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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Veröffentlicht in:	IEEE robotics and automation letters Jg. 7; H. 4; S. 9326 - 9333
Hauptverfasser:	Watson, Connor, Morimoto, Tania K.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.10.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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
Online-Zugang:	Volltext
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