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arXiv · 2311.13714

Learning Safe Control for Multi-Robot Systems: Methods, Verification, and Open Challenges

Kunal Garg·Songyuan Zhang·Oswin So·Charles Dawson·Chuchu Fan
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Abstract

In this survey, we review the recent advances in control design methods for robotic multi-agent systems (MAS), focussing on learning-based methods with safety considerations. We start by reviewing various notions of safety and liveness properties, and modeling frameworks used for problem formulation of MAS. Then we provide a comprehensive review of learning-based methods for safe control design for multi-robot systems. We start with various types of shielding-based methods, such as safety certificates, predictive filters, and reachability tools. Then, we review the current state of control barrier certificate learning in both a centralized and distributed manner, followed by a comprehensive review of multi-agent reinforcement learning with a particular focus on safety. Next, we discuss the state-of-the-art verification tools for the correctness of learning-based methods. Based on the capabilities and the limitations of the state of the art methods in learning and verification for MAS, we identify various broad themes for open challenges: how to design methods that can achieve good performance along with safety guarantees; how to decompose single-agent based centralized methods for MAS; how to account for communication-related practical issues; and how to assess transfer of theoretical guarantees to practice.

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BibTeXRIS

Kunal Garg, Songyuan Zhang, Oswin So, Charles Dawson, Chuchu Fan. 2023-11-22. Learning Safe Control for Multi-Robot Systems: Methods, Verification, and Open Challenges. https://arxiv.org/abs/2311.13714

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