Workshop Summary HRI ’20 Companion, March 23–26, 2020, Cambridge, United Kingdom Half Day Workshop on Mental Models of Robots Matthew Rueben University of Southern California mrueben@usc.edu Stefanos Nikolaidis University of Southern California nikolaid@usc.edu Elizabeth Phillips United States Air Force Academy elizabeth.phillips@usafa.edu Lionel Robert University of Michigan lprobert@umich.edu Minae Kwon Maartje de Graaf Utrecht University m.m.a.degraaf@uu.nl David Sirkin Stanford University sirkin@stanford.edu Sam Thellman Stanford University mnkwon@stanford.edu Linköping University sam.thellman@liu.se make decisions. These models are similar in structure to, though often simpler than, the thing or concept that they represent; they facilitate predicting, explaining, and understanding each interaction. Mental models are developed through interaction and determine how people focus their attention and behave [14]. People often hold inaccurate or overly presumptuous mental models about robots [11, 15], likely due to a lack of experience and the strong influence of robot design, e.g., appearance [21, 23], stated task/role [8], and social cues [10]. Thus, there is often a mismatch between user mental models or expectations of robots and actual robot capabilities [9]. This mismatch can lead to over trust, inappropriate reliance [16] or even discontinued use (especially of social robots) [1, 5]. As robotic systems become increasingly complex and their presence more commonplace, HRI researchers must find ways to help users understand them more accurately. Some researchers have already identified different factors influencing user mental models and how they can be exploited to increase user acceptance or improve human-robot interactions [12, 18]. Others have focused on anthropomorphism and humanlikeness [17, 25], mental state attribution to robots [13, 22], increasing the legibility [7] and transparency [24] of robot behavior and capabilities, and on estimating users’ mental models to inform robot behavior [6, 20]. The overarching aim of this workshop is to promote common ground and shared understanding within the HRI community of the role of user mental models in human-robot interactions, and to foster interdisciplinary collaboration in this broad research area. ABSTRACT Robotic systems are becoming increasingly complex, hindering people from understanding the robot’s inner workings [24]. Simply providing the robot’s source code may be useful for software and hardware engineers who need to test the system for traceability and verification [3], but not for the non-technical user. Plus, looks can be deceiving: robots that merely resemble humans or animals are perceived differently by users [25]. This workshop aims to provide a forum for researchers from both industry and academia to discuss the user’s understanding or mental model of a robot: what the robot is, what it does, and how it works. In many cases it will be useful for robots to estimate each user’s mental model and use this information when deciding how to behave during an interaction. Designing more transparent robot actions will also be important, giving users a window into what the robot is “thinking”, “feeling”, and “intending”. We envision a future in which robots can automatically detect and correct inaccurate mental models held by users. This workshop will develop a multidisciplinary vision for the next few years of research in pursuit of that future. ACM Reference Format: Matthew Rueben, Stefanos Nikolaidis, Maartje de Graaf, Elizabeth Phillips, Lionel Robert, David Sirkin, Minae Kwon, and Sam Thellman. 2020. Half Day Workshop on Mental Models of Robots. In Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction (HRI ’20 Companion), March 23–26, 2020, Cambridge, United Kingdom. ACM, New York, NY, USA, 2 pages. https://doi.org/10.1145/3371382.3374856 Background Mental models refer to an individual’s internal representation of a system, such as a robot [2, 4]. Specifically, they are knowledge frameworks used to describe, explain, and predict system purpose, function, form, and state [19]. Mental models provide utility because they allow individuals to more efficiently store and access information in order to perform tasks, understand phenomena, and Topics