# A Critical Review of Communications in Multi-Robot Systems

arXiv: https://arxiv.org/abs/2206.09484 · HTML: https://mlsanity.com/paper/2206.09484/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jun 2022.

## Built on by (most cited first, 16 of 16)

- [Large Language Models for Multi-Robot Systems: A Survey](https://mlsanity.com/paper/2502.03814/) (2025)
- [Work Smarter Not Harder: Simple Imitation Learning with CS-PIBT Outperforms Large Scale Imitation Learning for MAPF](https://mlsanity.com/paper/2409.14491/) (2024)
- [The Synergy Between Optimal Transport Theory and Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2401.10949/) (2024)
- [RecNet: An Invertible Point Cloud Encoding through Range Image Embeddings for Multi-Robot Map Sharing and Reconstruction](https://mlsanity.com/paper/2402.02192/) (2024)
- [Semantic-Aware Remote Estimation of Multiple Markov Sources Under Constraints](https://mlsanity.com/paper/2403.16855/) (2024)
- [4CNet: A Diffusion Approach to Map Prediction for Decentralized Multi-Robot Exploration](https://mlsanity.com/paper/2402.17904/) (2024)
- [Heterogeneous Robot Collaboration in Unstructured Environments with Grounded Generative Intelligence](https://mlsanity.com/paper/2510.26915/) (2025)
- [Multi-Robot Task Allocation for Homogeneous Tasks with Collision Avoidance via Spatial Clustering](https://mlsanity.com/paper/2505.10073/) (2025)
- [Efficient Incremental Belief Updates Using Weighted Virtual Observations](https://mlsanity.com/paper/2402.06940/) (2024)
- [Multi-source Plume Tracing via Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2505.08825/) (2025)
- [Regret Lower Bounds for Decentralized Multi-Agent Stochastic Shortest Path Problems](https://mlsanity.com/paper/2511.04594/) (2025)
- [Acting on the Unseen: Communication-Free Collaborative Filtering for Decentralized Multi-Robot Task Allocation](https://mlsanity.com/paper/2605.25584/) (2026)
- [Resource-Efficient Distributed Recursive Gaussian Processes](https://mlsanity.com/paper/2609.26979/) (2026)
- [Attention-based Hierarchical Variational Information Bottleneck for Robust Multi-Agent Communication under Variable Bandwidth](https://mlsanity.com/paper/2609.34860/) (2026)
- [Embodied Semantic Communication for Collective Autonomous Agents: A Tutorial on Representation, Wireless Delivery, and Closed-Loop Coordination](https://mlsanity.com/paper/2609.35936/) (2026)
- [Semantic Map Sharing and Capability-Aware Coverage Planning for AI-Native 6G Robotic Coordination](https://mlsanity.com/paper/2609.37666/) (2026)

## Abstract (arXiv)

Purpose of Review. This review summarizes the broad roles that communication formats and technologies have played in enabling multi-robot systems. We approach this field from two perspectives: of robotic applications that need communication capabilities in order to accomplish tasks, and of networking technologies that have enabled newer and more advanced multi-robot systems. Recent Findings. Through this review, we identify a dearth of work that holistically tackles the problem of co-design and co-optimization of robots and the networks they employ. We also highlight the role that data-driven and machine learning approaches play in evolving communication pipelines for multi-robot systems. In particular, we refer to recent work that diverges from hand-designed communication patterns, and also discuss the "sim-to-real" gap in this context. Summary. We present a critical view of the way robotic algorithms and their networking systems have evolved, and make the case for a more synergistic approach. Finally, we also identify four broad Open Problems for research and development, while offering a data-driven perspective for solving some of them.