Machine Learning‐Enhanced Modular Ionic Skin for Broad‐Spectrum Multimodal Discriminability in Bidirectional Human–Robot Interaction (Adv. Mater. 42/2025)

Abstract

Data-Driven Decoupled Multimodal Ionic Skin In their Research Article (DOI: 10.1002/adma.202508795), Geng Yang, Kaichen Xu, and co-workers report a machine learning-enhanced modular ionic skin capable of large-range multimodal decoupled sensing. Its outstanding performance is enabled by a synergistic sensor-algorithm optimization strategy, including hard-segment modulation of ionic gels and data-driven decoupling model. Functional validation via operator hand recognition and robotic sensing feedback underscore its potential for advanced human-robot interaction.