Analysis of Methods for Playing Human Robot Hide-and-Seek in a Simple Real World Urban Environment

  • Alex GoldhoornEmail author
  • Alberto Sanfeliu
  • René Alquézar
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 253)


The hide-and-seek game has many interesting aspects for studying cognitive functions in robots and the interactions between mobile robots and humans. Some MOMDP (Mixed Observable Markovian Decision Processes) models and a heuristic-based method are proposed and evaluated as an automated seeker. MOMDPs are used because the hider’s position is not always known (partially observable), and the seeker’s position is fully observable. The MOMDP model is used in an off-line method for which two reward functions are tried. Because the time complexity of this model grows exponentially with the number of (partially observable) states, an on-line hierarchical MOMDP model was proposed to handle bigger maps. To reduce the states in the on-line method a robot centered segmentation is used. In addition to extensive simulations, games with a human hider and a real mobile robot as a seeker have been done in a simple urban environment.


Robotics Human Robot Interaction Hide-and-Seek MOMDP 


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  1. 1.
    Johansson, E., Balkenius, C.: It’s a child’s game: Investigating cognitive development with playing robots. In: International Conference on Development and Learning, vol. 164 (2005)Google Scholar
  2. 2.
    Ong, S.C.W., Png, S.W., Hsu, D., Lee, W.S.: Planning under Uncertainty for Robotic Tasks with Mixed Observability. The International Journal of Robotics Research 29(8), 1053–1068 (2010)CrossRefGoogle Scholar
  3. 3.
    Araya-López, M., Thomas, V., Buffet, O., Charpillet, F.: A closer look at MOMDPs. In: 22nd International Conference on Tools with Artificial Intelligence - ICTAI (2010)Google Scholar
  4. 4.
    Braziunas, D.: Pomdp solution methods. Technical report, University of Toronto (2003)Google Scholar
  5. 5.
    Hauskrecht, M.: Value-function approximations for partially observable markov decision processes. Journal of Artificial Intelligence Research 13, 33–94 (2000)MathSciNetzbMATHGoogle Scholar
  6. 6.
    Cassandra, A.R., Kaelbling, L.P., Kurien, J.A.: Acting under uncertainty: discrete bayesian models for mobile-robot navigation. In: Proceedings of the 1996 IEEE/RSJ International Conference on Intelligent Robots and Systems 1996, IROS 1996, vol. 2, pp. 963–972 (1996)Google Scholar
  7. 7.
    Spaan, M.T.J., Vlassis, N.: A point-based pomdp algorithm for robot planning. In: Proceedings of the 2004 IEEE International Conference on Robotics and Automation, ICRA 2004, vol. 3, pp. 2399–2404 (2004)Google Scholar
  8. 8.
    Papadimitriou, C., Tsisiklis, J.N.: The complexity of markov decision processes. Mathematics of Operations Research 12(3), 441–450 (1987)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Kurniawati, H., Hsu, D., Lee, W.: Sarsop: efficient point-based pomdp planning by approximating optimally reachable belief spaces. In: Robotics: Science and Systems (2008)Google Scholar
  10. 10.
    Goldhoorn, A., Sanfeliu, A., Alquézar, R.: Comparison of momdp and heuristic methods to play hide-and-seek. Accepted for the Sixteenth International Conference of the Catalan Association of Artificial Intelligence (2013)Google Scholar
  11. 11.
    Trulls, E., Corominas Murtra, A., Pérez-Ibarz, J., Ferrer, G., Vasquez, D., Mirats-Tur, J., Sanfeliu, A.: Autonomous navigation for mobile service robots in urban pedestrian environments. Journal of Field Robotics (May 2010)Google Scholar
  12. 12.
    Sanfeliu, A., Andrade-Cetto, J., Barbosa, M., Bowden, R., Capitán, J., Corominas, A., Gilbert, A., Illingworth, J., Merino, L., Mirats, J.M., Moreno, P., Ollero, A., Sequeira, J.: Decentralized Sensor Fusion for Ubiquitous Networking Robotics in Urban Areas. Sensors 10(3), 2274–2314 (2010)CrossRefGoogle Scholar
  13. 13.
    Georgaraki, C.: A POMDP approach to the hide and seek game. Master’s thesis, Universitat Politècnica de Catalunya, Barcelona, Spain (2012)Google Scholar
  14. 14.
    Pineau, J., Gordon, G., Thrun, S.: Point-based value iteration: An anytime algorithm for pomdps. In: International Joint Conference on Artificial Intelligence, pp. 477–484 (2003)Google Scholar
  15. 15.
    Stockman, G., Shapiro, L.G.: Computer Vision, 1st edn. Prentice Hall, Upper Saddle River (2001)Google Scholar
  16. 16.
    Foka, A., Trahanias, P.: Real-time hierarchical POMDPs for autonomous robot navigation. Robotics and Autonomous Systems 55(7), 561–571 (2007)CrossRefGoogle Scholar
  17. 17.
    Ross, S., Pineau, J., Paquet, S., Chaib-Draa, B.: Online Planning Algorithms for POMDPs. The Journal of Artificial Intelligence Research 32(2), 663–704 (2008)MathSciNetzbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Alex Goldhoorn
    • 1
    Email author
  • Alberto Sanfeliu
    • 1
  • René Alquézar
    • 1
  1. 1.Institut de Robòtica i Informàtica IndustrialCSIC-UPCBarcelonaSpain

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