  The connection between visual input and tactile sensing is critical for
object manipulation tasks such as grasping and pushing. In this work, we
introduce the challenging task of estimating a set of tactile physical
properties from visual information. We aim to build a model that learns the
complex mapping between visual information and tactile physical properties. We
construct a first of its kind image-tactile dataset with over 400 multiview
image sequences and the corresponding tactile properties. A total of fifteen
tactile physical properties across categories including friction, compliance,
adhesion, texture, and thermal conductance are measured and then estimated by
our models. We develop a cross-modal framework comprised of an adversarial
objective and a novel visuo-tactile joint classification loss. Additionally, we
develop a neural architecture search framework capable of selecting optimal
combinations of viewing angles for estimating a given physical property.
