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SoftVTBench: A Deformation-Aware Visuo-Tactile Dataset and Benchmark for Deformable-Object Manipulation — arXiv:2608.18701

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.18701
License (as archived): CC0-1.0
Archived: 2026-10-18T14:07:02.461Z
SHA-256 of the archived text: c6f6b9397acdae60aafc01f685d90b99073be338ca1293aa1609f99640967901
Stable URL of this page: https://metraipolis.com/documento/dc_7e436bfb71e2

How to cite

SoftVTBench: A Deformation-Aware Visuo-Tactile Dataset and Benchmark for Deformable-Object Manipulation — arXiv:2608.18701 — arXiv (en), https://arxiv.org/abs/2608.18701 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_7e436bfb71e2 (Metraipolis archive dc_7e436bfb71e2, sha256 c6f6b9397acdae60aafc01f685d90b99073be338ca1293aa1609f99640967901)

BibTeX

@misc{dc7e436bfb71e2,
  title = {SoftVTBench: A Deformation-Aware Visuo-Tactile Dataset and Benchmark for Deformable-Object Manipulation — arXiv:2608.18701},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.18701},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_7e436bfb71e2; sha256 c6f6b9397acdae60aafc01f685d90b99073be338ca1293aa1609f99640967901},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.18701. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Physical interaction quality is central to deformable-object manipulation, yet most benchmarks evaluate task success alone. A policy may complete the task while allowing slip or causing excessive compression. A primary bottleneck is the absence of visuo-tactile datasets that pair policy-visible contact observations with independent physical ground truth over complete tasks. We introduce SoftVTBench, a visuo-tactile dataset for physical-interaction-aware deformable-object manipulation. It contains 4,000 expert demonstrations and more than 50 assets, including volumetric deformable objects and visually matched rigid twins. At 20 Hz, each episode synchronizes multi-view RGB, dual-finger tactile RGB and marker motion, proprioception, language, and binary and continuous gripper actions, alongside evaluator-only finite-element (FEM) states. Building upon this dataset, we establish a closed-loop benchmark that uses fixed object-specific calibration to define the Deformation-aware Success Rate (DSR), which counts a rollout as successful only when it completes the task and keeps peak normalized deformation within tolerance. Across Diffusion Policy, $\pi_{0.5}$, and FastWAM, all 12 in-distribution configurations contain successful rollouts that violate the deformation tolerance, accounting for 0.7--24% of each configuration's successes. Under distribution shift, visuo-tactile variants achieve higher task success in all six policy--suite comparisons and higher DSR in five, whereas their in-distribution benefits are mixed. These results show that making touch available does not by itself ensure effective multimodal fusion. SoftVTBench therefore provides a common visuo-tactile resource for studying not only whether a policy succeeds, but how it physically interacts with deformable objects and when touch improves that interaction.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-08-20 in cs.RO).
Categorie arXiv: Robotics (cs.RO).
Pagina del lavoro: https://arxiv.org/abs/2608.18701 — 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 2026-10-18T14:02:33.849Z dalla pagina https://arxiv.org/catchup/cs.RO/2026-08-20?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).