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.

翻译：本综述回顾了面向机器人多智能体系统（MAS）控制设计方法的最新进展，重点关注具有安全考虑的基于学习方法。我们首先回顾了安全性与活性属性（liveness properties）的各种概念，以及用于MAS问题建模的框架。随后，我们对多机器人系统安全控制设计的基于学习方法进行了全面评述。我们首先介绍了各类基于防护（shielding）的方法，例如安全证书、预测滤波器与可达性工具。接着，我们从集中式与分布式两个角度综述了控制障碍函数（control barrier certificate）学习的当前发展状况，并系统性地回顾了特别关注安全性的多智能体强化学习。之后，我们讨论了用于验证基于学习方法正确性的最新工具。基于MAS学习与验证领域前沿方法的优势与局限，我们识别出开放挑战的若干广泛主题：如何设计既能保证良好性能又具备安全保证的方法；如何针对MAS分解基于单智能体的集中式方法；如何考量与通信相关的实际问题；以及如何评估从理论保证到实际应用的迁移效果。