Hy-Embodied-VLM-1.0: Efficient Physical-World Agents
Building capable embodied agents requires not only multimodal perception and understanding, but also agentic capabilities for reasoning about actions, adapting to evolving situations, and interacting with the physical world. In this report, we introduce Hy-Embodied-VLM-1.0, an efficient and powerful embodied foundation model specifically designed for embodied agents operating in the physical world. To cultivate such capabilities from the pre-training stage onward, we define an action-centric capability taxonomy comprising three progressive dimensions: Action-Relevant State Understanding, Actio
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- PossiblePossibly related (embedding) · 52%Signet-AI/signetai →
- PossiblePossibly related (embedding) · 52%xlang-ai/OSWorld →
- PossiblePossibly related (embedding) · 50%EvolvingLMMs-Lab/LLaVA-OneVision-2 →
- PossiblePossibly related (embedding) · 50%Agentic AI for Robot Teams →
- PossiblePossibly related (embedding) · 49%AgentCore-8B →
- LinkedLinked via arxiv author · 85%Ziyi Wang →
“Hy-Embodied-VLM-1.0: Efficient Physical-World Agents”
- LinkedLinked via arxiv author · 85%Xumin Yu →
“Hy-Embodied-VLM-1.0: Efficient Physical-World Agents”
- LinkedLinked via arxiv author · 85%Yongming Rao →
“Hy-Embodied-VLM-1.0: Efficient Physical-World Agents”