On Studying Human Teaching Behavior with Robots: a Review
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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 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).
- Avrahami, J., and Y. Kareev. 1990. Decomposition, Working paper no. 33.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
- 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).
- 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.
- 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.
- 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.CrossRefGoogle 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
- 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.CrossRefGoogle 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
- Mouret, J.B. 2016. Micro-Data Learning: The other end of the spectrum. ERCIM News 107.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
- 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
- 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
- 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.
- 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
- Thomaz, A., and C. Breazeal. 2006. Transparency and socially guided machine learning. 5th Intl Conf on Development and Learning (ICDL) 1.Google Scholar
- 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
- 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.Google Scholar
- 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.