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The sense of touch is fundamental in several manipulation tasks, but rarelyused in robot manipulation. In this work we tackle the problem of learning richtouch features from cross-modal self-supervision. We evaluate them identifyingobjects and their properties in a few-shot classification setting. Two newdatasets are introduced using a simulated anthropomorphic robotic hand equippedwith tactile sensors on both synthetic and daily life objects. Severalself-supervised learning methods are benchmarked on these datasets, byevaluating few-shot classification on unseen objects and poses. Our experimentsindicate that cross-modal self-supervision effectively improves touchrepresentation, and in turn has great potential to enhance robot manipulationskills.

Learning rich touch representations through cross-modal self-supervision