Deep Domain Adaptation Regression for Force Calibration of Optical Tactile Sensors

TL;DR

This paper introduces a deep domain adaptation method for calibrating optical tactile sensors, enabling accurate force prediction across different sensor conditions without extensive labeled data.

Contribution

The novel unsupervised calibration approach transfers force prediction from a calibrated sensor to uncalibrated ones despite domain gaps, reducing data collection needs.

Findings

Achieved force prediction errors of 0.102N for normal force

Reduced calibration time by avoiding large labeled datasets

Effective across various domain differences like illumination and elastomer properties

Abstract

Optical tactile sensors provide robots with rich force information for robot grasping in unstructured environments. The fast and accurate calibration of three-dimensional contact forces holds significance for new sensors and existing tactile sensors which may have incurred damage or aging. However, the conventional neural-network-based force calibration method necessitates a large volume of force-labeled tactile images to minimize force prediction errors, with the need for accurate Force/Torque measurement tools as well as a time-consuming data collection process. To address this challenge, we propose a novel deep domain-adaptation force calibration method, designed to transfer the force prediction ability from a calibrated optical tactile sensor to uncalibrated ones with various combinations of domain gaps, including marker presence, illumination condition, and elastomer modulus. Experimental results show the effectiveness of the proposed unsupervised force calibration method, with lowest force prediction errors of 0.102N (3.4\% in full force range) for normal force, and 0.095N (6.3\%) and 0.062N (4.1\%) for shear forces along the x-axis and y-axis, respectively. This study presents a promising, general force calibration methodology for optical tactile sensors.