  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.
