Learning Non-Parametric Models in Real Time via Online Generalized Product of Experts

In this work, we address the problem of online learning, where models must be continually updated from an incoming stream of data, while retaining past information. We develop an approach that is nonparametric, models uncertainty, and requires minimal hand-tuning. Our proposed algorithm, which we te...

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Vydáno v:IEEE robotics and automation letters Ročník 7; číslo 4; s. 9326 - 9333
Hlavní autoři: Watson, Connor, Morimoto, Tania K.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.10.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Adaptation models
Algorithms
and learning for soft robots
Clustering
Clustering algorithms
Computational modeling
control
Data models
Gaussian process
Kinematics
Machine learning
machine learning for robot control
Model learning for control
modeling
Nonparametric statistics
Prediction algorithms
Predictive models
Robots
Training
ISSN:2377-3766, 2377-3766
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Shrnutí:In this work, we address the problem of online learning, where models must be continually updated from an incoming stream of data, while retaining past information. We develop an approach that is nonparametric, models uncertainty, and requires minimal hand-tuning. Our proposed algorithm, which we term online generalized product of experts (OGPoE), extends the powerful generalized product of experts (GPoE) framework to the online setting by leveraging methods for sparse, variational Gaussian process approximations, as well as nonparametric clustering. We devise a 1-D example learning problem to illustrate how our method works, and we verify that we achieve competitive results with other popular modeling approaches on a benchmark learning problem. Finally, we demonstrate how our algorithm can produce high accuracy predictions on a physical system, by learning the kinematics for a concentric tube robot, even when the robot is subject to changing, unknown loads.
Bibliografie:ObjectType-Article-1
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ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2022.3190809