# Graph-based Decentralized Task Allocation for Multi-Robot Target Localization

arXiv: https://arxiv.org/abs/2309.08896 · HTML: https://mlsanity.com/paper/2309.08896/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2023.

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

- [MAGNNET: Multi-Agent Graph Neural Network-based Efficient Task Allocation for Autonomous Vehicles with Deep Reinforcement Learning](https://mlsanity.com/paper/2502.02311/) (2025)
- [Can a Robot Walk the Robotic Dog: Triple-Zero Collaborative Navigation for Heterogeneous Multi-Agent Systems](https://mlsanity.com/paper/2603.21723/) (2026)
- [Learning Lagrangian Interaction Dynamics with Sampling-Based Model Order Reduction](https://mlsanity.com/paper/2407.03925/) (2024)
- [Optimizing Crowd-Aware Multi-Agent Path Finding through Local Communication with Graph Neural Networks](https://mlsanity.com/paper/2309.10275/) (2023)
- [HessianForge: Scalable LiDAR reconstruction with Physics-Informed Neural Representation and Smoothness Energy Constraints](https://mlsanity.com/paper/2503.08929/) (2025)
- [ARMATA: Auto-Regressive Multi-Agent Task Assignment](https://mlsanity.com/paper/2605.04225/) (2026)

## Abstract (arXiv)

We introduce a new graph neural operator-based approach for task allocation in a system of heterogeneous robots composed of Unmanned Ground Vehicles (UGVs) and Unmanned Aerial Vehicles (UAVs). The proposed model, textttmethod, or Graph Attention Task AllocatoR aggregates information from neighbors in the multi-robot system, with the aim of achieving globally optimal target localization. Being decentralized, our method is highly robust and adaptable to situations where the number of robots and the number of tasks may change over time. We also propose a heterogeneity-aware preprocessing technique to model the heterogeneity of the system. The experimental results demonstrate the effectiveness and scalability of the proposed approach in a range of simulated scenarios generated by varying the number of UGVs and UAVs and the number and location of the targets. We show that a single model can handle a heterogeneous robot team with a number of robots ranging between 2 and 12 while outperforming the baseline architectures.