Advertisement

Investigating Human-Human Approach and Hand-Over

  • Patrizia Basili
  • Markus Huber
  • Thomas Brandt
  • Sandra Hirche
  • Stefan Glasauer
Part of the Cognitive Systems Monographs book series (COSMOS, volume 6)

Abstract

Humans interact safely, effortlessly, and intuitively with each other. An efficient robot assistant should thus be able to interact in the same way. This requires not only that the robot can react appropriately to human behaviour, but also that robotic behaviour can be understood intuitively by the human partners. The latter can be achieved by the robot mimicking certain aspects of human behaviour so that the human partner can more easily infer the intentions of the robot. Here we investigate a simple interaction scenario, approach and hand-over, to gain better understanding of the behavioural patterns in human-human interactions. In our experiment, one human subject, holding an object, approached another subject with the goal to hand over the object. Head and object positions were measured with a motion tracking device to analyse the behaviour of the approaching human. Interaction indicated by lifting the object in order to prepare for hand-over started approximately 1.2 s before the actual hand-over. Interpersonal distance varied considerably between subjects with an average of 1.16 m. To test whether the behavioural patterns observed depended on two humans being present, we replaced in a second experiment the receiving subject with a table. We found that the behaviour of the transferring subject was very similar in both scenarios. Thus, the presence of the receiving subject plays a minor role in determining parameters such as start of interaction or interaction distance. We aim to implement and test the parameters derived experimentally in a robotic assistant to improve and facilitate human-robot interaction.

Keywords

Social Robot Human Partner Interaction Scenario Interpersonal Distance Standing Person 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Alami, R., Clodic, A., Montreuil, V., Sisbot, E.A., Chatila, R.: Toward Human-Aware Robot Task Planning. In: Proceedings of AAAI Spring Symposium: To boldly go where no human-robot team has gone before, Stanford, USA (2006)Google Scholar
  2. 2.
    Althoff, D., Kourakos, O., Lawitzky, M., Mörtl, A., Rambow, M., Rohrmüller, F., Brščić, D., Wollherr, D., Hirche, S., Buss, M.: A flexible architecture for real-time control in multi-robot systems. In: Ritter, H., et al. (eds.) Human Centered Robot Systems. COSMOS, vol. 6, pp. 43–52. Springer, Heidelberg (2009)Google Scholar
  3. 3.
    Balasuriya, J.C., Watanabe, K., Pallegedara, A.: ANFIS based active personal space for autonomous robots in ubiquitous environments. In: International Conference on Industrial and Information Systems, pp. 523–528 (2007)Google Scholar
  4. 4.
    Balasuriya, J.C., Watanabe, K., Pallegedara, A.: Giving robots some feelings towards interaction with humans in ubiquitous environment. In: International Conference on Industrial and Information Systems, pp. 529–534 (2007)Google Scholar
  5. 5.
    Fong, T., Nourbakhsh, I., Dautenhahn, K.: A survey of socially interactive robots. Robotics and Autonomous Systems, 143–166 (2003)Google Scholar
  6. 6.
    Hall, E.T.: The Hidden Dimension. Doubleday, New York (1966)Google Scholar
  7. 7.
    Hicheur, H., Pham, Q.-C., Arechavaleta, G., Laumond, J.-P., Berthoz, A.: The formation of trajectories during goal-oriented locomotion in humans. I. A stereotyped behaviour. European Journal of Neuroscience 26, 2376–2390 (2007)CrossRefGoogle Scholar
  8. 8.
    Huber, M., Knoll, A., Brandt, T., Glasauer, S.: Handing-over a cube: spatial features of physical joint action. Annals of the New York Academy of Sciences 1164, 380–382 (2009)CrossRefGoogle Scholar
  9. 9.
    Huber, M., Rickert, M., Knoll, A., Brandt, T., Glasauer, S.: Human-robot interaction in handing-over tasks. In: 7th IEEE International Symposium on Robot and Human Interactive Communication, pp. 107–112 (2008)Google Scholar
  10. 10.
    Kajikawa, S., Ishikawa, E.: Trajectory planning for hand-over between human and robot. In: 9th IEEE International Workshop on Robot and Human Interactive Communication, pp. 281–287 (2000)Google Scholar
  11. 11.
    Koay, K.L., Sisbot, E.A., Syrdal, D.S., Walters, M.L., Dautenhahn, K., Alami, R.: Exploratory Study of a Robot Approaching a Person in the Context of Handing Over an Object. In: Proceedings of AAAI Spring Symposium: Multidisciplinary Collaboration for Socially Assistive Robotics, AAAI Technical Report, pp. 18–24 (2007)Google Scholar
  12. 12.
    Nakauchi, Y., Simmons, R.: A social robot that stands in line. Autonomous Robots 12(3), 313–324 (2002)zbMATHCrossRefGoogle Scholar
  13. 13.
    Satake, S., Kanda, T., Glas, D.F., Imai, M., Ishiguro, H., Hagita, N.: How to approach humans? Strategies for social robots to initiate interaction. In: ACM/IEEE International Conference on Human-Robot Interaction, pp. 109–116 (2009)Google Scholar
  14. 14.
    Sisbot, E.A., Clodic, A., Alami, R., Ransan, M.: Supervision and motion planning for a mobile manipulator interacting with humans. In: ACM/IEEE International Conference on Human-Robot Interaction, pp. 327–334 (2008)Google Scholar
  15. 15.
    Walters, M.L., Dautenhahn, K., Woods, S., Koay, K.L.: Robotic etiquette: results from user studies involving a fetch and carry task. In: ACM/IEEE International Conference on Human-Robot Interaction, pp. 317–324 (2007)Google Scholar
  16. 16.
    Harris, C.M., Wolpert, D.M.: Signal-dependent noise determines motor planning. Nature 394, 780–784 (1998)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Patrizia Basili
    • 1
  • Markus Huber
    • 1
  • Thomas Brandt
    • 2
  • Sandra Hirche
    • 3
  • Stefan Glasauer
    • 1
  1. 1.Center for Sensorimotor Research, Institute of Clinical NeurosciencesLudwig-Maximilians-Universität München 
  2. 2.Chair of Clinical NeurosciencesLudwig-Maximilians-Universität München 
  3. 3.Institute of Automatic Control EngineeringTechnische Universität München 

Personalised recommendations