Human-robot collaborative manipulation through imitation and reinforcement learning

Abstract

This paper proposes a two-phase learning framework for human-robot collaborative manipulation tasks. A table-lifting task performed jointly by a human and a humanoid robot is considered. In order to perform the task, the robot should learn to hold the table at a suitable position and then perform the lifting task cooperatively with the human. Accordingly, learning is split into two phases. The first phase enables the robot to reach out and hold one end of the table. A Programming by Demonstration (PbD) algorithm based on GMM/GMR is used to accomplish this. In the second phase the robot switches its role to an agent learning to collaborate with the human on the task. A guided reinforcement learning algorithm is developed. Using the proposed framework, the robot can successfully learn to reach and hold the table and keep the table horizontal during lifting it up with human in a reasonable amount of time.