Wearable Technology for Robotic Manipulation and Learning (Hardcover, 1st ed. 2020)

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.

General

Imprint:	Springer Verlag, Singapore
Country of origin:	Singapore
Release date:	October 2020
First published:	2020
Authors:	Bin Fang • Fuchun Sun • Huaping Liu • Chunfang Liu • Di Guo
Dimensions:	235 x 155mm (L x W)
Format:	Hardcover
Pages:	208
Edition:	1st ed. 2020
ISBN-13:	978-981-15-5123-9
Categories:	Books > Computing & IT > Social & legal aspects of computing > Human-computer interaction Books > Computing & IT > Applications of computing > Artificial intelligence > General Books > Professional & Technical > Electronics & communications engineering > Electronics engineering > Automatic control engineering > Robotics Promotions
LSN:	981-15-5123-5
Barcode:	9789811551239

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