Publication
Triboelectric nanogenerators (TENGs) serve as a key technology in flexible electronic devices, especially tactile sensors, enabling flexible robotic manipulators to effectively interact with their external environment. Persistent challenges include inadequate system flexibility, a limited dataset for recognition, and less‐than‐optimal recognition accuracy. Herein, an intelligent soft robotic system incorporating triboelectric sensors to accurately acquire object‐grasping information is presented. The liquid metal triboelectric sensor, which utilized a liquid metal fingerprint electrode, exhibited excellent performance retention under real working conditions, both in bending and stretching states, and can identify four types of sensory information. Concurrently, a rotary triboelectric sensor featuring a rack‐and‐pinion structure is employed to augment the measurement of the dimensions and morphology of the grasped object. This innovative multi‐sensor fusion design enabled the entire system to acquire a greater amount of sensing information using fewer channels compared to conventional manipulators. Finally, by integrating convolutional neural networks with deep learning technology, the system achieved a target recognition accuracy of 96.67% across 15 distinct objects. This easy‐manufacturing and efficient self‐powered sensing system with a soft manipulator, endowing it with intelligent sensing capabilities and demonstrating great potential for digital real‐time recognition.