Learning from Demonstration Using GMM, CHMM and DHMM: A Comparison

Fenglu Ge, Wayne Moore, Michael Antolovich

Research output: Book chapter/Published conference paperConference paperpeer-review

1 Citation (Scopus)

Abstract

Greater production and improved safety in the mining industry can be enhanced by the use of automated vehicles. This paper presents results in applying Learning from Demonstration (LfD) to a laboratory semi-automated mine inspection robot following a path through a simulated mine. Three methods, Gaussian Mixture Model (GMM), Continuous Hidden Markov Model (CHMM), and Discrete Hidden Markov Model (DHMM) were used to implement the LfD and a comparison of the implementation results is presented. The results from the different models were then used to implement a novel, optimised path decomposition technique that may be suitable for possible robot use within an underground mine.
Original languageEnglish
Title of host publicationAI 2015
EditorsBernhard Pfahringer, Jochen Renz
Place of PublicationSwitzerland
PublisherSpringer
Pages204-217
Number of pages14
Volume9457
DOIs
Publication statusPublished - 2015
EventAdvances in Artificial Intelligence - Canberra, Australia
Duration: 30 Nov 201505 Dec 2015

Publication series

Name
ISSN (Print)0302-9743

Conference

ConferenceAdvances in Artificial Intelligence
Country/TerritoryAustralia
Period30/11/1505/12/15

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