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Robot-Matched Soft Fingers for UMI Data Collection: Lessons from 100 Hours of Pick and Place — arXiv:2611.03779

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.03779
License (as archived): CC0-1.0
Archived: 2026-11-04T19:05:05.680Z
SHA-256 of the archived text: 433220c154f5b03725f6c0a2df2d6704b7a08c926d385ccd8cc1f541a9d94581
Stable URL of this page: https://metraipolis.com/documento/dc_fe5cd7f84bbf

How to cite

Robot-Matched Soft Fingers for UMI Data Collection: Lessons from 100 Hours of Pick and Place — arXiv:2611.03779 — arXiv (en), https://arxiv.org/abs/2611.03779 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_fe5cd7f84bbf (Metraipolis archive dc_fe5cd7f84bbf, sha256 433220c154f5b03725f6c0a2df2d6704b7a08c926d385ccd8cc1f541a9d94581)

BibTeX

@misc{dcfe5cd7f84bbf,
  title = {Robot-Matched Soft Fingers for UMI Data Collection: Lessons from 100 Hours of Pick and Place — arXiv:2611.03779},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.03779},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_fe5cd7f84bbf; sha256 433220c154f5b03725f6c0a2df2d6704b7a08c926d385ccd8cc1f541a9d94581},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.03779. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Training robot policies for multi-task pick and place requires demonstrations on many objects, which handheld Universal Manipulation Interfaces (UMIs) can collect quickly and cheaply. However, existing UMIs depend on external base stations, use rigid fingers that give no signal about contact forces, or use parallel-jaw fingers that limit grip strength. We present a battery-powered handheld interface, built on YUBI, whose soft, angled fingers wrap around objects, deform visibly under load as a visual proxy for force, and match the grippers of the robot. With it, we collect a 99.7-hour dataset over 53 objects, train $\pi_{0.5}$ policies on 49 of them, and hold out the other 4 as unseen objects. We find that a top camera raises success by 13 points even when only about half of the data includes it, and that mid-training on our multi-task data raises success after fine-tuning on unseen objects from 35% to 57%. Finger design, dataset, and data processing code: 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/2611.03779 — 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 2026-11-04T18:39:52.377Z dalla pagina https://arxiv.org/list/cs.RO/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).