Advertisement

Affordance-Based Activity Placement in Human-Robot Shared Environments

  • Felix Lindner
  • Carola Eschenbach
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8239)

Abstract

When planning to carry out an activity, a mobile robot has to choose its placement during the activity. Within an environment shared by humans and robots, a social robot should take restrictions deriving from spatial needs of other agents into account. We propose a solution to the problem of obtaining a target placement to perform an activity taking the action possibilities of oneself and others into account. The approach is based on affordance spaces agents can use to perform activities and on socio-spatial reasons that count for or against using such a space.

Keywords

Mobile Robot Humanoid Robot Social Robot Dispositional Property Activity Placement 
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.
    Althaus, P., Ishiguro, H., Kanda, T., Miyashita, T., Christensen, H.I.: Navigation for human-robot interaction tasks. In: Proc. of ICRA 2004, pp. 368–377 (2004)Google Scholar
  2. 2.
    Bonnefon, J.-F., Dubois, D., Fargier, H., Leblois, S.: Qualitative heuristics for balancing the pros and cons. Theory and Decision 65(1), 71–95 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Dubois, D., Fargier, H., Bonnefon, J.-F.: On the qualitative comparison of decisions having positive and negative features. Journal of Artificial Intelligence Research 32(1), 385–417 (2008)MathSciNetzbMATHGoogle Scholar
  4. 4.
    Gibson, J.J.: The Theory of Affordances. In: The Ecological Approach to Visual Perception, pp. 127–143. Lawrence Erlbaum Association Inc. (1986)Google Scholar
  5. 5.
    Kendon, A.: Conducting Interaction: Patterns of Behavior and Focused Encounters. Cambridge University Press, Cambridge (1990)Google Scholar
  6. 6.
    Keshavdas, S., Zender, H., Kruijff, G.J.M., Liu, M., Colas, F.: Functional mapping: Spatial inferencing to aid human-robot rescue efforts in unstructured disaster environments. In: Proc. of the 2012 AAAI Spring Symposium on Designing Intelligent Robots, Stanford University. AAAI Press (2012)Google Scholar
  7. 7.
    Lemaignan, S., Ros, R., Mösenlechner, L., Alami, R., Beetz, M.: Oro, a knowledge management module for cognitive architectures in robotics. In: Proc. of the 2010 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (2010)Google Scholar
  8. 8.
    Lindner, F., Eschenbach, C.: Towards a formalization of social spaces for socially aware robots. In: Egenhofer, M., Giudice, N., Moratz, R., Worboys, M. (eds.) COSIT 2011. LNCS, vol. 6899, pp. 283–303. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  9. 9.
    Okada, K., Kojima, M., Sagawa, Y., Ichino, T., Sato, K., Inaba, M.: Vision based behavior verification system of humanoid robot for daily environment tasks. In: Proc. of the 6th IEEE-RAS Int. Conf. on Humanoid Robots, pp. 7–12. IEEE (2006)Google Scholar
  10. 10.
    Pandey, A.K., Alami, R.: Taskability graph: Towards analyzing effort based agent-agent affordances. In: Proc. of the 2012 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 791–796 (2012)Google Scholar
  11. 11.
    Raz, J.: Practical Reason and Norms. Oxford University Press (1999)Google Scholar
  12. 12.
    Shiotani, T., Maegawa, K., Lee, J.H.: A behavior model of autonomous mobile projection robot for the visual information. In: Proc. of the 8th Int. Conf. on Ubiquitous Robots and Ambient Intelligence, pp. 615–620 (2011)Google Scholar
  13. 13.
    Sisbot, E.A., Marin-Urias, L.F., Broquère, X., Sidobre, D., Alami, R.: Synthesizing robot motions adapted to human presence. Int. Journal of Social Robotics 2(3), 329–343 (2010)CrossRefGoogle Scholar
  14. 14.
    Stulp, F., Fedrizzi, A., Mösenlechner, L., Beetz, M.: Learning and reasoning with action-related places for robust mobile manipulation. Journal of Artificial Intelligence Research (JAIR) 43, 1–42 (2012)zbMATHGoogle Scholar
  15. 15.
    Tipaldi, G.D., Arras, K.O.: I want my coffee hot! Learning to find people under spatio-temporal constraints. In: Proc. of the Int. Conf. on Robotics and Automation, pp. 1217–1222 (2011)Google Scholar
  16. 16.
    Torta, E., Cuijpers, R.H., Juola, J.F., van der Pol, D.: Design of robust robotic proxemic behaviour. In: Mutlu, B., Bartneck, C., Ham, J., Evers, V., Kanda, T. (eds.) ICSR 2011. LNCS (LNAI), vol. 7072, pp. 21–30. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  17. 17.
    Williams, M.-A.: Robot social intelligence. In: Ge, S.S., Khatib, O., Cabibihan, J.-J., Simmons, R., Williams, M.-A. (eds.) ICSR 2012. LNCS (LNAI), vol. 7621, pp. 45–55. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  18. 18.
    Yamaoka, F., Kanda, T., Ishiguro, H., Hagita, N.: A model of proximity control for information-presenting robots. IEEE Trans. on Robotics 26(1), 187–195 (2010)CrossRefGoogle Scholar
  19. 19.
    Zacharias, F., Borst, C., Beetz, M., Hirzinger, G.: Positioning mobile manipulators to perform constrained linear trajectories. In: Proc. of IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), pp. 2578–2584. IEEE (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Felix Lindner
    • 1
  • Carola Eschenbach
    • 1
  1. 1.Knowledge and Language Processing, Department of InformaticsUniversity of HamburgHamburgGermany

Personalised recommendations