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TacMind: Benchmarking and Modeling Tactile Interaction States for Robotic Manipulation — arXiv:2611.00455

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.00455
License (as archived): CC0-1.0
Archived: 2026-11-03T18:56:59.464Z
SHA-256 of the archived text: abf4022bd3a175e4504440a10eac5659f62545882f5d141abb953f19b7ddf58a
Stable URL of this page: https://metraipolis.com/documento/dc_df0ef975daee

How to cite

TacMind: Benchmarking and Modeling Tactile Interaction States for Robotic Manipulation — arXiv:2611.00455 — arXiv (en), https://arxiv.org/abs/2611.00455 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_df0ef975daee (Metraipolis archive dc_df0ef975daee, sha256 abf4022bd3a175e4504440a10eac5659f62545882f5d141abb953f19b7ddf58a)

BibTeX

@misc{dcdf0ef975daee,
  title = {TacMind: Benchmarking and Modeling Tactile Interaction States for Robotic Manipulation — arXiv:2611.00455},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.00455},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_df0ef975daee; sha256 abf4022bd3a175e4504440a10eac5659f62545882f5d141abb953f19b7ddf58a},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.00455. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Tactile sensing provides robots with direct observations of contact physics, local geometry, and interaction dynamics, making it a vital source of feedback for contact-rich manipulation. However, existing tactile manipulation studies are often developed and evaluated within specific task settings, making it difficult to systematically compare how tactile information contributes under different interaction conditions. In this work, we develop a benchmark in simulation comprising 128 tasks organized around three complementary evaluation regimes: Precise, Recognition, and Dynamic. The benchmark further includes controlled static and dynamic task variants and 12 perturbation settings for evaluating robustness under changes in visual and physical conditions. These settings motivate studying tactile information as a temporally evolving interaction representation rather than only as an instantaneous observation. We therefore propose an action-conditioned latent predictive world model that integrates tactile history and learns a tactile state through prediction of future tactile latents. Building on this representation, we design a model that conditions action generation on the learned tactile state and uses predicted future tactile information to refine candidate actions through predictive tactile reflection. Experiments on the proposed benchmark show consistent gains from incorporating tactile information across the evaluated regimes. Project Page: 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/2611.00455 — 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 2026-11-03T18:17:07.703Z dalla pagina https://arxiv.org/list/cs.RO/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).