Pith Number

pith:4TSHRENS

pith:2025:4TSHRENSGHCP54ZSMZNFJQL3TZ

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InternVLA-M1: A Spatially Guided Vision-Language-Action Framework for Generalist Robot Policy

Bin Wang, Bolun Wang, Bowen Zhou, Chao Wu, Fangjing Wang, Feng Zheng, Hanqing Wang, Hao Li, Jiangmiao Pang, Jiaya Jia, Jia Zeng, Jingjing Zhang, Jinhui Ye, Jinyu Zhang, Junqiu Yu, Ning Gao, Shi Zhang, Shuai Yang, Tai Wang, Weiyang Jin, Xinyi Chen, Xueyuan Wei, Yangkun Zhu, Yang Tian, Yanwei Fu, Yao Mu, Yilun Chen, Yu Qiao, Ziqin Wang

Spatially guided pre-training on millions of examples teaches robots where to act before how, yielding gains up to 17 percent on standard benchmarks.

arxiv:2510.13778 v1 · 2025-10-15 · cs.RO · cs.AI · cs.CV

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Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

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Claims

C1strongest claim

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.

C2weakest assumption

That the spatial grounding learned in the first stage on embodiment-agnostic data will transfer effectively when used as prompts in the second stage to improve embodiment-specific action generation across diverse robots and tasks.

C3one line summary

InternVLA-M1 uses spatially guided pre-training on 2.3M examples followed by action post-training to deliver up to 17% gains on robot manipulation benchmarks and 20.6% on unseen objects.

References

46 extracted · 46 resolved · 28 Pith anchors

[1] Qwen2.5-VL Technical Report · arXiv:2502.13923

[2] GR00T N1: An Open Foundation Model for Generalist Humanoid Robots · arXiv:2503.14734

[3] $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control · arXiv:2410.24164

[4] RT-1: Robotics Transformer for Real-World Control at Scale · arXiv:2212.06817

[5] RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control · arXiv:2307.15818

Formal links

2 machine-checked theorem links

Cited by

30 papers in Pith

Humanoid Whole-Body Manipulation via Active Spatial Brain and Generalizable Action Cerebellum

PointACT: Vision-Language-Action Models with Multi-Scale Point-Action Interaction

Dynamic Skill Lifecycle Management for Agentic Reinforcement Learning

RoVLA: Multi-Consistency Constraints for Robust Vision-Language-Action Models

AsyncVLA: Asynchronous Flow Matching for Vision-Language-Action Models

Receipt and verification

First computed	2026-05-17T23:39:21.867905Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

e4e47891b231c4fef332665a54c17b9e4207f529db2e466342c9517b65942c0f

Aliases

arxiv: 2510.13778 · arxiv_version: 2510.13778v1 · doi: 10.48550/arxiv.2510.13778 · pith_short_12: 4TSHRENSGHCP · pith_short_16: 4TSHRENSGHCP54ZS · pith_short_8: 4TSHRENS

Agent API

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/4TSHRENSGHCP54ZSMZNFJQL3TZ \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: e4e47891b231c4fef332665a54c17b9e4207f529db2e466342c9517b65942c0f

Canonical record JSON

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    "cross_cats_sorted": [
      "cs.AI",
      "cs.CV"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.RO",
    "submitted_at": "2025-10-15T17:30:05Z",
    "title_canon_sha256": "e652664e01d46d787e429227dd91ef57352d92a97a312e6194194c8a985f6a68"
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