Titlebook: Inductive Biases in Machine Learning for Robotics and Control; Michael Lutter Book 2023 The Editor(s) (if applicable) and The Author(s), u

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Introduction,e of this technique is that these modules must be manually developed, arranged, and tuned for each task. Therefore, engineering these systems is labor-intensive and requires expert knowledge. For more complex tasks, unstructured environments, and unstructured observations, the associated complexity

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Book 2023ules 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 insight

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1610-7438 ing or wanting to learn more on robot learning with inductiv.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 relie

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Conclusion, main take-away of this book is that one can use deep networks in more creative ways than naive input-output mappings for learning dynamics models or policies. In the following, we summarize the contributions of the three chapters and discuss the open challenges of the presented algorithms.

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