A Multimodal Laser‐Induced Graphene‐Based Flexible Sensor for Soft Robotic Hand Environmental Perception

Abstract

Human skin exhibits a complex layered architecture endowed with diverse sensory receptors, facilitating the perception of rich, multimodal information during environmental interactions. This biological paradigm has inspired the development of advanced multimodal flexible sensors and intelligent robotic systems. In this study, it presents a novel multimodal laser‐induced graphene (LIG)‐based flexible sensor (MLFS) that leverages laser processing to rapidly fabricate distinct functional layers of LIG with tunable properties. By integrating triboelectric and piezoresistive mechanisms, the MLFS enables comprehensive detection of proximity and contact stimuli, providing sensitive and reliable environmental feedback. The sensor is seamlessly embedded onto a soft robotic finger, endowing the robotic hand with enhanced perception capabilities for environmental sensing and object discrimination. Experimental evaluations demonstrate that the soft robotic system can autonomously perform object searching and grasping tasks without reliance on visual cues. Moreover, employing a convolutional neural network (CNN), the system achieves high‐accuracy classification of object materials and textures, reaching 98.75% and 98.44%, respectively. This work significantly advances the sensory intelligence of soft robotic systems, offering a robust pathway for integrating multimodal sensing technologies with adaptive soft robotics and highlighting its potential for practical applications in autonomous manipulation and human‐robot interaction.