On Studying Human Teaching Behavior with Robots: a Review

Article
  • 52 Downloads

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.

Notes

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).

References

  1. 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.CrossRefGoogle Scholar
  2. Avrahami, J., and Y. Kareev. 1990. Decomposition, Working paper no. 33.Google Scholar
  3. 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.CrossRefGoogle Scholar
  4. 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.
  5. 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
  6. 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
  7. 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.CrossRefGoogle Scholar
  8. 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.CrossRefGoogle Scholar
  9. Brand, R.J., and S. Tapscott. 2007. Acoustic packaging of action sequences by infants. Infancy 11(3): 321–332. doi:10.1080/15250000701310413.CrossRefGoogle Scholar
  10. 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.CrossRefGoogle Scholar
  11. 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
  12. 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
  13. 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.CrossRefGoogle Scholar
  14. 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
  15. 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).
  16. 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.CrossRefGoogle Scholar
  17. 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.CrossRefGoogle Scholar
  18. 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.
  19. 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.CrossRefGoogle Scholar
  20. 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.
  21. 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.CrossRefGoogle Scholar
  22. 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.CrossRefGoogle Scholar
  23. 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.
  24. 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.CrossRefGoogle Scholar
  25. 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.CrossRefGoogle Scholar
  26. 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
  27. 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
  28. 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
  29. 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.CrossRefGoogle Scholar
  30. 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
  31. 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.
  32. 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.CrossRefGoogle Scholar
  33. 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/.
  34. Mori, M., and K. MacDorman. 1970. The uncanny valley. Energy 7(4): 33–35.Google Scholar
  35. 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.CrossRefGoogle Scholar
  36. Mouret, J.B. 2016. Micro-Data Learning: The other end of the spectrum. ERCIM News 107.Google Scholar
  37. 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.CrossRefGoogle Scholar
  38. 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.
  39. 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
  40. 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
  41. 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.Google Scholar
  42. 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.CrossRefGoogle Scholar
  43. 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
  44. 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.CrossRefGoogle Scholar
  45. 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.CrossRefGoogle Scholar
  46. 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.CrossRefGoogle Scholar
  47. 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).
  48. Sacks, H., E.a. Schegloff, and G. Jefferson. 1974. A simplest systematics for the organization of turn taking for conversation. doi:10.2307/412243.
  49. Schaal, S. 1999. Is Imitation Learning the Route to Humanoid Robots? Trends in Cognitive Sciences 3(6): 233–242.CrossRefGoogle Scholar
  50. 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.CrossRefGoogle Scholar
  51. 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.CrossRefGoogle Scholar
  52. 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.CrossRefGoogle Scholar
  53. Thomaz, A., and C. Breazeal. 2006. Transparency and socially guided machine learning. 5th Intl Conf on Development and Learning (ICDL) 1.Google Scholar
  54. 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/.CrossRefGoogle Scholar
  55. 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.CrossRefGoogle Scholar
  56. 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
  57. 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
  58. 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
  59. 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
  60. 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
  61. 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.
  62. 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.Google Scholar
  63. 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.
  64. 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.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2017

Authors and Affiliations

  1. 1.CITEC, Bielefeld UniversityBielefeldGermany

Personalised recommendations