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Notice

AISB miscellaneous Bulletin Item

Reminder/Final Call: Special Issue on Robot Learning in Autonomous Robots

http://www.editorialmanager.com/auro/

Please be reminded of the Final Call for Papers for the Special Issue on Robot Learning
of the Autonomous Robots journal! The deadline is this

SUNDAY, Nov. 8, 2008

Note that submission can only happen through Autonomous Robots
WebInterface

http://www.editorialmanager.com/auro/

If you have questions, please contact us by email ASAP.

Best wishes,
Jan Peters & Andrew Ng


===========================================================================
Final Call For Papers: Autonomous Robots - Special Issue on Robot Learning
===========================================================================
Quick Facts
=========
Editors:                                     	Jan Peters, Max Planck Institute for Biological Cybernetics,
                                                	Andrew Y. Ng, Stanford University
Journal:                                    	Autonomous Robots
Submission Deadline:              	November 8, 2008
Author Notification:                  	March 1, 2009
Revised Manuscripts:              	June 1, 2009
Approximate Publication Date:	4th Quarter, 2009

Abstract
======
Creating autonomous robots that can learn to act in unpredictable
environments has been a long standing goal of robotics, artificial
intelligence, and the cognitive sciences. In contrast, current
commercially available industrial and service robots mostly execute
fixed tasks and exhibit little adaptability. To bridge this gap,
machine learning offers a myriad set of methods some of which have
already been applied with great success to robotics problems. Machine
learning is also likely play an increasingly important role in
robotics as we take robots out of research labs and factory floors,
into the unstructured environments inhabited by humans and into other
natural environments.

To carry out increasingly difficult and diverse sets of tasks, future
robots will need to make proper use of perceptual stimuli such as
vision, lidar, proprioceptive sensing and tactile feedback, and
translate these into appropriate motor commands. In order to close
this complex loop from perception to action, machine learning will be
needed in various stages such as scene understanding, sensory-based
action generation, high-level plan generation, and torque level motor
control. Among the important problems hidden in these steps are
robotic perception, perceptuo-action coupling, imitation learning,
movement decomposition, probabilistic planning, motor primitive
learning, reinforcement learning, model learning, motor control, and
many others.

Driven by high-profile competitions such as RoboCup and the DARPA
Challenges, as well as the growing number of robot learning research
programs funded by governments around the world (e.g., FP7-ICT, the
euCognition initiative, DARPA Legged Locomotion and LAGR programs),
interest in robot learning has reached an unprecedented high point.
The interest in machine learning and statistics within robotics has
increased substantially; and, robot applications have also become
important for motivating new algorithms and formalisms in the machine
learning community.

In this Autonomous Robots Special Issue on Robot Learning, we intend
to outline recent successes in the application of domain-driven
machine learning methods to robotics. Examples of topics of interest
include, but are not limited to:
      learning models of robots, task or environments
      learning deep hierarchies or levels of representations from sensor
             & motor representations to task abstractions
      learning plans and control policies by imitation, apprenticeship
             and reinforcement learning
      finding low-dimensional embeddings of movement as implicit
             generative models
      integrating learning with control architectures
      methods for probabilistic inference from multi-modal sensory
             information (e.g., proprioceptive, tactile, vision)
      structured spatio-temporal representations designed for robot
             learning
      probabilistic inference in non-linear, non-Gaussian stochastic
             systems (e.g., for planning as well as for optimal or adaptive
             control)
From several recent workshops, it has become apparent that there is a
significant body of novel work on these topics. The special issue will
only focus on high quality articles based on sound theoretical
development as well as evaluations on real robot systems.

Time Line
========
Submission Deadline:                    November 8, 2008
Author Notification:                            March 1, 2009
Revised Manuscripts:                            June 1, 2009
Approximate Publication Date:           4th Quarter, 2009

Editors
======
Inquiries on this special issue should be send to one of the editors
listed below.

Jan Peters (http://www.jan-peters.net/)
Senior Research Scientist, Head of the Robot Learning Laboratory
Department for Machine Learning and Empirical Inference, Max Planck
Institute for Biological Cybernetics, Tuebingen, Germany

Andrew Y. Ng (http://ai.stanford.edu/~ang/)
Assistant Professor
Department of Computer Science, Stanford University, Palo Alto, USA