A cognitive architecture for Robotic hand posture learning

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 a...

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Published inIEEE 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
LanguageEnglish
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
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Abstract 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.
AbstractList 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.
Author Macaluso, I.
Infantino, I.
Dindo, H.
Chella, A.
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Issue 1
Keywords Human
Human operator
Video technique
Hand
Modeling
Robotics
Posture
Inverse problem
Kinematics
Gesture
Fast algorithm
Localization
Artificial intelligence
Visual control
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SubjectTerms 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
Title A cognitive architecture for Robotic hand posture learning
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Volume 35
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