Regression in Robotics Workshop: Call for Participation

Regression in Robotics Workshop: Call for Participation

Posted by Rebecca Martin on Tue, 09/06/2009 - 14:47

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

http://www.robreg.org

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:

- 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 behaviors".

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

Description:

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 missing?

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