Pith Number

pith:3RBEOIIO

pith:2025:3RBEOIIORZNQQE6VYXAA4YY2GQ

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DexVLA: Vision-Language Model with Plug-In Diffusion Expert for General Robot Control

Chaomin Shen, Feifei Feng, Jinming Li, Junjie Wen, Yichen Zhu, Zhibin Tang

DexVLA plugs a billion-parameter diffusion expert pre-trained across robot bodies into vision-language models for language-driven control on new embodiments.

arxiv:2502.05855 v3 · 2025-02-09 · cs.RO · cs.CV

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Claims

C1strongest claim

DexVLA demonstrates superior performance compared to state-of-the-art models like Octo, OpenVLA, and Diffusion Policy across multiple embodiments for complex, long-horizon tasks using only direct language prompting.

C2weakest assumption

That pre-training the diffusion expert on cross-embodiment data produces action representations that transfer effectively when plugged into a new VLA without requiring embodiment-specific action fine-tuning.

C3one line summary

DexVLA combines a scaled diffusion action expert with embodiment curriculum learning to achieve better generalization and performance than prior VLA models on diverse robot hardware and long-horizon tasks.

References

72 extracted · 72 resolved · 25 Pith anchors

[1] Learning visuotactile skills with two multifingered hands 2024

[2] Robocook: Long-horizon elasto-plastic object manipulation with diverse tools 2023

[4] Learning manipulation skills through robot chain-of-thought with sparse failure guidance 2024

[5] A. Zeng, S. Song, K.-T. Yu, E. Donlon, F. R. Hogan, M. Bauza, D. Ma, O. Taylor, M. Liu, E. Romo, et al. Robotic pick-and-place of novel objects in clutter with multi-affordance grasp- 9 ing and cross- 2022

[6] Y . Qin, Y .-H. Wu, S. Liu, H. Jiang, R. Yang, Y . Fu, and X. Wang. Dexmv: Imitation learning for dexterous manipulation from human videos. In European Conference on Computer Vision, pages 570–587. Sp 2022

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Cited by

33 papers in Pith

A Survey on Vision-Language-Action Models for Embodied AI

$\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization

AwareVLN: Reasoning with Self-awareness for Vision-Language Navigation

Sparse ActionGen: Accelerating Diffusion Policy with Real-time Pruning

OxyGen: Unified KV Cache Management for VLA Inference under Multi-Task Parallelism

Receipt and verification

First computed	2026-05-17T23:39:22.000957Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

dc4247210e8e5b0813d5c5c00e631a3437d7839aa2456c4e7c9c64d59f460424

Aliases

arxiv: 2502.05855 · arxiv_version: 2502.05855v3 · doi: 10.48550/arxiv.2502.05855 · pith_short_12: 3RBEOIIORZNQ · pith_short_16: 3RBEOIIORZNQQE6V · pith_short_8: 3RBEOIIO

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curl -sH 'Accept: application/ld+json' https://pith.science/pith/3RBEOIIORZNQQE6VYXAA4YY2GQ \
  | 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: dc4247210e8e5b0813d5c5c00e631a3437d7839aa2456c4e7c9c64d59f460424

Canonical record JSON

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