Learning Complex Assembly Skills from Kinect based Human Robot Interaction

Acquiring complex assembly skills is still a challenging task for robot programming. Because of the sensory and body structure differences, the human knowledge has to be demonstrated, recorded, converted and finally learned by the robot, in an inexplicit and indirect way. During this process, "how to demonstrate", "how to convert" and "how to learn" are the key problems. In this paper, Kinect sensor is utilized to provide the behavior information of the human demonstrator. Through natural human robot interaction, body skeleton and joint 3D coordinates are provided in real-time, which can fully describe the human intension and task related skills. To overcome the structural and individual differences, a Cartesian level unified mapping method is proposed to convert the human motion and match the specified robot. The recorded data set are modeled using Gaussian mixture model(GMM) and Gaussian mixture regression(GMR), which can extract redundancies across multiple demonstrations and build robust models to regenerate the dynamics of the recorded movements. The proposed methodologies are implemented in the imNEU humanoid robot platform. Experimental results verify the effectiveness.