CALL FOR PAPERS
Special Issue of the
IEEE Robotics and Automation Magazine
“Robot Learning in Practice”
Jun Morimoto (ATR Computational Neuroscience Laboratories, Japan)
Chad Jenkins (Brown University, USA)
Marc Toussaint (TU Berlin, Germany)
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.
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:
Submission deadline: October 1st, 2009
Issue date: June 2010
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 : xmorimo (at) atr.jp
Department of Computer Science
115 Waterman St, 4th Floor
Providence, RI, USA 02912-1910
E-mail: cjenkins (at) cs.brown.edu
Franklinstr. 28/29 FR6-9
10587 Berlin, Germany
E-mail: mtoussai (at) cs.tu-berlin.de
IEEE-RAS TC on Robot Learning web page: