Semantic visual recognition in a cognitive architecture for social robots

• Published in: Integrated computer-aided engineering Vol. 27; no. 3; pp. 301 - 316
• Main Authors: Martin-Rico, Francisco, Gomez-Donoso, Francisco, Escalona, Felix, Garcia-Rodriguez, Jose, Cazorla, Miguel
• Format: Journal Article
• Language: English
• Published: Amsterdam IOS Press BV 01.01.2020
• Subjects: Cognition & reasoning; Feature extraction; Image detection; Knowledge representation; Machine learning; Object recognition; Reasoning; Robots; Semantics
• ISSN: 1069-2509; 1875-8835
• DOI: 10.3233/ICA-200624

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.