Machine Learning‐Enhanced Modular Ionic Skin for Broad‐Spectrum Multimodal Discriminability in Bidirectional Human–Robot Interaction

(0-1.3 MPa). With respect to the decoupling algorithm, a data-driven decoupling model for the MIS is meticulously proposed and trained on a dedicated multi-stimuli dataset, achieving maximum decoupling ranges for temperature and pressure with prediction errors as low as 7.0%, while maintaining reliable strain detection despite temperature interference. The effectiveness and functionality of the system are demonstrated in a multimodal wearable hand kit for operator hand recognition and a robotic gripper kit for feedback, highlighting its potential in bidirectional human-robot interaction.