Imitation Learning Based on Trajectory Matching in the Writing Task

Aim  ing at the complexity of motion trajectory in the writing task, imitation learning based on trajectory matching was introduced to represent and generalize writing trajectory for the obtainment of writing skill. The robot acquires training data from a teacher and codes by Gaussian mixture model (GMM). Then, the essential feature of teaching behavior was learned and the motion trajectory was reconstructed by means of generalized output through Gaussian mixture regression (GMR). The results of simulation experiments show that this method possesses favourable ability of behavior coding and anti-interference performance. It can be used in the writing task of Chinese characters whose trajectory is continuous. Furthermore, the learning of Chinese characters’ writing skill with intermittent trajectory is realizable by multi-task learning extended from GMM and the generalization performance turns out to be good.