Surface Texture Recognition by Deep Learning-Enhanced Tactile Sensing

Tactile perception is a primary sensing channel for both humans and robots to be conscious of the surface properties of an object. Due to the unique functionalities of mechanoreceptors in human skin, humans can easily distinguish materials with different surface characteristics (e.g., compressibility, roughness, etc.) by simply pressing and sliding the fingertip over the samples. However, how to achieve such delicate texture recognition for robots remains an open challenge due to the lack of skin-comparable tactile sensing systems and smart pattern recognition algorithms. Herein, a novel texture recognition method is proposed by designing an arc-shaped soft tactile sensor and a bidirectional long short-term memory (LSTM) model with the attention mechanism. By using the proposed method, a respective recognition accuracy of 97% for Braille characters and 99% for 60 types of fabrics have been achieved, revealing the effectiveness of our method in surface texture recognition and the potential benefit to various applications, such as Braille reading for visually impaired people and defect detection in the textile industry.