InternVLA-M1: A Spatially Guided Vision-Language-Action Framework for Generalist Robot Policy Article Swipe
YOU?
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· 2025
· Open Access
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· DOI: https://doi.org/10.48550/arxiv.2510.13778
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.
Related Topics
- Type
- preprint
- Landing Page
- http://arxiv.org/abs/2510.13778
- https://arxiv.org/pdf/2510.13778
- OA Status
- green
- OpenAlex ID
- https://openalex.org/W4415277511
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4415277511Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2510.13778Digital Object Identifier
- Title
-
InternVLA-M1: A Spatially Guided Vision-Language-Action Framework for Generalist Robot PolicyWork title
- Type
-
preprintOpenAlex work type
- Publication year
-
2025Year of publication
- Publication date
-
2025-10-15Full publication date if available
- Authors
-
Xinyi Chen, Yilun Chen, Yanwei Fu, Ning Gao, Jiaya Jia, Weiyang Jin, Hao Li, Yao Mu, Jiangmiao Pang, Yu Qiao, Yang Tian, Bin Wang, Bolun Wang, Fangjing Wang, Hanqing Wang, Tai Wang, Zhaoxin Wang, Xueyuan Wei, Chao Wu, Shuai Yang, Jiayu Ye, Jinhui Yu, Jia Zeng, Jingjing Zhang, Ji Zhang, Shi Zhang, Feng Zheng, Bowen Zhou, Yuxuan ZhuList of authors in order
- Landing page
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https://arxiv.org/abs/2510.13778Publisher landing page
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https://arxiv.org/pdf/2510.13778Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2510.13778Direct OA link when available
- Cited by
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0Total citation count in OpenAlex
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| abstract_inverted_index.control | 11 |
| abstract_inverted_index.employs | 42 |
| abstract_inverted_index.further | 129 |
| abstract_inverted_index.objects | 171 |
| abstract_inverted_index.results | 188 |
| abstract_inverted_index.robots. | 202 |
| abstract_inverted_index.spatial | 7, 29, 47, 53, 85, 100, 119 |
| abstract_inverted_index.through | 83 |
| abstract_inverted_index.unified | 4 |
| abstract_inverted_index.variant | 98 |
| abstract_inverted_index.visual, | 65 |
| abstract_inverted_index.without | 99 |
| abstract_inverted_index.achieved | 167 |
| abstract_inverted_index.actions. | 40 |
| abstract_inverted_index.advances | 13 |
| abstract_inverted_index.aligning | 62 |
| abstract_inverted_index.critical | 34 |
| abstract_inverted_index.enabling | 144 |
| abstract_inverted_index.existing | 182 |
| abstract_inverted_index.guidance | 101 |
| abstract_inverted_index.improved | 160 |
| abstract_inverted_index.objects. | 154 |
| abstract_inverted_index.scalable | 198 |
| abstract_inverted_index.stronger | 118 |
| abstract_inverted_index.training | 90, 192 |
| abstract_inverted_index.unifying | 195 |
| abstract_inverted_index.Moreover, | 175 |
| abstract_inverted_index.available | 207 |
| abstract_inverted_index.clustered | 157 |
| abstract_inverted_index.determine | 57 |
| abstract_inverted_index.episodes, | 143 |
| abstract_inverted_index.framework | 5 |
| abstract_inverted_index.grounding | 8, 30, 48 |
| abstract_inverted_index.highlight | 189 |
| abstract_inverted_index.introduce | 1 |
| abstract_inverted_index.pipeline: | 45 |
| abstract_inverted_index.principle | 196 |
| abstract_inverted_index.reasoning | 54, 120 |
| abstract_inverted_index.resilient | 200 |
| abstract_inverted_index.scalable, | 17 |
| abstract_inverted_index.spatially | 24, 70, 88, 190 |
| abstract_inverted_index.surpassed | 181 |
| abstract_inverted_index.synthetic | 165 |
| abstract_inverted_index.training, | 27 |
| abstract_inverted_index.two-stage | 44 |
| abstract_inverted_index.SimplerEnv | 105 |
| abstract_inverted_index.capability | 121 |
| abstract_inverted_index.consistent | 93 |
| abstract_inverted_index.following, | 132 |
| abstract_inverted_index.generalist | 201 |
| abstract_inverted_index.generating | 80 |
| abstract_inverted_index.positions, | 67 |
| abstract_inverted_index.prompting. | 86 |
| abstract_inverted_index.real-world | 156 |
| abstract_inverted_index.scenarios, | 179 |
| abstract_inverted_index.simulation | 136 |
| abstract_inverted_index.improvement | 148 |
| abstract_inverted_index.instruction | 131 |
| abstract_inverted_index.outperforms | 96 |
| abstract_inverted_index.prediction. | 127 |
| abstract_inverted_index.InternVLA-M1 | 41, 95, 159 |
| abstract_inverted_index.co-training, | 166 |
| abstract_inverted_index.instructions | 37, 63 |
| abstract_inverted_index.long-horizon | 177 |
| abstract_inverted_index.pre-training | 49 |
| abstract_inverted_index.InternVLA-M1, | 2 |
| abstract_inverted_index.demonstrating | 117 |
| abstract_inverted_index.generalizable | 141 |
| abstract_inverted_index.intelligence. | 19 |
| abstract_inverted_index.plug-and-play | 84 |
| abstract_inverted_index.post-training | 73 |
| abstract_inverted_index.pick-and-place | 142 |
| abstract_inverted_index.configurations. | 174 |
| abstract_inverted_index.general-purpose | 18 |
| abstract_inverted_index.pick-and-place, | 158 |
| abstract_inverted_index.embodiment-aware | 81 |
| abstract_inverted_index.embodiment-agnostic | 66 |
| abstract_inverted_index.reasoning-intensive | 178 |
| abstract_inverted_index.instruction-following | 14 |
| abstract_inverted_index.vision-language-action | 26 |
| abstract_inverted_index.https://github.com/InternRobotics/InternVLA-M1. | 209 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 29 |
| citation_normalized_percentile |