Inductive Biases in Machine Learning for Robotics and Control
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
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

Inductive Biases in Machine Learning for Robotics and Control

by Michael Lutter
Inductive Biases in Machine Learning for Robotics and Control
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Inductive Biases in Machine Learning for Robotics and Control

Inductive Biases in Machine Learning for Robotics and Control

by Michael Lutter

Overview

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.

Product Details

ISBN-13: 9783031378317
Publisher: Springer Nature Switzerland
Publication date: 08/01/2023
Series: Springer Tracts in Advanced Robotics , #156
Edition description: 2023
Pages: 119
Product dimensions: 6.10(w) x 9.25(h) x (d)

Table of Contents

Introduction.- A Differentiable Newton-Euler Algorithm for Real-World Robotics.- Combining Physics and Deep Learning for Continuous-Time Dynamics Models.- Continuous-Time Fitted Value Iteration for Robust Policies.- Conclusion.
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