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arXiv · 2602.21736

Joint-Aligned Latent Action: Towards Scalable VLA Pretraining in the Wild

Hao Luo·Ye Wang·Wanpeng Zhang·Haoqi Yuan·Yicheng Feng·Haiweng Xu·Sipeng Zheng·Zongqing Lu
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Abstract

Despite progress, Vision-Language-Action models (VLAs) are limited by a scarcity of large-scale, diverse robot data. While human manipulation videos offer a rich alternative, existing methods are forced to choose between small, precisely-labeled datasets and vast in-the-wild footage with unreliable hand tracking labels. We present JALA, a pretraining framework that learns Jointly-Aligned Latent Actions. JALA bypasses full visual dynamic reconstruction, instead learns a predictive action embedding aligned with both inverse dynamics and real actions. This yields a transition-aware, behavior-centric latent space for learning from heterogeneous human data. We scale this approach with UniHand-Mix, a 7.5M video corpus (>2,000 hours) blending laboratory and in-the-wild footage. Experiments demonstrate that JALA generates more realistic hand motions in both controlled and unconstrained scenarios, significantly improving downstream robot manipulation performance in both simulation and real-world tasks. These results indicate that jointly-aligned latent actions offer a scalable pathway for VLA pretraining from human data.

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Hao Luo, Ye Wang, Wanpeng Zhang, Haoqi Yuan, Yicheng Feng, Haiweng Xu, Sipeng Zheng, Zongqing Lu. 2026-02-25. Joint-Aligned Latent Action: Towards Scalable VLA Pretraining in the Wild. https://arxiv.org/abs/2602.21736

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