Large‐Area Piezoresistive Tactile Sensor Developed by Training a Super‐Simple Single‐Layer Carbon Nanotube‐Dispersed Polydimethylsiloxane Pad

The revolutionary concept of creating a large‐area tactile sensor by training a simple, bulky material through deep learning (DL) is proposed. This enables the replacement of the conventional tactile sensor comprising a patterned array structure with a super‐simple, single‐layer, large‐area tactile sensor pad. A crude carbon nanotube‐dispersed polydimethylsiloxane pad—with a bias applied to the center and the resultant piezoresistive current detected at several electrodes located on the pad edge—plays a smart sensory role without the need for complicated fabrication of microengineered structures. The piezoresistive current while recording the indented location and the pressure thereon is measured, and then various DL models (a multimodel arrangement is necessary due to the viscoelasticity of the pad) using the collected data are trained. The proposed concept is realized using a tandem model comprising a combination of algorithms selected from deep neural networks, convolutional neural networks, long short‐term memory networks, and 16 state‐of‐the‐art machine learning algorithms. The hold‐out dataset test accuracy for the indented location identification reaches 98.89%, and the goodness of fit for pressure prediction is evaluated with mean squared error of 2.5 × 10−3 and coefficient of determination of 98.05%. A high‐precision tactile sensor is not achieved by means of complexly processed devices but by training simple bulk materials. A crude carbon nanotube‐dispersed polydimethylsiloxane pad—with a bias applied to the center and the resultant piezoresistive current detected at several electrodes located on the pad edge—plays a smart sensory role without the need for complicated fabrication of microengineered structures.