# Combining Graph Attention Networks and Distributed Optimization for Multi-Robot Mixed-Integer Convex Programming

arXiv: https://arxiv.org/abs/2503.21548 · HTML: https://mlsanity.com/paper/2503.21548/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Mar 2025.

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

- [Communication-Efficient Distributed Learning with Differential Privacy](https://mlsanity.com/paper/2604.02558/) (2026)

## Abstract (arXiv)

In this paper, we develop a fast mixed-integer convex programming (MICP) framework for multi-robot navigation by combining graph attention networks and distributed optimization. We formulate a mixed-integer optimization problem for receding horizon motion planning of a multi-robot system, taking into account the surrounding obstacles. To address the resulting multi-agent MICP problem in real time, we propose a framework that utilizes heterogeneous graph attention networks to learn the latent mapping from problem parameters to optimal binary solutions. Furthermore, we apply a distributed proximal alternating direction method of multipliers algorithm for solving the convex continuous optimization problem. We demonstrate the effectiveness of our proposed framework through experiments conducted on a robotic testbed.