Using Human Motion Estimation for Human-Robot Cooperative Manipulation

Traditionally the leader or follower role of the robot in a human-robot collaborative task has to be predetermined. However, humans performing collaborative tasks can switch between or share the leader-follower roles effortlessly even in the absence of audio-visual cues. This is because humans are capable of developing a mutual understanding while performing the collaborative task. This paper proposes a framework to endow robots with a similar capability. Behavior of the robot is controlled by two types of controllers such as reactive and proactive controllers each giving the robot follower and leader characteristics respectively. Proactive actions are based on human motion prediction. We propose that the role of the robot can be governed by the confidence of prediction. Hence, the robot can determine its role during the task autonomously and dynamically. The framework is demonstrated and evaluated through a table-lifting task. Experimental results confirm that the proposed system improves the overall task performance.