Research on tactile sensation by physical reservoir computing with a robot arm and a Ag<sub>2</sub>S reservoir
Kaiki Yoshimura, Tsuyoshi Hasegawa
Abstract
Abstract In recent years, physical reservoir computing has attracted much attention because of its low computational cost and low power consumption. In terms of social implementation of artificial intelligence, physical reservoir has a potential to meet the request, such as the need for AI robots to process information related to tactile sensation. It has been reported that a Ag 2 S polycrystalline thin film retains short-term memory and non-linearity when used as a physical reservoir. In this study, we applied the technique to tactile sensation by combining with a pressure sensor attached to a robot arm. In the object grasping task, a Ag 2 S physical reservoir enabled the objective recognition with the accuracy of 81.3%, although the task failed with linear regression of the direct output from the pressure sensor. We also demonstrate the potential of the system to detect anomalies in object grabbing.