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BiView-Touch: Learning Bimanual Tactile Representations by Cross-Hand Completion — arXiv:2609.23352

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.23352
License (as archived): CC0-1.0
Archived: 2026-10-09T21:59:32.659Z
SHA-256 of the archived text: 3f07e5ef5518149767b4263eb8c4362b845292676f8dfa1c1b3ef9d73ba51819
Stable URL of this page: https://metraipolis.com/documento/dc_ca64d57f2d32

How to cite

BiView-Touch: Learning Bimanual Tactile Representations by Cross-Hand Completion — arXiv:2609.23352 — arXiv (en), https://arxiv.org/abs/2609.23352 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ca64d57f2d32 (Metraipolis archive dc_ca64d57f2d32, sha256 3f07e5ef5518149767b4263eb8c4362b845292676f8dfa1c1b3ef9d73ba51819)

BibTeX

@misc{dcca64d57f2d32,
  title = {BiView-Touch: Learning Bimanual Tactile Representations by Cross-Hand Completion — arXiv:2609.23352},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.23352},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ca64d57f2d32; sha256 3f07e5ef5518149767b4263eb8c4362b845292676f8dfa1c1b3ef9d73ba51819},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.23352. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Bimanual interaction produces complementary tactile views of the same physical process, yet existing tactile representation learning largely models the two hands independently or combines them only for downstream prediction, leaving their cross-hand relationship unexplored. To exploit this overlooked structure, we introduce BiView-Touch, a tactile-only framework that completes masked target-hand latents from the remaining visible target-hand regions and the synchronized full contralateral hand. A student encoder with a geometry-conditioned directional decoder predicts full-view EMA latent targets, while temporal and layout counterfactuals encourage sensitivity to synchronized and anatomically organized source information. Controlled ablations and source-context interventions show that BiView-Touch learns structured cross-hand dependence on temporally aligned and anatomically organized contralateral tactile context, rather than benefiting from bilateral input alone. On the public HumanTouch dataset, its frozen representations consistently outperform representative self-supervised baselines across low-label settings. With only 5\% downstream labels, BiView-Touch achieves relative balanced-accuracy gains of 7.1\% on bilateral wrist-motion recognition and 14.1\% on force-derived interaction-phase recognition. We further introduce BVT-20, a 20-task bilateral tactile dataset, and demonstrate transfer across recording sessions and pretraining corpora, including transfer to a held-out bimanual task. Our code and dataset details are available on the anonymous project page: this https URL .

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-22 in cs.CV).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV) ; Robotics (cs.RO).
Pagina del lavoro: https://arxiv.org/abs/2609.23352 — 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 2026-10-09T21:45:20.877Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-09-22?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).