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ARS-Robot Learning 2012 : International Journal of Advanced Robotic Systems-Special issue on Robot Learning | |||||||||||
Link: http://www.intechopen.com/journals/invitation/international_journal_of_advanced_robotic_systems_robot_learning | |||||||||||
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Call For Papers | |||||||||||
For many years, building autonomous robots that can learn and gain new capabilities for accomplishing task(s) or adapt to changing environments, has been a major research area in AI, Robotics, and Cognitive Sciences.
Although Machine Learning methods have already been applied to some robotic problems, due to the difficulties of learning in natural environments e.g. partial observability, high-dimentionality, and time-complexity of learning process, few examles of successfull application of them is available for commercially existing robots. Learning techniques can be applied to various stages of robot control form perception to action e.g. combining multi-modal information, attention control and specifying the relevant sensory information, decision making and high-level planing, action generation and motor control. The growing number of research programs on robot learning shows an ever increasing interest on this field and its application specially to allow robots to be accepted as a companion by human. In this special issue of Journal of Advanced Robotic Systems on Robot Learning, we intend to outline recent progress in the application of machine learning techniques to robotics. Examples of topics of interest include, but are not limited to: Bio-inspired robot learning Evolutionary Robotics Imitation and learning by demonstration Reinforcement learning on robots Incremental Lifelong learning and behavior shaping Hierarchical task learning Distributed multi-robot learning Cooperative and Competitive learning and knowledge transfer between homogeneous and heteregenous robots Statistical learning and probabilistic reasoning for robotic tasks Locomotion learning and Environment adpatation Software and control architecture for robot learning Attention control and state abstraction Cognitive robotics |
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