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 .
Learning Variable Impedance Control(VIC) policy can help robot assistants to 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 the robot end-effector ) and inherits the efficiency of demonstration variance-based stiffness estimation method . 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 VIC papers. Real-world experiments on a 7 DoF runduant robot manipulator haven been conducted to validate the effectiveness of our framework .
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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
Learning Variable Impedance Control(VIC) policy can help robot assistants to intelligently adapt their manipulation compliance to ensure both safe interaction and proper task completion when operating in human-robot interaction environments. In this paper
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simple and effective stiffness estimation method that estimates a complete
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 the robot
R
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
) and inherits the efficiency of demonstration variance-based stiffness estimation method
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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
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 VIC papers. Real-world experiments on a
R
robot with the variable impedance controller
runduant robot manipulator haven been conducted to validate the effectiveness of our framework