[2603.28010] HeteroHub: An Applicable Data Management Framework for Heterogeneous Multi-Embodied Agent System

Computer Science > Artificial Intelligence arXiv:2603.28010 (cs) [Submitted on 30 Mar 2026] Title:HeteroHub: An Applicable Data Management Framework for Heterogeneous Multi-Embodied Agent System Authors:Xujia Li, Xin Li, Junquan Huang, Beirong Cui, Zibin Wu, Lei Chen View a PDF of the paper titled HeteroHub: An Applicable Data Management Framework for Heterogeneous Multi-Embodied Agent System, by Xujia Li and 5 other authors View PDF HTML (experimental) Abstract:Heterogeneous Multi-Embodied Agent Systems involve coordinating multiple embodied agents with diverse capabilities to accomplish tasks in dynamic environments. This process requires the collection, generation, and consumption of massive, heterogeneous data, which primarily falls into three categories: static knowledge regarding the agents, tasks, and environments; multimodal training datasets tailored for various AI models; and high-frequency sensor streams. However, existing frameworks lack a unified data management infrastructure to support the real-world deployment of such systems. To address this gap, we present \textbf{HeteroHub}, a data-centric framework that integrates static metadata, task-aligned training corpora, and real-time data streams. The framework supports task-aware model training, context-sensitive execution, and closed-loop control driven by real-world feedback. In our demonstration, HeteroHub successfully coordinates multiple embodied AI agents to execute complex tasks, illustrating how a robus...