Inductive Biases in Machine Learning for Robotics and Control

Lutter, Michael

ISBN 10: 3031378318 ISBN 13: 9783031378317
Verlag: Springer, 2023
Neu Hardcover

Verkäufer GreatBookPrices, Columbia, MD, USA Verkäuferbewertung 5 von 5 Sternen 5 Sterne, Erfahren Sie mehr über Verkäufer-Bewertungen

AbeBooks-Verkäufer seit 6. April 2009


Beschreibung

Beschreibung:

Bestandsnummer des Verkäufers 46421166-n

Diesen Artikel melden

Inhaltsangabe:

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.

Von der hinteren Coverseite:

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.

„Über diesen Titel“ kann sich auf eine andere Ausgabe dieses Titels beziehen.

Bibliografische Details

Titel: Inductive Biases in Machine Learning for ...
Verlag: Springer
Erscheinungsdatum: 2023
Einband: Hardcover
Zustand: New

Beste Suchergebnisse bei AbeBooks