Abstract
People are proficient at communicating their intentions in order to avoid conflicts when navigating in narrow, crowded environments. Mobile robots, on the other hand, often lack both the ability to interpret human intentions and the ability to clearly communicate their own intentions to people sharing their space. This work addresses the second of these points, leveraging insights about how people implicitly communicate with each other through gaze to enable mobile robots to more clearly signal their navigational intention. We present a human study measuring the importance of gaze in coordinating people’s navigation. This study is followed by the development of a virtual agent head which is added to a mobile robot platform. Comparing the performance of a robot with a virtual agent head against one with an LED turn signal demonstrates its ability to impact people’s navigational choices, and that people more easily interpret the gaze cue than the LED turn signal.
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Notes
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The questions from the survey are available online. https://drive.google.com/drive/folders/1qVj-gU1aFwY6Eq2a_l9ZdesfmQ_QQOC8?usp=sharing. The first question “Condition” was filled in before participants responded.
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Example interactions can be seen in the companion video to this paper, posted at https://youtu.be/rQziUQro9BU.
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Difficulties have been observed in the interpretation of the gaze direction of virtual agents. This effect is so named because it looks as though the Mona Lisa painting by Leonardo da Vinci is always looking at the observer.
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Acknowledgments
This work has taken place in the Learning Agents Research Group (LARG) at UT Austin. LARG research is supported in part by NSF (CPS-1739964, IIS-1724157, NRI-1925082), ONR (N00014-18-2243), FLI (RFP2-000), ARO (W911NF-19-2-0333), DARPA, Lockheed Martin, GM, and Bosch. Peter Stone serves as the Executive Director of Sony AI America and receives financial compensation for this work. The terms of this arrangement have been reviewed and approved by the University of Texas at Austin in accordance with its policy on objectivity in research. Studies in this work were approved under University of Texas at Austin IRB study numbers 2015-06-0058 and 2019-03-0139.
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Hart, J. et al. (2020). Using Human-Inspired Signals to Disambiguate Navigational Intentions. In: Wagner, A.R., et al. Social Robotics. ICSR 2020. Lecture Notes in Computer Science(), vol 12483. Springer, Cham. https://doi.org/10.1007/978-3-030-62056-1_27
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