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"ACM SIGCHI General Interest Announcements (Mailing List)" <[log in to unmask]>
Fri, 30 Oct 2009 15:28:00 +0100
Pierre-Yves Oudeyer <[log in to unmask]>
Pierre-Yves Oudeyer <[log in to unmask]>
Manuel Lopes <[log in to unmask]>
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IEEE Transactions on Autonomous Mental Development,
Special Issue on Active Learning and Intrinsically Motivated Exploration 
in Robots

This special issue is jointly supported by the
IEEE CIS Technical committee on Autonomous Mental Development,
and the IEEE RAS Technical committee on Robot Learning,

=== Topic

Learning techniques are increasingly being used in todays' complex 
robotic system. Robots are expected to deal with a large variety of 
tasks, using their high-dimensional and complex bodies, to interact with 
objects and humans in an intuitive and friendly way. In this new 
setting, not all relevant information is available at design time, thus 
self-experimentation and learning by interacting with the physical and 
social world is very important to acquire knowledge.

A major obstacle, in high and complex sensorimotor space, is that 
learning can become extremely slow or even impossible without adequate 
exploration strategies. To solve this problem, two main approaches are 
now converging. Active learning, from statistical learning theory, where 
the learner actively chooses experiments in order to collect highly 
informative examples, and where expected information gain can be 
evaluated with either theoretically optimal criteria or various 
computationally efficient heuristics. The second approach, intrinsically 
motivated exploration, from developmental psychology and recently 
operationalized in the developmental robotics community, aims at 
building robots capable of open-ended cumulative learning through 
task-independent efficient exploration of their sensorimotor space and 
to refine our understanding of how children learn and develop.

Although similar in some aspects, these two approaches differ in some of 
the underlying assumptions. Active learning implicitly assumes that 
samples with high uncertainty are the most informative and focuses on 
single tasks. On the contrary, Intrinsic motivation has been identified 
by psychologists as an innate incentive that pushes organisms to 
spontaneously explore activities or situations for the sole reason that 
they have a certain degree of novelty, challenge or surprise, hence the 
term curiosity-driven learning sometimes used.

Several open problems exist still and the goal of this special issue is 
to show state-of-the-art approaches to these problems and open new 
directions. Papers should address the following, non-exhaustive, topics 
applied to robotics or animal cognitive model:
.    How can traditional active learning heuristics be applied to 
robotics problems such as motor learning, affordance learning or 
interaction learning?
.    How to select an active strategy ? Are there general purpose 
methods or are they task dependent?
.    How can active and intrinsic motivated exploration enable 
long-life, task-independent learning and development?
.    Is there a unified formalism to both approaches?
.    How precisely do they model human active learning and exploration 
and its role in development?
.    Can these approaches be used for social tasks, e.g. joint-work and 
human-robot interaction ?

=== Editors:
Manuel Lopes, University of Plymouth,
Pierre-Yves Oudeyer, INRIA,

=== Two kinds of submissions are possible:
.    Regular papers, up to 15 double column pages ;
.    Correspondence papers either presenting a "perspective" that 
includes insights into issues of wider scope than a regular paper but 
without being highly computational in style or presenting concise 
description of recent technical results, up to 8 double column pages ;

=== Instructions for authors :

We are accepting submissions through Manuscript Central at :  (please select "Active 
Learning and Intrinsic Motivation" as the submission type)

When submitting your manuscript, please also send an email to 
[log in to unmask] <mailto:[log in to unmask]> and 
[log in to unmask] <mailto:[log in to unmask]>
with the title and name of the authors of the manuscript.

=== Timeline :

31 Jan 2010 -- Deadline for paper submission
15 March -- Notification
15 April -- Final version
20 April -- Electronic publication
15 June -- Printed publication

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