@article{49516, abstract = {{In this article, we present RISE—a Robotics Integration and Scenario-Management Extensible-Architecture—for designing human–robot dialogs and conducting Human–Robot Interaction (HRI) studies. In current HRI research, interdisciplinarity in the creation and implementation of interaction studies is becoming increasingly important. In addition, there is a lack of reproducibility of the research results. With the presented open-source architecture, we aim to address these two topics. Therefore, we discuss the advantages and disadvantages of various existing tools from different sub-fields within robotics. Requirements for an architecture can be derived from this overview of the literature, which 1) supports interdisciplinary research, 2) allows reproducibility of the research, and 3) is accessible to other researchers in the field of HRI. With our architecture, we tackle these requirements by providing a Graphical User Interface which explains the robot behavior and allows introspection into the current state of the dialog. Additionally, it offers controlling possibilities to easily conduct Wizard of Oz studies. To achieve transparency, the dialog is modeled explicitly, and the robot behavior can be configured. Furthermore, the modular architecture offers an interface for external features and sensors and is expandable to new robots and modalities.}}, author = {{Groß, André and Schütze, Christian and Brandt, Mara and Wrede, Britta and Richter, Birte}}, issn = {{2296-9144}}, journal = {{Frontiers in Robotics and AI}}, keywords = {{Artificial Intelligence, Computer Science Applications}}, publisher = {{Frontiers Media SA}}, title = {{{RISE: an open-source architecture for interdisciplinary and reproducible human–robot interaction research}}}, doi = {{10.3389/frobt.2023.1245501}}, volume = {{10}}, year = {{2023}}, } @article{48678, abstract = {{Explanation has been identified as an important capability for AI-based systems, but research on systematic strategies for achieving understanding in interaction with such systems is still sparse. Negation is a linguistic strategy that is often used in explanations. It creates a contrast space between the affirmed and the negated item that enriches explaining processes with additional contextual information. While negation in human speech has been shown to lead to higher processing costs and worse task performance in terms of recall or action execution when used in isolation, it can decrease processing costs when used in context. So far, it has not been considered as a guiding strategy for explanations in human-robot interaction. We conducted an empirical study to investigate the use of negation as a guiding strategy in explanatory human-robot dialogue, in which a virtual robot explains tasks and possible actions to a human explainee to solve them in terms of gestures on a touchscreen. Our results show that negation vs. affirmation 1) increases processing costs measured as reaction time and 2) increases several aspects of task performance. While there was no significant effect of negation on the number of initially correctly executed gestures, we found a significantly lower number of attempts—measured as breaks in the finger movement data before the correct gesture was carried out—when being instructed through a negation. We further found that the gestures significantly resembled the presented prototype gesture more following an instruction with a negation as opposed to an affirmation. Also, the participants rated the benefit of contrastive vs. affirmative explanations significantly higher. Repeating the instructions decreased the effects of negation, yielding similar processing costs and task performance measures for negation and affirmation after several iterations. We discuss our results with respect to possible effects of negation on linguistic processing of explanations and limitations of our study.}}, author = {{Groß, André and Singh, Amit and Banh, Ngoc Chi and Richter, Birte and Scharlau, Ingrid and Rohlfing, Katharina J. and Wrede, Britta}}, issn = {{2296-9144}}, journal = {{Frontiers in Robotics and AI}}, keywords = {{Artificial Intelligence, Computer Science Applications}}, publisher = {{Frontiers Media SA}}, title = {{{Scaffolding the human partner by contrastive guidance in an explanatory human-robot dialogue}}}, doi = {{10.3389/frobt.2023.1236184}}, volume = {{10}}, year = {{2023}}, } @article{34703, abstract = {{One of the many purposes for which social robots are designed is education, and there have been many attempts to systematize their potential in this field. What these attempts have in common is the recognition that learning can be supported in a variety of ways because a learner can be engaged in different activities that foster learning. Up to now, three roles have been proposed when designing these activities for robots: as a teacher or tutor, a learning peer, or a novice. Current research proposes that deciding in favor of one role over another depends on the content or preferred pedagogical form. However, the design of activities changes not only the content of learning, but also the nature of a human–robot social relationship. This is particularly important in language acquisition, which has been recognized as a social endeavor. The following review aims to specify the differences in human–robot social relationships when children learn language through interacting with a social robot. After proposing categories for comparing these different relationships, we review established and more specific, innovative roles that a robot can play in language-learning scenarios. This follows Mead’s (1946) theoretical approach proposing that social roles are performed in interactive acts. These acts are crucial for learning, because not only can they shape the social environment of learning but also engage the learner to different degrees. We specify the degree of engagement by referring to Chi’s (2009) progression of learning activities that range from active, constructive, toward interactive with the latter fostering deeper learning. Taken together, this approach enables us to compare and evaluate different human–robot social relationships that arise when applying a robot in a particular social role.}}, author = {{Rohlfing, Katharina and Altvater-Mackensen, Nicole and Caruana, Nathan and van den Berghe, Rianne and Bruno, Barbara and Tolksdorf, Nils Frederik and Hanulíková, Adriana}}, issn = {{2296-9144}}, journal = {{Frontiers in Robotics and AI}}, keywords = {{Artificial Intelligence, Computer Science Applications}}, publisher = {{Frontiers Media SA}}, title = {{{Social/dialogical roles of social robots in supporting children’s learning of language and literacy—A review and analysis of innovative roles}}}, doi = {{10.3389/frobt.2022.971749}}, volume = {{9}}, year = {{2022}}, } @article{24899, abstract = {{Temperamental traits can decisively influence how children enter into social interaction with their environment. Yet, in the field of child–robot interaction, little is known about how individual differences such as shyness impact on how children interact with social robots in educational settings. The present study systematically assessed the temperament of 28 preschool children aged 4–5 years in order to investigate the role of shyness within a dyadic child–robot interaction. Over the course of four consecutive sessions, we observed how shy compared to nonshy children interacted with a social robot during a word-learning educational setting and how shyness influenced children’s learning outcomes. Overall, results suggested that shy children not only interacted differently with a robot compared to nonshy children, but also changed their behavior over the course of the sessions. Critically, shy children interacted less expressively with the robot in general. With regard to children’s language learning outcomes, shy children scored lower on an initial posttest, but were able to close this gap on a later test, resulting in all children retrieving the learned words on a similar level. When intertest learning gain was considered, regression analyses even confirmed a positive predictive role of shyness on language learning gains. Findings are discussed with regard to the role of shyness in educational settings with social robots and the implications for future interaction design.}}, author = {{Tolksdorf, Nils Frederik and Viertel, Franziska E. and Rohlfing, Katharina J.}}, issn = {{2296-9144}}, journal = {{Frontiers in Robotics and AI}}, title = {{{Do Shy Preschoolers Interact Differently When Learning Language With a Social Robot? An Analysis of Interactional Behavior and Word Learning}}}, doi = {{10.3389/frobt.2021.676123}}, year = {{2021}}, } @article{19969, author = {{Hamann, Heiko and Khaluf, Yara and Botev, Jean and Divband Soorati, Mohammad and Ferrante, Eliseo and Kosak, Oliver and Montanier, Jean-Marc and Mostaghim, Sanaz and Redpath, Richard and Timmis, Jon and Veenstra, Frank and Wahby, Mostafa and Zamuda, Aleš}}, issn = {{2296-9144}}, journal = {{Frontiers in Robotics and AI}}, title = {{{Hybrid Societies: Challenges and Perspectives in the Design of Collective Behavior in Self-organizing Systems}}}, doi = {{10.3389/frobt.2016.00014}}, year = {{2016}}, }