Learning and Generalizing Variable Impedance Manipulation Skills from Human Demonstrations
This work addresses the need for more adaptable and safe robot assistants in human-robot interaction, though it is incremental as it builds on existing demonstration-based methods.
The paper tackles the problem of enabling robots to adapt their compliance for safe and effective manipulation in human-robot interaction by learning variable impedance control from human demonstrations, resulting in improved adaptability to object changes and generation of both translational and rotational stiffness profiles validated on a 7 DoF robot.
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.