Skip to main content
Log in

On Studying Human Teaching Behavior with Robots: a Review

  • Published:
Review of Philosophy and Psychology Aims and scope Submit manuscript

Abstract

Studying teaching behavior in controlled conditions is difficult. It seems intuitive that a human learner might have trouble reliably recreating response patterns over and over in interaction. A robot would be the perfect tool to study teaching behavior because its actions can be well controlled and described. However, due to the interactive nature of teaching, developing such a robot is not an easy task. As we will show in this review, respective studies require certain robot appearances and behaviors. These mainly should induce teaching behavior in humans, be interactive, match the study design, and be realizable in terms of effort. We discuss how remote controlling of the robot or simulating robot capabilities is used as an option. With this review, we introduce the field of research on studying human teaching behavior with robots as a tool in the experimental design. We will provide a structured overview of existing work, and identify main challenges of employing robots in such studies.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2

Similar content being viewed by others

Notes

  1. http://www.aliz-e.org

  2. http://www.l2tor.eu

  3. http://wyss.harvard.edu/viewpage/629

  4. https://flowers.inria.fr/research/kidlearn/

References

  • Argall, B.D., S. Chernova, M. Veloso, and B. Browning. 2009. A survey of robot learning from demonstration. Robotics and Autonomous Systems 57(5): 469–483. doi:10.1016/j.robot.2008.10.024.

    Article  Google Scholar 

  • Avrahami, J., and Y. Kareev. 1990. Decomposition, Working paper no. 33.

  • Avrahami, J., Y. Kareev, Y. Bogot, R. Caspi, S. Dunaevsky, and S. Lerner. 1997. Teaching by examples: Implications for the process of category acquisition. The Quarterly Journal of Experimental Psychology Section A 50(3): 586–606. doi:10.1080/713755719.

    Article  Google Scholar 

  • Baxter, P., J. Kennedy, E. Senft, S. Lemaignan, and T. Belpaeme. 2016. From characterising three years of HRI to methodology and reporting recommendations. In 2016 11th ACM/IEEE international conference on human-robot interaction (HRI), 391–398: IEEE. doi:10.1109/HRI.2016.7451777.

  • Becker-Asano, C., K. Ogawa, S. Nishio, and H. Ishiguro. 2010. Exploring the uncanny valley with Geminoid HI-1 in a real-world application. In IADIS International conference on interfaces and human computer interaction.

    Google Scholar 

  • Bengio, Y., J. Louradour, R. Collobert, and J. Weston. 2009. Curriculum learning. In Proceedings of the 26th annual international conference on machine learning - ICML ’09, ACM press, New York, USA, 1–8. doi:10.1145/1553374.1553380.

    Google Scholar 

  • Billard, A., S. Calinon, R. Dillmann, and S. Schaal. 2008. Robot Programming by Demonstration. In Springer handbook of robotics, eds. B. Siciliano, and O. Khatib, 1371–1394. Berlin, Heidelberg, Springer. doi:10.1007/978-3-540-30301-5_60.

    Chapter  Google Scholar 

  • Brand, R.J., and W.L. Shallcross. 2008. Infants prefer motionese to adult-directed action. Developmental Science 11(6): 853–861. doi:10.1111/j.1467-7687.2008.00734.x.

    Article  Google Scholar 

  • Brand, R.J., and S. Tapscott. 2007. Acoustic packaging of action sequences by infants. Infancy 11(3): 321–332. doi:10.1080/15250000701310413.

    Article  Google Scholar 

  • Brand, R.J., D.A. Baldwin, and L.A. Ashburn. 2002. Evidence for motionese: modifications in mothers’ infant-directed action. Developmental Science 5(1): 72–83. doi:10.1111/1467-7687.00211.

    Article  Google Scholar 

  • Cakmak, M., and A.L. Thomaz. 2010. Optimality of human teachers for robot learners. In 2010 IEEE 9th International conference on development and learning, ICDL-2010 - conference program, 64–69. doi:10.1109/DEVLRN.2010.5578865.

    Google Scholar 

  • Cakmak, M., and A.L. Thomaz. 2012. Designing robot learners that ask good questions. In Proceedings of the 7th annual ACM/IEEE international conference on human-robot interaction, 17–24. doi:10.1145/2157689.2157693.

