Learning for Adaptive and Reactive Robot Control / Intelligent Robotics and Autonomous Agents series (ePub)
A Dynamical Systems Approach
(Sprache: Englisch)
Methods by which robots can learn control laws that enable real-time reactivity using dynamical systems; with applications and exercises.
This book presents a wealth of machine learning techniques to make the control of robots more flexible and safe when...
This book presents a wealth of machine learning techniques to make the control of robots more flexible and safe when...
sofort als Download lieferbar
eBook (ePub)
92.00 €
- Lastschrift, Kreditkarte, Paypal, Rechnung
- Kostenloser tolino webreader
Produktdetails
Produktinformationen zu „Learning for Adaptive and Reactive Robot Control / Intelligent Robotics and Autonomous Agents series (ePub)“
Methods by which robots can learn control laws that enable real-time reactivity using dynamical systems; with applications and exercises.
This book presents a wealth of machine learning techniques to make the control of robots more flexible and safe when interacting with humans. It introduces a set of control laws that enable reactivity using dynamical systems, a widely used method for solving motion-planning problems in robotics. These control approaches can replan in milliseconds to adapt to new environmental constraints and offer safe and compliant control of forces in contact. The techniques offer theoretical advantages, including convergence to a goal, non-penetration of obstacles, and passivity. The coverage of learning begins with low-level control parameters and progresses to higher-level competencies composed of combinations of skills.
Learning for Adaptive and Reactive Robot Control is designed for graduate-level courses in robotics, with chapters that proceed from fundamentals to more advanced content. Techniques covered include learning from demonstration, optimization, and reinforcement learning, and using dynamical systems in learning control laws, trajectory planning, and methods for compliant and force control .
Features for teaching in each chapter:
applications, which range from arm manipulators to whole-body control of humanoid robots; pencil-and-paper and programming exercises; lecture videos, slides, and MATLAB code examples available on the author's website . an eTextbook platform website offering protected material[EPS2] for instructors including solutions.
This book presents a wealth of machine learning techniques to make the control of robots more flexible and safe when interacting with humans. It introduces a set of control laws that enable reactivity using dynamical systems, a widely used method for solving motion-planning problems in robotics. These control approaches can replan in milliseconds to adapt to new environmental constraints and offer safe and compliant control of forces in contact. The techniques offer theoretical advantages, including convergence to a goal, non-penetration of obstacles, and passivity. The coverage of learning begins with low-level control parameters and progresses to higher-level competencies composed of combinations of skills.
Learning for Adaptive and Reactive Robot Control is designed for graduate-level courses in robotics, with chapters that proceed from fundamentals to more advanced content. Techniques covered include learning from demonstration, optimization, and reinforcement learning, and using dynamical systems in learning control laws, trajectory planning, and methods for compliant and force control .
Features for teaching in each chapter:
Autoren-Porträt von Aude Billard, Sina Mirrazavi, Nadia Figueroa
Aude Billard is Professor, School of Engineering, Ecole Polytechnique Federale de Lausanne (EPFL) and Director of the Learning Algorithms and Systems Laboratory (LASA). Sina Mirrazavi is a Senior Researcher at Sony. Nadia Figueroa is the Shalini and Rajeev Misra Presidential Assistant Professor in the Mechanical Engineering and Applied Mechanics (MEAM) Department at the University of Pennsylvania.
Bibliographische Angaben
- Autoren: Aude Billard , Sina Mirrazavi , Nadia Figueroa
- 2022, 424 Seiten, Englisch
- Verlag: MIT Press
- ISBN-10: 0262367017
- ISBN-13: 9780262367011
- Erscheinungsdatum: 08.02.2022
Abhängig von Bildschirmgröße und eingestellter Schriftgröße kann die Seitenzahl auf Ihrem Lesegerät variieren.
eBook Informationen
- Dateiformat: ePub
- Größe: 140 MB
- Mit Kopierschutz
- Vorlesefunktion
Sprache:
Englisch
Kopierschutz
Dieses eBook können Sie uneingeschränkt auf allen Geräten der tolino Familie lesen. Zum Lesen auf sonstigen eReadern und am PC benötigen Sie eine Adobe ID.
Family Sharing
eBooks und Audiobooks (Hörbuch-Downloads) mit der Familie teilen und gemeinsam genießen. Mehr Infos hier.
Kommentar zu "Learning for Adaptive and Reactive Robot Control / Intelligent Robotics and Autonomous Agents series"
Schreiben Sie einen Kommentar zu "Learning for Adaptive and Reactive Robot Control / Intelligent Robotics and Autonomous Agents series".
Kommentar verfassen