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RobReg 2009 : RSS/PASCAL2 Workshop on Regression in Robotics


When Jun 28, 2009 - Jun 28, 2009
Where Seattle, WA, USA
Submission Deadline May 1, 2009
Notification Due May 12, 2009
Final Version Due Jun 28, 2009
Categories    robotics   machine learning   regression   robot learning

Call For Papers


Workshop on Regression in Robotics -- Approaches and Applications

June 28, 2009, Seattle, WA, USA
Co-located with Robotics: Science & Systems
Sponsored by PASCAL2 network of excellence


May 1, 2009: Submission of poster abstracts
May 12, 2009: Notification of acceptances
June 28, 2009: Workshop


Please send an extended abstract (1 or 2 pages incl. figures) for a poster presentation to

Partial travel funding for students is available through the generous support of PASCAL2. Please indicate your interest in the submission email.

As a core PASCAL 2 event, the workshop will feature videotaped proceedings.

Authors of selected poster presentations will be invited to submit a full length paper for an edited book to be potentially published by Cambridge University Press.


Function approximatittton 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:

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?

Workshop Organizers:

Christian Plagemann
Stanford University

Jo-Anne Ting
University of Edinburgh

Sethu Vijayakumar
University of Edinburgh

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