Synopsis
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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Etat : new. Questo è un articolo print on demand. N° de réf. du vendeur BUV2HUOVMT
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Inductive Biases in Machine Learning for Robotics and Control
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Springer, Berlin, Springer Nature Switzerland, Springer Aug 2024, 2024
ISBN 10 : 3031378342
ISBN 13 : 9783031378348
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Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
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Taschenbuch. Etat : 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. N° de réf. du vendeur 9783031378348
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Inductive Biases in Machine Learning for Robotics and Control
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Springer Verlag GmbH, 2024
ISBN 10 : 3031378342
ISBN 13 : 9783031378348
Neuf
Couverture souple
Vendeur : moluna, Greven, Allemagne
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Inductive Biases in Machine Learning for Robotics and Control (Springer Tracts in Advanced Robotics)
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Inductive Biases in Machine Learning for Robotics and Control
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Taschenbuch. Etat : 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]hartmann[at]springer[dot]com | Anbieter: preigu. N° de réf. du vendeur 129767022
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Inductive Biases in Machine Learning for Robotics and Control
Edité par
Springer, Springer International Publishing Aug 2024, 2024
ISBN 10 : 3031378342
ISBN 13 : 9783031378348
Neuf
Taschenbuch
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
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Taschenbuch. Etat : 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. N° de réf. du vendeur 9783031378348
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Inductive Biases in Machine Learning for Robotics and Control
Edité par
Springer Nature Switzerland, 2024
ISBN 10 : 3031378342
ISBN 13 : 9783031378348
Neuf
Taschenbuch
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
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Taschenbuch. Etat : 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. N° de réf. du vendeur 9783031378348
Acheter neuf
EUR 128,39
Expédition à EUR 61,10
Expédition depuis Allemagne vers Etats-Unis
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