Description

Book Synopsis
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

Learning for Adaptive and Reactive Robot Control

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    A Hardback by Aude Billard, Sina Mirrazavi

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      Book details

      Publisher MIT Press Ltd
      Published 1 February 2022
      ISBN-13 9780262046169
      978-0262046169
      ISBN-10 0262046164
      Also in:
      Technology, Engineering & Agriculture Technology: general issues

      Description

      Book Synopsis
      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

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