DigiTac: A DIGIT-TacTip Hybrid Tactile Sensor for Comparing Low-Cost High-Resolution Robot Touch

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 performances at servo control, offering guidance for researchers selecting or innovating tactile sensors. All hardware and software for reproducing this study will be openly released. Project website: www.lepora.com/digitac. Project repository: www.github.com/nlepora/digitac-design.