Caccavale, Riccardo
(2017)
Flexible Task Execution and Cognitive Control in Human-Robot Interaction.
[Tesi di dottorato]
| Item Type: |
Tesi di dottorato
|
| Resource language: |
English |
| Title: |
Flexible Task Execution and Cognitive Control in Human-Robot Interaction |
| Creators: |
| Creators | Email |
|---|
| Caccavale, Riccardo | riccardo.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: |
| nome | email |
|---|
| Riccio, Daniele | daniele.riccio@unina.it |
|
| Tutor: |
| nome | email |
|---|
| Finzi, Alberto | UNSPECIFIED |
|
| Date: |
10 April 2017 |
| Number of Pages: |
160 |
| Keywords: |
Cognitive robotics, attentional system, cognitive control, robot architecture |
| Settori scientifico-disciplinari del MIUR: |
Area 01 - Scienze matematiche e informatiche > INF/01 - Informatica |
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| 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 |
Collection description
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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