# Graph Neural Networks for Multi-Robot Active Information Acquisition

arXiv: https://arxiv.org/abs/2209.12091 · HTML: https://mlsanity.com/paper/2209.12091/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 43 references that are in the map.
- First posted on arXiv 24 Sep 2022.

## Builds on (most cited first, 21 of 21)

- [Relational inductive biases, deep learning, and graph networks](https://mlsanity.com/paper/1806.01261/) (2018)
- [Graph Neural Networks for Decentralized Multi-Robot Path Planning](https://mlsanity.com/paper/1912.06095/) (2019)
- [Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learning](https://mlsanity.com/paper/1709.10082/) (2017)
- [Learning Decentralized Controllers for Robot Swarms with Graph Neural Networks](https://mlsanity.com/paper/1903.10527/) (2019)
- [Message-Aware Graph Attention Networks for Large-Scale Multi-Robot Path Planning](https://mlsanity.com/paper/2011.13219/) (2020)
- [Autonomous Exploration Under Uncertainty via Deep Reinforcement Learning on Graphs](https://mlsanity.com/paper/2007.12640/) (2020)
- [Multi-Robot Coverage and Exploration using Spatial Graph Neural Networks](https://mlsanity.com/paper/2011.01119/) (2020)
- [Efficient Informative Sensing using Multiple Robots](https://mlsanity.com/paper/1401.3462/) (2014)
- [Graph Neural Networks for Decentralized Multi-Robot Submodular Action Selection](https://mlsanity.com/paper/2105.08601/) (2021)
- [Adaptive Informative Path Planning Using Deep Reinforcement Learning for UAV-based Active Sensing](https://mlsanity.com/paper/2109.13570/) (2021)
- [Coverage Control in Multi-Robot Systems via Graph Neural Networks](https://mlsanity.com/paper/2109.15278/) (2021)
- [Active Information Acquisition](https://mlsanity.com/paper/1602.02181/) (2016)
- [Multi-Robot Active Mapping via Neural Bipartite Graph Matching](https://mlsanity.com/paper/2203.16319/) (2022)
- [Information Acquisition with Sensing Robots: Algorithms and Error Bounds](https://mlsanity.com/paper/1309.5390/) (2013)
- [Informative Planning and Online Learning with Sparse Gaussian Processes](https://mlsanity.com/paper/1609.07560/) (2016)
- [Learning to Gather Information via Imitation](https://mlsanity.com/paper/1611.04180/) (2016)
- [Joint Estimation and Localization in Sensor Networks](https://mlsanity.com/paper/1404.3580/) (2014)
- [Learning Q-network for Active Information Acquisition](https://mlsanity.com/paper/1910.10754/) (2019)
- [Reactive Informative Planning for Mobile Manipulation Tasks under Sensing and Environmental Uncertainty](https://mlsanity.com/paper/2205.06301/) (2022)
- [Decentralized Multi-target Tracking in Urban Environments: Overview and Challenges](https://mlsanity.com/paper/1906.00770/) (2019)
- [Technical Report: Distributed Sampling-based Planning for Non-Myopic Active Information Gathering](https://mlsanity.com/paper/2107.11163/) (2021)

## Built on by (most cited first, 12 of 12)

- [Graph learning in robotics: a survey](https://mlsanity.com/paper/2310.04294/) (2023)
- [LPAC: Learnable Perception-Action-Communication Loops with Applications to Coverage Control](https://mlsanity.com/paper/2401.04855/) (2024)
- [NeuroSymActive: Differentiable Neural-Symbolic Reasoning with Active Exploration for Knowledge Graph Question Answering](https://mlsanity.com/paper/2602.15353/) (2026)
- [Decentralized Multi-Agent Active Search and Tracking when Targets Outnumber Agents](https://mlsanity.com/paper/2401.03154/) (2024)
- [Transferability of Convolutional Neural Networks in Stationary Learning Tasks](https://mlsanity.com/paper/2307.11588/) (2023)
- [A Scalable Multi-Robot Framework for Decentralized and Asynchronous Perception-Action-Communication Loops](https://mlsanity.com/paper/2309.10164/) (2023)
- [Scalable Multi-Robot Informative Path Planning for Target Mapping via Deep Reinforcement Learning](https://mlsanity.com/paper/2409.16967/) (2024)
- [Symmetries-enhanced Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2501.01136/) (2025)
- [Towards Robust Multi-UAV Collaboration: MARL with Noise-Resilient Communication and Attention Mechanisms](https://mlsanity.com/paper/2503.02913/) (2025)
- [MATT-Diff: Multimodal Active Target Tracking by Diffusion Policy](https://mlsanity.com/paper/2511.11931/) (2025)
- [Active Scout: Multi-Target Tracking Using Neural Radiance Fields in Dense Urban Environments](https://mlsanity.com/paper/2406.07431/) (2024)
- [Distributed Training of Large Graph Neural Networks with Variable Communication Rates](https://mlsanity.com/paper/2406.17611/) (2024)

## Abstract (arXiv)

This paper addresses the Multi-Robot Active Information Acquisition (AIA) problem, where a team of mobile robots, communicating through an underlying graph, estimates a hidden state expressing a phenomenon of interest. Applications like target tracking, coverage and SLAM can be expressed in this framework. Existing approaches, though, are either not scalable, unable to handle dynamic phenomena or not robust to changes in the communication graph. To counter these shortcomings, we propose an Information-aware Graph Block Network (I-GBNet), an AIA adaptation of Graph Neural Networks, that aggregates information over the graph representation and provides sequential-decision making in a distributed manner. The I-GBNet, trained via imitation learning with a centralized sampling-based expert solver, exhibits permutation equivariance and time invariance, while harnessing the superior scalability, robustness and generalizability to previously unseen environments and robot configurations. Experiments on significantly larger graphs and dimensionality of the hidden state and more complex environments than those seen in training validate the properties of the proposed architecture and its efficacy in the application of localization and tracking of dynamic targets.