Wearable Technology for Robotic Manipulation and Learning / by Bin Fang, Fuchun Sun, Huaping Liu, Chunfang Liu, Di Guo.
2020
TJ210.2-211.495
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Details
Title
Wearable Technology for Robotic Manipulation and Learning / by Bin Fang, Fuchun Sun, Huaping Liu, Chunfang Liu, Di Guo.
Author
Fang, Bin, author.
Edition
1st ed. 2020.
ISBN
9789811551246
9811551243
9789811551246
9811551235
9789811551239
9811551243
9789811551246
9811551235
9789811551239
Published
Singapore : Springer Singapore : Imprint: Springer, 2020.
Language
English
Description
1 online resource (XXIV, 208 pages) : illustrations.
Call Number
TJ210.2-211.495
Dewey Decimal Classification
629.892
Summary
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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Access limited to authorized users.
Digital File Characteristics
text file PDF
Available in Other Form
Print version: 9789811551260
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Online Access
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