@inproceedings{df3de113035742cd94ec0a6e7f601786,
title = "Linear Gaussian Processes for Data-Efficient Robot Dynamics Learning",
abstract = "This paper proposes a linear Gaussian Process learning framework which can be used in the both of semi-parametric and non-parametric models. Inspired by the linear relationship in Euler–Lagrange equation, this approach reduces the dimension of the previous models, which in turn improve the learning efficiency. Besides, the linear relationship results in the better generalization. Simulational results verify the feasibility of the proposed method by predicting the status of a two degrees-of-freedom (DOF) manipulator.",
keywords = "Forward dynamics, Gaussian processes, Rigid body dynamics (RBD), Semi-parametric model",
author = "Desong Du and Changhao Liu and Chenrui Ni and Naiming Qi and Yanfang Liu",
note = "Publisher Copyright: {\textcopyright} 2022, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; International Conference on Guidance, Navigation and Control, ICGNC 2020 ; Conference date: 23-10-2020 Through 25-10-2020",
year = "2022",
doi = "10.1007/978-981-15-8155-7\_350",
language = "英语",
isbn = "9789811581540",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "4201--4211",
editor = "Liang Yan and Haibin Duan and Xiang Yu",
booktitle = "Advances in Guidance, Navigation and Control - Proceedings of 2020 International Conference on Guidance, Navigation and Control, ICGNC 2020",
address = "德国",
}