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Learning Physical Interaction: A Survey of Tactile- and Force-aware Robot Learning — arXiv:2608.07558

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.07558
License (as archived): CC0-1.0
Archived: 2026-10-19T09:12:21.642Z
SHA-256 of the archived text: 9b1df15f894d128d66e91de42eae540573fb240add9f239eda21f9f033cb5b92
Stable URL of this page: https://metraipolis.com/documento/dc_616d971ecbbd

How to cite

Learning Physical Interaction: A Survey of Tactile- and Force-aware Robot Learning — arXiv:2608.07558 — arXiv (en), https://arxiv.org/abs/2608.07558 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_616d971ecbbd (Metraipolis archive dc_616d971ecbbd, sha256 9b1df15f894d128d66e91de42eae540573fb240add9f239eda21f9f033cb5b92)

BibTeX

@misc{dc616d971ecbbd,
  title = {Learning Physical Interaction: A Survey of Tactile- and Force-aware Robot Learning — arXiv:2608.07558},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.07558},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_616d971ecbbd; sha256 9b1df15f894d128d66e91de42eae540573fb240add9f239eda21f9f033cb5b92},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.07558. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Physically grounded robot intelligence requires robots to perceive, reason about, and regulate their interactions with the physical world. This capability is particularly critical in contact-sensitive manipulation, where successful task execution depends not only on visual perception and motion generation, but also on force regulation and adaptive control. In this context, recent robot learning methods have made substantial progress by integrating force, tactile, vision, language, and proprioceptive sensing into learned manipulation policies. In parallel, many systems adopt multi-phase architectures that combine high-level policies, action-refinement modules, and low-level controllers to bridge semantic task understanding with reactive physical execution. Despite these advances, existing surveys have not explicitly reviewed force- and tactile-aware robot learning from a unified perspective that jointly captures multimodal sensing and multi-phase system design. This survey addresses this gap by proposing TF-ART, a Tactile/Force-Aware Robot learning Taxonomy for multimodal and multi-phase frameworks, which maps individual methods into a unified hierarchical structure. The framework characterizes how recent works organize observation modalities, encode and fuse heterogeneous sensory inputs, generate and refine actions across multiple phases, and connect learned policies to reactive robot-end control. Building on this methodological view, we further examine the task settings and infrastructure requirements of physical interaction, thereby integrating both algorithmic and practical perspectives on force- and tactile-aware robot learning.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-08-11 in cs.RO).
Categorie arXiv: Robotics (cs.RO) ; Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2608.07558 — 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 2026-10-19T09:11:58.717Z dalla pagina https://arxiv.org/catchup/cs.RO/2026-08-11?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).