Universal slip detection of robotic hand with tactile sensing

Slip detection is to recognize whether an object remains stable during grasping, which can significantly enhance manipulation dexterity. In this study, we explore slip detection for five-finger robotic hands being capable of performing various grasp types, and detect slippage across all five fingers as a whole rather than concentrating on individual fingertips. First, we constructed a dataset collected during the grasping of common objects from daily life across six grasp types, comprising more than 200 k data points. Second, according to the principle of deep double descent, we designed a lightweight universal slip detection convolutional network for different grasp types (USDConvNet-DG) to classify grasp states (no-touch, slipping, and stable grasp). By combining frequency with time domain features, the network achieves a computation time of only 1.26 ms and an average accuracy of over 97% on both the validation and test datasets, demonstrating strong generalization capabilities. Furthermore, we validated the proposed USDConvNet-DG in real-time grasp force adjustment in real-world scenarios, showing that it can effectively improve the stability and reliability of robotic manipulation.