Customizing Tactile Sensors via Machine Learning‐Driven Inverse Design

ABSTRACT Replicating the sophisticated sense of touch in artificial systems requires tactile sensors with precisely tailored properties. However, manually navigating the complex microstructure‐property relationship results in inefficient and suboptimal designs. Here, we present a machine learning‐accelerated, multi‐objective inverse design methodology for the automatic customization of tactile sensors. At its core is a data‐efficient microstructure‐property predictor designed to ensure robust accuracy with minimal experimental data. It achieves this by synergistically combining support vector machine‐based boundary definition with dual‐phase active learning. This predictor then drives a multi‐objective inverse design software, enabling real‐time, on‐demand sensor customization. This methodology not only dramatically enhances the design efficiency but also yields sensors with exceptional characteristics—high sensitivity (1.2 V/kPa), high linearity ( R 2 = 0.999), and wide detection range (0–400 kPa). The resulting sensors are successfully applied to pulse monitoring, material identification, and robotic grasping. Furthermore, the underlying microstructure‐property mechanisms are elucidated using interpretable machine learning. This work establishes a general paradigm for automated sensor customization, accelerating the development of next‐generation wearable and robotic sensing systems.