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

Multimodal tactile perception systems that mimic the functionality of human skin are able to perceive complex external stimuli, facilitating advanced applications in human-machine interactions. However, current systems still struggle with limited sensing ranges and suboptimal decoupling strategies, restricting their effective multimodal sensing. To achieve broad-spectrum multimodal discriminability, a machine learning-enhanced modular ionic skin (MIS) is developed via a synergistic sensor-algorithm optimization strategy. From the sensing material perspective, process-controlled hard-segment modulation in the ionic gel enables the development of diverse ionic conductors with enhanced sensing properties: a minimum temperature coefficient of -4.00% °C^-1 (10-160 °C), a linear gauge factor of 2.95 (0-100%), and a maximum pressure sensitivity of 80.5 kPa^-1 (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.