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From Teleoperation to Long-Horizon Dexterity: Evaluating Visuomotor Skills and Offline Proxies for Dexterous Manipulation — arXiv:2610.40828

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.40828
License (as archived): CC0-1.0
Archived: 2026-11-03T07:52:12.611Z
SHA-256 of the archived text: 92e59d0e3714a7bfd62fc29cef6f86a523ca4bac0cdb8e626ed6b31a2c79f056
Stable URL of this page: https://metraipolis.com/documento/dc_ce25efec520f

How to cite

From Teleoperation to Long-Horizon Dexterity: Evaluating Visuomotor Skills and Offline Proxies for Dexterous Manipulation — arXiv:2610.40828 — arXiv (en), https://arxiv.org/abs/2610.40828 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ce25efec520f (Metraipolis archive dc_ce25efec520f, sha256 92e59d0e3714a7bfd62fc29cef6f86a523ca4bac0cdb8e626ed6b31a2c79f056)

BibTeX

@misc{dcce25efec520f,
  title = {From Teleoperation to Long-Horizon Dexterity: Evaluating Visuomotor Skills and Offline Proxies for Dexterous Manipulation — arXiv:2610.40828},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.40828},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ce25efec520f; sha256 92e59d0e3714a7bfd62fc29cef6f86a523ca4bac0cdb8e626ed6b31a2c79f056},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.40828. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Imitation learning policies are intended to make robotic manipulation robust to variations in object pose, appearance, and environment that challenge pre-programmed motion. However, real-world performance remains difficult to predict, as conventional optimization signals such as validation loss show no consistent relationship with robot success. To identify the design choices that govern real-world performance, 281 imitation-learning policies spanning seven policy families were evaluated in approximately 1,700 real-robot trials of RJ45 connector grasping and insertion using a five-fingered robotic hand. Controlled ablations showed that observation and action representations dominated conventional optimizer and sampling choices. For point-cloud policies, region-of-interest cropping, representation in the robot-base frame, and normalization within the point encoder each improved success by 70-100 percentage points. For the vision-language-action policy, absolute rather than relative action prediction improved insertion success by up to 70 percentage points. Skill decomposition, multi-view perception, and recovery demonstrations were associated with the strongest-performing systems, although their effects were not independently isolated. Of 67 offline metrics evaluated, only five measures of predicted-motion smoothness and directness reliably correlated with insertion success (r = -0.70 to -0.80), and only within a single policy family. These results identify representation choices as key determinants of real-world imitation-learning performance and show that offline metrics can shortlist policies, but cannot replace real-robot evaluation.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Robotics (cs.RO) ; Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Systems and Control (eess.SY).
Pagina del lavoro: https://arxiv.org/abs/2610.40828 — 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 2026-11-03T07:49:31.116Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).