A DMP-based Framework for Efficiently Generating Complete Stiffness Profiles of Human-like Variable Impedance Skill from Demonstrations

Human manipulation skills can be transferred to robots conveniently through learning from demonstrations (LfD) methods. However, most of these works either only encode motion trajectories or suffer from the complexity and incompleteness when estimating stiffness profiles. To solve these problems, we propose a simple and effective stiffness estimation method that estimates a complete end-effector stiffness matrix from the variation of demonstrations. To that end, Gaussian Mixture Regression (GMR) is applied to extract the reference pose trajectory and the variability. Quaternion logarithmic map is integrated to generate complete rotational stiffness. Besides, the Dynamic Movement Primitives (DMPs) model is further developed to encode and schedule both the movement trajectory and stiffness profiles in task space simultaneously. Finally, the effectiveness of our approach is validated on a real-world 7 DoF robot with the variable impedance controller.