Human behavior learning for robot in joint space
Many robots, such as human guide robots and service robots, need to learn the human behavior such that the robots can merge into human society. This learning process is in task space. However, the desired trajectories should be in joint space such that each joint can be moved like human. This needs inverse kinematics. In most of cases, the inverse kinematics do not have analytical solutions. Few training methods work in joint space directly, because they need dynamic time warping to remove speed influence. Both inverse kinematics and dynamic time warping destroy the accuracy of the learning. 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.
human behavior learning hidden markov model