Cover not available
Article published In:
Interaction Studies
Vol. 22:2 (2021) ► pp.244–279

Why robots should be technical

Correcting mental models through technical architecture concepts

| CoR-Lab Bielefeld University
| CoR-Lab Bielefeld University
| Honda Research Institute Europe
| Medical Assistance Systems Bielefeld University
| Medical Assistance Systems Bielefeld University
Research in social robotics is commonly focused on designing robots that imitate human behavior. While this might increase a user’s satisfaction and acceptance of robots at first glance, it does not automatically aid a non-expert user in naturally interacting with robots, and might hurt their ability to correctly anticipate a robot’s capabilities. We argue that a faulty mental model, that the user has of the robot, is one of the main sources of confusion. In this work, we investigate how communicating technical concepts of robotic systems to users affect their mental models, and how this can increase the quality of human-robot interaction. We conducted an online study and investigated possible ways of improving users’ mental models. Our results underline that communicating technical concepts can form an improved mental model. Consequently, we show the importance of consciously designing robots that express their capabilities and limitations.
Keywords: human-robot interaction, mental models, interaction design, robot architecture, technical concepts, robot transparency
Article outline
  • 1.Introduction
  • 2.Related work
    • 2.1Dual nature of computational artifacts: Relevance and architecture
    • 2.2Relation to human interactive learning and pragmatic frames
    • 2.3Communicating technical concepts
      • 2.3.1Instructions
      • 2.3.2Feedback
  • 3.Hypotheses
  • 4.Methods
    • 4.1Scenario
    • 4.2System and concepts
      • 4.2.1Robot
        • Robotic platform
        • System
      • 4.2.2Concepts
        • Object recognition
        • Speech recognition
        • State machine
    • 4.3Experimental design
      • 4.3.1Architecture instruction video
      • 4.3.2Robot visualization
        • Marker detection for object identification
        • Verbal communication
        • Finite state machines for robot control
      • 4.3.3Course of online study
      • 4.3.4Human-robot interaction videos
        • Object detection error
        • Speech recognition error
        • State machine error
  • 5.Results
    • 5.1Hypothesis 1: Providing architectural concepts allows users to gain more knowledge about the functionality of a robot
      • Which components does the robot have that allow it to observe or interact with its environment? (hardware)
      • What skills and abilities does the robot have? (software)
    • 5.2Hypothesis 2: Insights into the architecture of a robot increases the ability to recognize and explain errors in human-robot interaction
    • 5.3Hypothesis 3: Technical concepts differ in terms of their familiarity and observability. These factors influence the user’s ability to recognize and understand problems in human-robot interactions
    • 5.4ATI, godspeed and system-usability-scale
  • 6.Discussion
  • 7.Conclusion
  • 8.Future work
  • Notes
  • References
Published online: 28 February 2022
DOI logo
https://doi.org/10.1075/is.20023.hin
References (44)
References
Bangor, A., Kortum, P. T., & Miller, J. T. (2008). An empirical evaluation of the system usability scale. Intl. Journal of Human-Computer Interaction, 24 (6), 574–594. DOI logoGoogle Scholar
Bartneck, C., Kulié, D., Croft, E., & Zoghbi, S. (2009). Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots. International journal of social robotics, 1 (1), 71–81. DOI logoGoogle Scholar
Beller, W. E., & Wang, Y. P. (1997). Bar code dataform scanning and labeling apparatus and method [US Patent 5,602,377].Google Scholar
Brand, R. J., Baldwin, D. A., & Ashburn, L. A. (2002). Evidence for ‘motionese’: Modifications in mothers’ infant-directed action. Developmental Science, 5 (1), 72–83. DOI logoGoogle Scholar
Breazeal, C., Dautenhahn, K., & Kanda, T. (2016). Social robotics. Springer handbook of robotics (pp. 1935–1972). Springer. DOI logoGoogle Scholar
Breazeal, C., Kidd, C., Thomaz, A., Hoffman, G., & Berlin, M. (2005). Effects of nonverbal communication on efficiency and robustness in human-robot teamwork. IEEE/RSJ International Conference on Intelligent Robots and Systems, 708–713. DOI logoGoogle Scholar
Breslow, N. (1970). A generalized kruskal-wallis test for comparing k samples subject to unequal patterns of censorship. Biometrika, 57 (3), 579–594. DOI logoGoogle Scholar
Bruner, J. (1985). Child’s talk: Learning to use language. Child Language Teaching and Therapy, 1 (1), 111–114. DOI logoGoogle Scholar
Cakmak, M., & Takayama, L. (2014). Teaching people how to teach robots: The effect of instructional materials and dialog design. Proceedings of the 2014 ACM/IEEE international conference on Human-robot interaction, 431–438. DOI logoGoogle Scholar
Clement, J. (2020). Most popular mobile messaging apps worldwide as of october 2019, based on number of monthly active users [Retrieved: 2020-06-09, from [URL]].
de Greeff, J., & Belpaeme, T. (2015). Why robots should be social: Enhancing machine learning through social human-robot interaction. PLOS ONE, 10 (9), 1–26. DOI logoGoogle Scholar
Duffy, B. R. (2006). Fundamental issues in social robotics. International Review of Information Ethics, 6 (12), 2006. DOI logoGoogle Scholar
Dunn, O. J. (1964). Multiple comparisons using rank sums. Technometrics, 6 (3), 241–252. DOI logoGoogle Scholar
Franke, T., Attig, C., & Wessel, D. (2019a). A personal resource for technology interaction: Development and validation of the affinity for technology interaction (ati) scale. International Journal of Human-Computer Interaction, 35 (6), 456–467. DOI logoGoogle Scholar
