Multifunctional Tactile Sensors for Object Recognition

Abstract Humans can grab and identify objects by capturing the features of physical shape, temperature, and material. However, creating artificial sensors with finger‐like tactile capabilities is challenging due to the contradiction between structural compliance and multifunctional sensing. Here, a sensory system for a robot hand that can achieve object recognition is reported. The sensor with a simple horizontal sensing structure can respond to strain, material, and temperature stimuli using piezoresistive, triboelectric, and thermoelectric effects. A machine learning algorithm to train and classify tactile sensing signals, enabling a high accuracy of 98.5% in recognizing five objects is also developed. This work contributes to the development of sensory systems for object recognition in soft robotics.