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  • 1.
    Lamb, Maurice
    et al.
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment. University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment. Center for Cognition, Action and Perception, Department of Psychology, University of Cincinnati, USA.
    Nalepka, Patrick
    Department of Psychology, Center for Elite Performance, Expertise and Training, Macquarie University, Australia.
    Kallen, Rachel W.
    Department of Psychology, Center for Elite Performance, Expertise and Training, Macquarie University, Australia.
    Lorenz, Tamara
    Center for Cognition, Action and Perception, Department of Psychology, University of Cincinnati, USA / Department of Electrical Engineering and Computer Science, University of Cincinnati, USA / Department of Mechanical and Materials Engineering, University of Cincinnati, USA.
    Harrison, Steven J.
    Department of Kinesiology, University of Connecticut, USA.
    Minai, Ali A.
    Department of Electrical Engineering and Computer Science, University of Cincinnati, USA.
    Richardson, Michael J.
    Department of Psychology, Center for Elite Performance, Expertise and Training, Macquarie University, Australia.
    A Hierarchical Behavioral Dynamic Approach for Naturally Adaptive Human-Agent Pick-and-Place Interactions2019In: Complexity, ISSN 1076-2787, E-ISSN 1099-0526, article id 5964632Article in journal (Refereed)
    Abstract [en]

    Interactive or collaborative pick-and-place tasks occur during all kinds of daily activities, for example, when two or more individuals pass plates, glasses, and utensils back and forth between each other when setting a dinner table or loading a dishwasher together. In the near future, participation in these collaborative pick-and-place tasks could also include robotic assistants. However, for human-machine and human-robot interactions, interactive pick-and-place tasks present a unique set of challenges. A key challenge is that high-level task-representational algorithms and preplanned action or motor programs quickly become intractable, even for simple interaction scenarios. Here we address this challenge by introducing a bioinspired behavioral dynamic model of free-flowing cooperative pick-and-place behaviors based on low-dimensional dynamical movement primitives and nonlinear action selection functions. Further, we demonstrate that this model can be successfully implemented as an artificial agent control architecture to produce effective and robust human-like behavior during human-agent interactions. Participants were unable to explicitly detect whether they were working with an artificial (model controlled) agent or another human-coactor, further illustrating the potential effectiveness of the proposed modeling approach for developing systems of robust real/embodied human-robot interaction more generally.

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