Please use this identifier to cite or link to this item:
Title: A nonparametric Bayesian approach toward robot learning by demonstration
Authors: Chatzis, Sotirios P. 
Korkinof, Dimitrios 
Demiris, Yiannis 
Keywords: Robots;Galvanomagnetic effects;Computer science;Nonparametric statistics
Category: Computer and Information Sciences
Field: Engineering and Technology
Issue Date: Jun-2012
Publisher: Elsevier
Source: Robotics and autonomous systems, 2012, vol. 60, no. 6, pp. 789–802
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
ISSN: 0921-8890
DOI: 10.1016/j.robot.2012.02.005
Rights: © 2012 Elsevier. All rights reserved
Type: Article
Appears in Collections:Άρθρα/Articles

Show full item record

Citations 10

checked on Feb 13, 2018


checked on Jun 13, 2019

Page view(s)

Last Week
Last month
checked on Jun 14, 2019

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.