Name: Human–Robot Interaction Control Using Reinforcement Learning (Ieee Press Series on Systems Science and Engineering) (en Inglés)
Price: 149840.00 CLP
Availability: InStock
Author: Yu, Wen ; Perrusquia, Adolfo
ISBN: 9781119782742

portada Human–Robot Interaction Control Using Reinforcement Learning (Ieee Press Series on Systems Science and Engineering) (en Inglés)

Formato

Libro Físico

Autor

Wen Yu

Adolfo Perrusquia

Editorial

Wiley-IEEE Press

Año

2021

Idioma

Inglés

N° páginas

260

Encuadernación

Tapa Dura

Dimensiones

22.9 x 15.2 x 1.8 cm

Peso

0.56 kg.

ISBN13

9781119782742

N° edición

Categorías

Robótica
Hardware de redes
Redes neuronales y sistemas difusos

Human–Robot Interaction Control Using Reinforcement Learning (Ieee Press Series on Systems Science and Engineering) (en Inglés)

Wen Yu (Autor) · Adolfo Perrusquia (Autor) · Wiley-IEEE Press · Tapa Dura

Libro Físico

$ 149.840

$ 299.680

Ahorras: $ 149.840

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Envío normal

Origen: Estados Unidos (Costos de importación incluídos en el precio)

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Reseña del libro "Human–Robot Interaction Control Using Reinforcement Learning (Ieee Press Series on Systems Science and Engineering) (en Inglés)"

A comprehensive exploration of the control schemes of human-robot interactions In Human-Robot Interaction Control Using Reinforcement Learning, an expert team of authors delivers a concise overview of human-robot interaction control schemes and insightful presentations of novel, model-free and reinforcement learning controllers. The book begins with a brief introduction to state-of-the-art human-robot interaction control and reinforcement learning before moving on to describe the typical environment model. The authors also describe some of the most famous identification techniques for parameter estimation. Human-Robot Interaction Control Using Reinforcement Learning offers rigorous mathematical treatments and demonstrations that facilitate the understanding of control schemes and algorithms. It also describes stability and convergence analysis of human-robot interaction control and reinforcement learning based control. The authors also discuss advanced and cutting-edge topics, like inverse and velocity kinematics solutions, H2 neural control, and likely upcoming developments in the field of robotics. Readers will also enjoy: A thorough introduction to model-based human-robot interaction control Comprehensive explorations of model-free human-robot interaction control and human-in-the-loop control using Euler angles Practical discussions of reinforcement learning for robot position and force control, as well as continuous time reinforcement learning for robot force control In-depth examinations of robot control in worst-case uncertainty using reinforcement learning and the control of redundant robots using multi-agent reinforcement learning Perfect for senior undergraduate and graduate students, academic researchers, and industrial practitioners studying and working in the fields of robotics, learning control systems, neural networks, and computational intelligence, Human-Robot Interaction Control Using Reinforcement Learning is also an indispensable resource for students and professionals studying reinforcement learning.