Inductive Biases Machine Learning by Lutter Michael (20 results)
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Inductive Biases in Machine Learning for Robotics and Control (Springer Tracts in Advanced Robotics)
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Taschenbuch. Condition: Neu. Inductive Biases in Machine Learning for Robotics and Control | Michael Lutter | Taschenbuch | Springer Tracts in Advanced Robotics | xv | Englisch | 2024 | Springer | EAN 9783031378348 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartman…n[at]springer[dot]com | Anbieter: preigu.
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Taschenbuch. Condition: 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.
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Buch. Condition: 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 mo…dels 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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Language: English
Published by Springer, Berlin, Springer Nature Switzerland, Springer Aug 2024, 2024
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Taschenbuch. Condition: 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. 119 pp. Englisch.
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Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermanyBuchWeltWeit Ludwig Meier e.K.
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Buch. Condition: 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.
Language: English
Published by Springer, Berlin|Springer Nature Switzerland|Springer, 2023
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Condition: 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 .
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Seller: moluna, Greven, Germanymoluna
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Language: English
Published by Springer, Springer International Publishing Aug 2024, 2024
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Taschenbuch. Condition: 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 rel…ies 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.
Language: English
Published by Springer, Springer International Publishing Aug 2023, 2023
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Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germanybuchversandmimpf2000
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Buch. Condition: 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 bl…ack-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.
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