  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.
