Emergence of Human to Robot Transfer in Vision-Language-Action Models

• URL: http://arxiv.org/abs/2512.22414v1
• Date: Sat, 27 Dec 2025 00:13:11 GMT
• Title: Emergence of Human to Robot Transfer in Vision-Language-Action Models
• Authors: Simar Kareer, Karl Pertsch, James Darpinian, Judy Hoffman, Danfei Xu, Sergey Levine, Chelsea Finn, Suraj Nair,
• Abstract summary: Vision-language-action (VLA) models can enable broad open world generalization, but require large and diverse datasets.<n>We show that human-to-robot transfer emerges once the VLA is pre-trained on sufficient scenes, tasks, and embodiments.
• Score: 88.76648919814771
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision-language-action (VLA) models can enable broad open world generalization, but require large and diverse datasets. It is appealing to consider whether some of this data can come from human videos, which cover diverse real-world situations and are easy to obtain. However, it is difficult to train VLAs with human videos alone, and establishing a mapping between humans and robots requires manual engineering and presents a major research challenge. Drawing inspiration from advances in large language models, where the ability to learn from diverse supervision emerges with scale, we ask whether a similar phenomenon holds for VLAs that incorporate human video data. We introduce a simple co-training recipe, and find that human-to-robot transfer emerges once the VLA is pre-trained on sufficient scenes, tasks, and embodiments. Our analysis suggests that this emergent capability arises because diverse pretraining produces embodiment-agnostic representations for human and robot data. We validate these findings through a series of experiments probing human to robot skill transfer and find that with sufficiently diverse robot pre-training our method can nearly double the performance on generalization settings seen only in human data.

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