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EgoAlign: Bridging the Human-Humanoid Gap for Long-Range Loco-Manipulation — arXiv:2609.38046

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.38046
License (as archived): CC0-1.0
Archived: 2026-10-07T19:15:49.882Z
SHA-256 of the archived text: 35086803f6ccfa214e5d100bdbf72c70537868dc55628a44027fcb22b6d16505
Stable URL of this page: https://metraipolis.com/documento/dc_201413bebaea

How to cite

EgoAlign: Bridging the Human-Humanoid Gap for Long-Range Loco-Manipulation — arXiv:2609.38046 — arXiv (en), https://arxiv.org/abs/2609.38046 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_201413bebaea (Metraipolis archive dc_201413bebaea, sha256 35086803f6ccfa214e5d100bdbf72c70537868dc55628a44027fcb22b6d16505)

BibTeX

@misc{dc201413bebaea,
  title = {EgoAlign: Bridging the Human-Humanoid Gap for Long-Range Loco-Manipulation — arXiv:2609.38046},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.38046},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_201413bebaea; sha256 35086803f6ccfa214e5d100bdbf72c70537868dc55628a44027fcb22b6d16505},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.38046. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Egocentric human demonstrations offer an accessible source of task experience, but differences in body scale and controller response, together with missing robot states, limit their value as humanoid training supervision. We present EgoAlign, a data-construction framework that converts these demonstrations into action and state supervision compatible with a general-purpose, continuous whole-body controller, without collecting physical-robot demonstrations. Using the target-robot model and simulator, EgoAlign guides demonstration collection through execution feedback. It preserves locomotion references for visually guided periodic stepping while adapting upper-body interaction geometry through scale alignment and controller-in-the-loop refinement. A final causal replay reconstructs the corresponding robot states and motion-token labels for training with the human observations. We assess the resulting supervision by fine-tuning a vision--language--action model solely on adapted human demonstrations and deploying it zero-shot on a physical humanoid. The resulting policies perform long-range object relocation, navigation to unseen goal positions, and independently evaluated foot interaction. Refinement improves simulated hand alignment and physical pickup success over kinematic alignment alone, while human collection reduces on-site acquisition time relative to teleoperation. this https URL

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-30 in cs.RO).
Categorie arXiv: Robotics (cs.RO).
Pagina del lavoro: https://arxiv.org/abs/2609.38046 — 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 2026-10-07T18:40:59.550Z dalla pagina https://arxiv.org/catchup/cs.RO/2026-09-30?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).