Contact Shape and Pose Recognition: Utilizing a Multipole Magnetic Tactile Sensor With a Metalearning Model

Soft magnetic tactile sensors have been gradually applied to robotic systems due to their low-cost and simple fabrication. The previous soft magnetic tactile sensor was developed for tactile features of a single point (i.e., force/location) estimation and proved the feasibility by experiments. However, extracting tactile features of a surface (i.e., contact shape) by magnetic sensors remains a challenge, which limits the application. In this article, a soft magnetic tactile sensor that can extract contact surface shape and pose features is fabricated, and a multipole magnetization method is developed to improve the performance of the tactile sensor. Furthermore, we propose a metric-based metalearning method to extract the tactile feature of the contact surface shape and pose from magnetic data under limited sample conditions, and the method is verified by a series of experiments. The experimental results show that our method can achieve more than 80% accuracy in contact shape recognition and more than 95% accuracy in contact pose recognition. The experimental results demonstrate that our method can extract tactile features under limited data conditions and has a certain generalization ability for new contact data.