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.
CITATION STYLE
Erlhagen, W., & Bicho, E. (2014). A dynamic neural field approach to natural and efficient human-robot collaboration. In Neural Fields: Theory and Applications (Vol. 9783642545931, pp. 341–365). Springer-Verlag Berlin Heidelberg. https://doi.org/10.1007/978-3-642-54593-1_13
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