    Google Scholar 

  • De Jaegher, H., E. Di Paolo, and S. Gallagher. 2010. Can social interaction constitute social cognition? Trends in Cognitive Sciences 14(10): 441–447. doi:10.1016/j.tics.2010.06.009.

    Article  Google Scholar 

  • Fischer, K., K.S. Lohan, and K. Foth. 2012. Levels of embodiment: Linguistic analyses of factors influencing HRI. In Proceedings of the 7th annual ACM/IEEE international conference on human-robot interaction - HRI ’12, ACM press, New York, USA, 463. doi:10.1145/2157689.2157839.

    Google Scholar 

  • Fischer, K., K. Lohan, J. Saunders, C. Nehaniv, B. Wrede, and K. Rohlfing. 2013. The impact of the contingency of robot feedback on HRI. In 2013 International Conference on Collaboration Technologies and Systems (CTS), 210–217: IEEE, doi:10.1109/CTS.2013.6567231, (to appear in print).

  • Goodrich, M.A., and A.C. Schultz. 2007. Human-robot interaction: a survey. Foundations and Trends®, in Human-Computer Interaction 1(3): 203–275. doi:10.1561/1100000005.

    Article  Google Scholar 

  • de Greeff, J., and T. Belpaeme. 2015. Why robots should be social: Enhancing machine learning through social Human-Robot interaction. PloS one 10(9): e0138,061. doi:10.1371/journal.pone.0138061, doi:10.1371/journal.pone.0138061.g002.

    Article  Google Scholar 

  • Hegel, F., M. Lohse, and B. Wrede. 2009. Effects of visual appearance on the attribution of applications in social robotics. In RO-MAN 2009 - The 18th IEEE International symposium on robot and human interactive communication, 64–71: IEEE. doi:10.1109/ROMAN.2009.5326340.

  • Herberg, J.S., M.M. Saylor, P. Ratanaswasd, D.T. Levin, and D.M. Wilkes. 2008. Audience-contingent variation in action demonstrations for humans and computers. Cognitive Science 32(6): 1003–1020.

    Article  Google Scholar 

  • Kaochar, T., R.T. Peralta, C.T. Morrison, I.R. Fasel, T.J. Walsh, and P.R. Cohen. 2011. Towards understanding how humans teach robots. In Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics) LNCS, (Vol. 6787(1), 347–352). doi:10.1007/978-3-642-22362-4_31.

  • Kemler Nelson, D.G., K. Hirsh-Pasek, P.W. Jusczyk, and K.W. Cassidy. 1989. How the prosodic cues in motherese might assist language learning. Journal of child language 16(1): 55–68.

    Article  Google Scholar 

  • Khamassi, M., S. Lallée, P. Enel, E. Procyk, and P.F. Dominey. 2011. Robot cognitive control with a neurophysiologically inspired reinforcement learning model. Frontiers in Neurorobotics 5: 1. doi:10.3389/fnbot.2011.00001.

    Article  Google Scholar 

  • Khan, F., X. Zhu, and B. Mutlu. 2011. How do humans teach: on curriculum learning and teaching dimension. Nips pp 1–9. https://commons.wikimedia.org/wiki/File:Wakamaru-fullshot2011.jpg, https://creativecommons.org/licenses/by-sa/3.0/legalcode.

  • Kim, E.S., E.S. Kim, D. Leyzberg, D. Leyzberg, B. Scassellati, and B. Scassellati. 2009. How people talk when teaching a robot. In Proceedings of the 4th ACM/IEEE international conference on human robot interaction - HRI ’09, 23. doi:10.1145/1514095.1514102, https://commons.wikimedia.org/wiki/File:Pleo_2.jpg, https://creativecommons.org/licenses/by-sa/2.0/de/legalcode.

    Chapter  Google Scholar 

  • Knox, W.B., B.D. Glass, B.C. Love, W.T. Maddox, and P. Stone. 2012. How humans teach agents: a new experimental perspective. International Journal of Social Robotics 4(4): 409–421. doi:10.1007/s12369-012-0163-x.

