Imitation learning framework based on principal component analysis

Abstract

In this paper, an imitation learning framework that includes an evolutionary process based on principal component analysis (PCA) is presented. The framework comprises offline and online processes. In the offline process, human demonstrations are used to develop a motion database. The database covers the workspace and includes robot properties. The evolved database has a clustered structure for efficiency. In the online process, a robot can generate desired motions using a real-time motion reconstruction method based on PCA. The performance of this method is verified through two case studies. The proposed framework is applied to the generation of reaching motions to an object on a table and a shelf.