Human Adaptation to Human-Robot Shared Control

• Published in: IEEE transactions on human-machine systems Vol. 49; no. 2; pp. 126 - 136
• Main Authors: Amirshirzad, Negin, Kumru, Asiye, Oztop, Erhan
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.04.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Collaboration; Control systems; Human behavior; Human–robot interaction; Manipulators; motor skill acquisition; Performance evaluation; Robot control; Robot kinematics; Robot sensing systems; Robots; sensorimotor learning; shared control; Task analysis
• ISSN: 2168-2291; 2168-2305
• DOI: 10.1109/THMS.2018.2884719

Human-in-the-loop robot control systems naturally provide the means for synergistic human-robot collaboration through control sharing. The expectation in such a system is that the strengths of each partner are combined to achieve a task performance higher than that can be achieved by the individual partners alone. However, there is no general established rule to ensure a synergistic partnership. In particular, it is not well studied how humans adapt to a nonstationary robot partner whose behavior may change in response to human actions. If the human is not given the choice to turn on or off the control sharing, the robot-human system can even be unstable depending on how the shared control is implemented. In this paper, we instantiate a human-robot shared control system with the "ball balancing task," where a ball must be brought to a desired position on a tray held by the robot partner. The experimental setup is used to assess the effectiveness of the system and to find out the differences in human sensorimotor learning when the robot is a control sharing partner, as opposed to being a passive teleoperated robot. The results of the four-day 20-subject experiments conducted show that 1) after a short human learning phase, task execution performance is significantly improved when both human and robot are in charge. Moreover, 2) even though the subjects are not instructed about the role of the robot, they do learn faster despite the nonstationary behavior of the robot caused by the goal estimation mechanism built in.