  This article illustrates the application of deep learning to robot touch by
considering a basic yet fundamental capability: estimating the relative pose of
part of an object in contact with a tactile sensor. We begin by surveying deep
learning applied to tactile robotics, focussing on optical tactile sensors,
which help bridge from deep learning for vision to touch. We then show how deep
learning can be used to train accurate pose models of 3D surfaces and edges
that are insensitive to nuisance variables such as motion-dependent shear. This
involves including representative motions as unlabelled perturbations of the
training data and using Bayesian optimization of the network and training
hyperparameters to find the most accurate models. Accurate estimation of pose
from touch will enable robots to safely and precisely control their physical
interactions, underlying a wide range of object exploration and manipulation
tasks.
