Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Inductive Biases in Machine Learning for Robotics and Control

Inductive Biases in Machine Learning for Robotics and Control

Lutter, Michael

Hardcover

Series: Springer Tracts in Advanced Robotics, Book 156

Technology & Engineering

ISBN10: 3031378318
ISBN13: 9783031378317
Publisher: Springer Nature
Published: Aug 1 2023
Pages: 119
Weight: 0.82
Height: 0.38 Width: 6.14 Depth: 9.21
Language: English

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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