# Graph Neural Networks for Learning Robot Team Coordination

arXiv: https://arxiv.org/abs/1805.03737 · HTML: https://mlsanity.com/paper/1805.03737/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 May 2018.

## Built on by (most cited first, 4 of 4)

- [Beyond Robustness: A Taxonomy of Approaches towards Resilient Multi-Robot Systems](https://mlsanity.com/paper/2109.12343/) (2021)
- [ModGNN: Expert Policy Approximation in Multi-Agent Systems with a Modular Graph Neural Network Architecture](https://mlsanity.com/paper/2103.13446/) (2021)
- [Graph learning in robotics: a survey](https://mlsanity.com/paper/2310.04294/) (2023)
- [CoDreamer: Communication-Based Decentralised World Models](https://mlsanity.com/paper/2406.13600/) (2024)

## Abstract (arXiv)

This paper shows how Graph Neural Networks can be used for learning distributed coordination mechanisms in connected teams of robots. We capture the relational aspect of robot coordination by modeling the robot team as a graph, where each robot is a node, and edges represent communication links. During training, robots learn how to pass messages and update internal states, so that a target behavior is reached. As a proxy for more complex problems, this short paper considers the problem where each robot must locally estimate the algebraic connectivity of the team's network topology.