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Developing artificial tactile sensing capabilities that rival human touch isa long-term goal in robotics and prosthetics. Gradually more elaboratebiomimetic tactile sensors are being developed and applied to grasping andmanipulation tasks to help achieve this goal. Here we present the neuroTac, anovel neuromorphic optical tactile sensor. The neuroTac combines the biomimetichardware design from the TacTip sensor which mimicks the layered papillaestructure of human glabrous skin, with an event-based camera (DAVIS240,iniVation) and algorithms which transduce contact information in the form ofspike trains. The performance of the sensor is evaluated on a textureclassification task, with four spike coding methods being implemented andcompared: Intensive, Spatial, Temporal and Spatiotemporal. We foundtiming-based coding methods performed with the highest accuracy over bothartificial and natural textures. The spike-based output of the neuroTac couldenable the development of biomimetic tactile perception algorithms in roboticsas well as non-invasive and invasive haptic feedback methods in prosthetics.

NeuroTac: A Neuromorphic Optical Tactile Sensor applied to Texture Recognition