Visual Embodied Brain: Let Multimodal Large Language Models See, Think,
  and Control in Spaces
The remarkable progress of Multimodal Large Language Models (MLLMs) has
attracted increasing attention to extend them to physical entities like legged
robot. This typically requires MLLMs to not only grasp multimodal understanding
abilities, but also integrate visual-spatial reasoning and physical interaction
capabilities. Nevertheless,existing methods struggle to unify these
capabilities due to their fundamental differences.In this paper, we present the
Visual Embodied Brain (VeBrain), a unified framework for perception, reasoning,
and control in real world. VeBrain reformulates robotic control into common
text-based MLLM tasks in the 2D visual space, thus unifying the objectives and
mapping spaces of different tasks. Then, a novel robotic adapter is proposed to
convert textual control signals from MLLMs to motion policies of real robots.
From the data perspective, we further introduce VeBrain-600k, a high-quality
instruction dataset encompassing various capabilities of VeBrain. In
VeBrain-600k, we take hundreds of hours to collect, curate and annotate the
data, and adopt multimodal chain-of-thought(CoT) to mix the different
capabilities into a single conversation. Extensive experiments on 13 multimodal
benchmarks and 5 spatial intelligence benchmarks demonstrate the superior
performance of VeBrain to existing MLLMs like Qwen2.5-VL. When deployed to
legged robots and robotic arms, VeBrain shows strong adaptability, flexibility,
and compositional capabilities compared to existing methods. For example,
compared to Qwen2.5-VL, VeBrain not only achieves substantial gains on MMVet by
+5.6%, but also excels in legged robot tasks with +50% average gains.
683e709a3a4c2c3b2750fc32
Gen Luo
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Ganlin Yang
claimed_verified
2025-06-03T08:45:03.857Z
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Ganlin Yang
user
ganlinyang
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Ziyang Gong
claimed_verified
2025-06-03T08:45:00.364Z
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ZiYang Gong
user
Cusyoung
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Guanzhou Chen
claimed_verified
2025-06-09T10:12:49.709Z
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Guanzhou Chen
user
Rayment
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Haonan Duan
claimed_verified
2025-06-04T09:03:03.236Z
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Haonan Duan
user
robot-haonan
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Erfei Cui
683e709a3a4c2c3b2750fc38
Ronglei Tong
683e709a3a4c2c3b2750fc39
Zhi Hou
683e709a3a4c2c3b2750fc3a
Tianyi Zhang
claimed_verified
2025-06-07T05:49:46.792Z
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/avatars/61a84989dbbc1898ebcba3236dbed039.svg
Tianyi Zhang
user
TianyiZhang0213
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Zhe Chen
683e709a3a4c2c3b2750fc3c
Shenglong Ye
683e709a3a4c2c3b2750fc3d
Lewei Lu
683e709a3a4c2c3b2750fc3e
Jingbo Wang
683e709a3a4c2c3b2750fc3f
Wenhai Wang
683e709a3a4c2c3b2750fc40
Jifeng Dai
683e709a3a4c2c3b2750fc41
Yu Qiao
683e709a3a4c2c3b2750fc42
Rongrong Ji
683e709a3a4c2c3b2750fc43
Xizhou Zhu
Visual Embodied Brain: Let Multimodal Large Language Models See, Think,
  and Control in Spaces
The remarkable progress of Multimodal Large Language Models (MLLMs) has
attracted increasing attention to extend them to physical entities like legged
robot. This typically requires MLLMs to not only grasp multimodal understanding
abilities, but also integrate visual-spatial reasoning and physical interaction
capabilities. Nevertheless,existing methods struggle to unify these
capabilities due to their fundamental differences.In this paper, we present the
Visual Embodied Brain (VeBrain), a unified framework for perception, reasoning,
and control in real world. VeBrain reformulates robotic control into common
text-based MLLM tasks in the 2D visual space, thus unifying the objectives and
mapping spaces of different tasks. Then, a novel robotic adapter is proposed to
convert textual control signals from MLLMs to motion policies of real robots.
From the data perspective, we further introduce VeBrain-600k, a high-quality
instruction dataset encompassing various capabilities of VeBrain. In
VeBrain-600k, we take hundreds of hours to collect, curate and annotate the
data, and adopt multimodal chain-of-thought(CoT) to mix the different
capabilities into a single conversation. Extensive experiments on 13 multimodal
benchmarks and 5 spatial intelligence benchmarks demonstrate the superior
performance of VeBrain to existing MLLMs like Qwen2.5-VL. When deployed to
legged robots and robotic arms, VeBrain shows strong adaptability, flexibility,
and compositional capabilities compared to existing methods. For example,
compared to Qwen2.5-VL, VeBrain not only achieves substantial gains on MMVet by
+5.6%, but also excels in legged robot tasks with +50% average gains.
VeBrain is a unified framework that integrates multimodal understanding, visual-spatial reasoning, and physical interaction for legged robots, demonstrating superior performance compared to existing methods across various benchmarks.
Multimodal Large Language Models VeBrain Visual Embodied Brain text-based MLLM tasks robotic adapter VeBrain-600k multimodal chain-of-thought MMVet compositional capabilities