DeTac: Dual-branch Decoupling of Texture and Force Signals in Vision-Based Tactile Sensors

Abstract:Vision-based tactile sensors enable fine-grained perception for dexterous robotic manipulation, but current marker-based designs tightly couple surface texture and contact force signals within a single observation. Existing data-driven decoupling methods rely on serial mask-based inpainting, which suffers from mask-sizing dilemmas caused by physical indentation effects, error retention, and lack of real paired data for supervision. To address these challenges, we propose DeTac, a novel dual-branch generative framework that concurrently extracts clean shading and marker images from a single mixed tactile image. DeTac leverages full-image synthesis to naturally bypass mask dependency and introduces a cross-branch synergistic optimization that enables mutual supervision, effectively preventing error retention. Furthermore, we construct a strictly aligned, real-world paired tactile dataset using custom hardware to provide high-quality supervision. Extensive experiments demonstrate that DeTac achieves state-of-the-art decoupling accuracy, significantly facilitating downstream surface reconstruction and force estimation tasks while exhibiting cross-sensor generalization.

Accepted at the 10th Conference on Robot Learning (CoRL 2026). 17 pages, 11 figures

Robotics (cs.RO)

arXiv:2610.16463 [cs.RO]

(or arXiv:2610.16463v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2610.16463 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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