    Article  Google Scholar 

  • Krämer, N.C., A.v.d. Pütten, and S. Eimler. 2012. Human-Agent and Human-Robot Interaction Theory: Similarities to and Differences from Human-Human Interaction. In Human-computer interaction: The agency perspective, studies in computational intelligence, Vol. 396, eds. M. Zacarias, and J.V. de Oliveira. Berlin, Heidelberg: Springer. doi:10.1007/978-3-642-25691-2.

    Google Scholar 

  • Lohan, K., K. Rohlfing, S. Gieselmann, A.L. Vollmer, and B. Wrede. 2010. Does embodiment affect tutoring behavior?. In 9th International conference on development and learning, 214668. https://commons.wikimedia.org/wiki/File:ICub_Innorobo_Lyon_2014.JPG, https://creativecommons.org/licenses/by-sa/3.0/legalcode.

    Google Scholar 

  • Lohse, M., B. Wrede, and L. Schillingmann. 2013. Enabling robots to make use of the structure of human actions - a user study employing Acoustic Packaging. In 22Nd IEEE international symposium on robot and human interactive communication (IEEE RO-MAN 2013).

    Google Scholar 

  • Lopes, M., F. Melo, and L. Montesano. 2009. Active Learning for Reward Estimation in Inverse Reinforcement Learning. In Machine learning and knowledge discovery in databases, (Vol. 177, 31–46). Berlin Heidelberg: Springer-Verlag. doi:10.1007/978-3-642-04174-7_3.

    Chapter  Google Scholar 

  • Lütkebohle, I., J. Peltason, L. Schillingmann, B. Wrede, S. Wachsmuth, C. Elbrechter, and R. Haschke. 2009. The curious robot-structuring interactive robot learning. In International conference on robotics and automations, 2154–2160.

    Google Scholar 

  • Lütkebohle, I., J. Peltason, L. Schillingmann, C. Elbrechter, S. Wachsmuth, B. Wrede, and R. Haschke. 2012. A Mixed-Initiative Approach to Interactive Robot Tutoring. In Springer tracts in advanced robotics, (Vol. 76, 483–502). Springer. doi:10.1007/978-3-642-25116-0_34.

  • McCandliss, B.D., J.A. Fiez, A. Protopapas, M. Conway, and J.L. McClelland. 2002. Success and failure in teaching the [r]-[l] contrast to Japanese adults: tests of a Hebbian model of plasticity and stabilization in spoken language perception. Cognitive, affective &, behavioral neuroscience 2(2): 89–108.

    Article  Google Scholar 

  • Moulin-Frier, C., and P.-Y. Oudeyer. 2013. Exploration strategies in developmental robotics: A unified probabilistic framework. In 2013 IEEE Third Joint International Conference on Development and Learning and Epigenetic Robotics (ICDL), 1–6: IEEE. doi:10.1109/DevLrn.2013.6652535, https://flowers.inria.fr/CMF_PYO_ICDL2013.pdf, http://ieeexplore.ieee.org/document/6652535/.

  • Mori, M., and K. MacDorman. 1970. The uncanny valley. Energy 7(4): 33–35.

    Google Scholar 

  • Mori, M., K. MacDorman, and N. Kageki. 2012. The uncanny valley [From the field]. IEEE Robotics &, Automation Magazine 19(2): 98–100. doi:10.1109/MRA.2012.2192811.

    Article  Google Scholar 

  • Mouret, J.B. 2016. Micro-Data Learning: The other end of the spectrum. ERCIM News 107.

  • Muhl, C., and Y. Nagai. 2007. Does Disturbance Discourage People from Communicating with a Robot?. In The 16th IEEE International symposium on robot and human interactive communication, 2007. RO-MAN 2007, 1137–1142. doi:10.1109/ROMAN.2007.4415251.

    Chapter  Google Scholar 

  • Nagai, Y., C. Muhl, and K. Rohlfing. 2008. Toward designing a robot that learns actions from parental demonstrations. In 2008. ICRA 2008. IEEE International conference on robotics and automation, 3545–3550. doi:10.1109/ROBOT.2008.4543753.

  • Nagai, Y., A. Nakatani, and M. Asada. 2010. How a robot’s attention shapes the way people teach. In Proceedings of the 10th international conference on epigenetic robotics, november, 81–88.

