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CALL FOR CONTRIBUTIONS
Workshop on Regression in Robotics -- Approaches and Applications
June 28, 2009, Seattle, WA, USA
http://www.robreg.org
Co-located with Robotics: Science & Systems
Sponsored by PASCAL2 network of excellence
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Dates:
April 24, 2009: Submission of poster abstracts
May 8, 2009: Notification of acceptances
June 28, 2009: Workshop
Description:
Function approximation is a central task in robot learning.
Relevant problems include sensor modeling, manipulation,
control, and many others. A large number of regression methods
have been proposed from statistics, machine learning and control
system theory to address robotics-related issues such as online
updates, active sampling, high dimensionality, non-homoge neous
noise and missing features. However, with minimal communication
and collaboration between communities, work is sometimes
reproduced or re-discovered, making research progress challenging.
Our goal is to draw researchers from the different communities of
robotics, control systems theory and machine learning into a discussion
of the relevant problems in function approximation to be learned in
robotics. We would like to develop a common understanding of the
benefits and drawbacks of different regression approaches and to
derive practical guidelines for selecting a suitable approach to a
given problem. In addition, we would like to discuss two key points
of criticism in current robot learning research. First, data-driven
machine learning meth ods do, in fact, not necessarily outperform
models designed by human experts and we would like to explore
what regression problems in robotics really have to be learned.
Second, regres sion methods are typically evaluated using different
metrics and data sets, mak ing standardized comparisons challenging.
Goal & Topics:
We invite abstract submissions from researchers working on machine
learning, robotics and/or control theory with a general interest in
regression
and function approximation. Ideally, submissions should contribute to
one or several of the following topics:
**Approaches: Which learning approaches have been applied successfully
to solve regression problems in robotics or have a high potential for
doing so?
**Problem settings: Which robot learning problems contain regression or
function approximation as a central component? What are the specific
aspects that make the problem challenging?
**Theoretical foundations: How can challenging requirements such as
online
updates, active sampling, high dimensionality, non-homogeneous noise and
missing features be addressed?
**Benchmarking and evaluation: What are suitable methods for
evaluation of
regression methods? What metrics are being used and, subsequently, which
should be used? Which benchmark data sets are available and which are
missing?
Submission Details:
Interested parties should send an abstract in PDF form of up to 2
pages to
submiss...@robreg.org by Friday, April 24, 2009. Accepted abstracts
will take
the form of poster presentations and a selection will be chosen for
additional
short talks.
Travel Funding:
With the generous support of Pascal2, we can also offer travel
funding for students,
and interested parties should indicate their interest in their email
submission.
Workshop Organizers:
Christian Plagemann
Stanford University
plagem...@stanford.edu
Jo-Anne Ting
University of Edinburgh
jt...@ed.ac.uk
Sethu Vijayakumar
University of Edinburgh
sethu.vijayaku...@ed.ac.uk
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--
The University of Edinburgh is a charitable body, registered in
Scotland, with registration number SC005336.
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