Autonomous robots have been a vision of robotics, artificial intelligence, and cognitive sciences. An important step towards this goal is to create robots that can learn to accomplish a multitude of different tasks triggered by environmental context and higher-level instruction. Early approaches to this goal during the heydays of artificial intelligence research in the late 1980s showed that handcrafted approaches do not suffice and that machine learning is needed. However, off the shelf learning techniques often do not scale into real-time or to the high-dimensional domains of manipulator and humanoid robotics. In this book, we investigate the foundations for a general approach to motor skill learning that employs domain-specific machine learning methods. A theoretically well-founded general approach to representing the required control structures for task representation and execution is presented along with novel learning algorithms that can be applied in this setting. The resulting framework is shown to work well both in simulation and on real robots.
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Kartoniert / Broschiert. Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Peters JanJan Peters heads the Robot Learning Lab at Max-Planck Institute for Biological Cybernetics. He graduated from Univ. of Southern California with a PhD in Computer Science. He studied Computer Science, Electrical & Mechanic. Bestandsnummer des Verkäufers 4950053
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Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Autonomous robots have been a vision of robotics, artificial intelligence, and cognitive sciences. An important step towards this goal is to create robots that can learn to accomplish a multitude of different tasks triggered by environmental context and higher-level instruction. Early approaches to this goal during the heydays of artificial intelligence research in the late 1980s showed that handcrafted approaches do not suffice and that machine learning is needed. However, off the shelf learning techniques often do not scale into real-time or to the high-dimensional domains of manipulator and humanoid robotics. In this book, we investigate the foundations for a general approach to motor skill learning that employs domain-specific machine learning methods. A theoretically well-founded general approach to representing the required control structures for task representation and execution is presented along with novel learning algorithms that can be applied in this setting. The resulting framework is shown to work well both in simulation and on real robots. Bestandsnummer des Verkäufers 9783639021103
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Taschenbuch. Zustand: Neu. Machine Learning for Robotics | Learning Methods for Robot Motor Skills | Jan Peters | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2013 | VDM Verlag Dr. Müller | EAN 9783639021103 | Verantwortliche Person für die EU: OmniScriptum GmbH & Co. KG, Bahnhofstr. 28, 66111 Saarbrücken, info[at]akademikerverlag[dot]de | Anbieter: preigu. Bestandsnummer des Verkäufers 101816780
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