NDLI: An adaptive probabilistic approach to goal-level imitation learning

Content Provider	IEEE Xplore Digital Library
Author	Dindo, H. Schillaci, G.
Copyright Year	2010
Description	Author affiliation: Department of Computer Science, University of Palermo, Italy (Dindo, H.; Schillaci, G.)
Abstract	Imitation learning has been recognized as a promising technique to teach robots advanced skills. It is based on the idea that robots could learn new behaviors by observing and imitating the behaviors of other skilled actors. We propose an adaptive probabilistic graphical model which copes with three core issues of any imitative behavior: observation, representation and reproduction of skills. Our model, Growing Hierarchical Dynamic Bayesian Network (GHDBN), is hierarchical (i.e. able to characterize structured behaviors at different levels of abstraction), and growing (i.e. skills are learned or updated incrementally - and at each level of abstraction - every time a new observation sequence is available). A GHDBN, once trained, is able to recognize skills being observed and to reproduce them by exploiting the generative power of the model. The system has been successfully tested in simulation, and initial tests have been conducted on a NAO humanoid robot platform.
Starting Page	4452
Ending Page	4457
File Size	2362890
Page Count	6
File Format	PDF
ISBN	9781424466740
ISSN	21530866
e-ISBN	9781424466764
DOI	10.1109/IROS.2010.5654298
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-10-18
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Robots Hidden Markov models Probabilistic logic Aerospace electronics Probability distribution Bayesian methods Graphical models
Content Type	Text
Resource Type	Article

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