# Multi-Robot Coordination and Planning in Uncertain and Adversarial Environments

arXiv: https://arxiv.org/abs/2105.00389 · HTML: https://mlsanity.com/paper/2105.00389/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 May 2021.

## Built on by (most cited first, 6 of 6)

- [Large Language Models for Multi-Robot Systems: A Survey](https://mlsanity.com/paper/2502.03814/) (2025)
- [Beyond Robustness: A Taxonomy of Approaches towards Resilient Multi-Robot Systems](https://mlsanity.com/paper/2109.12343/) (2021)
- [Security Considerations in AI-Robotics: A Survey of Current Methods, Challenges, and Opportunities](https://mlsanity.com/paper/2310.08565/) (2023)
- [Advancing Investment Frontiers: Industry-grade Deep Reinforcement Learning for Portfolio Optimization](https://mlsanity.com/paper/2403.07916/) (2024)
- [AdverSAR: Adversarial Search and Rescue via Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2212.10064/) (2022)
- [TAB-Fields: A Maximum Entropy Framework for Mission-Aware Adversarial Planning](https://mlsanity.com/paper/2412.02570/) (2024)

## Abstract (arXiv)

Deploying a team of robots that can carefully coordinate their actions can make the entire system robust to individual failures. In this report, we review recent algorithmic development in making multi-robot systems robust to environmental uncertainties, failures, and adversarial attacks. We find the following three trends in the recent research in the area of multi-robot coordination: (1) resilient coordination to either withstand failures and/or attack or recover from failures/attacks; (2) risk-aware coordination to manage the trade-off risk and reward, where the risk stems due to environmental uncertainty; (3) Graph Neural Networks based coordination to learn decentralized multi-robot coordination policies. These algorithms have been applied to tasks such as formation control, task assignment and scheduling, search and planning, and informative data collection. In order for multi-robot systems to become practical, we need coordination algorithms that can scale to large teams of robots dealing with dynamically changing, failure-prone, contested, and uncertain environments. There has been significant recent research on multi-robot coordination that has contributed resilient and risk-aware algorithms to deal with these issues and reduce the gap between theory and practice. Learning-based approaches have been seen to be promising, especially since they can learn who, when, and how to communicate for effective coordination. However, these algorithms have also been shown to be vulnerable to adversarial attacks, and as such developing learning-based coordination strategies that are resilient to such attacks and robust to uncertainties is an important open area of research.