Affiliations: [a] Departamento de Teoria de la Señal y Sistemas Telematicos y Computacion, University Rey Juan Carlos, Madrid, Spain | [b] Institute for Computing Research, University of Alicante, Alicante, Spain
Correspondence:
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Corresponding author: Francisco Martin-Rico, Departamento de Teoria de la Señal y Sistemas Telematicos y Computacion, University Rey Juan Carlos, Madrid, Spain. E-mail: [email protected].
Abstract: 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.
Keywords: Cognitive architectures, people recognition, pose estimation, social robotics
