Matt Rueben

PreviouslyPostdoctoral scholar in the Intergroup Human-Robot Interaction Lab at New Mexico State University and the Interaction Lab at the University of Southern California
Education:Oregon State University Ph.D. Robotics in the Personal Robotics Group
Oregon State University H.B.S. Mechanical Engineering
Me on:Google Scholar

About Me

I am an educator and human-robot interaction scholar. I recently taught a course I developed for the Honors College at New Mexico State University called, "Exploring the Future through Robotics and Artificial Intelligence." The goal of my research is to help people understand robots' perceptual capabilities for effective, informed interactions.

I recently finished a postdoc in the Intergroup Human-Robot Interaction (iHRI) Lab directed by Dr. Marlena Fraune at New Mexico State University. Prior to that I did a postdoc in the USC Interaction Lab. I received the Ph.D. in Robotics from Oregon State University in 2018 for a dissertation about privacy in human-robot interaction. My undergraduate studies were also at OSU—I received the H.B.S. Degree in Mechanical Engineering in 2013.

Research Interests

Human-Robot Interaction, Mental Models of Robots, Ethics and Societal Impacts of Robotics

Teaching Experience and Course Materials

Current Work

Passengers' Mental Models of Self-Driving Cars: As self-driving capabilities for cars become commercially available, people are trying to figure out whether they are safe. I am part of a project studying how groups of passengers come to understand the Tesla Autopilot system over the course of an hour long drive. I am specifically focusing on how interactions between the passengers influence mental model formation.

Video Games as Inspiration for Vision Cues in Human-Robot Interaction: Robots should be able to indicate when they notice someone with their sensors. Fortunately, video game developers have already implemented a variety of cues for in-game agents that we can learn from and apply to human-robot interaction.

We are doing a thematic analysis of over 50 cues from video games to produce design recommendations for robots that can communicate about what they "see". Some non-diegetic cues that were only possible in video games in the past -- e.g., a floating "!" exclamation point over an agent's head -- are now becoming possible for human-robot interactions via augmented reality (AR) headsets, which makes this work especially timely and exciting.

Describing Robots with Analogies -- Impacts on Trust: When people start working with a new robot, they often either trust it too much or too little when its actual abilities warrant something in between. We are investigating how the analogies we use to introduce people to a new robot could improve trust calibration. Currently we are focusing on zoomorphic analogies--i.e., how trustworthy people believe different animals would be at tasks commonly done by robots.

Past Work

Estimating and Influencing User Mental Models of a Robot’s Perceptual Capabilities: 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 sense and understand about the world. This project represents pioneering work towards enabling robots to autonomously estimate and influence human beliefs about robot perceptual capabilities. A custom-designed, web-based game is being used to establish feasibility and build computational models of how the user's mental model develops.

Telepresence Robots for Homebound K-12 Students: Many students must stay home from school for prolonged periods of time because of illness, disability, or other reasons. These students might get instruction at home, but miss out on both the normal social interactions and the full learning experience at school. In this project we are deploying telepresence robots in high schools whereby homebound students can attend school for weeks at a time. We will study the effect of this new situation on social interactions and develop algorithms that improve student outcomes.

Privacy-Sensitive Robotics: Robots present a new concern for personal privacy: not only can a remote operator see through the robot's video cameras, but he or she can also drive the robot, potentially so as to view private objects against the owner's wishes. Our group is studying how people think about privacy around robots. Our scope goes beyond personal information to include proxemics, territory, and feelings of crowding or "being watched."

Shared Autonomy Interfaces for Using Household Appliances: Robots can help people around the house by operating appliances: washing mashines, microwaves, light switches, or even television sets. Thus far, this project has yielded interfaces through which a human can help the robot locate knobs, light switches, and push-buttons on a device, after which the robot can actuate the controls autonomously. In the future, this work could be extended by building large, semantic maps of the robot's environment to help the robot do useful tasks on its own.

