Skip to main content

Advertisement

SpringerLink
Log in

The More the Merrier? Effects of Humanlike Learning Abilities on Humans’ Perception and Evaluation of a Robot

  • Published:
International Journal of Social Robotics Aims and scope Submit manuscript

Abstract

In this paper, we present three experimental studies in which subjects trained a robot to do a card game via reinforcement learning. In the first two studies participants interacted with the robot either without any learning ability (control group) or with one out of three versions of a learning algorithm implementing gradually aspects of more humanlike learning abilities. Results show that the implementation of a learning algorithm had positive effects regarding the evaluation of the robot, its learning abilities and the interaction. We found that more humanlike learning abilities do not always lead to better performance and evaluation and that results were to some extend influenced by longer or shorter interaction times. In a third study, we additionally explored the influence of other behavioral variations such as low or high verbal skills and interaction modalities in perceived intelligence of the robot irrespective of the implemented learning algorithm, but did not find significant effects. Results are discussed with regard to the socialness of future interaction scenarios.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Schaal S (1999) Is imitation learning the route to humanoid robots? Trends Cogn Sci 3:233–242

    Article  Google Scholar 

  2. Nicolescu MN, Mataric MJ (2003) Natural methods for robot task learning: instructive demonstrations, generalization and practice. In: Proceedings of the second international joint conference on autonomous agents and multiagent systems. ACM, pp 241–248

  3. Argall BD, Chernova S, Veloso M, Browning B (2009) A survey of robot learning from demonstration. Robot Auton Syst 57(5):469–483

    Article  Google Scholar 

  4. Billard A, Calinon S, Dillmann R, Schaal S (2008) Robot programming by demonstration. In: Siciliano B, Khatib O (eds) Springer handbook of robotics. Springer, Berlin, pp 1371–1394

  5. Clouse JA, Utgoff PE (1992) A teaching method for reinforcement learning. In: Proceedings of the ninth international conference on machine learning (ICML92), pp 92–101

  6. Thomaz AL, Breazeal C (2006) Reinforcement learning with human teachers: evidence of feedback and guidance with implications for learning performance. AAAI 6:1000–1005

    Google Scholar 

  7. Hoefinghoff J, Rosenthal-von der Pütten A, Pauli J, Krämer N (2015) You and your robot companion—a framework for creating robotic applications usable by non-experts. In: Proceedings of the 24th IEEE international symposium on robot and human interactive communication, pp 615–620

  8. Meltzoff AN (1988) Infant imitation and memory: nine-month-olds in immediate and deferred tests. Child Dev 59:217–225

    Article  Google Scholar 

  9. Minsky M (1975) A framework for representing knowledge. In: Winston PH (ed) Psychol Comput Vis. McGraw-Hill, New York, pp 211–277

    Google Scholar 

  10. Dube WV, McIlvane WJ, Maguire RW, Mackay HA, Stoddard LT (1989) Stimulus class formation and stimulusreinforcer relations. J Exp Anal Behav 51:65–76

    Article  Google Scholar 

  11. Sloutsky VM (2003) The role of similarity in the development of categorization. Trends Cogn Sci 7:246–251

    Article  Google Scholar 

  12. Calinon S, Guenter F, Billard A (2007) On learning, representing, and generalizing a task in a humanoid robot. IEEE Trans Syst Man Cybern B Cybern 37:286–298

    Article  Google Scholar 

  13. Hester T, Quinlan M, Stone P (2010) Generalized model learning for reinforcement learning on a humanoid robot. In: IEEE International conference on robotics and automation (ICRA), pp 2369–2374

  14. Hoefinghoff J, Rosenthal-von der Pütten AM, Pauli J, Krämer N (2015) “Yes dear, that belongs into the shelf!”—exploratory studies with elderly people who learn to train an adaptive robot companion. In: Proceedings of the international conference of social robotics, pp 235–244

  15. Sutton RS, Barto AG (1998) Reinforcement learning: an introduction. MIT Press, Cambridge

    Google Scholar 

  16. Tapus A, Mataric MJ (2008) Socially assistive robots: the link between personality, empathy, physiological signals, and task performance. In: AAAI spring symposium: emotion, personality, and social behavior, pp 133–140

  17. Tapus A, Tapus C, Mataric MJ (2008) User robot personality matching and assistive robot behavior adaptation for post-stroke rehabilitation therapy. Intell Serv Robot 1:169–183

    Article  Google Scholar 

  18. Kober J, Bagnell JA, Peters J (2013) Reinforcement learning in robotics: a survey. Int J Robot Res. https://doi.org/10.1177/0278364913495721

  19. Hoefinghoff J, Pauli J (2012) Decision making based on somatic markers. In: Proceedings of the 25th international Florida Artificial Intelligence Research Society conference. AAAI Press, pp 163–168

  20. Hoefinghoff J (2015) A decision making framework for robot companion systems usable by non-experts, PhD dissertation, Lehrstuhl Intelligente Systeme, Universität Duisburg-Essen, Duisburg

  21. Damasio A (1994) Descartes error: emotion, reason, and the human brain. Putnam Press, Mahopac

    Google Scholar 

  22. O’Doherty JP (2004) Reward representations and reward-related learning in the human brain: insights from neuroimaging. Curr Opin Neurobiol 14:769–776

    Article  Google Scholar 

  23. Wunderlich K, Rangel A, O’Doherty JP (2009) Neural computations underlying action-based decision making in the human brain. In: Proceedings of the National Academy of Sciences, vol 106, pp 17 199–17 204

  24. Zeelenberg M, Beattie J, Van der Pligt J, de Vries NK (1996) Consequences of regret aversion: effects of expected feedback on risky decision making. Organ Behav Hum Decis Process 65:148–158

    Article  Google Scholar 

  25. Blais AR, Weber EU (2006) A domain-specific risk-taking (DOSPERT) scale for adult populations. Judgm Decis Mak 1:33–47

    Google Scholar 

Download references

Acknowledgements

We thank Prof. Josef Pauli for his support and advice and for the Nao he kindly provided us with. Moreover, we would like to thank Christina Müller, Patrick Pecak and Svea Delsing for their help in data acquisition.

Author information

Authors and Affiliations

  1. University of Duisburg-Essen, Forsthausweg 2, 47048, Duisburg, Germany

    Astrid M. Rosenthal-von der Pütten & Jens Hoefinghoff

Authors
  1. Astrid M. Rosenthal-von der Pütten
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jens Hoefinghoff
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Astrid M. Rosenthal-von der Pütten.

Ethics declarations

Ethical Standard

APA ethical standards were followed in the conduct of the study.

Conflict of interest

The authors do not have any interests that might be interpreted as influencing the research. The authors declare that they have no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rosenthal-von der Pütten, A.M., Hoefinghoff, J. The More the Merrier? Effects of Humanlike Learning Abilities on Humans’ Perception and Evaluation of a Robot. Int J of Soc Robotics 10, 455–472 (2018). https://doi.org/10.1007/s12369-017-0445-4

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12369-017-0445-4

Keywords

Search

Navigation