To Explore or to Exploit? Learning Humans’ Behaviour to Maximize Interactions with Them

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9991)


Assume a robot operating in a public space (e.g., a library, a museum) and serving visitors as a companion, a guide or an information stand. To do that, the robot has to interact with humans, which presumes that it actively searches for humans in order to interact with them. This paper addresses the problem how to plan robot’s actions in order to maximize the number of such interactions in the case human behavior is not known in advance. We formulate this problem as the exploration/exploitation problem and design several strategies for the robot. The main contribution of the paper than lies in evaluation and comparison of the designed strategies on two datasets. The evaluation shows interesting properties of the strategies, which are discussed.


Distant experimentation e-Learning Mobile robots Robot programming 



This work has been supported by the Technology Agency of the Czech Republic under the project no. TE01020197 “Centre for Applied Cybernetics” and by the EU ICT project 600623 ‘STRANDS’.


  1. 1.
    Amigoni, F., Caglioti, V.: An information-based exploration strategy for environment mapping with mobile robots. Robot. Auton. Syst. 58(5), 684–699 (2010)CrossRefGoogle Scholar
  2. 2.
    Basilico, N., Amigoni, F.: Exploration strategies based on multi-criteria decision making for an autonomous mobile robot. In: Proceedings of 4th European Conference on Mobile Robots, pp. 259–264. KoREMA (2009)Google Scholar
  3. 3.
    Basilico, N., Amigoni, F.: Exploration strategies based on multi-criteria decision making for searching environments in rescue operations. Auton. Robot. 31(4), 401–417 (2011)CrossRefGoogle Scholar
  4. 4.
    Cook, D.J.: Learning setting-generalized activity models for smart spaces. IEEE Intell. Syst. 2010(99), 1 (2010)Google Scholar
  5. 5.
    Gerling, K., Hebesberger, D., Dondrup, C., Körtner, T., Hanheide, M.: Robot deployment in long-term care. Zeitschrift für Gerontologie und Geriatrie 1–9 (2016).
  6. 6.
    Gonzalez-Banos, H.H., Latombe, J.C.: Navigation strategies for exploring indoor environments. Int. J. Robot. Res. 21(10–11), 829–848 (2002)CrossRefGoogle Scholar
  7. 7.
    Hebesberger, D., Dondrup, C., Koertner, T., Gisinger, C., Pripfl, J.: Lessons learned from the deployment of a long-term autonomous robot as companion in physical therapy for older adults with dementia: a mixed methods study. In: 11th ACM/IEEE International Conference on Human Robot Interaction, HRI 2016, pp. 27–34. IEEE Press, Piscataway (2016).
  8. 8.
    Hollinger, G., Djugash, J., Singh, S.: Coordinated search in cluttered environments using range from multiple robots. In: Laugier, C., Siegwart, R. (eds.) Field and Service Robotics. STAR, vol. 42, pp. 433–442. Springer, Berlin Heidelberg (2008)CrossRefGoogle Scholar
  9. 9.
    Koenig, S., Tovey, C., Halliburton, W.: Greedy mapping of terrain. In: Proceedings of IEEE International Conference on Robotics and Automation, vol. 4, pp. 3594–3599 (2001)Google Scholar
  10. 10.
    Koutsoupias, E., Papadimitriou, C., Yannakakis, M.: Searching a fixed graph. In: Meyer auf der Heide, F., Monien, B. (eds.) ICALP 1996. LNCS, vol. 1099, pp. 280–289. Springer, Heidelberg (1996). doi: 10.1007/3-540-61440-0_135 CrossRefGoogle Scholar
  11. 11.
    Krajník, T., Santos, J.M., Duckett, T.: Life-long spatio-temporal exploration of dynamic environments. In: 2015 European Conference on Mobile Robots (ECMR), pp. 1–8, September 2015Google Scholar
  12. 12.
    Krajník, T., Fentanes, J.P., Cielniak, G., Dondrup, C., Duckett, T.: Spectral analysis for long-term robotic mapping. In: 2014 IEEE International Conference on Robotics and Automation (ICRA) (2014)Google Scholar
  13. 13.
    Krajník, T., Fentanes, J.P., Mozos, O.M., Duckett, T., Ekekrantz, J., Hanheide, M.: Long-term topological localization for service robots in dynamic environments using spectral maps. In: International Conference on Intelligent Robots and Systems (IROS) (2014)Google Scholar
  14. 14.
    Krajník, T., Kulich, M., Mudrová, L., Ambrus, R., Duckett, T.: Where’s Waldo at time t? Using spatio-temporal models for mobile robot search. In: 2014 IEEE International Conference on Robotics and Automation (ICRA) (2015)Google Scholar
  15. 15.
    Kulich, M., Faigl, J., Přeučil, L.: On distance utility in the exploration task. In: 2011 IEEE International Conference on Robotics and Automation (ICRA), pp. 4455–4460, May 2011Google Scholar
  16. 16.
    Kulich, M., Přeučil, L., Miranda Bront, J.: Single robot search for a stationary object in an unknown environment. In: 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 5830–5835, May 2014Google Scholar
  17. 17.
    Kulich, M., Miranda-Bront, J.J., Přeučil, L.: A meta-heuristic based goal-selection strategy for mobile robot search in an unknown environment. Comput. Oper. Res. (2016). ISSN 0305-0548,
  18. 18.
    Makarenko, A.A., Williams, S.B., Bourgault, F., Durrant-Whyte, H.F.: An experiment in integrated exploration. In: IEEE/RSJ International Conference on Intelligent Robots and System, pp. 534–539. IEEE (2002)Google Scholar
  19. 19.
    Santos, J.M., Krajnik, T., Pulido Fentanes, J., Duckett, T.: Lifelong information-driven exploration to complete and refine 4D spatio-temporal maps. Robot. Autom. Lett. 1, 684–691 (2016)CrossRefGoogle Scholar
  20. 20.
    Sarmiento, A., Murrieta-Cid, R., Hutchinson, S.: A multi-robot strategy for rapidly searching a polygonal environment. In: Lemaître, C., Reyes, C.A., González, J.A. (eds.) IBERAMIA 2004. LNCS (LNAI), vol. 3315, pp. 484–493. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  21. 21.
    Tovar, B., Muñoz-Gómez, L., Murrieta-Cid, R., Alencastre-Miranda, M., Monroy, R., Hutchinson, S.: Planning exploration strategies for simultaneous localization and mapping. Robot. Auton. Syst. 54(4), 314–331 (2006)CrossRefGoogle Scholar
  22. 22.
    Tovey, C., Koenig, S.: Improved analysis of greedy mapping. In: 2003 Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, (IROS 2003), vols. 3 and 4, pp. 3251–3257, October 2003Google Scholar
  23. 23.
    Yamauchi, B.: A frontier-based approach for autonomous exploration. In: Proceedings of IEEE International Symposium on Computational Intelligence in Robotics and Automation, pp. 146–151. IEEE Computer Society Press (1997)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Czech Institute of Informatics, Robotics, and CyberneticsCzech Technical University in PraguePragueCzech Republic
  2. 2.Lincoln Centre for Autonomous SystemsUniversity of LincolnLincolnUK

Personalised recommendations