Semantic visual recognition in a cognitive architecture for social robots
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http://hdl.handle.net/10045/106868
Título: | Semantic visual recognition in a cognitive architecture for social robots |
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Autor/es: | Martín Rico, Francisco | Gomez-Donoso, Francisco | Escalona, Félix | Garcia-Rodriguez, Jose | Cazorla, Miguel |
Grupo/s de investigación o GITE: | Robótica y Visión Tridimensional (RoViT) | Arquitecturas Inteligentes Aplicadas (AIA) |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Ciencia de la Computación e Inteligencia Artificial | Universidad de Alicante. Departamento de Tecnología Informática y Computación | Universidad de Alicante. Instituto Universitario de Investigación Informática |
Palabras clave: | Cognitive architectures | People recognition | Pose estimation | Social robotics |
Área/s de conocimiento: | Ciencia de la Computación e Inteligencia Artificial | Arquitectura y Tecnología de Computadores |
Fecha de publicación: | 8-may-2020 |
Editor: | IOS Press |
Cita bibliográfica: | Integrated Computer-Aided Engineering. 2020, 27(3): 301-316. doi:10.3233/ICA-200624 |
Resumen: | Cognitive architectures allow robots to perform their operations by drawing on a process that aims to simulate human reasoning. This paper presents an integrated semantic artificial memory system in cognitive architecture based on symbolic reasoning and a connective representation of the knowledge. This memory system attempts to simulate how humans learn to distinguish instances of particular objects within their class using a convolutional network to detect the relevant elements of an image. We use a vector with the extracted features to learn to discriminate an instance of another element from the same class. A novel feature of our approach is its autonomous learning process during the operation of the robot, integrating a deep learning embedding with a statistical classifier. The usefulness and robustness of this method are demonstrated by applying it to a social robot that learns to differentiate people. Finally, experiments are carried out to validate our approach, comparing the detection results with several alternative methods. |
Patrocinador/es: | This work has been funded by the Spanish Government TIN2016-76515-R grant for the COMBAHO project, supported with Feder funds. This work has also been supported by a Spanish grant for PhD studies ACIF/2017/243 and FPU16/00887. |
URI: | http://hdl.handle.net/10045/106868 |
ISSN: | 1069-2509 (Print) | 1875-8835 (Online) |
DOI: | 10.3233/ICA-200624 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © 2020 – IOS Press and the author(s) |
Revisión científica: | si |
Versión del editor: | https://doi.org/10.3233/ICA-200624 |
Aparece en las colecciones: | INV - RoViT - Artículos de Revistas INV - AIA - Artículos de Revistas |
Archivos en este ítem:
Archivo | Descripción | Tamaño | Formato | |
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Martin-Rico_etal_2020_IntegratedComp-AidedEng_final.pdf | Versión final (acceso restringido) | 13,27 MB | Adobe PDF | Abrir Solicitar una copia |
Martin-Rico_etal_2020_IntegratedComp-AidedEng_revised.pdf | Versión revisada (acceso abierto) | 8,28 MB | Adobe PDF | Abrir Vista previa |
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