# Learning Optimal Topology for Ad-hoc Robot Networks

arXiv: https://arxiv.org/abs/2201.12900 · HTML: https://mlsanity.com/paper/2201.12900/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 24 references that are in the map.
- First posted on arXiv 30 Jan 2022.

## Builds on (most cited first, 4 of 4)

- [SuperGlue: Learning Feature Matching with Graph Neural Networks](https://mlsanity.com/paper/1911.11763/) (2019)
- [GPT-GNN: Generative Pre-Training of Graph Neural Networks](https://mlsanity.com/paper/2006.15437/) (2020)
- [Learning Graph Normalization for Graph Neural Networks](https://mlsanity.com/paper/2009.11746/) (2020)
- [Data-Driven Convergence Prediction of Astrobots Swarms](https://mlsanity.com/paper/2005.14703/) (2020)

## Built on by (most cited first, 1 of 1)

- [Topology Recoverability Prediction for Ad-Hoc Robot Networks: A Data-Driven Fault-Tolerant Approach](https://mlsanity.com/paper/2310.20024/) (2023)

## Abstract (arXiv)

In this paper, we synthesize a data-driven method to predict the optimal topology of an ad-hoc robot network. This problem is technically a multi-task classification problem. However, we divide it into a class of multi-class classification problems that can be more efficiently solved. For this purpose, we first compose an algorithm to create ground-truth optimal topologies associated with various configurations of a robot network. This algorithm incorporates a complex collection of optimality criteria that our learning model successfully manages to learn. This model is an stacked ensemble whose output is the topology prediction for a particular robot. Each stacked ensemble instance constitutes three low-level estimators whose outputs will be aggregated by a high-level boosting blender. Applying our model to a network of 10 robots displays over 80% accuracy in the prediction of optimal topologies corresponding to various configurations of the cited network.