Grinding motion generation based on human motion by variational autoencoder

Abstract

This paper proposes grinding motion generation based on human motions by variational autoencoder, a generative model based on neural networks. Unlike robots, human motions are not always the same even if they are repeated for the same purpose. By generating motions that take this difference into account, it is possible to make the robot perform grinding tasks with motion diversity. The proposed method uses variational autoencoder to learn human grinding motions. In order to generate a long time motion, the task was divided by using two variational autoencoders. The proposed method can generate grinding motions with motion diversity.