A Neuromorphic Tactile System for Reliable Braille Reading in Noisy Environments

Neuromorphic sensors are a promising technology in artificial touch due to their low latency and low computational and power requirements, particularly when paired with spiking neural networks (SNNs). Here, we explore the ability of these systems to adapt to and generalize across varying sources of uncertainty in tactile tasks. We choose Braille reading as an application task and collect event-based data for 27 braille characters with a neuromorphic tactile sensor (NeuroTac) under varying conditions of tapping speed, center position and indentation depth using a 6-DOF robot arm. We initially analyze the effect of spatial location and speed on classification performance with spiking convolutional neural networks (SCNNs). We then show that SCNNs are able to generalize across each dimension. The final general SCNN model reaches 95.33% accuracy with uncertainty in all 4 dimensions. This research demonstrates the noise degradation performance of SCNNs in a tactile task, and outlines the potential of a single SCNN to generalize across several dimensions of uncertainty.