[robotics-worldwide] [journals] CfP : Special Issue on Continual Unsupervised Sensorimotor Learning

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[robotics-worldwide] [journals] CfP : Special Issue on Continual Unsupervised Sensorimotor Learning

Nguyen, Sao Mai
Dear Colleagues,

IEEE Transactions on Cognitive and Developmental Systems is currently
running a Special Issue entitled " Continual Unsupervised Sensorimotor
Learning" :
https://urldefense.proofpoint.com/v2/url?u=http-3A__projects.au.dk_socialrobotics_news-2Devents_show_artikel_special-2Dissue-2Don-2Dcontinual-2Dunsupervised-2Dsensorimotor-2Dlearning_&d=DwIFaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=l43O7nQ8-SFPawSHhigBjuf_Bimk-HJm2sCrv0pBFCk&s=SbTyCq9UxRw46_Sn-1kpAxluozGHSh3Ot_TVps8amwk&e=

We would like to invite you to prepare a research article or a
comprehensive review to be published in
this special issue.


AIM AND SCOPE

Although machine learning algorithms continue to improve at a rapid
pace enabling technologies and products such as autonomous driving
cars and sophisticated image and speech recognition, it is often
forgotten that these applications represent tailored solutions to
specific tasks. Thus it is not clear if or how these autonomous
systems can pave the road to general purpose machines envisioned by
many.
The pursuit for higher levels of autonomy and versatility in robotics
is arguably lead by two main factors. Firstly, as we push robots out
of the labs and productions lines, it becomes increasingly difficult
to design for all possible scenarios that a particular robot might
encounter. Secondly, the cost of designing, manufacturing, and
maintaining such systems becomes prohibitive.
As the algorithms for learning single tasks in restricted environments
are improving, new challenges have gained relevance in order to get
more autonomous artificial systems. These challenges include
multi-task learning, multimodal sensorimotor learning and lifelong
adaptation to injury, growth and ageing. Addressing these challenges
promise higher levels of autonomy and versatility of future robots.
This special issue on Continual Unsupervised Sensorimotor Learning is
primarily concerned with the developmental processes involved in
unsupervised sensorimotor learning in a life-long perspective, and in
particular the emergence of representations of action and perception
in humans and artificial agents in continual learning. These processes
include action-perception cycle, active perception, continual
sensory-motor learning, environmental-driven scaffolding, and
intrinsic motivation.
The special issue will highlight behavioural and neural data, and
cognitive and developmental approaches to research in the areas of
robotics, computer science, psychology, neuroscience, etc.
Contributions might focus on mathematical and computational models to
improve robot performance and/or attempt to unveil the underlying
mechanisms that lead to continual adaptation to changing environment
or embodiment and continual learning in open-ended environments.
Contributions from multiple disciplines including cognitive systems,
cognitive robotics, developmental and epigenetic robotics, autonomous
and evolutionary robotics, social structures, multi-agent and
artificial life systems, computational neuroscience, and developmental
psychology, on theoretical, computational, application-oriented, and
experimental studies as well as reviews in these areas are welcome.


THEMES

This special issue aims to report state-of-the-art approaches and
recent advances on Continual Unsupervised Sensorimotor Learning with a
cross-disciplinary perspective. Topics relevant to this special issue
include but are not limited to:

Emergence of representations via continual interaction
Continual sensory-motor learning
Action-perception cycle
Active perception
Environmental-driven scaffolding
Intrinsic motivation
Neural substrates, neural circuits and neural plasticity
Human and animal behaviour experiments and models
Reinforcement learning and deep reinforcement learning for life-long learning
Multisensory robot learning
Multimodal sensorimotor learning
Affordance learning
Prediction learning


SUBMISSION

Manuscripts should be prepared according to the “Information for
Authors” of the journal found at
https://urldefense.proofpoint.com/v2/url?u=http-3A__cis.ieee.org_component_content_article_7_131-2Dieee-2Dtransactions-2Don-2Dautonomous-2Dmental-2Ddevelopment-2Dinformation-2Dfor-2Dauthors.html&d=DwIFaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=l43O7nQ8-SFPawSHhigBjuf_Bimk-HJm2sCrv0pBFCk&s=97uR3duEZfvXRX_sy460npumNC1C5cOvSVthCm6w03E&e=.
Submissions must be done through the IEEE TCDS Manuscript center:
https://urldefense.proofpoint.com/v2/url?u=https-3A__mc.manuscriptcentral.com_tcds-2Dieee&d=DwIFaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=l43O7nQ8-SFPawSHhigBjuf_Bimk-HJm2sCrv0pBFCk&s=VstsM24zwNzbmbH3Z6ezgfeURWm91-UZLGgGk2vA0pc&e=.
During the submission process, please select the category “SI:
Continual Unsupervised Sensorimotor Learning”.


IMPORTANT DATES

6th January 2019 – Paper submission deadline
15th March 2019 – Notification for authors
31st May 2019 – Deadline revised papers submission
30th June 2019 – Final notification for authors
31st July 2019 – Deadline for camera-ready versions
September 2019 – Expected publication date
More information on Continual Unsupervised Sensorimotor Learning
https://urldefense.proofpoint.com/v2/url?u=http-3A__projects.au.dk_socialrobotics_news-2Devents_show_artikel_special-2Dissue-2Don-2Dcontinual-2Dunsupervised-2Dsensorimotor-2Dlearning_&d=DwIFaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=l43O7nQ8-SFPawSHhigBjuf_Bimk-HJm2sCrv0pBFCk&s=SbTyCq9UxRw46_Sn-1kpAxluozGHSh3Ot_TVps8amwk&e=



GUEST EDITORS

Nicolás Navarro-Gerrero
Aarhus University, Aarhus, Denmark [hidden email]

Sao Mai Nguyen
IMT Atlantique, [hidden email]

Erhan Öztop
Özyeğin University, [hidden email]

Junpei Zhong
National Institute of Advanced Industrial Science and Technology
(AIST), [hidden email]
----

Nguyen Sao Mai
[hidden email]
Researcher in Cognitive Developmental Robotics
https://urldefense.proofpoint.com/v2/url?u=http-3A__nguyensmai.free.fr&d=DwIFaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=l43O7nQ8-SFPawSHhigBjuf_Bimk-HJm2sCrv0pBFCk&s=5K68otWf7ms3snU7oj5ro6vLC6jpIce8rWNZgVSeukQ&e=
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