[robotics-worldwide] Reminder: CFP- IROS'10 Workshop on Probabilistic Graphical Models in Robotics

GraphBot IROS2010 graphbot2010 at gmail.com
Fri Jul 2 09:41:30 PDT 2010

               Taipei, Taiwan,  October 22, 2010

Motivation and Objectives:

Among the large family of machine learning techniques, the concept of
probabilistic graphical models has gained significant interest in
robotics, as it offers a good combination of graph-theoretic design
and probabilistic reasoning together with a solid mathematical
background.  In particular, graphical models such as Bayes nets,
Markov random fields and factor graphs, have been proven to be very
powerful tools in the area of robotic perception, scene analysis, and
simultaneous localization and mapping (SLAM).

The objective of this workshop is to collect, discuss, and analyze in
detail recent approaches based on graphical models for typical
robotics problems. Additionally, the workshop aims at bringing
together researchers that are either interested or already working in
this field, to talk about the successes, limitations, and open
problems in the use of graphical models in robotics.

Important Dates:

- Submission of extended abstracts: Jul 12, 2010
- Notification of acceptance: Aug 10, 2010
- Final submission: Sep 13, 2010
- Workshop date: Oct 22, 2010


We invite contributions in areas that are relevant in robotics such as
classification and state estimation. Both theoretical and applied
papers are encouraged. The common theme is probabilistic graphical
models within robotics that includes papers spanning one or more of
the following threads:

- types of graphical models (e.g. Bayesian Networks, Factor
 Graphs, Markov Random Fields, Conditional Random Fields, Associative
  Markov Networks etc.)
- inference algorithms (belief propagation, Junction Tree, MCMC etc.)
- training algorithms ((un-)constrained optimization, approximations
- network topology design (heuristics, theoretical motivations)
- combination with other techniques (e.g. smoothed classification)
- graphical models and sparse linear algebra
- application scenarios (classification, state estimation, etc.)
- comparison of methods, benchmarking
- implementation issues

Submission Procedure:

Participants are invited to submit an extended abstract of maximum 3
pages using IEEE format. The page limit for the final paper is 6
pages. The authors of selected contributions will have the opportunity
to present their work during the workshop either as a regular
presentation or as a poster along with a short spotlight presentation.

In case of a sufficient number of high quality submissions, extended
versions of the selected contributions will be considered for
publication as a Special Issue in the Journal on Autonomous Robots.

One major aim of the workshop is also to contribute to the exchange of
sample data and robust implementations of the algorithms to facilitate
benchmarking. Therefore, in addition to the paper submissions, the
authors are encouraged to upload at least one relevant data set or one

Accepted papers, together with the data and code will be available
online at the workshop website.

Invited Speakers:

Wolfram Burgard,  Univ. of Freiburg
Frank Dellaert, Georgia Tech
Michael Kaess, MIT
Kristian Kersting,   Univ. of Bonn
Kurt Konolige, Willow Garage
Edwin Olson, Univ. of Michigan
Fabio Ramos, ACFR
Gian Diego Tipaldi,  Univ. of Freiburg


Viorela Ila, Georgia Tech, USA
Rudolph Triebel, ETH Zurich, Switzerland
Teresa Vidal-Calleja, ACFR, Australia


mailto:graphbot2010 at gmail.com

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