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DexHOT: Learning Hand-Object Configuration Transfer for Dexterous Manipulation — arXiv:2611.05119

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.05119
License (as archived): CC0-1.0
Archived: 2026-11-04T19:05:19.889Z
SHA-256 of the archived text: 374f35b7bb6308879956aa6bafb1b0411a93e68a70e515e77c079d1bdf072e5e
Stable URL of this page: https://metraipolis.com/documento/dc_ba090eee7bc2

How to cite

DexHOT: Learning Hand-Object Configuration Transfer for Dexterous Manipulation — arXiv:2611.05119 — arXiv (en), https://arxiv.org/abs/2611.05119 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ba090eee7bc2 (Metraipolis archive dc_ba090eee7bc2, sha256 374f35b7bb6308879956aa6bafb1b0411a93e68a70e515e77c079d1bdf072e5e)

BibTeX

@misc{dcba090eee7bc2,
  title = {DexHOT: Learning Hand-Object Configuration Transfer for Dexterous Manipulation — arXiv:2611.05119},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.05119},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ba090eee7bc2; sha256 374f35b7bb6308879956aa6bafb1b0411a93e68a70e515e77c079d1bdf072e5e},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.05119. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Dexterous manipulation involves not only moving objects, but also arranging the hand around them to maintain useful contacts and enable subsequent actions. Yet many learning-based systems specify goals primarily through object poses or trajectories, leaving hand-object configurations to emerge implicitly. We introduce DexHOT, which formulates dexterous manipulation as hand-object configuration transfer: moving between feasible configurations that jointly specify hand articulation and object pose relative to the wrist. DexHOT learns a reusable relation-conditioned transfer primitive from synthetic feasible configuration pairs, without human reference motions, and composes long-horizon behaviors through closed-loop tracking of configuration subgoals. On DexHOTBench with 120 objects, a single policy achieves 80.5% transfer success on seen objects and 69.4% on unseen objects; on a real dexterous hand, it achieves 61.5% success over 200 real-robot transfers and 90% task success across six demonstration-guided tasks. These results show that explicit hand-object goals provide an expressive and composable interface for dexterous manipulation. More details and videos are available at this https URL .

Tema degli esploratori: statistica e scienza dei dati (elenco arXiv cs.RO, nuovi lavori del giorno).
Categorie arXiv: Robotics (cs.RO).
Pagina del lavoro: https://arxiv.org/abs/2611.05119 — 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 2026-11-04T18:39:52.426Z dalla pagina https://arxiv.org/list/cs.RO/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).