Machine Learning for Robotics: Learning Methods for Robot Motor Skills

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9783639021103: Machine Learning for Robotics: Learning Methods for Robot Motor Skills
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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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Jan 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 & Mechanical Engineering in Munich, Singapore and Los Angeles and was a visiting researcher at National Univ. of Singapore and ATR in Japan. His research centers on motor skill learning and robotics.

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Book Description Condition: New. Publisher/Verlag: VDM Verlag Dr. Müller | Learning Methods for Robot Motor Skills | 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 amultitude 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 skilllearning that employs domain-specific machine learning methods. A theoretically well-founded general approach to representing the required control structures for task representation and executionis 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. | Format: Paperback | Language/Sprache: english | 180 gr | 220x150x7 mm | 128 pp. Seller Inventory # K9783639021103

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Book Description VDM Verlag Dr. Mueller e.K., Germany, 2013. Paperback. Condition: New. Language: English . Brand New Book ***** Print on Demand *****.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. Seller Inventory # AAV9783639021103

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Book Description VDM Verlag Dr. Müller E.K. Nov 2013, 2013. Taschenbuch. Condition: Neu. Neuware - 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 amultitude 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 skilllearning that employs domain-specific machine learning methods. A theoretically well-founded general approach to representing the required control structures for task representation and executionis 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. 128 pp. Englisch. Seller Inventory # 9783639021103

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Book Description VDM Verlag Dr. Müller E.K. Nov 2013, 2013. Taschenbuch. Condition: Neu. Neuware - 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 amultitude 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 skilllearning that employs domain-specific machine learning methods. A theoretically well-founded general approach to representing the required control structures for task representation and executionis 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. 128 pp. Englisch. Seller Inventory # 9783639021103

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Book Description VDM Verlag. Paperback. Condition: New. 128 pages. Dimensions: 9.1in. x 6.2in. x 0.4in.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. This item ships from multiple locations. Your book may arrive from Roseburg,OR, La Vergne,TN. Paperback. Seller Inventory # 9783639021103

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