Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/1621
Title: A Nonparametric Bayesian Approach Toward Robot Learning by Demonstration
Authors: Chatzis, Sotirios P. 
Korkinof, Dimitrios 
Demiris, Yiannis 
metadata.dc.contributor.other: Χατζής, Σωτήριος
Δεμίρης, Γιάννης
Major Field of Science: Engineering and Technology
Field Category: Computer and Information Sciences
Keywords: Robots;Galvanomagnetic effects;Computer science;Nonparametric statistics;Gaussian mixture regression;Variational Bayes;Dirichlet process;Robot learning by demonstration
Issue Date: Jun-2012
Source: Robotics and autonomous systems, 2012, vol. 60, no. 6, pp. 789–802
Volume: 60
Issue: 6
Start page: 789
End page: 802
Journal: Robotics and Autonomous Systems 
Abstract: In the past years, many authors have considered application of machine learning methodologies to effect robot learning by demonstration. Gaussian mixture regression (GMR) is one of the most successful methodologies used for this purpose. A major limitation of GMR models concerns automatic selection of the proper number of model states, i.e., the number of model component densities. Existing methods, including likelihood- or entropy-based criteria, usually tend to yield noisy model size estimates while imposing heavy computational requirements. Recently, Dirichlet process (infinite) mixture models have emerged in the cornerstone of nonparametric Bayesian statistics as promising candidates for clustering applications where the number of clusters is unknown a priori. Under this motivation, to resolve the aforementioned issues of GMR-based methods for robot learning by demonstration, in this paper we introduce a nonparametric Bayesian formulation for the GMR model, the Dirichlet process GMR model. We derive an efficient variational Bayesian inference algorithm for the proposed model, and we experimentally investigate its efficacy as a robot learning by demonstration methodology, considering a number of demanding robot learning by demonstration scenarios.
URI: https://hdl.handle.net/20.500.14279/1621
ISSN: 09218890
DOI: 10.1016/j.robot.2012.02.005
Rights: © 2012 Elsevier.
Type: Article
Affiliation : Imperial College London 
Publication Type: Peer Reviewed
Appears in Collections:Άρθρα/Articles

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