Deep Learning–Powered Robust Tactile Perception: Bridging Graphene Electronic Skin and Dynamic Decoding

Decoding algorithm–based approaches emerge as transformative technologies for artificial sensory systems, transcending traditional methods and enabling robust and versatile applications. Herein, the development of an electronic skin (e‐skin) that integrates multifunctional tactile sensing capabilities, including dynamic pressure sensing and continuous sliding touch detection, along with human–robot interface is reported. To address the limitations of early works on multifunctionality in strain sensors based on resistive values, the innovative scheme harnesses the synergy of facile e‐skin fabrication and advanced decoding algorithms, creating a robust stimuli‐responsive platform. At the core of the system lies a straightforward integration of e‐skin, achieved by generating laser‐induced graphene on a liquid‐crystal polymer film, followed by embedding the transfer‐printed conductive graphene layer into an elastomeric substrate. This streamlined methodology optimizes existing sensor arrays without the need for intricate material combinations or interconnections, avoiding susceptibility to damage. The advanced decoding algorithms bypass geometric engineering and complex numerical calculations within the deep learning hyper‐redundant system. In the experimental results, it is demonstrated that the e‐skin system successfully achieves a Braille‐readable e‐skin and a surgery‐enabled human–robot interface, highlighting the scalability and adaptability of the e‐skin in coordination with decoding algorithm systems.