Robotics: Science and Systems XXII

LAP: Language-Action Pre-training Enables Zero-Shot Cross-Embodiment Transfer

Lihan Zha, Asher James Hancock, Mingtong Zhang, Tenny Yin, Yixuan Huang, Dhruv Shah, Allen Z. Ren, Anirudha Majumdar

Abstract:

A long-standing goal in robotics is a generalist policy that can be deployed zero-shot on new robot embodiments without per-embodiment adaptation. Despite large-scale multi-embodiment pre-training, existing Vision--Language--Action models (VLAs) remain tightly coupled to their training embodiments and typically require costly fine-tuning. We introduce Language-Action Pre-training (LAP), a simple recipe that represents low-level robot actions directly in natural language, aligning action supervision with the pre-trained vision--language model’s input--output distribution. LAP requires no learned tokenizer, no costly annotation, and no embodiment-specific architectural design. Based on LAP, we present LAP-3B, which to the best of our knowledge is the first VLA to achieve substantial zero-shot transfer to previously unseen robot embodiments without any embodiment-specific fine-tuning. Across multiple novel robots and manipulation tasks, LAP-3B attains over 50% average zero-shot success, delivering roughly a 2× improvement over the strongest prior VLAs. We further show that LAP enables efficient adaptation and favorable scaling, while unifying action prediction and VQA in a shared language-action format that yields additional gains through co-training.

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Bibtex:

  
@INPROCEEDINGS{ZhaL-RSS-26, 
    AUTHOR    = {Lihan Zha AND Asher James Hancock AND Mingtong Zhang AND Tenny Yin AND Yixuan Huang AND Dhruv Shah AND Allen Z. Ren AND Anirudha Majumdar}, 
    TITLE     = {{LAP: Language-Action Pre-training Enables Zero-Shot Cross-Embodiment Transfer}}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2026}, 
    ADDRESS   = {Sydney, Australia}, 
    MONTH     = {July}, 
    DOI       = {10.15607/RSS.2026.XXII.203} 
}