and Target Audience This workshop brings together all researchers and practitioners interested in helping users understand robotic systems. We welcome contributions from those working on estimating aspects of users’ mental models using perception and learning algorithms, designing communicative robot actions or other interventions, and developing decision making systems that connect all these elements to create autonomous robot behaviors. Applicants with a background in human-computer interaction, natural language processing, design, human factors, psychology, neuroscience, cognitive science, and any other related disciplines are welcome to apply. We especially encourage submissions from researchers and practitioners contributing theories, methods and applications that are only Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). HRI ’20 Companion, March 23–26, 2020, Cambridge, United Kingdom © 2020 Copyright held by the owner/author(s). ACM ISBN 978-1-4503-7057-8/20/03. https://doi.org/10.1145/3371382.3374856 658 Workshop Summary HRI ’20 Companion, March 23–26, 2020, Cambridge, United Kingdom sparsely used in the HRI community such as human factors, gaming, and employee education for working with industrial robots. Prospective participants are welcome to submit papers (max. 4 pages) and poster presentations (max. 2 pages) covering topics within the broad scope of this workshop. Example areas of relevance include, but not limited to: user modeling, explainability, anthropomorphism, and personalization. All papers should be submitted in PDF format using the ACM template for late-breaking reports and will be peer reviewed based on their originality, relevance, technical soundness, and clear presentation. Paper acceptance requires that at least one author registers for and attends the workshop. We will provide online access to the workshop proceedings on the workshop website after the conference. REFERENCES [1] Anol Bhattacherjee. 2001. Understanding information systems continuance: an expectation-confirmation model. MIS quarterly (2001), 351–370. [2] John M Carroll and Judith Reitman Olson. 1988. Mental models in humancomputer interaction. In Handbook of human-computer interaction. Elsevier, 45–65. [3] Jane Cleland-Huang, Orlena Gotel, Andrea Zisman, et al. 2012. Software and systems traceability. Vol. 2. Springer. [4] K Craik. 1943. The Nature of Explanation, Cambridge University Press. Cambridge, UK (1943). [5] Maartje De Graaf, Somaya Ben Allouch, and Jan Van Dijk. 2017. Why do they refuse to use my robot?: Reasons for non-use derived from a long-term home study. In Proc. 2017 ACM/IEEE Intl. Conf. on Human-Robot Interaction. ACM, 224–233. [6] Sandra Devin and Rachid Alami. 2016. An implemented theory of mind to improve human-robot shared plans execution. In 2016 11th ACM/IEEE Intl. Conf. on Human-Robot Interaction (HRI). IEEE, 319–326. [7] Anca D Dragan, Kenton CT Lee, and Siddhartha S Srinivasa. 2013. Legibility and predictability of robot motion. In Proc. 8th ACM/IEEE Intl. Conf. on Human-robot interaction. IEEE Press, 301–308. [8] Jennifer Goetz, Sara Kiesler, and Aaron Powers. 2003. Matching robot appearance and behavior to tasks to improve human-robot cooperation. In Proc. 12th IEEE Intl. Workshop on Robot and Human Interactive Communication. Ieee, 55–60. [9] Kerstin S Haring, Katsumi Watanabe, Mari Velonaki, Chad C Tossell, and Victor Finomore. 2018. FFAB—The Form Function Attribution Bias in Human–Robot Interaction. IEEE Transactions on Cognitive and Developmental Systems 10, 4 (2018), 843–851. [10] Sara Kiesler. 2005. Fostering common ground in human-robot interaction. In ROMAN 2005. IEEE Intl. Workshop on Robot and Human Interactive Communication, 2005. IEEE, 729–734. [11] Takanori Komatsu, Rie Kurosawa, and Seiji Yamada. 2012. How does the difference between users’ expectations and perceptions about a robotic agent affect their behavior? Intl. Journal of Social Robotics 4, 2 (2012), 109–116. [12] Sau-lai Lee, Ivy Yee-man Lau, Sara Kiesler, and Chi-Yue Chiu. 2005. Human mental models of humanoid robots. In Proc. 2005 IEEE International Conference on Robotics and Automation. 2767–2772. [13] Serena Marchesi, Davide Ghiglino, Francesca Ciardo, Jairo Perez-Osorio, Ebru Baykara, and Agnieszka Wykowska. 