# Multi-Robot Coverage and Exploration using Spatial Graph Neural Networks

arXiv: https://arxiv.org/abs/2011.01119 · HTML: https://mlsanity.com/paper/2011.01119/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Nov 2020.

## Built on by (most cited first, 19 of 19)

- [Graph Neural Networks in IoT: A Survey](https://mlsanity.com/paper/2203.15935/) (2022)
- [Graph Neural Networks for Multi-Robot Active Information Acquisition](https://mlsanity.com/paper/2209.12091/) (2022)
- [ModGNN: Expert Policy Approximation in Multi-Agent Systems with a Modular Graph Neural Network Architecture](https://mlsanity.com/paper/2103.13446/) (2021)
- [Geometric Graph Filters and Neural Networks: Limit Properties and Discriminability Trade-offs](https://mlsanity.com/paper/2305.18467/) (2023)
- [Learning Control Admissibility Models with Graph Neural Networks for Multi-Agent Navigation](https://mlsanity.com/paper/2210.09378/) (2022)
- [Graph learning in robotics: a survey](https://mlsanity.com/paper/2310.04294/) (2023)
- [Learning Connectivity for Data Distribution in Robot Teams](https://mlsanity.com/paper/2103.05091/) (2021)
- [Solving the flexible job-shop scheduling problem through an enhanced deep reinforcement learning approach](https://mlsanity.com/paper/2310.15706/) (2023)
- [LPAC: Learnable Perception-Action-Communication Loops with Applications to Coverage Control](https://mlsanity.com/paper/2401.04855/) (2024)
- [Learning Decentralized Flocking Controllers with Spatio-Temporal Graph Neural Network](https://mlsanity.com/paper/2309.17437/) (2023)
- [A Scalable Multi-Robot Framework for Decentralized and Asynchronous Perception-Action-Communication Loops](https://mlsanity.com/paper/2309.10164/) (2023)
- [Active Neural Topological Mapping for Multi-Agent Exploration](https://mlsanity.com/paper/2311.00252/) (2023)
- [Graph Learning-based Fleet Scheduling for Urban Air Mobility under Operational Constraints, Varying Demand & Uncertainties](https://mlsanity.com/paper/2401.04851/) (2024)
- [Bigraph Matching Weighted with Learnt Incentive Function for Multi-Robot Task Allocation](https://mlsanity.com/paper/2403.07131/) (2024)
- [Learning-aided Bigraph Matching Approach to Multi-Crew Restoration of Damaged Power Networks Coupled with Road Transportation Networks](https://mlsanity.com/paper/2506.19703/) (2025)
- [Time-Constrained Intelligent Adversaries for Automation Vulnerability Testing: A Multi-Robot Patrol Case Study](https://mlsanity.com/paper/2509.11971/) (2025)
- [Dual-Attention Heterogeneous GNN for Multi-robot Collaborative Area Search via Deep Reinforcement Learning](https://mlsanity.com/paper/2601.03686/) (2026)
- [A Gated Graph Neural Network Approach to Fast-Convergent Dynamic Average Estimation](https://mlsanity.com/paper/2606.20955/) (2026)
- [Coverage Path Planning: Classical Foundations, Recent Advances, and Future Directions](https://mlsanity.com/paper/2607.10649/) (2026)

## Abstract (arXiv)

The multi-robot coverage problem is an essential building block for systems that perform tasks like inspection or search and rescue. We discretize the coverage problem to induce a spatial graph of locations and represent robots as nodes in the graph. Then, we train a Graph Neural Network controller that leverages the spatial equivariance of the task to imitate an expert open-loop routing solution. This approach generalizes well to much larger maps and larger teams that are intractable for the expert. In particular, the model generalizes effectively to a simulation of ten quadrotors and dozens of buildings. We also demonstrate the GNN controller can surpass planning-based approaches in an exploration task.