Adaptive Robot to Person Encounter by Motion Patterns

  • Hans Jørgen Andersen
  • Thomas Bak
  • Mikael Svenstrup
Part of the Communications in Computer and Information Science book series (CCIS, volume 33)


This paper introduces a new method for adaptive control of a robot approaching a person controlled by the person’s interest in interaction. For adjustment of the robot behavior a cost function centered in the person is adapted according to an introduced person evaluator method relying on the three variables: the distance between the person and the robot, the relative velocity between the two, and position of the person. The person evaluator method determine the person’s interest by evaluating the spatial relationship between robot and person in a Case Based Reasoning (CBR) system that is trained to determine to which degree the person is interested in interaction. The outcome of the CBR system is used to adapt the cost function around the person, so that the robot’s behavior is adapted to the expressed interest. The proposed methods are evaluated by a number of physical experiments that demonstrate the effectiveness of the adaptive cost function approach, which allows the robot to locate itself in front of a person who has expressed interest through his or hers spatial motion.


Human-robot interaction Adaptive Control Social situatedness Patterns of behavior 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Dautenhahn, K.: Methodology & themes of human-robot interaction: A growing research field. International Journal of Advanced Robotic Systems 4(1), 103–108 (2007)Google Scholar
  2. 2.
    Bruce, A., Nourbakhsh, I., Simmons, R.: The role of expressiveness and attention in human-robot interaction. In: Proceedings, AAAI Fall Symposium (2001)Google Scholar
  3. 3.
    Christensen, H.I., Pacchierotti, E.: Embodied social interaction for robots. In: Dautenhahn, K. (ed.) AISB 2005, Hertsfordshire, April 2005, pp. 40–45 (2005)Google Scholar
  4. 4.
    Dautenhahn, K., Walters, M., Woods, S., Koay, K.L., Sisbot, E.A., Alami, R., Siméon, T.: How i serve you? a robot companion approaching a seated person in a helping context. In: HRI Human Robot Interaction 2006 (HRI 2006), Salt Lake City, Utah, USA (2006)Google Scholar
  5. 5.
    Hanajima, N., Ohta, Y., Hikita, H., Yamashita, M.: Investigation of impressions for approach motion of a mobile robot based on psychophysiological analysis. In: IEEE International Workshop on Robots and Human Interactive Communication ROMAN 2005, August 2005, pp. 79–84 (2005)Google Scholar
  6. 6.
    Michalowski, M., Sabanovic, S., Simmons, R.: A spatial model of engagement for a social robot. In: The 9th International Workshop on Advanced Motion Control, AMC 2006, Istanbul (March 2006)Google Scholar
  7. 7.
    Michalowski, M., Sabanovic, S., DiSalvo, C., Busquets, D., Hiatt, L., Melchior, N., Simmons, R.: Socially distributed perception: Grace plays social tag at aaai 2005. Autonomous Robots 22(4), 385–397 (2007)CrossRefGoogle Scholar
  8. 8.
    Hall, E.: The Hidden Dimension. Doubleday (1966)Google Scholar
  9. 9.
    Walters, M.L., Dautenhahn, K., te Boekhorst, R., Koay, K.L., Kaouri, C., Woods, S.N., Lee, D., Werry, I.: The influence of subjects’ personality traits on personal spatial zones in a human-robot interaction experiment. In: Proc. IEEE Ro-man, Hashville, August 2005, pp. 347–352 (2005)Google Scholar
  10. 10.
    Sisbot, E.A., Clodic, A., Urias, L., Fontmarty, M., Bréthes, L., Alami, R.: Implementing a human-aware robot system. In: IEEE International Symposium on Robot and Human Interactive Communication 2006 (RO-MAN 2006), Hatfield, United Kingdom (2006)Google Scholar
  11. 11.
    Sisbot, E.A., Alami, R., Siméon, T., Dautenhahn, K., Walters, M., Woods, S., Koay, K.L., Nehaniv, C.: Navigation in the presence of humans. In: IEEE-RAS International Conference on Humanoid Robots (Humanoids 2005), Tsukuba, Japan (December 2005)Google Scholar
  12. 12.
    Woods, S., Walters, M.L., Koay, K., Dautenhahn, K.: Methodological issues in hri: A comparison of live and video-based methods in robot to human approach direction trials. In: The 15th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN 2006), Hatfield, UK, September 2006, pp. 51–58 (2006)Google Scholar
  13. 13.
    Kolodner, J.: Case-based reasoning. Morgan Kaufmann Publishers Inc., San Francisco (1993)CrossRefMATHGoogle Scholar
  14. 14.
    Ram, A., Arkin, R.C., Moorman, K., Clark, R.J.: Case-based reactive navigation: A case-based method for on-line selection and adaptation of reactive control parameters in autonomous robotic systems. IEEE Transactions on Systems, Man, and Cybernetics 27, 376–394 (1997)CrossRefGoogle Scholar
  15. 15.
    Collett, T.H., MacDonald, B.A., Gerkey, B.P.: Player 2.0: Toward a practical robot programming framework. In: Sammut, C. (ed.) Proceedings of the Australasian Conference on Robotics and Automation (ACRA 2005), Sydney, Australia (December 2005),
  16. 16.
    Bruce, J., Balch, T., Veloso, M.: Fast and inexpensive color image segmentation for interactive robots. In: Proceedings of IROS 2000, Japan (October 2000)Google Scholar
  17. 17.
    Ulrich, I., Borenstein, J.: Vfh*: Local obstacle avoidance with look-ahead verification. In: IEEE International Conference on Robotics and Automation, San Francisco, April 2000, pp. 2505–2511 (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Hans Jørgen Andersen
    • 1
  • Thomas Bak
    • 2
  • Mikael Svenstrup
    • 2
  1. 1.Department for Media TechnologyAalborg UniversityAalborgDenmark
  2. 2.Department of Electronic Systems, Automation & ControlAalborg UniversityAalborgDenmark

Personalised recommendations