A Framework of Hybrid Force/Motion Skills Learning for Robots

Abstract

Human factors and human-centered design philosophy are highly desired in today's robotics applications such as human-robot interaction (HRI). Several studies showed that endowing robots of human-like interaction skills can not only make them more likeable but also improve their performance. In particular, skill transfer by imitation learning can increase the usability and acceptability of robots by users without computer programming skills. In fact, besides positional information, muscle stiffness of the human arm and contact force with the environment also play important roles in understanding and generating human-like manipulation behaviors for robots, e.g., in physical HRI and teleoperation. To this end, we present a novel robot learning framework based on dynamic movement primitives (DMPs), taking into consideration both the positional and contact force profiles for human-robot skills transferring. Distinguished from the conventional method involving only the motion information, the proposed framework combines two sets of DMPs, which are built to model the motion trajectory and the force variation of the robot manipulator, respectively. Thus, a hybrid force/motion control approach is taken to ensure the accurate tracking and reproduction of the desired positional and force motor skills. Meanwhile, in order to simplify the control system, a momentum-based force observer is applied to estimate the contact force instead of employing force sensors. To deploy the learned motion-force robot manipulation skills to a broader variety of tasks, the generalization of these DMP models in actual situations is also considered. Comparative experiments have been conducted using a Baxter robot to verify the effectiveness of the proposed learning framework on real-world scenarios like cleaning a table.