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Singing the Body Electric: The Impact of Robot Embodiment on User Expectations (2401.06977v1)

Published 13 Jan 2024 in cs.RO, cs.AI, and cs.HC

Abstract: Users develop mental models of robots to conceptualize what kind of interactions they can have with those robots. The conceptualizations are often formed before interactions with the robot and are based only on observing the robot's physical design. As a result, understanding conceptualizations formed from physical design is necessary to understand how users intend to interact with the robot. We propose to use multimodal features of robot embodiments to predict what kinds of expectations users will have about a given robot's social and physical capabilities. We show that using such features provides information about general mental models of the robots that generalize across socially interactive robots. We describe how these models can be incorporated into interaction design and physical design for researchers working with socially interactive robots.

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References (31)
  1. Deep reinforcement learning at the edge of the statistical precipice. Advances in neural information processing systems, 34:29304–29320, 2021.
  2. Shape it–the influence of robot body shape on gender perception in robots. In Social Robotics: 9th International Conference, ICSR 2017, Tsukuba, Japan, November 22-24, 2017, Proceedings 9, pages 75–84. Springer, 2017.
  3. The (fe) male robot: how robot body shape impacts first impressions and trust towards robots. International Journal of Social Robotics, 13:477–489, 2021.
  4. Experience grounds language. arXiv preprint arXiv:2004.10151, 2020.
  5. Gender representation and humanoid robots designed for domestic use. International Journal of Social Robotics, 1(3):261–265, 2009.
  6. The robotic social attributes scale (rosas) development and validation. In Proceedings of the 2017 ACM/IEEE International Conference on human-robot interaction, pages 254–262, 2017.
  7. Perceived robot capability. In 2015 24th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pages 541–548. IEEE, 2015.
  8. Fred D Davis. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, pages 319–340, 1989.
  9. Embodiment in socially interactive robots. Foundations and Trends® in Robotics, 7(4):251–356, 2019.
  10. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248–255. Ieee, 2009.
  11. Design metaphors for understanding user expectations of socially interactive robot embodiments. ACM Transactions on Human-Robot Interaction, 12(2):1–41, 2023.
  12. Universal dimensions of social cognition: Warmth and competence. Trends in cognitive sciences, 11(2):77–83, 2007.
  13. The peculiarities of robot embodiment (emcorp-scale) development, validation and initial test of the embodiment and corporeality of artificial agents scale. In Proceedings of the 2018 ACM/IEEE international conference on human-robot interaction, pages 370–378, 2018.
  14. Metaphors, materialities, and affordances: Hybrid morphologies in the design of interactive artifacts. Design Studies, 53:24–46, 2017.
  15. Characterizing the Design Space of Rendered Robot Faces, page 96–104. Association for Computing Machinery, New York, NY, USA, 2018. ISBN 9781450349536. URL https://doi.org/10.1145/3171221.3171286.
  16. Conceptual metaphors impact perceptions of human-ai collaboration. arXiv preprint arXiv:2008.02311, 2020.
  17. The role of a mental model in learning to operate a device. Cognitive science, 8(3):255–273, 1984.
  18. Mental models of robotic assistants. In CHI’02 extended abstracts on Human Factors in Computing Systems, pages 576–577, 2002.
  19. Conceptual metaphors for designing smart environments: Device, robot, and friend. Frontiers in Psychology, 11:198, 2020.
  20. Human expectations of social robots. In 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pages 463–464. IEEE, 2016.
  21. Introducing representations of facial affect in automated multimodal deception detection. In Proceedings of the 2020 International Conference on Multimodal Interaction, pages 305–314, 2020.
  22. How “real” are computer personalities? psychological responses to personality types in human-computer interaction. Communication research, 23(6):651–674, 1996.
  23. Scikit-learn: Machine learning in python. the Journal of machine Learning research, 12:2825–2830, 2011.
  24. The shape of our bias: Perceived age and gender in the humanoid robots of the abot database. In 2022 17th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pages 110–119. IEEE, 2022.
  25. What is human-like? decomposing robots’ human-like appearance using the anthropomorphic robot (abot) database. In Proceedings of the 2018 ACM/IEEE international conference on human-robot interaction, pages 105–113, 2018.
  26. The influence of height in robot-mediated communication. In 2013 8th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pages 1–8. IEEE, 2013.
  27. Mental models of a mobile shoe rack: exploratory findings from a long-term in-the-wild study. ACM Transactions on Human-Robot Interaction (THRI), 10(2):1–36, 2021.
  28. Four years in review: Statistical practices of likert scales in human-robot interaction studies. In Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, pages 43–52, 2020.
  29. Re-framing the desktop interface around the activities of knowledge work. In Proceedings of the 21st annual ACM symposium on User interface software and technology, pages 211–220, 2008.
  30. Transformers: State-of-the-art natural language processing. In Proceedings of the 2020 conference on empirical methods in natural language processing: system demonstrations, pages 38–45, 2020.
  31. Aligning books and movies: Towards story-like visual explanations by watching movies and reading books. In Proceedings of the IEEE international conference on computer vision, pages 19–27, 2015.
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Authors (3)
  1. Nathaniel Dennler (16 papers)
  2. Stefanos Nikolaidis (65 papers)
  3. Maja Matarić (35 papers)

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