[Submitted on 22 Jul 2026]
Title:FELT: Generating Tactile Signals from Vision for Visuo-Tactile Manipulation
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Abstract:The sense of touch is central to manipulation, especially when vision is occluded or ambiguous. Although combining vision and touch improves manipulation, learning robust visuo-tactile policies requires substantial tactile data. Such data remains scarcer than visual data, because tactile sensors are fragile, specialized, and hard to standardize. To address this, we present Feature-Extracted Latent Tactile (FELT), a learning-based framework that synthesizes per-finger pressure tactile images from RGB observations, reducing the need for tactile-equipped data collection. FELT uses a large frozen visual encoder and a lightweight query decoder to predict tactile signals in a single feed-forward pass. To respect the physical topology of dual-finger tactile sensors, FELT decodes the left and right tactile sensor panels through separate branches, capturing the asymmetric contact patterns during interactions such as wiping, insertion, and in-hand rotation. At inference time, FELT only requires RGB data, allowing us to augment existing vision-only data with tactile observations, either as generated tactile images or as latent tactile features. Experiments on four contact-rich manipulation tasks demonstrate that both generated tactile images and latent tactile features improve policy success over vision-only baselines, with latent feature requiring no real tactile sensor during policy training or deployment. Supplementary material is available on our anonymous website: this https URL.
Comments: 26 pages, including supplementary material
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2607.20683 [cs.RO]
(or arXiv:2607.20683v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.20683
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Zinan Li [view email] [v1] Wed, 22 Jul 2026 19:32:42 UTC (5,205 KB)
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