Inductive Biases in Machine Learning for Robotics and Control: 156 (Springer Tracts in Advanced Robotics, 156) - Tapa dura
Sinopsis
One important robotics problem is "How can one program a robot to perform a task"? Classical robotics solves this problem by manually engineering modules for state estimation, planning, and control. In contrast, robot learning solely relies on black-box models and data. This book shows that these two approaches of classical engineering and black-box machine learning are not mutually exclusive. To solve tasks with robots, one can transfer insights from classical robotics to deep networks and obtain better learning algorithms for robotics and control. To highlight that incorporating existing knowledge as inductive biases in machine learning algorithms improves performance, this book covers different approaches for learning dynamics models and learning robust control policies. The presented algorithms leverage the knowledge of Newtonian Mechanics, Lagrangian Mechanics as well as the Hamilton-Jacobi-Isaacs differential equation as inductive bias and are evaluated on physical robots.
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One important robotics problem is “How can one program a robot to perform a task”? Classical robotics solves this problem by manually engineering modules for state estimation, planning, and control. In contrast, robot learning solely relies on black-box models and data. This book shows that these two approaches of classical engineering and black-box machine learning are not mutually exclusive. To solve tasks with robots, one can transfer insights from classical robotics to deep networks and obtain better learning algorithms for robotics and control. To highlight that incorporating existing knowledge as inductive biases in machine learning algorithms improves performance, this book covers different approaches for learning dynamics models and learning robust control policies. The presented algorithms leverage the knowledge of Newtonian Mechanics, Lagrangian Mechanics as well as the Hamilton-Jacobi-Isaacs differential equation as inductive bias and are evaluated on physical robots.
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Inductive Biases in Machine Learning for Robotics and Control (eng)
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Inductive Biases in Machine Learning for Robotics and Control (Springer Tracts in Advanced Robotics, 156)
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Inductive Biases in Machine Learning for Robotics and Control
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Springer Nature Switzerland Aug 2023, 2023
ISBN 10: 3031378318
ISBN 13: 9783031378317
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Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -One important robotics problem is 'How can one program a robot to perform a task' Classical robotics solves this problem by manually engineering modules for state estimation, planning, and control. In contrast, robot learning solely relies on black-box models and data. This book shows that these two approaches of classical engineering and black-box machine learning are not mutually exclusive. To solve tasks with robots, one can transfer insights from classical robotics to deep networks and obtain better learning algorithms for robotics and control. To highlight that incorporating existing knowledge as inductive biases in machine learning algorithms improves performance, this book covers different approaches for learning dynamics models and learning robust control policies. The presented algorithms leverage the knowledge of Newtonian Mechanics, Lagrangian Mechanics as well as the Hamilton-Jacobi-Isaacs differential equation as inductive bias and are evaluated on physical robots. 136 pp. Englisch. Nº de ref. del artículo: 9783031378317
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Inductive Biases in Machine Learning for Robotics and Control (Springer Tracts in Advanced Robotics, 156)
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Inductive Biases in Machine Learning for Robotics and Control (Springer Tracts in Advanced Robotics, 156)
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Inductive Biases in Machine Learning for Robotics and Control
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Springer, Berlin|Springer Nature Switzerland|Springer, 2023
ISBN 10: 3031378318
ISBN 13: 9783031378317
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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. One important robotics problem is How can one program a robot to perform a task ? Classical robotics solves this problem by manually engineering modules for state estimation, planning, and control. In contrast, robot learning solely relies on black-box . Nº de ref. del artículo: 888374518
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Inductive Biases in Machine Learning for Robotics and Control
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Springer, Springer International Publishing Aug 2023, 2023
ISBN 10: 3031378318
ISBN 13: 9783031378317
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Buch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -One important robotics problem is How can one program a robot to perform a task Classical robotics solves this problem by manually engineering modules for state estimation, planning, and control. In contrast, robot learning solely relies on black-box models and data. This book shows that these two approaches of classical engineering and black-box machine learning are not mutually exclusive. To solve tasks with robots, one can transfer insights from classical robotics to deep networks and obtain better learning algorithms for robotics and control. To highlight that incorporating existing knowledge as inductive biases in machine learning algorithms improves performance, this book covers different approaches for learning dynamics models and learning robust control policies. The presented algorithms leverage the knowledge of Newtonian Mechanics, Lagrangian Mechanics as well as the Hamilton-Jacobi-Isaacs differential equation as inductive bias and are evaluated on physical robots.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 136 pp. Englisch. Nº de ref. del artículo: 9783031378317
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Inductive Biases in Machine Learning for Robotics and Control
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Springer Nature Switzerland, 2023
ISBN 10: 3031378318
ISBN 13: 9783031378317
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Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - One important robotics problem is 'How can one program a robot to perform a task' Classical robotics solves this problem by manually engineering modules for state estimation, planning, and control. In contrast, robot learning solely relies on black-box models and data. This book shows that these two approaches of classical engineering and black-box machine learning are not mutually exclusive. To solve tasks with robots, one can transfer insights from classical robotics to deep networks and obtain better learning algorithms for robotics and control. To highlight that incorporating existing knowledge as inductive biases in machine learning algorithms improves performance, this book covers different approaches for learning dynamics models and learning robust control policies. The presented algorithms leverage the knowledge of Newtonian Mechanics, Lagrangian Mechanics as well as the Hamilton-Jacobi-Isaacs differential equation as inductive bias and are evaluated on physical robots. Nº de ref. del artículo: 9783031378317
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