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A Novel Approach for Efficient Gaussian Mixture Model using Dynamics-motivated Optimal Excitation
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Title
A Novel Approach for Efficient Gaussian Mixture Model using Dynamics-motivated Optimal Excitation
Issued Date
2024-06-19
Citation
Kim, Taehoon. (2024-06-19). A Novel Approach for Efficient Gaussian Mixture Model using Dynamics-motivated Optimal Excitation. 33rd IEEE International Symposium on Industrial Electronics, ISIE 2024, 1–4. doi: 10.1109/ISIE54533.2024.10595794
Type
Conference Paper
ISBN
9798350394085
ISSN
2163-5145
Abstract
EffiDynaMix, a novel, efficient, gray-box, nonparametric dynamics modeling method, integrates mathematical dynamics with Gaussian Mixture Model (GMM) for simplified training data creation and improved generalization. It outperforms traditional methods like conv-GMM, GP, and LSTM in training efficiency and accuracy with new data. By leveraging dynamic equations, EffiDynaMix enhances learning efficiency and adaptability, offering advancements in robotic system precision and computational efficiency, leading to faster and more responsive robots. © 2024 IEEE.
URI
http://hdl.handle.net/20.500.11750/57556
DOI
10.1109/ISIE54533.2024.10595794
Publisher
IEEE Industrial Electronics Society
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오세훈
Oh, Sehoon오세훈

Department of Robotics and Mechatronics Engineering

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