Human behavior learning for robot in joint space

In this paper, the desired trajectory is trained in joint space without the dynamic time warping. In order to learn the demonstrations in joint space, we use two techniques, Lloyd׳s algorithm and modified hidden Markov model, to solve the problems in joint space learning. Since the desired trajectories are the joint angles, they can be applied directly without inverse kinematics. Experimental results show that the proposed algorithm works well for human behavior learning.