  Developing artificial tactile sensing capabilities that rival human touch is
a long-term goal in robotics and prosthetics. Gradually more elaborate
biomimetic tactile sensors are being developed and applied to grasping and
manipulation tasks to help achieve this goal. Here we present the neuroTac, a
novel neuromorphic optical tactile sensor. The neuroTac combines the biomimetic
hardware design from the TacTip sensor which mimicks the layered papillae
structure of human glabrous skin, with an event-based camera (DAVIS240,
iniVation) and algorithms which transduce contact information in the form of
spike trains. The performance of the sensor is evaluated on a texture
classification task, with four spike coding methods being implemented and
compared: Intensive, Spatial, Temporal and Spatiotemporal. We found
timing-based coding methods performed with the highest accuracy over both
artificial and natural textures. The spike-based output of the neuroTac could
enable the development of biomimetic tactile perception algorithms in robotics
as well as non-invasive and invasive haptic feedback methods in prosthetics.