    Google Scholar 

  • Nishio, S., H. Ishiguro, and N. Hagit. 2007. Geminoid: Teleoperated android of an existing person. In Humanoid robots: new developments, I-Tech education and publishing. doi:10.5772/4876.

    Google Scholar 

  • Oudeyer, P.Y., and F. Kaplan. 2004. Intelligent Adaptive Curiosity: a source of Self-Development. In: Berthouze, L., Kozima, H., Prince, C.G., Sandini, G., Stojanov, G., Metta, G., Balkenius, C. (eds). Proceedings of the fourth international workshop on epigenetic robotics, lund university cognitive studies. Vol 117, 127–130.

  • Oudeyer, P.Y., F. Kaplan, and V.V. Hafner. 2007. Intrinsic motivation systems for autonomous mental development. IEEE Transactions on Evolutionary Computation 11(2): 265–286. doi:10.1109/TEVC.2006.890271.

    Article  Google Scholar 

  • Pitsch, K., K.S. Lohan, K. Rohlfing, J. Saunders, C.L. Nehaniv, and B. Wrede. 2012. Better be reactive at the beginning. Implications of the first seconds of an encounter for the tutoring style in human-robot-interaction. In Proceedings - IEEE International workshop on robot and human interactive communication, 974–981. doi:10.1109/ROMAN.2012.6343876, https://commons.wikimedia.org/wiki/File:ICub_Innorobo_Lyon_2014.JPG, https://creativecommons.org/licenses/by-sa/3.0/legalcode.

    Google Scholar 

  • Pitsch, K., A.L. Vollmer, and M. Mühlig. 2013. Robot feedback shapes the tutor’s presentation: How a robot’s online gaze strategies lead to micro-adaptation of the human’s conduct. Interaction Studies 14(2): 268–296. doi:10.1075/is.14.2.06pit.

    Article  Google Scholar 

  • Pitsch, K., A.L. Vollmer, K.J. Rohlfing, J. Fritsch, and B. Wrede. 2014. Tutoring in adult-child interaction: on the loop of the tutor’s action modification and the recipient’s gaze. Interaction Studies 15(1): 55–98. doi:10.1075/is.15.1.03pit.

    Article  Google Scholar 

  • Rohlfing, K.J., J. Fritsch, B. Wrede, and T. Jungmann. 2006. How can multimodal cues from child-directed interaction reduce learning complexity in robots? Advanced Robotics 20(10): 1183–1199. doi:10.1163/156855306778522532.

    Article  Google Scholar 

  • Rosenthal, S., A.K. Dey, and M. Veloso. 2009. How robots’ questions affect the accuracy of the human responses. In Proceedings - IEEE International workshop on robot and human interactive communication, 1137–1142, doi:10.1109/ROMAN.2009.5326291, (to appear in print).

  • Sacks, H., E.a. Schegloff, and G. Jefferson. 1974. A simplest systematics for the organization of turn taking for conversation. doi:10.2307/412243.

  • Schaal, S. 1999. Is Imitation Learning the Route to Humanoid Robots? Trends in Cognitive Sciences 3(6): 233–242.

    Article  Google Scholar 

  • Schillingmann, L., B. Wrede, and K.J. Rohlfing. 2009. A computational model of acoustic packaging. IEEE Transactions on Autonomous Mental Development 1(4): 226–237. doi:10.1109/TAMD.2009.2039135.

    Article  Google Scholar 

  • Strauss, S., and M. Ziv. 2012. Teaching is a natural cognitive ability for humans. Mind, Brain, and Education 6(4): 186–196. doi:10.1111/j.1751-228X.2012.01156.x.

    Article  Google Scholar 

  • Tapus, A., A. Peca, A. Aly, C. Pop, L. Jisa, S. Pintea, A.S. Rusu, and D.O. David. 2012. Children with autism social engagement in interaction with Nao, an imitative robot – A series of single case experiments. Interaction Studies 13(Charman 1997): 315–347. doi:10.1075/is.13.3.01tap.

    Article  Google Scholar 

  • Thomaz, A., and C. Breazeal. 2006. Transparency and socially guided machine learning. 5th Intl Conf on Development and Learning (ICDL) 1.

