InternVLA-M1: A Spatially Guided Vision-Language-Action Framework for
  Generalist Robot Policy
Authors: name: Xinyi Chen; name: Yilun Chen; name: Yanwei Fu; name: Ning Gao; name: Jiaya Jia; name: Weiyang Jin; name: Hao Li; name: Yao Mu; name: Jiangmiao Pang; name: Yu Qiao; name: Yang Tian; name: Bin Wang; name: Bolun Wang; name: Fangjing Wang; name: Hanqing Wang; name: Tai Wang; name: Ziqin Wang; name: Xueyuan Wei; name: Chao Wu; name: Shuai Yang; name: Jinhui Ye; name: Junqiu Yu; name: Jia Zeng; name: Jingjing Zhang; name: Jinyu Zhang; name: Shi Zhang; name: Feng Zheng; name: Bowen Zhou; name: Yangkun Zhu
Published: 2025-10-15 17:30:05

Abstract: 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.