[Comp-neuro] IEEE RAS Technical Committee on Robot Learning

Jan Peters mail at jan-peters.net
Thu May 29 09:42:57 CEST 2008


		  Call for Participation
       IEEE RAS Technical Committee on Robot Learning


To foster the development of learning robots, we are pleased
to announce the formation of the IEEE RAS Technical
Committee on Robot Learning. We invite people interested in
participating in this technical committee to contact the
corresponding chair, Nicholas Roy, at nickroy at mit.edu, or
one of the other chairs.

There is an increasing interest in machine learning and
statistics within the robotics community. At the same time,
there has been a growth in the learning community in using
robots as motivating applications for new algorithms and
formalisms. Considerable evidence of this exists in the use
of learning in high-profile competitions such as RoboCup and
the DARPA Challenges, and the growing number of research
programs funded by governments around the
world. Additionally, the volume of research is increasing,
as shown by the number of learning papers accepted to IROS
and ICRA, and the corresponding number of learning sessions.

The primary vision of our technical committee is as a focus
for widely distributing technically rigorous results in
shared areas of interest. Without being exclusive, areas of
shared research interest include

* learning models of robots, task or environments;
* learning deep hierarchies or levels of representations
  from sensor & motor representations to task abstractions;
* learning of plans and control policies by imitation and
  reinforcement learning;
* integrating learning with control architectures;
* methods for probabilistic inference from multi-modal
  sensory information (e.g., proprioceptive, tactile, vison);
* structured spatio-temporal representations designed for
  robot learning such as low-dimensional embedding of

Our activities will include regular meetings at the primary
robotics conferences, including a workshop series beginning at
IROS 2008.

See http://www.learning-robots.de for more information.

Jun Morimoto, ATR
Jan Peters, Max Planck Institute
Nicholas Roy, MIT (Corresponding chair)
Russ Tedrake, MIT

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