  Robotic touch, particularly when using soft optical tactile sensors, suffers
from distortion caused by motion-dependent shear. The manner in which the
sensor contacts a stimulus is entangled with the tactile information about the
geometry of the stimulus. In this work, we propose a supervised convolutional
deep neural network model that learns to disentangle, in the latent space, the
components of sensor deformations caused by contact geometry from those due to
sliding-induced shear. The approach is validated by reconstructing unsheared
tactile images from sheared images and showing they match unsheared tactile
images collected with no sliding motion. In addition, the unsheared tactile
images give a faithful reconstruction of the contact geometry that is not
possible from the sheared data, and robust estimation of the contact pose that
can be used for servo control sliding around various 2D shapes. Finally, the
contact geometry reconstruction in conjunction with servo control sliding were
used for faithful full object reconstruction of various 2D shapes. The methods
have broad applicability to deep learning models for robots with a
shear-sensitive sense of touch.
