[info] [Comp-neuro] Call For Papers: Autonomous Robots - Special Issue on Robot Learning

[Eugen Leitl]

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From: Jan Peters <mail at jan-peters.net>
Date: Sat, 28 Jun 2008 15:20:33 +0200
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Subject: [Comp-neuro] Call For Papers: Autonomous Robots - Special Issue on
	Robot Learning
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Call For Papers: Autonomous Robots - Special Issue on Robot Learning
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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)