InternVLA-M1: A Spatially Guided Vision-Language-Action Framework for
  Generalist Robot Policy
We introduce InternVLA-M1, a unified framework for spatial grounding and
robot control that advances instruction-following robots toward scalable,
general-purpose intelligence. Its core idea is spatially guided
vision-language-action training, where spatial grounding serves as the critical
link between instructions and robot actions. InternVLA-M1 employs a two-stage
pipeline: (i) spatial grounding pre-training on over 2.3M spatial reasoning
data to determine ``where to act'' by aligning instructions with visual,
embodiment-agnostic positions, and (ii) spatially guided action post-training
to decide ``how to act'' by generating embodiment-aware actions through
plug-and-play spatial prompting. This spatially guided training recipe yields
consistent gains: InternVLA-M1 outperforms its variant without spatial guidance
by +14.6% on SimplerEnv Google Robot, +17% on WidowX, and +4.3% on LIBERO
Franka, while demonstrating stronger spatial reasoning capability in box,
point, and trace prediction. To further scale instruction following, we built a
simulation engine to collect 244K generalizable pick-and-place episodes,
enabling a 6.2% average improvement across 200 tasks and 3K+ objects. In
real-world clustered pick-and-place, InternVLA-M1 improved by 7.3%, and with
synthetic co-training, achieved +20.6% on unseen objects and novel
configurations. Moreover, in long-horizon reasoning-intensive scenarios, it
surpassed existing works by over 10%. These results highlight spatially guided
training as a unifying principle for scalable and resilient generalist robots.
Code and models are available at
https://github.com/InternRobotics/InternVLA-M1.
68f0545236f8b025381e191a
Xinyi Chen
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2025-10-16T10:38:30.945Z
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xinyi chen
user
quasdo
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Yilun Chen
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Yanwei Fu
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Ning Gao
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Jiaya Jia
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Weiyang Jin
claimed_verified
2025-10-16T06:38:55.583Z
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Weiyang Jin
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Wayne-King
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Hao Li
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Yao Mu
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Jiangmiao Pang
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Yu Qiao
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Yang Tian
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Bin Wang
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Bolun Wang
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Fangjing Wang
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Hanqing Wang
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Tai Wang
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Ziqin Wang
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wang
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wz7in
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Xueyuan Wei
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Chao Wu
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Shuai Yang
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2025-10-17T04:14:05.519Z
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Shuai Yang
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ShuaiYang03
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Jinhui Ye
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Junqiu Yu
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Jia Zeng
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Jingjing Zhang
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Jinyu Zhang
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Shi Zhang
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Feng Zheng
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Bowen Zhou
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Yangkun Zhu
claimed_verified
2025-10-16T07:25:41.023Z
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zhuyangkun
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tenstep
InternVLA-M1: A Spatially Guided Vision-Language-Action Framework for
  Generalist Robot Policy
We introduce InternVLA-M1, a unified framework for spatial grounding and
robot control that advances instruction-following robots toward scalable,
general-purpose intelligence. Its core idea is spatially guided
vision-language-action training, where spatial grounding serves as the critical
link between instructions and robot actions. InternVLA-M1 employs a two-stage
pipeline: (i) spatial grounding pre-training on over 2.3M spatial reasoning
data to determine ``where to act'' by aligning instructions with visual,
embodiment-agnostic positions, and (ii) spatially guided action post-training
to decide ``how to act'' by generating embodiment-aware actions through
plug-and-play spatial prompting. This spatially guided training recipe yields
consistent gains: InternVLA-M1 outperforms its variant without spatial guidance
by +14.6% on SimplerEnv Google Robot, +17% on WidowX, and +4.3% on LIBERO
Franka, while demonstrating stronger spatial reasoning capability in box,
point, and trace prediction. To further scale instruction following, we built a
simulation engine to collect 244K generalizable pick-and-place episodes,
enabling a 6.2% average improvement across 200 tasks and 3K+ objects. In
real-world clustered pick-and-place, InternVLA-M1 improved by 7.3%, and with
synthetic co-training, achieved +20.6% on unseen objects and novel
configurations. Moreover, in long-horizon reasoning-intensive scenarios, it
surpassed existing works by over 10%. These results highlight spatially guided
training as a unifying principle for scalable and resilient generalist robots.
Code and models are available at
https://github.com/InternRobotics/InternVLA-M1.
A unified framework for spatial grounding and robot control, InternVLA-M1, enhances instruction-following robots through spatially guided vision-language-action training, achieving significant improvements across various tasks and simulations.
spatial grounding vision-language-action training spatial reasoning spatial prompting SimplerEnv WidowX LIBERO Franka pick-and-place long-horizon reasoning