Caccavale, Riccardo (2017) Flexible Task Execution and Cognitive Control in Human-Robot Interaction. [Tesi di dottorato]

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Item Type: Tesi di dottorato
Lingua: English
Title: Flexible Task Execution and Cognitive Control in Human-Robot Interaction
Creators:
CreatorsEmail
Caccavale, Riccardoriccardo.caccavale@unina.it
Date: 10 April 2017
Number of Pages: 160
Institution: Università degli Studi di Napoli Federico II
Department: Ingegneria Elettrica e delle Tecnologie dell'Informazione
Dottorato: Information technology and electrical engineering
Ciclo di dottorato: 29
Coordinatore del Corso di dottorato:
nomeemail
Riccio, Danieledaniele.riccio@unina.it
Tutor:
nomeemail
Finzi, AlbertoUNSPECIFIED
Date: 10 April 2017
Number of Pages: 160
Uncontrolled Keywords: Cognitive robotics, attentional system, cognitive control, robot architecture
Settori scientifico-disciplinari del MIUR: Area 01 - Scienze matematiche e informatiche > INF/01 - Informatica
Date Deposited: 08 May 2017 22:08
Last Modified: 08 Mar 2018 11:34
URI: http://www.fedoa.unina.it/id/eprint/11842
DOI: 10.6093/UNINA/FEDOA/11842

Abstract

A robotic system that interacts with humans is expected to flexibly execute structured cooperative tasks while reacting to unexpected events and behaviors. In this thesis, these issues are faced presenting a framework that integrates cognitive control, executive attention, structured task execution and learning. In the proposed approach, the execution of structured tasks is guided by top-down (task-oriented) and bottom-up (stimuli-driven) attentional processes that affect behavior selection and activation, while resolving conflicts and decisional impasses. Specifically, attention is here deployed to stimulate the activations of multiple hierarchical behaviors orienting them towards the execution of finalized and interactive activities. On the other hand, this framework allows a human to indirectly and smoothly influence the robotic task execution exploiting attention manipulation. We provide an overview of the overall system architecture discussing the framework at work in different applicative contexts. In particular, we show that multiple concurrent tasks/plans can be effectively orchestrated and interleaved in a flexible manner; moreover, in a human-robot interaction setting, we test and assess the effectiveness of attention manipulation and learning processes.

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