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

Emergence of human to robot transfer: We observe that the transfer from human data to robot policies scales with the size and diversity of VLA pre-training data.The x-axis represents the diversity of the pre-training robot dataset, and the yellow and blue lines show the finetuning performance with and without human embodiment data.While both increase, the gain from leveraging human data only appears beyond a certain pre-training scale.We evaluate on a suite of four generalization scenarios shown only in the human data.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 pretrained 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.