A DMP-based Online Adaptive Stiffness Adjustment Method

Learning from demonstration (LfD) is a promising method for robots to learn and generalize human-like skills. It has the advantages of high programming efficiency, easy optimization, and non-professionals can also operate. There is a lot of research work that learn motion trajectories and stiffness curves from human demonstrations simutaneously to make the robot compliant, but previous work rarely consider the changes of environment. In this article, we propose an adaptive stiffness method that enables the robot to learn motion and stiffness trajectories from a single demonstration. When the environment changes, it can spontaneously tune the stiffness according to environmental feedback to ensure the smoothness of the task. Thus the robot has the ability to adapt to environmental changes. We first proved the theoretical feasibility of the method, and then we conducted physical experiments on the Baxter robot to verify the effectiveness of the proposed method.