Customizing Tactile Sensors via Machine Learning‐Driven Inverse Design (Adv. Sci. 29/2026)

Machine Learning-Driven Inverse Design In their Research Article (DOI: 10.1002/advs.202524250), Honghao Lyu, Geng Yang, and co-workers introduce a machine learning-accelerated inverse design methodology to automatically customize high-performance tactile sensors. By synergizing support vector machines with active learning, a data-efficient predictor rapidly navigates complex microstructure-property relationships. This real-time, on-demand framework delivers sensors with exceptional sensitivity, linearity, and range, establishing a powerful new paradigm for next-generation robotic sensing systems.