| 書目名稱 | Wearable Technology for Robotic Manipulation and Learning | | 編輯 | Bin Fang,Fuchun Sun,Di Guo | | 視頻video | http://file.papertrans.cn/1022/1021408/1021408.mp4 | | 概述 | Provides a systematic and comprehensive introduction to robotic imitation learning.Introduces demonstration-learning-based solutions for robotic manipulation.Showcase the applications of wearable devi | | 圖書封面 |  | | 描述 | .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 | | 出版日期 | Book 2020 | | 關(guān)鍵詞 | Imitation learning; robotics; robots; wearable device; motion capture; inertial sensors; robotic manipulat | | 版次 | 1 | | doi | https://doi.org/10.1007/978-981-15-5124-6 | | isbn_softcover | 978-981-15-5126-0 | | isbn_ebook | 978-981-15-5124-6 | | copyright | Springer Nature Singapore Pte Ltd. 2020 |
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