  Current Virtual Reality (VR) environments lack the rich haptic signals that
humans experience during real-life interactions, such as the sensation of
texture during lateral movement on a surface. Adding realistic haptic textures
to VR environments requires a model that generalizes to variations of a user's
interaction and to the wide variety of existing textures in the world. Current
methodologies for haptic texture rendering exist, but they usually develop one
model per texture, resulting in low scalability. We present a deep
learning-based action-conditional model for haptic texture rendering and
evaluate its perceptual performance in rendering realistic texture vibrations
through a multi part human user study. This model is unified over all materials
and uses data from a vision-based tactile sensor (GelSight) to render the
appropriate surface conditioned on the user's action in real time. For
rendering texture, we use a high-bandwidth vibrotactile transducer attached to
a 3D Systems Touch device. The result of our user study shows that our
learning-based method creates high-frequency texture renderings with comparable
or better quality than state-of-the-art methods without the need for learning a
separate model per texture. Furthermore, we show that the method is capable of
rendering previously unseen textures using a single GelSight image of their
surface.
