{
  "id": 6607,
  "url": "https://arxiv.org/abs/2603.28010v1",
  "title": "HeteroHub: An Applicable Data Management Framework for Heterogeneous Multi-Embodied Agent System",
  "summary": "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 u",
  "authors": "Xujia Li, Xin Li, Junquan Huang, Beirong Cui, Zibin Wu, Lei Chen",
  "category": "research",
  "topics": "agents-autonomy,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-03-30T04:01:05.000Z",
  "fetched_at": "2026-07-14T16:32:37.309Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/6607",
  "original_url": "https://arxiv.org/abs/2603.28010v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}