NDLI: Gestalt-based action segmentation for robot task learning

Content Provider	IEEE Xplore Digital Library
Author	Pardowitz, M. Haschke, R. Steil, J. Ritter, H.
Copyright Year	2008
Description	Author affiliation: Neuroinformatics Group, Bielefeld Univ., Bielefeld (Pardowitz, M.; Haschke, R.; Ritter, H.) \|\| CoR-Lab., Bielefeld Univ., Bielefeld (Steil, J.)
Abstract	In programming by demonstration (PbD) systems, the problem of task segmentation and task decomposition has not been addressed with satisfactory attention. In this article we propose a method relying on psychological gestalt theories originally developed for visual perception and apply it to the domain of action segmentation. We propose a computational model for gestalt-based segmentation called competitive layer model (CLM). The CLM relies on features mutually supporting or inhibiting each other to form segments by competition. We analyze how gestalt laws for actions can be learned from human demonstrations and how they can be beneficial to the CLM segmentation method. We validate our approach with two reported experiments on action sequences and present the results obtained from those experiments.
Starting Page	347
Ending Page	352
File Size	4162930
Page Count	6
File Format	PDF
ISBN	9781424428212
DOI	10.1109/ICHR.2008.4756003
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-12-01
Publisher Place	South Korea
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Learning systems Image segmentation Computer vision Computational modeling Humanoid robots Humans Psychology Cognitive robotics Robot programming Visual perception
Content Type	Text
Resource Type	Article

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