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

jun_brro, Wook Jung, Taeyoung Kim, Huiwon Jang, Jinwoo Shin

Human-to-Robot Transfer via Bridging Representation and Action Space in VLAs: 15 upvotes on Hugging Face Daily Papers, #17 of 38 papers on 2026-11-05. Day-by-day upvote history.

Upvotes over time

It had 13 upvotes by its first day, 14 upvotes by the next day, 16 upvotes by day 3, 15 upvotes by day 7, and has 15 now. It ranked #87 of 207 papers in its week and #129 of 340 in its month. Its code repository has 4 GitHub stars.

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 (Human-To-Robot Bridge), a human-robot co-training framework for adapting pretrained VLA policies from unpaired human and robot demonstrations. H2RB bridges the embodiment gap at both interfaces: geometry-anchored alignment grounds human and robot observations in a compatible representation, while a decoupled-decoder tokenizer shares action latents without forcing both domains through a single action mapping. 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 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 baselines 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, reducing the dependence on costly target-robot data collection.

Paper page on Hugging Face · arXiv

Data: hysts-bot-data/daily-papers-stats and the Daily Papers API. Open data: tardellirs/paper-pulse-data. Sister project: Model Pulse, the download history of every model on the Hub.