Hierarchical robot learning for physical collaboration between humans and robots

摘要

Human-in-the-loop robot learning is an important ability for robotics in human-robot collaborative (HRC) tasks. The research of interactive learning mainly focuses on robot learning with human cognitive interaction. However, robot learning with human physical interaction remains a challenging problem, due to the stochastic of human control. In this paper, we present a hierarchical robot learning approach that includes two learning hierarchies for HRC tasks. High-level motion learning is to learn the motion policy for objects which used as the shared plan of robot and human. In low-level interactive learning, human action is first predicted by an Extend Kalman Filter (EKF) algorithm. Q-learning with function approximation is applied to select the optimal robot action with the guidance of the predicted human action. Finally, the proposed learning approach is validated on a UR5 robot. The results of our experiments show the presented learning approach enables the robot to adaptively coordinate with a human and produce an active contribution to the HRC tasks.