Guest Editors:
Jun Morimoto (ATR Computational Neuroscience Laboratories, Japan)
Chad Jenkins (Brown University, USA)
Marc Toussaint (TU Berlin, Germany)
Scope:
IEEE Robotics and Automation Magazine (RAM) seeks articles for this
special issue, scheduled for publication in June 2010.
There is an increasing interest in machine learning and statistics
within the robotics community. At the same time, there has been a
growth in the learning community in using robots as motivating
applications for new algorithms and formalisms. Considerable evidence
of this exists in the use of learning in high-profile competitions
such as RoboCup and the DARPA Challenges, and the growing number of
research programs funded by governments around the world.
The proposed special issue is intended to publish contributions on
robot learning algorithms with practical applications. Areas of
research interest include:
* learning models of robots, task or environments.
* learning hierarchical representations from sensor inputs and motor
outputs to task abstractions.
* learning of plans and control policies by imitation and
reinforcement learning.
* extraction of low-dimensional task relevant representations for
robot learning.
* learning robust policies that work in real environments.
* state estimation algorithms for robot learning.
Submission instructions:
Articles must be around a nominal length of eight pages each. We
encourage submission of supplementary material such as experiment
videos and source code. For further details see the instruction page:
http://www.ieee-ras.org/ram/for_authors
Submission deadline: October 1st, 2009
Issue date: June 2010
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Jun Morimoto (point of contact)
Department of Brain Robot Interface
ATR Computational Neuroscience Laboratories
2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto, Japan
E-mail : xmo...@atr.jp
Chad Jenkins
Department of Computer Science
Brown University,
115 Waterman St, 4th Floor
Providence, RI, USA 02912-1910
E-mail: cjen...@cs.brown.edu
Marc Toussaint
TU Berlin
Franklinstr. 28/29 FR6-9
10587 Berlin, Germany
E-mail: mtou...@cs.tu-berlin.de
IEEE-RAS TC on Robot Learning web page:
http://www.learning-robots.de/