Selected Publications

[1] Matthew Rueben, Matthew R. Horrocks, Jennifer Eleanor Martinez, Michelle V. Cormier, Nicolas LaLone, Marlena R. Fraune, and Z Toups Dugas. "I See You!": A Design Framework for Interface Cues about Agent Visual Perception from a Thematic Analysis of Videogames. In Proceedings of the ACM CHI Conference on Human Factors in Computing Systems, April 29–May 5, 2022, New Orleans, LA, USA. ACM, 2022.
[2] Matthew Rueben, Mohammad Syed, Emily London, Mark Camarena, Eunsook Shin, Yulun Zhang, Timothy S. Wang, Thomas R. Groechel, Rhianna Lee, and Maja J. Matarić. Long-Term, in-the-Wild Study of Feedback about Speech Intelligibility for K-12 Students Attending Class via a Telepresence Robot. In Proceedings of the International Conference on Multimodal Interaction (ICMI), pages 567–576. ACM, 2021. [ pdf ]
[3] Matthew Rueben, Eitan Rothberg, and Maja J. Matarić. Applying the Theory of Make-Believe to Human-Robot Interaction. In Culturally Sustainable Social Robotics, pages 40–50. IOS Press, 2020. [ pdf ]
[4] Matthew Rueben, Shirley A. Elprama, Dimitrios Chrysostomou, and An Jacobs. Introduction to (Re)Using Questionnaires in Human-Robot Interaction Research. In Jost et al. (Eds.), Human-Robot Interaction: Evaluation Methods and Their Standardization, pages 125–144. Springer Series on Bio- and Neurosystems Vol. 12 No. 1, 2020. [ html ]
[5] Matthew Rueben, Jeffrey Klow, Madelyn Duer, Eric Zimmerman, Jennifer Piacentini, Madison Browning, Frank J. Bernieri, Cindy M. Grimm, and William D. Smart. Mental Models of a Mobile Shoe Rack: Exploratory Findings from a Long-term In-the-Wild Study. ACM Transactions on Human-Robot Interaction. 10, 2, Article 16 (February 2021), 36 pages. [ pdf | abstract | bib ]
[6] Jeffrey Klow, Jordan Proby, Matthew Rueben, Ross T Sowell, Cindy M Grimm, and William D Smart. Privacy, utility, and cognitive load in remote presence systems. In International Conference on Social Robotics, pages 730–739. Springer, 2019. [ bib ]
[7] Matthew Rueben, Alexander Mois Aroyo, Christoph Lutz, Johannes Schmölz, Pieter Van Cleynenbreugel, Andrea Corti, Siddharth Agrawal, and William D Smart. Themes and research directions in privacy-sensitive robotics. In 2018 IEEE Workshop on Advanced Robotics and its Social Impacts (ARSO), pages 77–84. IEEE, 2018. [ bib ]
[8] Margaret M. Krupp, Matthew Rueben, Cindy M. Grimm, and William D. Smart. A focus group study of privacy concerns about telepresence robots. In Proceedings of the 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pages 1451–1458. IEEE, 2017. [ http ]
[9] Matthew Rueben, Cindy M Grimm, Frank J Bernieri, and William D Smart. A taxonomy of privacy constructs for privacy-sensitive robotics. 2017. arXiv:1701.00841v1 [cs.CY]. [ bib | .pdf ]
[10] Matthew Rueben, Frank J Bernieri, Cindy M Grimm, and William D Smart. Framing effects on privacy concerns about a home telepresence robot. In Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction, pages 435–444. ACM, 2017. [ bib | http ]
[11] Margot E Kaminski, Matthew Rueben, William D Smart, and Cindy M Grimm. Averting robot eyes. Md. L. Rev., 76:983, 2017. Nominated for the Future of Privacy Forum's Privacy Papers for Policymakers Award.bib ]
[12] Matthew Rueben and William D Smart. Privacy in human-robot interaction: Survey and future work. In We Robot 2016: the Fifth Annual Conference on Legal and Policy Issues relating to Robotics. University of Miami School of Law, 2016. Discussant: Ashkan Soltani, Independent Researcher. [ bib | .pdf ]
[13] Leo Bowen-Biggs, Suzanne Dazo, Yili Zhang, Alexander Hubers, Matthew Rueben, Ross Sowell, William D Smart, and Cindy M Grimm. A method for establishing correspondences between hand-drawn and sensor-generated maps. In Proceedings of the Eighth International Conference on Social Robotics (ICSR), pages 1003–1013. Springer, 2016. [ bib ]
[14] Matthew Rueben, Frank J Bernieri, Cindy M Grimm, and William D Smart. Evaluation of physical marker interfaces for protecting visual privacy from mobile robots. In Proceedings of the 25th IEEE International Symposium on Robot and Human Interactive Communication (RO–MAN 2016), pages 787–794. IEEE, 2016. [ bib | http ]
[15] Alexander Hubers, Emily Andrulis, Levi Scott, Tanner Stirrat, Ruonan Zhang, Ross Sowell, Matthew Rueben, Cindy M Grimm, and William D Smart. Using video manipulation to protect privacy in remote presence systems. In Social Robotics, Proceedings of the Seventh International Conference on Social Robotics, pages 245–254. Springer, 2015. [ bib ]
[16] Matthew Rueben and William D Smart. Shared autonomy perception and manipulation of physical device controls. In Proceedings of the Eleventh International Symposium on Visual Computing (ISVC), pages 830–841. Springer, 2015. [ bib ]
[17] Matthew Rueben, Daniel T Cox, Rob A Holman, Sungwon Shin, and John Stanley. Optical Measurements of Tsunami Inundation and Debris Movement in a Large-Scale Wave Basin. Journal of Waterway, Port, Coastal, and Ocean Engineering, 2014. Named a 2015 Outstanding Paper by this journal. [ bib ]
[18] Matthew Rueben, Rob A Holman, Daniel T Cox, Sungwon Shin, Jason Killian, and John Stanley. Optical measurements of tsunami inundation through an urban waterfront modeled in a large-scale laboratory basin. Coastal Engineering, 58(3):229–238, March 2011. [ bib ]

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Last updated Feb 1, 2020.