Special issue on Robot Learning in Practice

A special issue of the IEEE Robotics and Automation Magazine

Introduction

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.

This special issue, organized by the Robot Learning Technical Committee of the IEEE Robotics and Automation Society, is intended to address recent advances, related works, and future research directions in robot learning.

IEEE-RAS TC on Robot Learning web page

Scope, description and more information

This 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 need to be around a nominal length of eight pages each.

We encourage submission of supplementary material such as experiment videos and source code. See also the instruction page: http://www.ieee-ras.org/ram/for_authors#multimedia

To submit a paper, go here

Important dates
Call for papers	July 29, 2009
Deadline for paper submission	October 15, 2009 (extended)
First review	January 15, 2010
Final review	February 15, 2010
Publication	June 2010