2019. Do We Adopt the Intentional Stance Toward Humanoid Robots? Frontiers in Psychology 10 (2019). [14] Donald A Norman. 2014. Some observations on mental models. In Mental models. Psychology Press, 15–22. [15] Steffi Paepcke and Leila Takayama. 2010. Judging a bot by its cover: an experiment on expectation setting for personal robots. In 2010 5th ACM/IEEE Intl. Conf. on Human-Robot Interaction (HRI). IEEE, 45–52. [16] Raja Parasuraman and Victor Riley. 1997. Humans and automation: Use, misuse, disuse, abuse. Human factors 39, 2 (1997), 230–253. [17] Elizabeth Phillips, Daniel Ullman, Maartje MA de Graaf, and Bertram F Malle. 2017. What Does A Robot Look Like?: A Multi-Site Examination of User Expectations About Robot Appearance. In Proc. Human Factors and Ergonomics Society Annual Meeting, Vol. 61. SAGE Publications Sage CA: Los Angeles, CA, 1215–1219. [18] Aaron Powers and Sara Kiesler. 2006. The advisor robot: Tracing people’s mental model from a robot’s physical attributes. In Proc. 1st ACM SIGCHI/SIGART Conf. on Human-Robot Interaction. ACM, 218–225. [19] William B Rouse and Nancy M Morris. 1986. On looking into the black box: Prospects and limits in the search for mental models. Psychological bulletin 100, 3 (1986), 349. [20] Dorsa Sadigh, Anca D Dragan, Shankar Sastry, and Sanjit A Seshia. 2017. Active Preference-Based Learning of Reward Functions.. In Robotics: Science and Systems. [21] Valerie K Sims, Matthew G Chin, David J Sushil, Daniel J Barber, Tatiana Ballion, Bryan R Clark, Keith A Garfield, Michael J Dolezal, Randall Shumaker, and Neal Finkelstein. 2005. Anthropomorphism of robotic forms: A response to affordances?. In Proc. Human Factors and Ergonomics Society Annual Meeting, Vol. 49. SAGE Publications Sage CA: Los Angeles, CA, 602–605. [22] Sam Thellman and Tom Ziemke. 2019. The Intentional Stance Toward Robots: Conceptual and Methodological Considerations. In The 41st Annual Conf. of the Cognitive Science Society, July 24-26, Montreal, Canada. 1097–1103. [23] Sarah Woods, Kerstin Dautenhahn, and Joerg Schulz. 2004. The design space of robots: Investigating children’s views. In 13th IEEE Intl. Workshop on Robot and Human Interactive Communication. IEEE, 47–52. [24] Robert H Wortham and Andreas Theodorou. 2017. Robot transparency, trust and utility. Connection Science 29, 3 (2017), 242–248. [25] Jakub Złotowski, Diane Proudfoot, Kumar Yogeeswaran, and Christoph Bartneck. 2015. Anthropomorphism: opportunities and challenges in human–robot interaction. Intl. Journal of Social Robotics 7, 3 (2015), 347–360. Workshop Objectives and Activities The vision for the workshop schedule is to first develop a complete, high-level picture of the problem space by inundating ourselves with all the aspects of user mental model research from all the related disciplines. This holistic overview then serves as a common ground for the break-out session. The half day workshop will feature oral presentations of accepted papers, keynote speakers, and over an hour of interactive activities. Activities will be done in small groups and will include summarizing the problem space, brainstorming use cases and collaboration ideas, and making consensus recommendations to difficult questions. After the workshop, products from these efforts will be made available on the workshop website. Time 9:00 9:15 10:15 11:00 11:30 12:45 Activity Welcoming speech by organizers Invited speaker talks Contributed talks Coffee break Break-out activities and discussions Close for lunch Duration 15 60 45 30 75 Organizing Team The team of eight organizers will recruit participants from their networks as well as through major mailing lists for all the relevant disciplines. The diversity of disciplinary backgrounds on the organizing team will ensure the recruitment of a multidisciplinary audience and presenters. In addition, we aim to represent audience members and presenters from different geographic backgrounds, genders, and levels of seniority. Matthew Rueben Privacy Website Stefanos Nikolaidis Computational modeling Website Maartje de Graaf Behavioral science Website Elizabeth Phillips Human factors Website Lionel Robert Teamwork, autonomous vehicles Website David Sirkin Design Website Minae Kwon Algorithms Website Sam Thellman Cognitive science Website 659
