Spatio-temporal encoding improves neuromorphic tactile texture classification

TL;DR

This paper demonstrates that spatio-temporal encoding of mechanoreceptor responses significantly enhances tactile texture classification in neuromorphic systems, leading to more robust and accurate robotic touch sensing.

Contribution

It introduces a novel spatio-temporal encoding method using gray-level co-occurrence matrices to improve texture classification in neuromorphic tactile systems.

Findings

Spatio-temporal encoding greatly improves classification accuracy.

Performance is more robust to sliding velocity changes.

Removing temporal or spatial information causes significant performance drops.

Abstract

With the increase in interest in deployment of robots in unstructured environments to work alongside humans, the development of human-like sense of touch for robots becomes important. In this work, we implement a multi-channel neuromorphic tactile system that encodes contact events as discrete spike events that mimic the behavior of slow adapting mechanoreceptors. We study the impact of information pooling across artificial mechanoreceptors on classification performance of spatially non-uniform naturalistic textures. We encoded the spatio-temporal activation patterns of mechanoreceptors through gray-level co-occurrence matrix computed from time-varying mean spiking rate-based tactile response volume. We found that this approach greatly improved texture classification in comparison to use of individual mechanoreceptor response alone. In addition, the performance was also more robust to changes in sliding velocity. The importance of exploiting precise spatial and temporal correlations between sensory channels is evident from the fact that on either removal of precise temporal information or altering of spatial structure of response pattern, a significant performance drop was observed. This study thus demonstrates the superiority of population coding approaches that can exploit the precise spatio-temporal information encoded in activation patterns of mechanoreceptor populations. It, therefore, makes an advance in the direction of development of bio-inspired tactile systems required for realistic touch applications in robotics and prostheses.