Application of Unsupervised Clustering to Complex Robot Training Tasks

Ulrich Nehmzow, Otar Akanyeti, S. A. Billings

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)

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Abstract

The interest in obtaining sensor-motor competences by robot training – by expressing the desired actions of the robot in terms of its sensory perception using non-linear mapping techniques – has been growing steadily in mobile robotics applications. However if the sensor-motor task under investigation is subject to state transitions – in the sense that the casual relationship between sensor perception of the robot and the desired motor commands exhibits different characteristics over time or space -, it may not be possible to identify the whole sensor-motor relationship in a single model using standard non-linear mapping techniques.
This paper proposes a novel method based on first using a classifier which divides the perception-action space of the robot into subspaces and then generating a sperate model for each subspace. The viability of the proposed methodology has been demonstrated by teaching Scitos G5 mobile robot to achieve right wall following and complex route learning behaviours.
Original languageEnglish
Title of host publicationProceedings of Towards Autonomous Robotic Systems 2008
EditorsSubramanian Ramamoorthy, Gillian M. Hayes
PublisherEdinburgh University Press
Pages57-64
Number of pages8
ISBN (Electronic)9781906849016
ISBN (Print)9781906849009
Publication statusPublished - 30 Sept 2008
Externally publishedYes

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