Robotic Motion Optimization for Tactile Object Recognition Learned From Human Behaviors

Touch is one of the most important human senses. With the development of artificial intelligence, an increasing number of scholars are investigating how robots can be endowed with the sense of touch. Tactile information acquisition relies heavily on action patterns, as touch is generated by direct contact. This paper proposes a robot active tactile perception framework inspired by human behaviors for object recognition. Human recognition experiments were designed to explore the action patterns used to recognize unknown objects. The analysis of these experiments revealed three primary action patterns: pressing, sliding, and rubbing. Robotic motions were conducted to collect tactile data from the same objects using these actions. Given the limited number of available datasets, which restricts the application of active perception learning methods inspired by human behavior patterns, this work aims to address this challenge by developing a novel framework. A tactile convolutional neural network was established for object recognition using the collected data. Additionally, a human-behavior-pattern-inspired tactile feedback control unit was integrated to optimize robotic motions and improve recognition accuracy. The proposed method achieved a recognition accuracy of 97.5%, surpassing the human experiment. This work provides valuable insights into the active tactile perception of robots, offering a reference for future research in this field.