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A Minimal Optical-Flow Representation for Vision-Based Tactile Rotation Classification in Robotic Manipulation Across Gravity Domains — arXiv:2610.12073

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.12073
License (as archived): CC0-1.0
Archived: 2026-10-09T06:33:05.346Z
SHA-256 of the archived text: 0b36dc2419afd0352ac9137b064f09c8b92ad0079d8ca83d94e7980e0b245c64
Stable URL of this page: https://metraipolis.com/documento/dc_dc40cd8a519d

How to cite

A Minimal Optical-Flow Representation for Vision-Based Tactile Rotation Classification in Robotic Manipulation Across Gravity Domains — arXiv:2610.12073 — arXiv (en), https://arxiv.org/abs/2610.12073 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_dc40cd8a519d (Metraipolis archive dc_dc40cd8a519d, sha256 0b36dc2419afd0352ac9137b064f09c8b92ad0079d8ca83d94e7980e0b245c64)

BibTeX

@misc{dcdc40cd8a519d,
  title = {A Minimal Optical-Flow Representation for Vision-Based Tactile Rotation Classification in Robotic Manipulation Across Gravity Domains — arXiv:2610.12073},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.12073},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_dc40cd8a519d; sha256 0b36dc2419afd0352ac9137b064f09c8b92ad0079d8ca83d94e7980e0b245c64},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.12073. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Vision-based tactile sensors provide rich contact information, but processing high-resolution images can be costly for resource-constrained platforms such as space robots. This work investigates whether a compact representation of tactile motion can classify object rotation across different gravity conditions. Dense optical flow from a simulated GelSight Mini is aggregated over a 7x9 grid into 126 features and used to classify the direction of load-induced rotation under Earth, Mars, Moon, and orbital gravity. Gravity causes a small but significant shift in these features, accounting for 1.6% of their variance (R2 = 0.016). Despite its small magnitude, this shift affects models trained only on Earth data: XGBoost accuracy decreases from 94.4% on Earth to 75.9% in orbit. In contrast, a single model trained across all four gravity domains achieves 96.3% overall accuracy and 95.1%-97.0% across individual domains, without using gravity as an input. The representation can also be reduced to 40 features while retaining 95.7% accuracy, with XGBoost requiring only 0.14 ms per inference. These findings show that Earth-gravity performance alone is insufficient to establish the transferability of tactile perception for space robotic manipulation, highlighting the need to account for gravity-induced domain shifts during training and validation.

Tema degli esploratori: statistica e scienza dei dati (elenco arXiv cs.CV, nuovi lavori del giorno).
Categorie arXiv: Robotics (cs.RO) ; Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2610.12073 — 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 2026-10-09T05:55:07.886Z dalla pagina https://arxiv.org/list/cs.CV/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).