Adversarial Learning of Robust and Safe Controllers for Cyber-Physical Systems

Luca Bortolussi; Francesca Cairoli; Ginevra Carbone; Francesco Franchina; Enrico Regolin

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arXiv · 2009.02019

Adversarial Learning of Robust and Safe Controllers for Cyber-Physical Systems

Luca Bortolussi·Francesca Cairoli·Ginevra Carbone·Francesco Franchina·Enrico Regolin

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Abstract

We introduce a novel learning-based approach to synthesize safe and robust controllers for autonomous Cyber-Physical Systems and, at the same time, to generate challenging tests. This procedure combines formal methods for model verification with Generative Adversarial Networks. The method learns two Neural Networks: the first one aims at generating troubling scenarios for the controller, while the second one aims at enforcing the safety constraints. We test the proposed method on a variety of case studies.

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Luca Bortolussi, Francesca Cairoli, Ginevra Carbone, Francesco Franchina, Enrico Regolin. 2021-03-26. Adversarial Learning of Robust and Safe Controllers for Cyber-Physical Systems. https://arxiv.org/abs/2009.02019

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