Author

Yongming Rao

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Preprint Sep 2026

Transferring the Intelligence of VLMs to Robotic Control

Humans can seamlessly adapt to both physical and digital worlds, suggesting that while a digital-to-real gap exists in embodiment, environment and task, human intelligence itself may transfer across this gap. This naturally raises a fundamental question: can the intelligence of vision-language models (VLMs) similarly g...

Meng-Hao Guo, Zhe-Han Mo, Jia-Jun Wang et al. · 0 citations

Jul 2026

Hy-Embodied-VLM-1.0: Efficient Physical-World Agents

The model achieves the best performance among similarly sized models on 19 of the 38 benchmarks and substantially outperforms strong competitors, including Qwen3.6-A3B and Cosmos 3.5 MoT-2B, and demonstrates strong performance on embodied agentic tasks requiring multi-turn interaction and long-horizon reasoning.

Ziyi Wang, Xu-Min Yu, Yongming Rao et al. · arXiv.org · 6 citations