, Dorsa (Herausgeber:in) / Tokekar, Pratap (Herausgeber:in) / Luo, Wenhao (Autor:in) / Sun, Wen (Autor:in) / Kapoor, Ashish (Autor:in)

Kongress:

International Workshop on the Algorithmic Foundations of Robotics ; 2022 ; , MD, USA June 22, 2022 - June 24, 2022

Erschienen in:

Algorithmic Foundations of Robotics XV ; Kapitel : 25 ; 419-435

Springer Proceedings in Advanced Robotics ; 25

Erscheinungsdatum :

2022-12-15

Format / Umfang :

17 pages

ISBN :

978-3-031-21090-7 , 978-3-031-21089-1

ISSN :