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 demonstra
H2RB adapts pretrained vision-language-action policies to new robots using unpaired human demonstrations via geometry-anchored alignment and a decoupled-decoder tokenizer, substantially improving cross-embodiment skill transfer.
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…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
Human-to-Robot Transfer via Bridging Representation and Action Space in VLAs was first seen on Hugging Face · Daily papers on Nov 5, 2026. Since then it has appeared in 14 checks on 2 different days. Its best position was #12 of 30, reached on Nov 5, 15:28 UTC. It dropped off the list after Nov 6, 05:28 UTC.
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#12 on Hugging Face · Daily papers, on Nov 5, 15:28 UTC. Trendoday checks this list every 120 minutes.
It was first seen on Nov 5, 2026 and has been on the list on 2 days (14 checks) since.