Article published In: Interaction Studies
Vol. 22:2 (2021) ► pp.244–279
Why robots should be technical
Correcting mental models through technical architecture concepts
Published online: 28 February 2022
https://doi.org/10.1075/is.20023.hin
https://doi.org/10.1075/is.20023.hin
Abstract
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.
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
- 4.2.1Robot
- 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
- 5.1Hypothesis 1: Providing architectural concepts allows users to gain more knowledge about the functionality of a
robot
- 6.Discussion
- 7.Conclusion
- 8.Future work
- Notes
References
References (44)
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.
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.
Beller, W. E., & Wang, Y. P. (1997). Bar
code dataform scanning and labeling apparatus and method [US Patent 5,602,377].
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.
Breazeal, C., Dautenhahn, K., & Kanda, T. (2016). Social
robotics. Springer handbook of
robotics (pp. 1935–1972). Springer.
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.
Breslow, N. (1970). A
generalized kruskal-wallis test for comparing k samples subject to unequal patterns of
censorship. Biometrika, 57 (3), 579–594.
Bruner, J. (1985). Child’s
talk: Learning to use language. Child Language Teaching and
Therapy, 1 (1), 111–114.
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.
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.
Duffy, B. R. (2006). Fundamental
issues in social robotics. International Review of Information
Ethics, 6 (12), 2006.
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.
(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.
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.
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.
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.
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.
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).
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.
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.
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.
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.
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.
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.
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.
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, 71, 470.
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.
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.
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.
Shapiro, S. S., & Wilk, M. B. (1965). An
analysis of variance test for normality (complete
samples). Biometrika, 52(3–4), 591–611.
Staggers, N., & Norcio, A. F. (1993). Mental
models: Concepts for human-computer interaction research. International Journal of Man-machine
studies, 38(4), 587–605.
Stanford Artificial Intelligence Laboratory
et al. (2014, July 22). Robotic operating
system (Version ROS Indigo Igloo). [URL]
Sweller, J., van Merriënboer, J. J., & Paas, F. (2019). Cognitive
architecture and instructional design: 20 years later. Educational Psychology
Review, 1–32.
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.
Vollmer, A.-L., Lohan, K. S., Fritsch, J., Wrede, B., & Rohlfing, K. (2009). Which
motionese parameters change with children’s age?
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).
Vollmer, A.-L., & Schillingmann, L. (2018). On
studying human teaching behavior with robots: A review. Review of Philosophy and
Psychology, 9 (4), 863–903.
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