A cognitive architecture for Robotic hand posture learning

• Published in: IEEE transactions on systems, man and cybernetics. Part C, Applications and reviews Vol. 35; no. 1; pp. 42 - 52
• Main Authors: Infantino, I., Chella, A., Dindo, H., Macaluso, I.
• Format: Journal Article
• Language: English
• Published: New-York, NY IEEE 01.02.2005; Institute of Electrical and Electronics Engineers
• Subjects: Anthropomorphism; Applied sciences; Cameras; Cognitive architecture; Cognitive robotics; Computer science; control theory; systems; Control systems; Control theory. Systems; Exact sciences and technology; gesture recognition; human-robot interface; Humans; Kinematics; Layout; Robot control; Robot vision systems; robotic vision; Robotics; Robustness; Human; Human operator; Video technique; Hand; Modeling; Robotics; Posture; Inverse problem; Kinematics; Gesture; Fast algorithm; Localization; Artificial intelligence; Visual control
• ISSN: 1094-6977; 1558-2442
• DOI: 10.1109/TSMCC.2004.840043

This paper deals with the design and implementation of a visual control of a robotic system composed of a dexterous hand and video camera. The aim of the proposed system is to reproduce the movements of a human hand in order to learn complex manipulation tasks or to interact with the user. A novel algorithm for robust and fast fingertips localization and tracking is presented. A suitable kinematic hand model is adopted to achieve a fast and acceptable solution to an inverse kinematics problem. The system is part of a cognitive architecture for posture learning that integrates the perceptions by a high-level representation of the scene and of the observed actions. The anthropomorphic robotic hand imitates the gestures acquired by the vision system in order to learn meaningful movements, to build its knowledge by different conceptual spaces, and to perform complex interactions with the human operator.