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Wearable technology for robotic mani...

Fang, Bin.

Wearable technology for robotic manipulation and learning

Record Type:	Electronic resources : Monograph/item
Title/Author:	Wearable technology for robotic manipulation and learningby Bin Fang ... [et al.].
other author:	Fang, Bin.
Published:	Singapore :Springer Singapore :2020.
Description:	xxiv, 208 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Robotics.
Online resource:	https://doi.org/10.1007/978-981-15-5124-6
ISBN:	9789811551246$q(electronic bk.)

Wearable technology for robotic manipulation and learning
Wearable technology for robotic manipulation and learning[electronic resource] /by Bin Fang ... [et al.]. - Singapore :Springer Singapore :2020. - xxiv, 208 p. :ill., digital ;24 cm.

Over the next few decades, millions of people, with varying backgrounds and levels of technical expertise, will have to effectively interact with robotic technologies on a daily basis. This means it will have to be possible to modify robot behavior without explicitly writing code, but instead via a small number of wearable devices or visual demonstrations. At the same time, robots will need to infer and predict humans' intentions and internal objectives on the basis of past interactions in order to provide assistance before it is explicitly requested; this is the basis of imitation learning for robotics. This book introduces readers to robotic imitation learning based on human demonstration with wearable devices. It presents an advanced calibration method for wearable sensors and fusion approaches under the Kalman filter framework, as well as a novel wearable device for capturing gestures and other motions. Furthermore it describes the wearable-device-based and vision-based imitation learning method for robotic manipulation, making it a valuable reference guide for graduate students with a basic knowledge of machine learning, and for researchers interested in wearable computing and robotic learning.

ISBN: 9789811551246$q(electronic bk.)

Standard No.: 10.1007/978-981-15-5124-6doiSubjects--Topical Terms:

181952
Robotics.

LC Class. No.: TJ211

Dewey Class. No.: 629.892

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520 $a Over the next few decades, millions of people, with varying backgrounds and levels of technical expertise, will have to effectively interact with robotic technologies on a daily basis. This means it will have to be possible to modify robot behavior without explicitly writing code, but instead via a small number of wearable devices or visual demonstrations. At the same time, robots will need to infer and predict humans' intentions and internal objectives on the basis of past interactions in order to provide assistance before it is explicitly requested; this is the basis of imitation learning for robotics. This book introduces readers to robotic imitation learning based on human demonstration with wearable devices. It presents an advanced calibration method for wearable sensors and fusion approaches under the Kalman filter framework, as well as a novel wearable device for capturing gestures and other motions. Furthermore it describes the wearable-device-based and vision-based imitation learning method for robotic manipulation, making it a valuable reference guide for graduate students with a basic knowledge of machine learning, and for researchers interested in wearable computing and robotic learning.
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