Exploratory Movement Strategies for Texture Discrimination with a Neuromorphic Tactile Sensor

Abstract

We propose a neuromorphic tactile sensing frame-work for robotic texture classification that is inspired by human exploratory strategies. Our system utilizes the NeuroTac sensor to capture neuromorphic tactile data during a series of exploratory motions. We first tested six distinct motions for texture classification under fixed environment: sliding, rotating, tapping, as well as the combined motions: sliding+rotating, tapping+rotating, and tapping+sliding. We chose sliding and sliding+rotating as the best motions based on final accuracy and the sample timing length needed to reach converged accuracy. In the second experiment designed to simulate complex real-world conditions, these two motions were further evaluated under varying contact depth and speeds. Under these conditions, our framework attained the highest accuracy of 87.33% with sliding+rotating while maintaining an extremely low power consumption of only 8.04 mW. These results suggest that the sliding+rotating motion is the optimal exploratory strategy for neuromorphic tactile sensing deployment in texture classification tasks and holds significant promise for enhancing robotic environmental interaction.