(2019b). A personal resource for technology interaction: Development and validation of the affinity for technology interaction (ati) scale. International Journal of Human-Computer Interaction, 35 (6), 456–467. DOI logoGoogle Scholar
Garrido-Jurado, S., Muñoz-Salinas, R., Madrid-Cuevas, F. J., & Marién-Jiménez, M. J. (2014). Automatic generation and detection of highly reliable fiducial markers under occlusion. Pattern Recognition, 47 (6), 2280–2292. DOI logoGoogle Scholar
Hamacher, A., Bianchi-Berthouze, N., Pipe, A. G., & Eder, K. (2016). Believing in bert: Using expressive communication to enhance trust and counteract operational error in physical human-robot interaction. 2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), 493–500. DOI logoGoogle Scholar
Hassenzahl, M., Borchers, J., Boll, S., Pütten, A. R.-V. D., & Wulf, V. (2020). Otherware: How to best interact with autonomous systems. Interactions, 28 (1), 54–57. DOI logoGoogle Scholar
Hegel, F., Gieselmann, S., Peters, A., Holthaus, P., & Wrede, B. (2011). Towards a typology of meaningful signals and cues in social robotics. 2011 RO-MAN, 72–78. DOI logoGoogle Scholar
Hindemith, L., Vollmer, A.-L., Wrede, B., & Joublin, F. (2019). Pragmatic frames as an approach to reduce misinterpretations in human-robot-interaction. Proc. Int. Conf. on Development and Learning (ICDL-EPIROB).Google Scholar
Kaptein, F., Broekens, J., Hindriks, K., & Neerincx, M. (2017). Personalised self-explanation by robots: The role of goals versus beliefs in robot-action explanation for children and adults. 2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), 676–682. DOI logoGoogle Scholar
Kwon, M., Huang, S. H., & Dragan, A. D. (2018). Expressing robot incapability. Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, 87–95. DOI logoGoogle Scholar
Liu, S. (2020). Global market share held by operating systems for desktop pcs, from january 2013 to january 2020 [Retrieved: 2020-06-09, from [URL]].
McCracken, D. D., & Reilly, E. D. (2003). Backus-naur form (bnf). Encyclopedia of computer science (pp. 129–131). John Wiley; Sons Ltd.Google Scholar
Nelson, D. G. K., Hirsh-Pasek, K., Jusczyk, P. W., & Cassidy, K. W. (1989). How the prosodic cues in motherese might assist language learning. Journal of child Language, 16 (1), 55–68. DOI logoGoogle Scholar
Otero, N., Alissandrakis, A., Dautenhahn, K., Nehaniv, C., Syrdal, D. S., & Koay, K. L. (2008). Human to robot demonstrations of routine home tasks: Exploring the role of the robot’s feedback. 2008 3rd ACM/IEEE International Conference on Human-Robot Interaction (HRI), 177–184. DOI logoGoogle Scholar
Pitsch, K., Vollmer, A.-L., Rohlfing, K. J., Fritsch, J., & Wrede, B. (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 logoGoogle Scholar
Rahwan, I., Cebrian, M., Obradovich, N., Bongard, J., Bonnefon, J.-F., Breazeal, C., Crandall, J. W., Christakis, N. A., Couzin, I. D., Jackson, M. O., et al. (2019). Machine behaviour. Nature, 568 (7753), 477–486. DOI logoGoogle Scholar
Rohlfing, K. J., Wrede, B., Vollmer, A.-L., & Oudeyer, P.-Y. (2016). An alternative to mapping a word onto a concept in language acquisition: Pragmatic frames. Frontiers in psychology, 7 1, 470. DOI logoGoogle Scholar
Saunders, J., Syrdal, D. S., Koay, K. L., Burke, N., & Dautenhahn, K. (2015). “teach me-show me” – end-user personalization of a smart home and companion robot. IEEE Transactions on Human-Machine Systems, 46 (1), 27–40. DOI logoGoogle Scholar
Schillinger, P., Kohlbrecher, S., & von Stryk, O. (2016). Human-robot collaborative high-level control with application to rescue robotics. Proc. IEEE Int. Conf. on Robotics and Automation (ICRA), 2796–2802. DOI logoGoogle Scholar
Schulte, C., & Budde, L. (2018). A framework for computing education: Hybrid interaction system: The need for a bigger picture in computing education. Proceedings of the 18th Koli Calling International Conference on Computing Education Research, 1–10. DOI logoGoogle Scholar
Shapiro, S. S., & Wilk, M. B. (1965). An analysis of variance test for normality (complete samples). Biometrika, 52 (3–4), 591–611. DOI logoGoogle Scholar
Soon, T. J. (2008). Qr code. Synthesis Journal, 2008 1, 59–78.Google Scholar
Staggers, N., & Norcio, A. F. (1993). Mental models: Concepts for human-computer interaction research. International Journal of Man-machine studies, 38 (4), 587–605. DOI logoGoogle Scholar
Stanford Artificial Intelligence Laboratory et al. (2014, July 22). Robotic operating system (Version ROS Indigo Igloo). [URL]
Sterelny, K. (1990). The representational theory of mind: An introduction. Basil Blackwell.Google Scholar
Sweller, J., van Merriënboer, J. J., & Paas, F. (2019). Cognitive architecture and instructional design: 20 years later. Educational Psychology Review, 1–32. DOI logoGoogle Scholar
Thomaz, A. L., & Cakmak, M. (2009). Learning about objects with human teachers. 2009 4th ACM/IEEE International Conference on Human-Robot Interaction (HRI), 15–22. DOI logoGoogle Scholar
Vollmer, A.-L., Lohan, K. S., Fritsch, J., Wrede, B., & Rohlfing, K. (2009). Which motionese parameters change with children’s age?Google Scholar
Vollmer, A.-L., Mühlig, M., Steil, J. J., Pitsch, K., Fritsch, J., Rohlfing, K. J., & Wrede, B. (2014). Robots show us how to teach them: Feedback from robots shapes tutoring behavior during action learning. PloS one, 9 (3). DOI logoGoogle Scholar
Vollmer, A.-L., & Schillingmann, L. (2018). On studying human teaching behavior with robots: A review. Review of Philosophy and Psychology, 9 (4), 863–903. DOI logoGoogle Scholar
Vollmer, A.-L., Wrede, B., Rohlfing, K. J., & Oudeyer, P.-Y. (2016). Pragmatic frames for teaching and learning in human-robot interaction: Review and challenges. Frontiers in neurorobotics, 10 1, 10. DOI logoGoogle Scholar
Wortham, R., Theodorou, A., & Bryson, J. (2017). Robot transparency: Improving understanding of intelligent behaviour for designers and users, 274–289. DOI logoGoogle Scholar
Cited by (5)

