Bioinspired Super‐Robust Conductive Hydrogels for Machine Learning‐Assisted Tactile Perception System

Abstract Conductive hydrogels have attracted significant attention due to exceptional flexibility, electrochemical property, and biocompatibility. However, the low mechanical strength can compromise their stability under high stress, making the material susceptible to fracture in complex or harsh environments. Achieving a balance between conductivity and mechanical robustness remains a critical challenge. In this study, super‐robust conductive hydrogels were designed and developed with highly oriented structures and densified networks, by employing techniques such as stretch‐drying‐induced directional assembly, salting‐out, and ionic crosslinking. The hydrogels showed remarkable mechanical property (tensile strength: 17.13–142.1 MPa; toughness: 50 MJ m⁻³), high conductivity (30.1 S m⁻¹), and reliable strain sensing performance. Additionally, it applied this hydrogel material to fabricate biomimetic electronic skin device, significantly improving signal quality and device stability. By integrating the device with 1D convolutional neural network algorithm, it further developed a real‐time material recognition system based on triboelectric and piezoresistive signal collection, achieving a classification accuracy of up to 99.79% across eight materials. This study predicted the potential of the high‐performance conductive hydrogels for various applications in flexible smart wearables, the Internet of Things, bioelectronics, and bionic robotics.