  Tactile information is important for gripping, stable grasp, and in-hand
manipulation, yet the complexity of tactile data prevents widespread use of
such sensors. We make use of an unsupervised learning algorithm that transforms
the complex tactile data into a compact, latent representation without the need
to record ground truth reference data. These compact representations can either
be used directly in a reinforcement learning based controller or can be used to
calibrate the tactile sensor to physical quantities with only a few datapoints.
We show the quality of our latent representation by predicting important
features and with a simple control task.
