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Learning attentional regulations for structured tasks execution in robotic cognitive control

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Abstract

We present a framework for robotic cognitive control endowed with adaptive mechanisms for attentional regulation and task execution. In cognitive psychology, cognitive control is the process that orchestrates executive and cognitive processes supporting adaptive responses and complex goal-directed behaviors. Similar mechanisms can be deployed in robotic systems in order to flexibly execute complex structured tasks. In this work, following a supervisory attentional system paradigm, we propose an approach that permits to learn how to exploit top-down and bottom-up attentional regulations to guide the execution of hierarchically structured tasks. We present the overall framework discussing its functioning in a mobile robot case study considering pick-carry-place tasks. In this setting, we show that the proposed system can be on-line trained by a user in order to execute incrementally complex activities.

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Acknowledgements

The research leading to these results has been partially supported by the Projects REFILLs (H2020-ICT-731590), RoMoLo (MISE F/050277/01-02-X32 under EU-funded Actions for R&D), and ICOSAF (PON R& I 2014-2020).

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  1. Dipartimento di Ingegneria Elettrica e Tecnologie dell’Informazione, Università di Napoli Federico II, via Claudio 21, 80125, Napoli, Italy

    Riccardo Caccavale & Alberto Finzi

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  1. Riccardo Caccavale
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  2. Alberto Finzi
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Correspondence to Alberto Finzi.

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Caccavale, R., Finzi, A. Learning attentional regulations for structured tasks execution in robotic cognitive control. Auton Robot 43, 2229–2243 (2019). https://doi.org/10.1007/s10514-019-09876-x

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