Improved Gaussian mixture model and Gaussian mixture regression for learning from demonstration based on Gaussian noise scattering

Feng, Chunhua, Liu, Zhuang, Li, Weidong, Lu, Xin, Jing, Yanguo ORCID: https://orcid.org/0000-0001-9581-4215 and Ma, Yongsheng (2025) Improved Gaussian mixture model and Gaussian mixture regression for learning from demonstration based on Gaussian noise scattering. Advanced Engineering Informatics, 65 (Part A). p. 103192. Full text not available from this repository.

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Abstract

Learning from Demonstration (LfD) is an effectual approach for robots to acquire new skills by implementing intuitive learning through imitating human demonstration. As one of the mainstream learning models for LfD, Gaussian mixture modeling (GMM) and Gaussian mixture regression (GMR) exhibit the advantages of ease of use and robust learning capabilities. To further improve the learning and regression performance of GMM/GMR, in this paper, improved GMM/GMR based on a Gaussian noise scattering strategy is designed. The main contributions of this study include: 1) the Gaussian noise scattering strategy is developed to eliminate the requirement of creating multiple demonstrations and overcome the jitter and sharp-turning defects of the demonstration; 2) based on a new evaluation criterion IBF and the sparrow search algorithm (SSA), GMM/GMR is optimized to achieve the balance of feature retention of the demonstration and the smoothness of the reproduced solution. Experimental results show that with the Gaussian noise scattering strategy, the geometric similarity of the reproduced solution and the demonstration increased for approximately 33.16 %, and the smoothness improved for 19.83 %. The challenges of underfitting and overfitting in GMM/GMR were effectively mitigated after incorporating the evaluation criterion IBF and leveraging SSA. This demonstrates the potential applicability of the improved GMM/GMR in practical industrial scenarios.

Item Type:	Article
Journal / Publication Title:	Advanced Engineering Informatics
Publisher:	Elsevier
ISSN:	1474-0346
Departments:	Institute of Business, Industry and Leadership > Business
Depositing User:	Anna Lupton
Date Deposited:	07 Jul 2025 09:09
Last Modified:	07 Jul 2025 09:09
URI:	https://insight.cumbria.ac.uk/id/eprint/8931