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arXiv · 2006.08465

Neural Certificates for Safe Control Policies

Wanxin Jin·Zhaoran Wang·Zhuoran Yang·Shaoshuai Mou
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Abstract

This paper develops an approach to learn a policy of a dynamical system that is guaranteed to be both provably safe and goal-reaching. Here, the safety means that a policy must not drive the state of the system to any unsafe region, while the goal-reaching requires the trajectory of the controlled system asymptotically converges to a goal region (a generalization of stability). We obtain the safe and goal-reaching policy by jointly learning two additional certificate functions: a barrier function that guarantees the safety and a developed Lyapunov-like function to fulfill the goal-reaching requirement, both of which are represented by neural networks. We show the effectiveness of the method to learn both safe and goal-reaching policies on various systems, including pendulums, cart-poles, and UAVs.

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BibTeXRIS

Wanxin Jin, Zhaoran Wang, Zhuoran Yang, Shaoshuai Mou. 2020-06-15. Neural Certificates for Safe Control Policies. https://arxiv.org/abs/2006.08465

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