构建统一数据框架，让不同智能体协同完成复杂任务
HeteroHub: An Applicable Data Management Framework for Heterogeneous Multi-Embodied Agent System
- 整合静态元数据、训练数据与实时传感流，实现数据驱动的多智能体协同
- 支持任务感知训练与闭环控制，实现在动态环境中的高效执行
- 适用于需长期维护和演进的多智能体系统，适合工程部署
异构多具身智能体系统需在动态环境中协调多个能力各异的具身智能体完成任务，过程中涉及大量异构数据，主要包括三类：关于智能体、任务与环境的静态知识；针对不同AI模型定制的多模态训练数据集；以及高频传感器数据流。然而现有框架缺乏统一的数据管理基础设施，难以支撑实际部署。为此，我们提出 HeteroHub——一个以数据为中心的框架，集成静态元数据、任务对齐的训练语料与实时数据流。该框架支持任务感知的模型训练、上下文敏感的执行，以及基于真实反馈的闭环控制。在演示中，HeteroHub成功协调多个具身智能体完成复杂任务，验证了稳健数据管理框架对可扩展、可维护、可演进的具身AI系统的关键作用。
原文摘要 · Abstract (English)
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 robust data management framework can enable scalable, maintainable, and evolvable embodied AI systems.
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