Special Issue on Robot Learning Deadline Nov 8 2008
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Call For Papers: Autonomous Robots - Special Issue on Robot Learning
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Quick Facts
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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
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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
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Submission Deadline: November 8, 2008
Author Notification: March 1, 2009
Revised Manuscripts: June 1, 2009
Approximate Publication Date: 4th Quarter, 2009
Editors
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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_______________________________________________
Comp-neuro mailing list
Comp-neuro@neuroinf.org
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Face and Gesture workshop: Facial and Bodily Expressions for Control and Adaptation of Games
Call:
http://hmi.ewi.utwente.nl/conference/ECAG08
Deadline: June 15
ICDL 2008: Deadline for Papers is March 14
This year is in Monterrey, California
August 9th-12th, 2008.
http://www.icdl08.org/
Important dates:
Feb. 15 Special session proposals due
March 14 Full 6-page paper submissions due
March 21 Tutorial proposals due
April 14 Notification of accept/reject
April 18 1-page poster abstracts due
May 9 Camera-Ready Copy due
AAAI deadline soon
AAAI may also be a decent venue for some of the projects done in mplab. Here is a link to past conferences to get an idea: http://www.aaai.org/Library/conferences-library.phpThe deadline for the 2008 conference is Jan 30, though some have abstracts due on the 25th. Here’s the link: http://www.aaai.org/Conferences/AAAI/aaai08.php
ICML and UAI deadlines soon
ICML and UAI have about the same audiences as NIPS. I’m not sure what should make you choose ICML versus UAI. They are opposite in schedule as NIPS: submit in winter, conference in summer. So a lot of people basically make two conference trips per year. This year UAI, ICML, and COLT are at the same place and location.Feb 8: ICML deadlineFeb 27: UAI abstract deadline, Feb 29 full submission. http://uai2008.cs.helsinki.fi/cfps.shtml
IEEE Workshop on Human Communicative Behavior. Deadline March 25th 2008
IEEE Workshop on CVPR for Human Communicative Behavior
http://www.doc.ic.ac.uk/~maja/cvpr4hb.html
Deadline for paper submissions is March 25th 2008