NeuroMesh: A Unified Neural Inference Framework for Decentralized Multi-Robot Collaboration
Yang Zhou, Yash Shetye, Long Quang, Devon Super, Jesse Milzman, Manohari Goarin, Aditya Azad, Devang Sunil Dhake
Abstract
Deploying learned multi-robot models on heterogeneous robots remains challenging due to hardware heterogeneity, communication constraints, and the lack of a unified execution stack. This paper presents NeuroMesh, a multi-domain, cross-platform, and modular decentralized neural inference framework that standardizes observation encoding, message passing, aggregation, and task decoding in a unified pipeline. NeuroMesh combines a dual-aggregation paradigm for reduction- and broadcast-based information fusion with a parallelized architecture that decouples cycle time from end-to-end latency. Our high-performance C++ implementation leverages Zenoh for inter-robot communication and supports hybrid GPU/CPU inference. We validate NeuroMesh on a heterogeneous team of aerial and ground robots across collaborative perception, decentralized control, and task assignment, demonstrating robust operation across diverse task structures and payload sizes. We plan to release NeuroMesh as an open-source framework to the community.
BibTeX
@inproceedings{ral2026_neuromeshaunifie,
title = {NeuroMesh: A Unified Neural Inference Framework for Decentralized Multi-Robot Collaboration},
author = {Yang Zhou and Yash Shetye and Long Quang and Devon Super and Jesse Milzman and Manohari Goarin and Aditya Azad and Devang Sunil Dhake and Jeffrey Mao and Carlos Nieto-Granda and Giuseppe Loianno},
booktitle = {RA-L 2026},
year = {2026}
}