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A Dynamic Neural Field Approach to Natural and Efficient Human-Robot Collaboration

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Neural Fields

Abstract

A major challenge in modern robotics is the design of autonomous robots that are able to cooperate with people in their daily tasks in a human-like way. We address the challenge of natural human-robot interactions by using the theoretical framework of Dynamic Neural Fields (DNFs) to develop processing architectures that are based on neuro-cognitive mechanisms supporting human joint action . By explaining the emergence of self-stabilized activity in neuronal populations, Dynamic Field Theory provides a systematic way to endow a robot with crucial cognitive functions such as working memory , prediction and decision making . The DNF architecture for joint action is organized as a large scale network of reciprocally connected neuronal populations that encode in their firing patterns specific motor behaviors, action goals, contextual cues and shared task knowledge. Ultimately, it implements a context-dependent mapping from observed actions of the human onto adequate complementary behaviors that takes into account the inferred goal of the co-actor. We present results of flexible and fluent human-robot cooperation in a task in which the team has to assemble a toy object from its components.

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Notes

  1. 1.

    But see http://www.youtube.com/watch?v=A0qemfXnWiE for a video with the complete construction task.

  2. 2.

    Video of the human-robot interactions depicted in Fig. 13.3 can be found in http://dei-s1.dei.uminho.pt/pessoas/estela/Videos/JAST/Video_Fig4_Aros_Human_Toy_Vehicle.mpg.

  3. 3.

    For the video see http://www.youtube.com/watch?v=7t5DLgH4DeQ.

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Acknowledgements

The present research was conducted in the context of the fp6-IST2 EU-IP Project JAST (proj. nr. 003747) and partly financed by fp7-people-2011-itn NETT (proj. nr. 289146) and FCT grants POCI/V.5/A0119/2005 and CONC-REEQ/17/2001. We would like to thank Luis Louro, Emanuel Sousa, Flora Ferreira, Eliana Costa e Silva, Rui Silva and Toni Machado for their assistance during the robotic experiments.

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Authors and Affiliations

  1. Department of Mathematics and Applications, Center for Mathematics, University of Minho, Braga, Portugal

    Wolfram Erlhagen

  2. Department of Industrial Electronics, Centre Algoritmi, University of Minho, Braga, Portugal

    Estela Bicho

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  1. Wolfram Erlhagen
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  2. Estela Bicho
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Correspondence to Wolfram Erlhagen .

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Editors and Affiliations

  1. School of Mathematical Sciences, University of Nottingham, Nottingham, United Kingdom

    Stephen Coombes

  2. Department of German Studies and Linguistics, Humboldt-Universität zu Berlin, Berlin, Germany

    Peter beim Graben

  3. Department of Mathematics, University of Reading, Reading, United Kingdom

    Roland Potthast

  4. School of Medicine, University of Auckland, Auckland, New Zealand

    James Wright

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Erlhagen, W., Bicho, E. (2014). A Dynamic Neural Field Approach to Natural and Efficient Human-Robot Collaboration. In: Coombes, S., beim Graben, P., Potthast, R., Wright, J. (eds) Neural Fields. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54593-1_13

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