# HeteroHub: An Applicable Data Management Framework for Heterogeneous Multi-Embodied Agent System

arXiv: https://arxiv.org/abs/2603.28010 · HTML: https://mlsanity.com/paper/2603.28010/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 10 references that are in the map.
- First posted on arXiv 30 Mar 2026.

## Builds on (most cited first, 4 of 4)

- [COHERENT: Collaboration of Heterogeneous Multi-Robot System with Large Language Models](https://mlsanity.com/paper/2409.15146/) (2024)
- [Towards Generalizable Vision-Language Robotic Manipulation: A Benchmark and LLM-guided 3D Policy](https://mlsanity.com/paper/2410.01345/) (2024)
- [EMOS: Embodiment-aware Heterogeneous Multi-robot Operating System with LLM Agents](https://mlsanity.com/paper/2410.22662/) (2024)
- [TensorSocket: Shared Data Loading for Deep Learning Training](https://mlsanity.com/paper/2409.18749/) (2024)

## Abstract (arXiv)

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 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.