マルチロボットシステムのための安全制御の学習：手法・検証・未解決課題
Learning Safe Control for Multi-Robot Systems: Methods, Verification, and Open Challenges
複数ロボットの安全な制御を学習ベースで実現する手法を、安全性の概念からシールディング、制御バリア関数学習、安全考慮型マルチエージェント強化学習、検証ツールまで体系的にサーベイし、今後の課題を整理した。
著者: Kunal Garg, Songyuan Zhang, Oswin So, Charles Dawson, Chuchu Fan
分類: cs.RO, cs.MA, cs.SY, eess.SY, math.OC
原文アブストラクト
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.
