In human-robot interaction, the behaviour of the robot is usually predefined and can easily be predicted. However, this is not true for the behaviour of the users. Throughout the interaction, users employ various behaviours which often cannot be foreseen intuitively. Therefore, theoretical models need to be developed to predict and explain the users’ behaviour in order to enable the robot to better understand its interaction partner. The current work suggests such a model which is based on the concepts of situations and expectations. It claims that both concepts support the prediction of user behaviour in the interaction and demonstrates the usefulness of the model in a case study based on data from human-robot interaction in a home tour situation.
Rosén, Julia, Jessica Lindblom, Maurice Lamb & Erik Billing
2024. Previous Experience Matters: An in-Person Investigation of Expectations in Human–Robot Interaction. International Journal of Social Robotics 16:3 ► pp. 447 ff.
San Martin, Ane, Johan Kildal & Elena Lazkano
2024. Taking Charge of One’s Own Safety While Collaborating with Robots: Enhancing Situational Awareness for a Safe Environment. Sustainability 16:10 ► pp. 4024 ff.
Croijmans, Ilja, Laura van Erp, Annelie Bakker, Lara Cramer, Sophie Heezen, Dana Van Mourik, Sterre Weaver & Ruud Hortensius
2023. No Evidence for an Effect of the Smell of Hexanal on Trust in Human–Robot Interaction. International Journal of Social Robotics 15:8 ► pp. 1429 ff.
Rosén, Julia, Erik Billing & Jessica Lindblom
2023. Applying the Social Robot Expectation Gap Evaluation Framework. In Human-Computer Interaction [Lecture Notes in Computer Science, 14013], ► pp. 169 ff.
Sobhani, Mehdi, Jim Smith, Anthony Pipe & Angelika Peer
2023. A Novel Mirror Neuron Inspired Decision-Making Architecture for Human–Robot Interaction. International Journal of Social Robotics
Zhang, Mengwei, Jinsheng Cui & Jianan Zhong
2023. How consumers react differently toward humanoid vs. nonhumanoid robots after service failures: a moderated chain mediation model. International Journal of Emerging Markets
Rosén, Julia, Jessica Lindblom & Erik Billing
2022. The Social Robot Expectation Gap Evaluation Framework. In Human-Computer Interaction. Technological Innovation [Lecture Notes in Computer Science, 13303], ► pp. 590 ff.
Holthaus, Patrick & Sven Wachsmuth
2021. It was a Pleasure Meeting You. International Journal of Social Robotics 13:7 ► pp. 1729 ff.
Kim, Hyek, Suk-Ho Lee & Hyunmin Kang
2021. Initial expectations with social robots : Focused on verbal and non-verbal interaction . Journal of Digital Contents Society 22:2 ► pp. 281 ff.
Lin, Weijane, Hong-Chun Chen & Hsiu-Ping Yueh
2021. Using Different Error Handling Strategies to Facilitate Older Users’ Interaction With Chatbots in Learning Information and Communication Technologies. Frontiers in Psychology 12
Mirnig, Nicole, Gerald Stollnberger, Markus Miksch, Susanne Stadler, Manuel Giuliani & Manfred Tscheligi
2017. To Err Is Robot: How Humans Assess and Act toward an Erroneous Social Robot. Frontiers in Robotics and AI 4
Lohse, Manja, Frederic Siepmann & Sven Wachsmuth
2014. A Modeling Framework for User-Driven Iterative Design of Autonomous Systems. International Journal of Social Robotics 6:1 ► pp. 121 ff.
Kruijff, Geert-Jan M.
2013. Symbol Grounding as Social, Situated Construction of Meaning in Human-Robot Interaction. KI - Künstliche Intelligenz 27:2 ► pp. 153 ff.
Hegel, Frank, Sebastian Gieselmann, Annika Peters, Patrick Holthaus & Britta Wrede
2011. 2011 RO-MAN, ► pp. 72 ff.
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