  Humans seemingly incorporate potential touch signals in their perception. Our
goal is to equip robots with a similar capability, which we term Imagine2touch.
Imagine2touch aims to predict the expected touch signal based on a visual patch
representing the area to be touched. We use ReSkin, an inexpensive and compact
touch sensor to collect the required dataset through random touching of five
basic geometric shapes, and one tool. We train Imagine2touch on two out of
those shapes and validate it on the ood. tool. We demonstrate the efficacy of
Imagine2touch through its application to the downstream task of object
recognition. In this task, we evaluate Imagine2touch performance in two
experiments, together comprising 5 out of training distribution objects.
Imagine2touch achieves an object recognition accuracy of 58% after ten touches
per object, surpassing a proprioception baseline.
