HeteroHub: An Applicable Data Management Framework for Heterogeneous Multi-Embodied Agent System
This addresses data management challenges for deploying scalable embodied AI systems, though it appears incremental as it builds on existing coordination concepts.
The paper tackles the lack of unified data management for heterogeneous multi-embodied agent systems, presenting HeteroHub, a framework that integrates static metadata, training corpora, and real-time streams to coordinate agents in complex tasks.
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.