DocumentCode :
3324375
Title :
Robot Learning by Demonstration with local Gaussian process regression
Author :
Schneider, Markus ; Ertel, Wolfgang
Author_Institution :
Univ. of Appl. Sci. Ravensburg-Weingarten, Ravensburg, Germany
fYear :
2010
fDate :
18-22 Oct. 2010
Firstpage :
255
Lastpage :
260
Abstract :
In recent years there was a tremendous progress in robotic systems, and however also increased expectations: A robot should be easy to program and reliable in task execution. Learning from Demonstration (LfD) offers a very promising alternative to classical engineering approaches. LfD is a very natural way for humans to interact with robots and will be an essential part of future service robots. In this work we first review heteroscedastic Gaussian processes and show how these can be used to encode a task. We then introduce a new Gaussian process regression model that clusters the input space into smaller subsets similar to the work in [11]. In the next step we show how these approaches fit into the Learning by Demonstration framework of [2], [3]. At the end we present an experiment on a real robot arm that shows how all these approaches interact.
Keywords :
Gaussian processes; human-robot interaction; intelligent robots; learning (artificial intelligence); regression analysis; Gaussian process; human-robot interface; learning from demonstration; regression model; robot programming; robotic system;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Robots and Systems (IROS), 2010 IEEE/RSJ International Conference on
Conference_Location :
Taipei
ISSN :
2153-0858
Print_ISBN :
978-1-4244-6674-0
Type :
conf
DOI :
10.1109/IROS.2010.5650949
Filename :
5650949
Link To Document :
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