Imitation learning of five-fingered electric prosthetic hand in simulation environment

Abstract

In order to improve the quality of life of people with upper limb deficiencies, electric prosthetic hands with grasping ability are being developed.However, it is difficult to implement complex grasping motions in conventional electric prosthetic hands because the control of the grasping motion is manually programmed. Therefore, we propose a method to acquire the grasping motion of a motorized hands prosthetic hand using imitation learning. In this study, we construct a simulation environment of a five-fingered electric prosthetic hands using Unity, and aim to acquire the grasping motion by imitation learning.