  • Thomaz, A.L., and C. Breazeal. 2008. Teachable robots: Understanding human teaching behavior to build more effective robot learners. Artificial Intelligence 172 (6–7): 716–737. doi:10.1016/j.artint.2007.09.009, http://robotic.media.mit.edu/portfolio/sophies-kitchen/.

    Article  Google Scholar 

  • Thomaz, A.L., and M. Cakmak. 2009. Learning about objects with human teachers. In Proceedings of the 4th ACM/IEEE international conference on human robot interaction - HRI ’09, 15. doi:10.1145/1514095.1514101.

    Chapter  Google Scholar 

  • Vollmer, A.L., K.S. Lohan, K. Fischer, Y. Nagai, K. Pitsch, J. Fritsch, K.J. Rohlfing, and B. Wrede. 2009a. People modify their tutoring behavior in Robot-Directed interaction for action learning. In International conference on development and learning, IEEE computer society, Shanghai, China.

    Google Scholar 

  • Vollmer, A.L., K.S. Lohan, J. Fritsch, K. Rohlfing, and B. Wrede. 2009b. Which motionese parameters change with children’s age?. In Paper presented at the Cognitive development society’s biennial meeting. San Antonia, Texas.

    Google Scholar 

  • Vollmer, A.L., K. Pitsch, K. Lohan, J. Fritsch, K. Rohlfing, and B. Wrede. 2010. Developing Feedback: How children of different age contribute to a tutoring interaction with adults. In IEEE 9th international conference on development and learning, cor-lab., bielefeld univ., bielefeld, 76–81. Germany: IEEE. doi:10.1109/DEVLRN.2010.5578863.

    Google Scholar 

  • Vollmer, A.L., M. Mühlig, K. Rohlfing, B. Wrede, and A. Cangelosi. 2013a. Demonstrating actions to a robot: How naïve users correct a robot’s replication of goal and manner-oriented actions. In The 17th workshop on the semantics and pragmatics of dialogue (DialDam), University of Amsterdam.

    Google Scholar 

  • Vollmer, A.L., B. Wrede, K.J. Rohlfing, and A. Cangelosi. 2013b. Do beliefs about a robot’s capabilities influence alignment to its actions?. In 2013 IEEE 3rd joint international conference on development and learning and epigenetic robotics, ICDL 2013 - Electronic Conference Proceedings. doi:10.1109/DevLrn.2013.6652521.

    Google Scholar 

  • Vollmer, A.L., M. Mühlig, J.J. Steil, K. Pitsch, J. Fritsch, K.J. Rohlfing, and B. Wrede. 2014. Robots show us how to teach them: Feedback from robots shapes tutoring behavior during action learning. PLoS ONE 9(3) doi:10.1371/journal.pone.0091349, https://commons.wikimedia.org/wiki/File:ASIMO_4.28.11.jpg, https://creativecommons.org/licenses/by-sa/3.0/legalcode.

  • Vollmer, A.L., B. Wrede, K.J. Rohlfing, and Py Oudeyer. 2016. Pragmatic frames for teaching and learning in human-robot interaction: review and challenges. Frontiers in neurorobotics, submitted.

  • Yu, C., M. Scheutz, and P. Schermerhorn. 2010. Investigating multimodal real-time patterns of joint attention in an HRI word learning task. In 2010 5th ACM/IEEE international conference on human-robot interaction (HRI), 309–316: IEEE. doi:10.1109/HRI.2010.5453181.

  • Zodik, I., and O. Zaslavsky. 2008. Characteristics of teachers’ choice of examples in and for the mathematics classroom. Educational Studies in Mathematics 69(2): 165–182. doi:10.1007/s10649-008-9140-6.

    Article  Google Scholar 

Download references

Acknowledgments

This research/work was supported by the Cluster of Excellence Cognitive Interaction Technology ‘CITEC’ (EXC 277) at Bielefeld University, which is funded by the German Research Foundation (DFG).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Anna-Lisa Vollmer.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Vollmer, AL., Schillingmann, L. On Studying Human Teaching Behavior with Robots: a Review. Rev.Phil.Psych. 9, 863–903 (2018). https://doi.org/10.1007/s13164-017-0353-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13164-017-0353-4

Navigation