Recent Advances in Smart Tactile Sensory Systems with Brain‐Inspired Neural Networks

Abstract

Tactile sensory systems play a vital role in various emerging fields including robotics, prosthetics, and human–machine interfaces. However, traditional tactile sensors are typically designed to detect a single stimulus through a lock‐and‐key mechanism, which poses substantial challenges in the realization of multimodal tactile sensors. To address this issue, the convergence of tactile sensory systems with artificial neural network and machine learning (ML) platforms has been utilized to enhance the capabilities of multimodal sensors and enable signal decoupling/interpretation of mixed tactile stimuli. Herein, recent progress in multimodal sensors that can simultaneously identify various stimuli such as strain, pressure, and temperature is reviewed, providing in‐depth understanding of materials, structures, and methodologies. In addition, accurate interpretation of signals from mixed tactile stimuli under complex conditions remains challenging. This review presents a comprehensive exploration of ML algorithms that mimic human neural networks, discussing their significance in advancing smart sensory systems and improving signal interpretation in complex and dynamic environments.