Haptic In-Sensor Computing Device Based on CNT/PDMS Nanocomposite Physical Reservoir

Abstract

The importance of haptic in-sensor computing devices has been increasing. Herein, a haptic sensor with a hierarchical structure is successfully fabricated via the sacrificial template method, using carbon nanotube-polydimethylsiloxane (CNT-PDMS) nanocomposites for in-sensor computing applications. The CNT-PDMS nanocomposite sensors, with different sensitivities, are obtained by varying the amount of CNTs. The input stimuli are transformed into higher-dimensional information, enabling a new path for the CNT-PDMS nanocomposite application, which is implemented on a robotic hand as an in-sensor computing device by applying a reservoir computing paradigm. The nonlinear output data obtained from the sensors are trained using linear regression and used to classify nine different objects used in everyday life with an object recognition accuracy of >80% for each object. This approach can enable tactile sensation in robots while reducing the computational cost.