  By learning Variable Impedance Control policy, robot assistants can
intelligently adapt their manipulation compliance to ensure both safe
interaction and proper task completion when operating in human-robot
interaction environments. In this paper, we propose a DMP-based framework that
learns and generalizes variable impedance manipulation skills from human
demonstrations. This framework improves robots$'$ adaptability to environment
changes(i.e. the weight and shape changes of grasping object at the robot
end-effector) and inherits the efficiency of demonstration-variance-based
stiffness estimation methods. Besides, with our stiffness estimation method, we
generate not only translational stiffness profiles but also rotational
stiffness profiles that are ignored or incomplete in most learning Variable
Impedance Control papers. Real-world experiments on a 7 DoF redundant robot
manipulator have been conducted to validate the effectiveness of our framework.
