A Cognitive Approach to Enhancing Human-Robot Interaction for Service Robots
As robots become more intelligent and their application fields continue to grow, the decisions and interaction of robots that share work domains with humans become increasingly important. Traditional robots have received only simple commands, and humans’ roles have been limited to supervisor. However, for successive task performance, robots’ decision making should be approached via collaboration between the human and robot. Interaction also should be regarded as an issue closely associated with joint work plans rather than a simple function. Interaction between the human and robot, moreover, should be systemized in order to decrease the workload of the human and maximize user satisfaction. Accordingly, we developed several cognitive models: a task model, truth-maintenance model, interaction model, and intention rule-base. These models can manage and initiate the interactions based on their tasks and modify robot’s activities by using the result of the interaction. We demonstrated the adaptability and usability of the developed models by applying them to the home-service robot, T-Rot.
KeywordsHuman-robot interaction Interaction-based robot behavior Cognitive model Task-oriented interaction Service robot
Unable to display preview. Download preview PDF.
- 2.U.N. and I.F.R.R.: United Nations and The International Federation of Robotics: World Robotics 2002. New York and Geneva: United Nations (2002)Google Scholar
- 4.Kang, W.L., Kim, H.R., Yoon, W.C., Yoon, Y.S., Kwon, D.S.: Designing a human-robot interaction framework for home service robot. In: ROMAN 2005. IEEE International Workshop, pp. 286–293 (2005)Google Scholar
- 5.Hollnagel, E., Woods, D.D.: Joint cognitive systems: Foundations of cognitive systems engineering, pp. 25–47. CRC Press / Taylor & Francis, Boca Raton, FL (2005)Google Scholar
- 6.Fong, T.: Collaborative control: A robot-centric model for vehicle teleoperation. Ph.D. dissertation, Robotics Inst. Carnegie Mellon Univ, Pittsburgh, PA (2001)Google Scholar
- 7.Horvitz, E.: Uncertainty, Action, and Interaction: In Pursuit of Mixed-Initiative Computing. IEEE Intelligent Systems 14(5), 17–20 (1999)Google Scholar
- 8.Alami, R., Clodic, V., Montreuil, E., Sisbot, A., Chatila, R.: Task planning for human-robot interaction. In: Joint sOc-EUSAI conference (October 2005)Google Scholar
- 9.Breazeal, C., Hoffman, G., Lockerd, A.: Teaching and Working with Robots as a Collaboration. In: Adaptive Agents and Multi-Agent Systems II. LNCS (LNAI), vol. 3394, pp. 1030–1037. Springer, Heidelberg (2005)Google Scholar
- 11.Miura, J., Iwase, K., Shirai, Y.: Interactive Teaching of a Mobile Robot. In: Proc. IEEE Int. Conf. on Robotics and Automation, Barcelona, Spain, pp. 3389–3394 (2005)Google Scholar
- 12.Huber, M.J.: JAM Agents in a Nutshell, Version 0.60 + 0.80iGoogle Scholar