Robot learning by a mining tunnel inspection robot

Feng Lu Ge, Wayne Moore, Michael Antolovich, Junbin Gao

Research output: Book chapter/Published conference paperConference paper

1 Citation (Scopus)

Abstract

Learning from Demonstration (LfD) is a method of teaching an agent a task by a number of suitable demonstrations. The agent will then perform the task without any further supervision. In this paper, Discrete Hidden MarkovModel (DHMM) is applied to train a robot for a mining inspection task. An initial training method based on the Gaussian Mixture Model (GMM) was developed and is compared to DHMM. Results show that the learning speedbased on DHMM is faster than the one for GMM and DHMM may prove to be more suitable for the mining inspection task under consideration. The proposed method has already been implemented, and some important problemson implementation are discussed.
Original languageEnglish
Title of host publicationIEEE International Conference on Ubiquitous Robots and Ambient Intelligence
Place of PublicationUnited States
PublisherInstitute of Electrical and Electronics Engineers
Pages200-204
Number of pages5
ISBN (Electronic)9781467331104
DOIs
Publication statusPublished - 2012
EventURAI 2012: 9th International Conference - Daejeon, Korea (South), Korea, Republic of
Duration: 26 Nov 201228 Nov 2012

Conference

ConferenceURAI 2012: 9th International Conference
CountryKorea, Republic of
Period26/11/1228/11/12

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