Learning-Based Slip Detection and Fine Control Using the Tactile Sensor for Robot Stable Grasping

Abstract

Slip detection and control is critical to achieving stable grasping in robotics. However, accurate and robust slip detection and control remains a challenging task. This letter proposes a learning framework with contrastive learning and feature alignment to improve the accuracy of end-to-end slip detection under small sample conditions. In addition, a fuzzy logic control system is designed based on the stiffness perception of the grasped object for estimating the increment of reflective force to suppress the slip. To validate the effectiveness of the proposed method, we conduct online tests on various objects in two scenarios prone to slip, based on a developed hardware platform. Experimental results show that the proposed slip detection method demonstrates high accuracy and good generalization capability, while the slip control method incorporating the object stiffness property can achieve safe and fine control after slip occurs.