A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Designing Sociable Robots
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Robot learning driven by emotions
Adaptive Behavior
A Context-Dependent Attention System for a Social Robot
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
ICDL '02 Proceedings of the 2nd International Conference on Development and Learning
Pose Estimation using 3D View-Based Eigenspaces
AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
Applying Learning by Tutelage and Multimodal Interface to Sociable Robots
ICMLA '08 Proceedings of the 2008 Seventh International Conference on Machine Learning and Applications
Modelling Shared Attention Through Relational Reinforcement Learning
Journal of Intelligent and Robotic Systems
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Sociable robots are embodied agents that are part of a heterogeneous society of robots and humans. They should be able to recognize human beings and each other, and to engage in social interactions. The use of a robotic architecture may strongly reduce the time and effort required to construct a sociable robot. Such architecture must have structures and mechanisms to allow social interaction, behavior control and learning from environment. Learning processes described on Science of Behavior Analysis may lead to the development of promising methods and structures for constructing robots able to behave socially and learn through interactions from the environment by a process of contingency learning. In this paper, we present a robotic architecture inspired from Behavior Analysis. Methods and structures of the proposed architecture, including a hybrid knowledge representation, are presented and discussed. The architecture has been evaluated in the context of a nontrivial real problem: the learning of the shared attention, employing an interactive robotic head. The learning capabilities of this architecture have been analyzed by observing the robot interacting with the human and the environment. The obtained results show that the robotic architecture is able to produce appropriate behavior and to learn from social interaction.