Cited by five other publications

Kopp, Tobias
2024. Facets of Trust and Distrust in Collaborative Robots at the Workplace: Towards a Multidimensional and Relational Conceptualisation. International Journal of Social Robotics DOI logo
Groß, André, Christian Schütze, Mara Brandt, Britta Wrede & Birte Richter
2023. RISE: an open-source architecture for interdisciplinary and reproducible human–robot interaction research. Frontiers in Robotics and AI 10 DOI logo
Wrede, Britta, Anna-Lisa Vollmer & Sören Krach
2023. On the potentials of interaction breakdowns for HRI. Behavioral and Brain Sciences 46 DOI logo
Gopfert, Jan Philip, Ulrike Kuhl, Lukas Hindemith, Heiko Wersing & Barbara Hammer
2022. Intuitiveness in Active Teaching. IEEE Transactions on Human-Machine Systems 52:3 ► pp. 458 ff. DOI logo
Zhao, Liuzhen, Yingli Yang & Tao Cui
2022. A Study of the Morphological Design of Architecture from a Geometric Logic Perspective. Security and Communication Networks 2022 ► pp. 1 ff. DOI logo

This list is based on CrossRef data as of 4 july 2024. Please note that it may not be complete. Sources presented here have been supplied by the respective publishers. Any errors therein should be reported to them.