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

Wolfram Erlhagen () and Estela Bicho ()
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Wolfram Erlhagen: University of Minho, Department of Mathematics and Applications, Center for Mathematics
Estela Bicho: University of Minho, Department of Industrial Electronics, Centre Algoritmi

Chapter Chapter 13 in Neural Fields, 2014, pp 341-365 from Springer

Abstract: 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 Dynamic neural fields (DNF) (DNFs) to develop processing architectures that are based on neuro-cognitive mechanisms supporting human joint action Joint action . By explaining the emergence of self-stabilized activity in neuronal populations, Dynamic Field Theory Dynamic field theory (DFT) provides a systematic way to endow a robot with crucial cognitive functions Cognition cognitive functions such as working memory Working memory , prediction Prediction and decision making 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.

Keywords: Dynamic Neural Field (DNF); Natural Human-robot Interaction; DNF Model; Suprathreshold Activity; Goal Inference (search for similar items in EconPapers)
Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-642-54593-1_13

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DOI: 10.1007/978-3-642-54593-1_13

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