  In this paper, we address the Sim2Real gap in the field of vision-based
tactile sensors for classifying object surfaces. We train a Diffusion Model to
bridge this gap using a relatively small dataset of real-world images randomly
collected from unlabeled everyday objects via the DIGIT sensor. Subsequently,
we employ a simulator to generate images by uniformly sampling the surface of
objects from the YCB Model Set. These simulated images are then translated into
the real domain using the Diffusion Model and automatically labeled to train a
classifier. During this training, we further align features of the two domains
using an adversarial procedure. Our evaluation is conducted on a dataset of
tactile images obtained from a set of ten 3D printed YCB objects. The results
reveal a total accuracy of 81.9%, a significant improvement compared to the
34.7% achieved by the classifier trained solely on simulated images. This
demonstrates the effectiveness of our approach. We further validate our
approach using the classifier on a 6D object pose estimation task from tactile
data.
