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    Virtual Reality and Photogrammetry for Improved Reproducibility of Human-Robot Interaction Studies

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    Virtual Reality and Photogrammetry for Improved Reproducibility.pdf (134.7Kb)
    Permanent Link
    https://doi.org/10.1109/VR.2019.8798186
    http://hdl.handle.net/11603/14974
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    • UMBC Computer Science and Electrical Engineering Department
    • UMBC Faculty Collection
    • UMBC Office for the Vice President of Research
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    Author/Creator
    Murnane, Mark
    Breitmeyer, Max
    Matuszek, Cynthia
    Engel, Don
    Date
    2019-08-15
    Type of Work
    2 pages
    Text
    conference papers and proceedings preprints
    Citation of Original Publication
    M. Murnane, M. Breitmeyer, C. Matuszek and D. Engel, "Virtual Reality and Photogrammetry for Improved Reproducibility of Human-Robot Interaction Studies," 2019 IEEE Conference on Virtual Reality and 3D User Interfaces (VR), Osaka, Japan, 2019, pp. 1092-1093. doi: 10.1109/VR.2019.8798186. URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8798186&isnumber=8797678
    Rights
    This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
    © 2019 IEEE.
    Subjects
    Virtual Reality
    VR
    Photogrammetry
    Human-Robot Interaction
    Virtual Presence
    H.5.1 [Information Interfaces and Presentation]: Multimedia Information Systems—Artificial, Augmented and Virtual Realities
    I.2.9 [Artificial Intelligence]: Robotics—Operator Interfaces
    I.2.9 [Artificial Intelligence]: Robotics—Sensors
    Abstract
    Collecting data in robotics, especially human-robot interactions, traditionally requires a physical robot in a prepared environment, which presents substantial scalability challenges. First, robots provide many possible points of system failure, while the availability of human participants is limited. Second, for tasks such as language learning, it is important to create environments which provide interesting, varied use cases. Traditionally, this requires prepared physical spaces for each scenario being studied. Finally, the expense associated with acquiring robots and preparing spaces places serious limitations on the reproducible quality of experiments. We therefore propose a novel mechanism for using virtual reality to simulate robotic sensor data in a series of prepared scenarios. This allows for a reproducible data set which other labs can recreate using commodity VR hardware. The authors demonstrate the effectiveness of this approach with an implementation that includes a simulated physical context, a reconstruction of a human actor, and a reconstruction of a robot. This evaluation shows that even a simple “sandbox” environment allows us to simulate robot sensor data, as well as the movement (e.g. view-port) and speech of humans interacting with the robot in a prescribed scenario


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    Albin O. Kuhn Library & Gallery
    University of Maryland, Baltimore County
    1000 Hilltop Circle
    Baltimore, MD 21250
    www.umbc.edu/scholarworks

    Contact information:
    Email: scholarworks-group@umbc.edu
    Phone: 410-455-3021


    If you wish to submit a copyright complaint or withdrawal request, please email mdsoar-help@umd.edu.