Biped Walking Learning from Imitation Using Dynamic Movement Primitives

Exploring the full potential of humanoid robots requires their ability to learn, generalize and reproduce complex tasks that will be faced in dynamic environments. In recent years, significant attention has been devoted to recovering kinematic information from the human motion using a motion capture system. This paper demonstrates the use of a VICON system to capture human locomotion that is used to train a set of Dynamic Movement Primitives. These DMP can then be used to directly control a humanoid robot on the task space. The main objectives of this paper are: (1) to study the main characteristics of human natural locomotion and human “robot-like” locomotion; (2) to use the captured motion to train a DMP; (3) to use the DMP to directly control a humanoid robot in task space. Numerical simulations performed on V-REP demonstrate the effectiveness of the proposed solution.