Human-to-Robot Transfer via Bridging Representation and Action Space in VLAs

Pretrained Vision-Language-Action (VLA) policies provide a strong starting point for deployment, but adapting them to a target robot and environment still requires costly embodiment-specific demonstrations. Human demonstrations are far easier to collect, yet directly leveraging them is challenging due to the substantial human-robot embodiment gap, particularly when the underlying VLA has been pretrained primarily on robot data. We introduce **H2RB** (**H**uman-**T**o-**R**obot **B**ridge), a human-robot co-training framework for adapting pretrained VLA policies from unpaired human and robot demonstrations. H2RB addresses the embodiment mismatch at both the representation and action levels: Hand geometry-anchored alignment brings human and robot observations into a compatible task representation, while a decoupled-decoder action tokenizer shares action latents but embodiment-specific action generation. This design allows human demonstrations to provide useful behavioral supervision while retaining the rich visual, semantic, and control priors of the pretrained VLA. Across real-world human-to-robot transfer tasks, H2RB substantially improves object transfer performance from 8.0 to 51.8 and enables skill transfer to score 62.7, in settings where co-train baseline methods totally fail to transfer human-demonstrated skills. The benefit also persists under object-placement shifts, improving out-of-distribution transfer from 6.0 to 45.3. H2RB suggests a way for human demonstrations to serve as a scalable supervision source for adapting pretrained robotic policies, substantially reducing the dependence on costly target-robot data collection.