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RoboTryOn: Synthesizing Dexterous Robot Demonstrations from Human Videos via Virtual Try-On — arXiv:2611.05163

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.05163
License (as archived): CC0-1.0
Archived: 2026-11-04T21:40:22.727Z
SHA-256 of the archived text: 6aac4b4e2eea848bf7d350a6f77cc618603cf37a1946fa64dd4d944ec3aa3abb
Stable URL of this page: https://metraipolis.com/documento/dc_a577f555e280

How to cite

RoboTryOn: Synthesizing Dexterous Robot Demonstrations from Human Videos via Virtual Try-On — arXiv:2611.05163 — arXiv (en), https://arxiv.org/abs/2611.05163 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_a577f555e280 (Metraipolis archive dc_a577f555e280, sha256 6aac4b4e2eea848bf7d350a6f77cc618603cf37a1946fa64dd4d944ec3aa3abb)

BibTeX

@misc{dca577f555e280,
  title = {RoboTryOn: Synthesizing Dexterous Robot Demonstrations from Human Videos via Virtual Try-On — arXiv:2611.05163},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.05163},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_a577f555e280; sha256 6aac4b4e2eea848bf7d350a6f77cc618603cf37a1946fa64dd4d944ec3aa3abb},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.05163. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Robot foundation models have shown remarkable performance in robot control, yet adapting them to a new task still relies heavily on teleoperated demonstrations. Egocentric human videos offer a scalable substitute, but overlaying rendered robots on human videos or retargeting human poses into robot actions leaves significant visual or action gaps for high-DoF robot embodiments. To address this limitation, we propose RoboTryOn, which treats human-to-robot translation as virtual try-on: try-on keeps the person and swaps the garment, RoboTryOn keeps the demonstrated motion and swaps the human for the robot. Specifically, RoboTryOn converts a human video into a video of the target robot specified by a reference image, while preserving the original task, objects, and background. To maintain visual consistency, we translate selected keyframes autoregressively and use them to guide video synthesis with motion conditioned on the human hand skeleton. Additionally, we use an inverse dynamics model (IDM) that incorporates depth features to infer action labels from synthesized videos for policy learning. Our experiments across multiple humanoid embodiments show that policies trained with our synthetic data improve the average success rate on novel tasks, including challenging bimanual manipulation, from 2% to 60%, without any real robot demonstration of those tasks. Moreover, training with our synthetic data matches the performance of policies trained solely on teleoperated demonstrations at approximately a third of the collection cost, demonstrating the cost efficiency of our approach.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Robotics (cs.RO) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2611.05163 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-04T21:35:49.711Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).