NeuroMesh: 分散型マルチロボット協調のための統合ニューラル推論フレームワーク
NeuroMesh: A Unified Neural Inference Framework for Decentralized Multi-Robot Collaboration
異種ロボット間での学習モデル展開を容易にする、統一パイプラインを持つ分散型ニューラル推論フレームワークを提案。空中・地上ロボットの協調タスクで有効性を実証した。
詳しい要約
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著者: Yang Zhou, Yash Shetye, Long Quang, Devon Super, Jesse Milzman, Manohari Goarin, Aditya Azad, Devang Sunil Dhake, Jeffery Mao, Carlos Nieto-Granda, Giuseppe Loianno
分類: cs.RO, cs.MA
原文アブストラクト
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.
