CfP: RSS10 Workshop Towards Closing the Loop: Active Learning for Robotics

Towards Closing the Loop: Active Learning for Robotics
Zaragoza, Spain, June 27, 2010

Important Dates:
* Submission of extended abstracts: May 19, 2010
(later submission might not be considered for review)
* Notification of acceptance: May 28, 2010
* Workshop date: June 27, 2010

The ability to adapt to changing environment autonomously will be
essential for future robots. While this need is well-recognized, most
machine learning research focuses largely on perception and static
data sets. Instead, future robots need to interact with the
environment to generate the data that is needed to foster real-time
adaptation based on all information collected in previous interactions
and observations. In other words, we need to close the loop between
the robot acting, robot sensing and robot learning. Novel active
methods need to outperform passive methods by a margin that
compensates the potential the extra computational burden and the cost
of the active data sampling.

During the last years, there has been an increasing interest in
related techniques that could potentially become applicable in this
context. These include techniques from statistics such as adaptive
sensing or sequential experimental design as well novel reinforcement
learning methods that have the potential to scale into robotics. In
this context, we would like to bring together researchers from both
the robotics and active machine learning in order to discuss for which
problems the autonomous learning loop can be closed using learning,
and to identify the machine learning methods that can be used to close

Invited speakers:
* Mark Coates, McGill University
* Andrew Davison, Imperial College London
* Manuel Lopes, University of Plymouth
* Pierre-Yves Oudeyers, INRIA
* Nick Roy, MIT
* Jo-Ann Ting, UBC
* John K. Tsotsos, York University

Submission instructions:
We invite submission of extended abstracts to the workshop. Extended
abstracts should be up to 2 pages in length, formatted in according to
RSS style. However, submissions should not be blind. Extended
abstracts should be sent in PDF or PS file format by email to

The selected submission may be accepted either as an oral presentation
or as a poster presentation. We encourage participants who can
contribute in the following areas:

* Active learning
* Active filtering
* Sequential experimental design
* Adaptive sensing
* Optimal information gathering
* Autonomous exploration
* Bayesian optimization
* Active cognitive development
* Attention systems or gaze control
* Sensor placement
* Active vision
* Online decision making
* Selection criteria/Utility functions
* Information theoretic metrics in the context of robotics.

The above list is not exhaustive, and we welcome submissions on highly
related topics too. Accepted extended abstracts will be made available
online at the workshop website.

Steering Committee:
* Florence d’Alché-Buc
* Jun Morimoto
* Nick Roy
* Marc Toussaint

* Ruben Martinez-Cantin, Instituto Superior Tecnico
* Jan Peters, Max Plank Institute for Biological Cybernetics
* Andreas Krause, Caltech