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In the next few decades, millions of people, with different backgrounds and technical expertise levels, will have to interact effectively with robot technologies on a daily basis. This means that it would be possible to modify robot behavior without clearly writing the code, but instead through a low number of wearable equipment or visual performances. At the same time, robots will require to estimate and predict the intentions and internal objectives of humans based on previous interactions to provide assistance before it is clearly requested; This is the basis of duplication for robotics.
This book introduces readers from learning robotic copy based on human performance with wearable equipment. It presents an advanced calibration method for the weedable sensor and fusion approach under the Kalman filter framework, as well as a novel wearing tool to capture gestures and other movements. In addition, it describes the method of learning wearable-device-based and vision-based copy for robot manipulation, so that this machine is a valuable reference guide for graduate students with basic knowledge of learning, and for researchers interested in wearable computing and wearable computing and robotic learning.
Publisher: Springer; 1 Ed. 2020 edition (October 7, 2020)
language English
Hardcawar: 232 pages
ISBN -10: 9811551235
ISBN -13: 978-9811551239
Items Weight: 1.14 pounds
Dimensions: 6.14 x 0.56 x 9.21 inch
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