# Efficient Planning of Multi-Robot Collective Transport using Graph Reinforcement Learning with Higher Order Topological Abstraction

arXiv: https://arxiv.org/abs/2303.08933 · HTML: https://mlsanity.com/paper/2303.08933/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Mar 2023.

## Built on by (most cited first, 5 of 5)

- [Fast Decision Support for Air Traffic Management at Urban Air Mobility Vertiports using Graph Learning](https://mlsanity.com/paper/2308.09075/) (2023)
- [Topological Data Analysis in smart manufacturing: State of the art and futuredirections](https://mlsanity.com/paper/2310.09319/) (2023)
- [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)
- [Automated Generation of Diverse Courses of Actions for Multi-Agent Operations using Binary Optimization and Graph Learning](https://mlsanity.com/paper/2506.20031/) (2025)

## Abstract (arXiv)

Efficient multi-robot task allocation (MRTA) is fundamental to various time-sensitive applications such as disaster response, warehouse operations, and construction. This paper tackles a particular class of these problems that we call MRTA-collective transport or MRTA-CT -- here tasks present varying workloads and deadlines, and robots are subject to flight range, communication range, and payload constraints. For large instances of these problems involving 100s-1000's of tasks and 10s-100s of robots, traditional non-learning solvers are often time-inefficient, and emerging learning-based policies do not scale well to larger-sized problems without costly retraining. To address this gap, we use a recently proposed encoder-decoder graph neural network involving Capsule networks and multi-head attention mechanism, and innovatively add topological descriptors (TD) as new features to improve transferability to unseen problems of similar and larger size. Persistent homology is used to derive the TD, and proximal policy optimization is used to train our TD-augmented graph neural network. The resulting policy model compares favorably to state-of-the-art non-learning baselines while being much faster. The benefit of using TD is readily evident when scaling to test problems of size larger than those used in training.