  The sense of touch is fundamental in several manipulation tasks, but rarely
used in robot manipulation. In this work we tackle the problem of learning rich
touch features from cross-modal self-supervision. We evaluate them identifying
objects and their properties in a few-shot classification setting. Two new
datasets are introduced using a simulated anthropomorphic robotic hand equipped
with tactile sensors on both synthetic and daily life objects. Several
self-supervised learning methods are benchmarked on these datasets, by
evaluating few-shot classification on unseen objects and poses. Our experiments
indicate that cross-modal self-supervision effectively improves touch
representation, and in turn has great potential to enhance robot manipulation
skills.
