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About this paper
Gen Luo, Ganlin Yang, Ziyang Gong, Guanzhou Chen, Haonan Duan, Erfei Cui, Ronglei Tong, Zhi Hou and 10 more
Researchers from Shanghai AI Laboratory, Tsinghua University, and SenseTime Research developed VeBrain, a unified framework enabling Multimodal Large Language Models to integrate multimodal understanding, visual-spatial reasoning, and robotic control. This approach reformulates robot control tasks into 2D visual keypoint detection and embodied skill recognition, achieving up to +31.5% average performance gains against other frameworks across diverse benchmarks while preserving general multimodal understanding.
Key insights
- Robot control tasks can be effectively reformulated as text-based Multimodal Large Language Model (MLLM) objectives (2D keypoint detection and embodied skill recognition), allowing MLLMs to leverage their strong understanding for physical control.
- A specialized robotic adapter is crucial for robustly translating 2D visual predictions from MLLMs into precise 3D real-world robot actions, addressing challenges like ego-view changes and dynamic state awareness.
- Training a unified embodied AI requires a comprehensive, high-quality instruction dataset that balances multimodal understanding, visual-spatial reasoning, and robot control, leveraging Chain-of-Thought for complex tasks.
Summary by alphaXiv.
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