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Transferring Tactile Data Across Sensors

Wadhah Zai El Amri, Malte Kuhlmann, Nicolás Navarro-Guerrero

TL;DR

A novel method for translating data between tactile sensors by exploiting sensor deformation information rather than output signals is introduced, enabling the continued use of valuable datasets.

Abstract

Tactile perception is essential for human interaction with the environment and is becoming increasingly crucial in robotics. Tactile sensors like the BioTac mimic human fingertips and provide detailed interaction data. Despite its utility in applications like slip detection and object identification, this sensor is now deprecated, making many existing datasets obsolete. This article introduces a novel method for translating data between tactile sensors by exploiting sensor deformation information rather than output signals. We demonstrate the approach by translating BioTac signals into the DIGIT sensor. Our framework consists of three steps: first, converting signal data into corresponding 3D deformation meshes; second, translating these 3D deformation meshes from one sensor to another; and third, generating output images using the converted meshes. Our approach enables the continued use of valuable datasets.

Transferring Tactile Data Across Sensors

TL;DR

A novel method for translating data between tactile sensors by exploiting sensor deformation information rather than output signals is introduced, enabling the continued use of valuable datasets.

Abstract

Tactile perception is essential for human interaction with the environment and is becoming increasingly crucial in robotics. Tactile sensors like the BioTac mimic human fingertips and provide detailed interaction data. Despite its utility in applications like slip detection and object identification, this sensor is now deprecated, making many existing datasets obsolete. This article introduces a novel method for translating data between tactile sensors by exploiting sensor deformation information rather than output signals. We demonstrate the approach by translating BioTac signals into the DIGIT sensor. Our framework consists of three steps: first, converting signal data into corresponding 3D deformation meshes; second, translating these 3D deformation meshes from one sensor to another; and third, generating output images using the converted meshes. Our approach enables the continued use of valuable datasets.

Paper Structure

This paper contains 3 sections, 2 figures.

Table of Contents

  1. Introduction
  2. Methodology
  3. Results and Conclusion

Figures (2)

  • Figure 1: Framework for translating BioTac signals into DIGIT images. Step 1: Convert BioTac input signals to BioTac surface deformation. Step 2: Convert BioTac surface mesh deformation to DIGIT surface mesh deformation. Step 3: Generate DIGIT's output from the surface mesh deformation.
  • Figure 2: Converted samples. First row: Real electrode values. Second row: Ground-truth BioTac mesh deformations. The outer frame represents the "unfolded" BioTac surface. Third row: Converted DIGIT mesh deformations. Fourth row: DIGIT output images. The third and fourth rows were generated using the first row as input.