A novel curved Gaussian Mixture Model and its application in motion skill encoding

Disi Chen, Gongfa Li, Dalin Zhou, Zhaojie Ju

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

The purpose of this paper is to present a novel curved Gaussian Mixture Model (CGMM) and to study the application of it in motion skill encoding. Primarily, Gaussian mixture model (GMM) has been widely applied on many occasions when a probability density function is needed to approximate a complex probability distribution. However, GMM cannot efficiently approach highly non-linear distributions. Thus, the proposed novel CGMM, as a weighted mixture of curved Gaussian models (CGM), is structured with non-linear transfers, which reshapes the flat GMM into a geo-metrically curved one. As a consequence, CGMM has more freedoms and flexibilities than the flat GMM so a CGMM requires fewer number of components in fitting highly non-linear motion trajectories. Moreover, we derive a dedicated iterative parameter estimation algorithm for the CGMM based on maximum likelihood estimation (MLE) theory. To evaluate the performance of the CGMM and its parameter estimation algorithm, a series of quantitative experiments are carried out. We first test the model performance in the data fitting task with the generated synthetic data. Then a motion skill encoding test is carried out on a human motion trajectory dataset built by a Virtual Reality (VR) based motion tracking system. The empirical results support that CGMM outperforms state-of-the-arts in the model performance test. Meanwhile, CGMM has a significant improvement in encoding high dimensional non-linear trajectory data compared to the GMM in motion skill encoding test with its dedicated parameter estimation algorithm.
Original languageEnglish
Title of host publication2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021)
PublisherInstitute of Electrical and Electronics Engineers
Pages7813-7818
Number of pages6
ISBN (Electronic)9781665417143
ISBN (Print)9781665417150
DOIs
Publication statusPublished - 16 Dec 2021
Event2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) -
Duration: 27 Sep 20211 Oct 2021
https://www.iros2021.org

Publication series

NameProceedings of 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
PublisherIEEE
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866

Conference

Conference2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Period27/09/211/10/21
Internet address

Keywords

  • non-linear transfer
  • curved Gaussian Mixture Model
  • expectation-maximization algorithm
  • motion skill encoding

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