Metraipolis › archive › dc_5dc026fa50ce

OpenViTac: Learning and Benchmarking Visuo-Tactile Policies in a Unified Sim-and-Real Framework — arXiv:2610.10384

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.10384
License (as archived): CC0-1.0
Archived: 2026-10-08T12:49:24.901Z
SHA-256 of the archived text: 2289358aaef76e1439efe651afce0d390ab138c571bfa5e407934878751affca
Stable URL of this page: https://metraipolis.com/documento/dc_5dc026fa50ce

How to cite

OpenViTac: Learning and Benchmarking Visuo-Tactile Policies in a Unified Sim-and-Real Framework — arXiv:2610.10384 — arXiv (en), https://arxiv.org/abs/2610.10384 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_5dc026fa50ce (Metraipolis archive dc_5dc026fa50ce, sha256 2289358aaef76e1439efe651afce0d390ab138c571bfa5e407934878751affca)

BibTeX

@misc{dc5dc026fa50ce,
  title = {OpenViTac: Learning and Benchmarking Visuo-Tactile Policies in a Unified Sim-and-Real Framework — arXiv:2610.10384},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.10384},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_5dc026fa50ce; sha256 2289358aaef76e1439efe651afce0d390ab138c571bfa5e407934878751affca},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.10384. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Tactile feedback provides embodied agents with physical information beyond visual observations, enabling more reliable interaction with the real world. However, despite the rapid progress of vision-tactile-language-action (VTLA) policies, there remains a lack of unified benchmarks for evaluating tactile-enabled robot manipulation across simulation and the real world. To address this gap, we introduce OpenViTac, a visuo-tactile manipulation benchmark for evaluating robot policies across simulation and the real world. OpenViTac organizes contact-rich manipulation into four tactile-relevant capability dimensions and provides paired simulation-real-world settings for consistent evaluation of VLA, WAM, and VTLA policies. Building upon this benchmark, we investigate how different tactile representations and integration strategies affect the performance of pretrained VLA models. Correspondingly, we introduce OpenVTLA, a tactile augmentation framework that combines the best-performing representation and integration strategy. Furthermore, we leverage the paired benchmark setting to study sim-real co-training and analyze factors affecting cross-domain policy learning. Together, OpenViTac provides a unified platform for evaluating and advancing visuo-tactile robot manipulation.

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/2610.10384 — 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 2026-10-08T12:18:30.581Z dalla pagina https://arxiv.org/list/cs.RO/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).