Using structured UKR manifolds for motion classification and segmentation

Authors:
Jan Steffen;Michael Pardowitz;Helge Ritter
Affiliations:
Faculty of Technology, University of Bielefeld, Germany;Faculty of Technology, University of Bielefeld, Germany;Faculty of Technology, University of Bielefeld, Germany
Venue:
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Year:
2009

Citing 2
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Abstract

Task learning from observations of nonexpert human users will be a core feature of future cognitive robots. However, the problem of task segmentation has only received minor attention. In this paper, we present a new approach to classifying and segmenting series of observations into a set of candidate motions. As basis for these candidates, we use Structured UKR manifolds, a modified version of Unsupervised Kernel Regression which has been introduced in order to easily reproduce and synthesise represented dextrous manipulation tasks. Together with the presented mechanism, it then realises a system that is able both to reproduce and recognise the represented motions.