Human-Robot Collaborative Learning of a Bag Shaking Trajectory

Abstract—This paper presents a collaborative reinforcement learning algorithm,)(λCQ, designed to accelerate learning by integrating a human operator into the learning process. The)(λCQ-learning algorithm enables collaboration of knowledge between the robot and a human; the human, responsible for remotely monitoring the robot, suggests solutions when intervention is required. Based on the robot’s learning performance, it switches between fully autonomous operation, and the integration of human commands. The)(λCQ-learning algorithm was tested on a Motoman UP-6 fixed-arm robot required to empty the contents of a suspicious bag. We demonstrate experimental results that support our hypothesis of evaluating whether learning is faster while human collaboration is triggered than when the system functions autonomously. Index Terms—robot learning, reinforcement learning, human-robot collaboration. I