Talk@Princeton - Ensuring Robot Safety Through Safety Index Synthesis
Changliu gave a talk at Princeton Robotics Seminar, titled Ensuring Robot Safety Through Safety Index Synthesis.
Safety Index is a special class of high order control barrier functions. Its purpose is to ensure forward invariance within a user-specified safe set and achieve finite time convergence to that set. Synthesizing a valid safety index poses significant challenges, particularly when dealing with control limits, uncertainties, and time-varying dynamics. In this talk, I will introduce a variety of approaches that can be used for safety index synthesis, including a rule-based method, an evolutionary optimization-based approach, a constrained reinforcement learning-based approach, an adversarial optimization-based approach, as well as sum of square programming. The parameterization of the safety index can either take an analytical form or be a neural network. I will conclude the talk by highlighting the limitations of existing work and discuss potential future directions, including integrating formal verification into neural safety index synthesis.
This talk highlights the following work from ICL:
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[C38] Model-free Safe Control for Zero-Violation Reinforcement Learning
Weiye Zhao, Tairan He and Changliu Liu
Conference on Robot Learning, 2021
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[C40] Safe Control with Neural Network Dynamic Models
Tianhao Wei and Changliu Liu
Learning for Dynamics and Control Conference, 2022
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[C41] Joint Synthesis of Safety Certificate and Safe Control Policy Using Constrained Reinforcement Learning
Haitong Ma, Changliu Liu, Shengbo Eben Li, Sifa Zheng and Jianyu Chen
Learning for Dynamics and Control Conference, 2022
Best Paper Finalist
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[C53] Safe Control Under Input Limits with Neural Control Barrier Functions
Simin Liu, Changliu Liu and John Dolan
Conference on Robot Learning, 2022
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[C56] Safety index synthesis via sum-of-squares programming
Weiye Zhao, Tairan He, Tianhao Wei, Simin Liu and Changliu Liu
American Control Conference, 2023
The work is supported by National Science Foundation. Read more about the work at this research page.