DigiTac: A DIGIT-TacTip Hybrid Tactile Sensor for Comparing Low-Cost High-Resolution Robot Touch
Nathan F. Lepora, Yijiong Lin, Ben Money-Coomes, John Lloyd

TL;DR
This paper introduces DigiTac, a low-cost, high-resolution hybrid tactile sensor combining DIGIT and TacTip technologies, enabling direct comparison and analysis of tactile sensing performance for robotic applications.
Contribution
The study customizes the DIGIT sensor with a TacTip-based surface and develops a system for comparing different tactile sensors using deep learning and robotic control.
Findings
All three sensors achieved similar pose prediction accuracy.
Sensor construction influenced servo control performance.
Open-source hardware and software are provided for reproducibility.
Abstract
Deep learning combined with high-resolution tactile sensing could lead to highly capable dexterous robots. However, progress is slow because of the specialist equipment and expertise. The DIGIT tactile sensor offers low-cost entry to high-resolution touch using GelSight-type sensors. Here we customize the DIGIT to have a 3D-printed sensing surface based on the TacTip family of soft biomimetic optical tactile sensors. The DIGIT-TacTip (DigiTac) enables direct comparison between these distinct tactile sensor types. For this comparison, we introduce a tactile robot system comprising a desktop arm, mounts and 3D-printed test objects. We use tactile servo control with a PoseNet deep learning model to compare the DIGIT, DigiTac and TacTip for edge- and surface-following over 3D-shapes. All three sensors performed similarly at pose prediction, but their constructions led to differing…
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