A Spiking Neural Network Model for Object Recognition with Tactile Properties

• Published in: International Conference on Automation, Control and Robotics Engineering (Online) pp. 210 - 215
• Main Authors: Wang, Fei, Wu, Juan, Pan, Yanxin
• Format: Conference Proceeding
• Language: English
• Published: IEEE 16.07.2025
• Subjects: Distributed three-dimensional tactile sensor; Grasping; Hardware; MIMICs; Object recognition; Pressing; Process control; Representation learning; Robot perception; Skin; SNN; Spiking neural networks; Tactile properties; Tactile sensors
• ISSN: 2997-6278
• DOI: 10.1109/CACRE66141.2025.11119548

Tactile properties such as hardness, roughness, and texture are critical information for robots interacting with their surrounding environment, forming the foundation for various tasks including grasping, autonomous navigation, and collaboration. Inspired by human perception mechanisms, this paper presents a tactile property recognition method based on spiking neural network (SNN). We designed a distributed three-dimensional tactile sensor that mimics the structure of human skin, aiming to collect tactile signals through active exploration modes of pressing and sliding over artificially synthesized samples with varying textures and roughness. The collected signals are converted into pulse sequences using Poisson encoding, with object classification determined by the frequency of spike emissions. Experimental results indicate that the proposed method exhibits a straightforward structure and robust predictive performance, achieving an object classification accuracy of 95.56%, effectively simulating human tactile perception capabilities.