Trajectory Learning Using Principal Component Analysis

Robots are increasingly used in numerous life applications. Therefore, humans are looking forward to create productive robots. Robot learning is the process of obtaining additional information to accomplish an objective configuration. Moreover, robot learning from demonstration is to guide the robot the way to perform a particular task derived from human directions. Traditionally, modeling the demonstrated data was applied on discrete data which would result in learning outcome distortions. So as to overcome such distortion, preprocessing of the raw data is necessary. In this paper, trajectory learning from demonstration scheme is proposed. In our proposed scheme, the raw data are initially preprocessed by employing the principal component analysis algorithm. We experimentally compare our proposed scheme with the most recent proposed schemes. It is found that the proposed scheme is capable of increasing the efficiency by minimizing the error in comparison to the other recent work with significant reduced computational cost.