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A Hierarchical Behavioral Dynamic Approach for Naturally Adaptive Human-Agent Pick-and-Place Interactions
Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi. Center for Cognition, Action and Perception, Department of Psychology, University of Cincinnati, USA. (Interaction Lab (ILAB))ORCID-id: 0000-0003-2254-1396
Department of Psychology, Center for Elite Performance, Expertise and Training, Macquarie University, Australia.
Department of Psychology, Center for Elite Performance, Expertise and Training, Macquarie University, Australia.
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.
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2019 (Engelska)Ingår i: Complexity, ISSN 1076-2787, E-ISSN 1099-0526, artikel-id 5964632Artikel i tidskrift (Refereegranskat) Published
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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John Wiley & Sons, 2019. artikel-id 5964632
Nationell ämneskategori
Data- och informationsvetenskap Robotteknik och automation
Forskningsämne
Interaction Lab (ILAB)
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URN: urn:nbn:se:his:diva-17433DOI: 10.1155/2019/5964632ISI: 000472844800001Scopus ID: 2-s2.0-85068211658OAI: oai:DiVA.org:his-17433DiVA, id: diva2:1337197
Tillgänglig från: 2019-07-12 Skapad: 2019-07-12 Senast uppdaterad: 2019-08-27Bibliografiskt granskad

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