Zero-Shot Transfer of Force Map Estimation Across GelSight Mini Sensors
arXiv:2608.18240v1 Announce Type: new Abstract: Despite the rapid industrialization of the touch sensor manufacturing process, most of these sensors are still handmade in research laboratories. This complicates standardizing their performance, requiring the repetition of data collection and training models for each unit produced. To address this problem, this paper presents a method that can generalize the estimation of 3D force maps across different GelSight Mini sensor units, regardless of the sensor version. Specifically, the method consists of two stages: a domain adaptation stage, in which the input tactile image is reconstructed as a general tactile image using a UniT-based model; and a stage for estimating 3D force maps employing a U-Net network. Our proposal achieves promising results in both steps, such as an SSIM of 0.9338 +- 0.0358 in the image reconstruction phase and an MAE_F of 1.1294 +- 1.5934(N) in the force estimation phase.
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[Submitted on 18 Aug 2026]
Title:Zero-Shot Transfer of Force Map Estimation Across GelSight Mini Sensors
View a PDF of the paper titled Zero-Shot Transfer of Force Map Estimation Across GelSight Mini Sensors, by Julio Casta\~no Amoros and Pablo Gil
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Abstract:Despite the rapid industrialization of the touch sensor manufacturing process, most of these sensors are still handmade in research laboratories. This complicates standardizing their performance, requiring the repetition of data collection and training models for each unit produced. To address this problem, this paper presents a method that can generalize the estimation of 3D force maps across different GelSight Mini sensor units, regardless of the sensor version. Specifically, the method consists of two stages: a domain adaptation stage, in which the input tactile image is reconstructed as a general tactile image using a UniT-based model; and a stage for estimating 3D force maps employing a U-Net network. Our proposal achieves promising results in both steps, such as an SSIM of 0.9338 +- 0.0358 in the image reconstruction phase and an MAE_F of 1.1294 +- 1.5934(N) in the force estimation phase.
Comments: Accepted for publication in IEEE Sensors Letter
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
Cite as: arXiv:2608.18240 [cs.CV]
(or arXiv:2608.18240v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2608.18240
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Julio Castaño-Amorós [view email] [v1] Tue, 18 Aug 2026 18:30:20 UTC (1,733 KB)
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