Due to many requests, the deadline of the following IEEE TAMD special
issue has been extended to 15th february.
CALL FOR PAPER
IEEE Transactions on Autonomous Mental Development,
Special Issue on Active Learning and Intrinsically Motivated Exploration
Deadline extension: 15th february
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,
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
. 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 ?
Manuel Lopes, University of Plymouth, http://www.plymouth.ac.uk/staff/mlopes
Pierre-Yves Oudeyer, INRIA, http://www.pyoudeyer.com
=== 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 :
http://mc.manuscriptcentral.com/tamd-ieee (please select "Active
Learning and Intrinsic Motivation" as the submission type)
When submitting your manuscript, please also send an email to
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with the title and name of the authors of the manuscript.
=== Timeline :
15th Feb 2010 -- Deadline for paper submission
15 March -- Notification
15 April -- Final version
20 April -- Electronic publication
15 June -- Printed publication
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