[robotics-worldwide] Call for Participation: RSS/PASCAL2 Workshop on Regression in Robotics

Christian Plagemann plagemann at stanford.edu
Mon Jun 8 09:19:07 PDT 2009

Call for Participation

RSS'09/PASCAL2 Workshop on
  Regression in Robotics -- Approaches and Applications

Sunday, June 28, 2009, Seattle, WA, USA

Co-located with Robotics: Science & Systems, RSS 2009
Sponsored by PASCAL2 Network of Excellence


Dear colleagues,

we invite you to attend this full-day workshop, to be held on Sunday, June
28, 2009 in Seattle at the University of Washington campus. The workshop
will feature invited speakers, selected poster presentations and a moderated
panel discussion.

Please note the following recent updates:

- As a core PASCAL2 event, the workshop program will be be videotaped and
archived online.

- We are pleased to announce that a Pascal Best Poster Presentation Award of
US$ 350 will be given to the authors of a selected poster. Results will be
determined by a panel of judges *and* by popular vote.

- After the workshop, we invite all participants to an informal Robot
Learning Cocktail Night organized jointly with the RSS workshop on "Bridging
the gap between high-level discrete representations and low-level continuous

Please refer to http://www.robreg.org for a more detailed program schedule.

Invited Speakers:

** Pieter Abbeel, University of California at Berkeley
** Dieter Fox, University of Washington
** Raia Hadsell, Carnegie Mellon University
** Andreas Krause, California Institute of Technology
** Jan Peters, Max-Planck Institute of Biological Cybernetics
** Rajesh Rao, University of Washington
** Nick Roy, Massachusetts Institute of Technology


Function approximation from noisy data 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-homogeneous 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 methods 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,
regression methods are typically evaluated using different metrics and data
sets, making standardized comparisons challenging.

Goal & Topics:

The workshop will address topics such as the following:

*** 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

Workshop Organizers:

Christian Plagemann
Stanford University
plagemann at stanford.edu

Jo-Anne Ting
University of Edinburgh
jting at ed.ac.uk

Sethu Vijayakumar
University of Edinburgh
sethu.vijayakumar at ed.ac.uk

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