abstract

Half Day Workshop on Mental Models of Robots

Authors:

Matthew Rueben,

Stefanos Nikolaidis,

Maartje de Graaf,

Elizabeth Phillips,

Sam ThellmanAuthors Info & Claims

HRI '20: Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction

Pages 658 - 659

https://doi.org/10.1145/3371382.3374856

Published: 01 April 2020 Publication History

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.

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 human-computer interaction. In Handbook of human-computer interaction. Elsevier, 45--65.

[3]

Jane Cleland-Huang, Orlena Gotel, Andrea Zisman, et almbox. 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.

Digital Library

[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.

Digital Library

[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, Vol. 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, Vol. 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, Vol. 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, Vol. 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.

Digital Library

[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, Vol. 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, Vol. 29, 3 (2017), 242--248.

Digital Library

[25]

Jakub Złotowski, Diane Proudfoot, Kumar Yogeeswaran, and Christoph Bartneck. 2015. Anthropomorphism: opportunities and challenges in human--robot interaction. Intl. Journal of Social Robotics, Vol. 7, 3 (2015), 347--360.

Cited By

Eldrandaly KAbdel-Basset MIbrahim MAbdel-Aziz N(2022)Explainable and secure artificial intelligence: taxonomy, cases of study, learned lessons, challenges and future directionsEnterprise Information Systems10.1080/17517575.2022.209853717:9Online publication date: 26-Jul-2022
https://doi.org/10.1080/17517575.2022.2098537
Ding WAbdel-Basset MHawash HAli A(2022)Explainability of artificial intelligence methods, applications and challengesInformation Sciences: an International Journal10.1016/j.ins.2022.10.013615:C(238-292)Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1016/j.ins.2022.10.013
Allan DVonasch ABartneck C(2021)The Doors of Social Robot Perception: The Influence of Implicit Self-theoriesInternational Journal of Social Robotics10.1007/s12369-021-00767-914:1(127-140)Online publication date: 25-Mar-2021
https://doi.org/10.1007/s12369-021-00767-9

Recommendations

Estimating and Influencing User Mental Models of a Robot's Perceptual Capabilities: Initial Development and Pilot Study
HRI '20: Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction

People develop mental models of robots to improve their interactions with them, but predictions from these models are not always accurate. Robots often fail to communicate their capabilities, especially perceptual capabilities---i.e., what they can ...
Mental State Attribution to Robots: A Systematic Review of Conceptions, Methods, and Findings
The topic of mental state attribution to robots has been approached by researchers from a variety of disciplines, including psychology, neuroscience, computer science, and philosophy. As a consequence, the empirical studies that have been conducted so far ...
Mental Models of a Mobile Shoe Rack: Exploratory Findings from a Long-term In-the-Wild Study

Most people do not have direct access to knowledge about the inner workings of robots. Instead, they must develop mental models of the robot, a process that is not well understood. This article presents findings from a long-term, in-the-wild, ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

HRI '20: Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction

March 2020

702 pages

ISBN:9781450370578

DOI:10.1145/3371382

General Chairs:
Tony Belpaeme
Ghent University, Belgium
,
James Young
University of Manitoba, Canada
,
Program Chairs:
Hatice Gunes
University of Cambridge, UK
,
Laurel Riek
UC San Diego, USA

Copyright © 2020 Owner/Author.

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.

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 April 2020

Check for updates

Author Tags

Qualifiers

Abstract

Conference

HRI '20

Sponsor:

HRI '20: ACM/IEEE International Conference on Human-Robot Interaction

March 23 - 26, 2020

Cambridge, United Kingdom

Acceptance Rates

Overall Acceptance Rate 192 of 519 submissions, 37%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
165
Total Downloads

Downloads (Last 12 months)12
Downloads (Last 6 weeks)2

Reflects downloads up to 18 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Eldrandaly KAbdel-Basset MIbrahim MAbdel-Aziz N(2022)Explainable and secure artificial intelligence: taxonomy, cases of study, learned lessons, challenges and future directionsEnterprise Information Systems10.1080/17517575.2022.209853717:9Online publication date: 26-Jul-2022
https://doi.org/10.1080/17517575.2022.2098537
Ding WAbdel-Basset MHawash HAli A(2022)Explainability of artificial intelligence methods, applications and challengesInformation Sciences: an International Journal10.1016/j.ins.2022.10.013615:C(238-292)Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1016/j.ins.2022.10.013
Allan DVonasch ABartneck C(2021)The Doors of Social Robot Perception: The Influence of Implicit Self-theoriesInternational Journal of Social Robotics10.1007/s12369-021-00767-914:1(127-140)Online publication date: 25-Mar-2021
https://doi.org/10.1007/s12369-021-00767-9

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents