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

arXiv: https://arxiv.org/abs/2311.13714 · HTML: https://mlsanity.com/paper/2311.13714/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Nov 2023.

## Built on by (most cited first, 13 of 13)

- [Real-Time Out-of-Distribution Failure Prevention via Multi-Modal Reasoning](https://mlsanity.com/paper/2505.10547/) (2025)
- [Discrete GCBF Proximal Policy Optimization for Multi-agent Safe Optimal Control](https://mlsanity.com/paper/2502.03640/) (2025)
- [Solving Multi-Agent Safe Optimal Control with Distributed Epigraph Form MARL](https://mlsanity.com/paper/2504.15425/) (2025)
- [Foundation Models to the Rescue: Deadlock Resolution in Connected Multi-Robot Systems](https://mlsanity.com/paper/2404.06413/) (2024)
- [A Principle of Targeted Intervention for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2510.17697/) (2025)
- [Reliable and Efficient Multi-Agent Coordination via Graph Neural Network Variational Autoencoders](https://mlsanity.com/paper/2503.02954/) (2025)
- [Pareto Control Barrier Function for Inner Safe Set Maximization Under Input Constraints](https://mlsanity.com/paper/2410.04260/) (2024)
- [Model Checking for Reinforcement Learning in Autonomous Driving: One Can Do More Than You Think!](https://mlsanity.com/paper/2411.14375/) (2024)
- [Distributed Risk-Sensitive Safety Filters for Uncertain Discrete-Time Systems](https://mlsanity.com/paper/2506.07347/) (2025)
- [Towards safe control parameter tuning in distributed multi-agent systems](https://mlsanity.com/paper/2508.13608/) (2025)
- [Rainbow Deep Q-Learning with Kinematics-Aware Design for Cooperative Delta and 3-RRS Parallel Robot Insertion](https://mlsanity.com/paper/2605.11697/) (2026)
- [Formal Verification of Learned Multi-Agent Communication Policies via Decision Tree Distillation](https://mlsanity.com/paper/2606.19632/) (2026)
- [Decentralized Safe Multi-Agent Reinforcement Learning via Predictive Shielding](https://mlsanity.com/paper/2609.07618/) (2026)

## Abstract (arXiv)

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.