Learning Complex Trajectories by Imitation Using Orthogonal Basis Functions and Template Matching

摘要

In this paper a new method based on Orthogonal basis Functions and Template Matching (OFTM) for learning trajectory by imitation is introduced. In this method, the robot uses primitive movements including template and orthogonal basis trajectories, which are learnt by using Gaussian Mixture Model (GMM), to construct new given trajectories. To obtain this goal, the robot calculates the dissimilarity between the new trajectory and arbitrary templates, then the similar parts will be replaced by the template, and the rest of the new trajectory will be constructed by using the orthogonal learnt trajectories. The results show that our method is more accurate and requires less computation in comparison with learning the whole trajectory by GMM.