[robotics-worldwide] [journals] Call for papers: IEEE TCDS Special Issue on Continual Unsupervised Sensorimotor Learning

Erhan Oztop erhan at atr.jp
Fri Feb 15 03:56:59 PST 2019


Dear Colleagues,

This is another announcement for the special issue "Continual 
Unsupervised Sensorimotor Learning" (with new dates) that will appear 
under IEEE Transactions on Cognitive and Developmental Systems.

We would like to invite you to contribute to the special issue by 
submitting a research article or a review. The deadline is now set as 
March 15th, 2019. The scope, aim, submission and other details are given 
below.

URL:
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=DwIDaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=oCMIckjwUAQBrllj6wmvY5ztHhP-h-MtEEUVUQBT-p0&s=pgvgviylplAIhADuco3iiazaX81SVGtqM_EHnaBTQEw&e=

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=https-3A__cis.ieee.org_publications_t-2Dcognitive-2Dand-2Ddevelopmental-2Dsystems_tcds-2Dinformation-2Dfor-2Dauthors&d=DwIDaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=oCMIckjwUAQBrllj6wmvY5ztHhP-h-MtEEUVUQBT-p0&s=zyi5got3Q4spv-dB5ZMK65Dk5XRW8PWHCe57YwXRrnE&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=DwIDaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=oCMIckjwUAQBrllj6wmvY5ztHhP-h-MtEEUVUQBT-p0&s=zl1fONf1h1muS8Q0CJNmLYAqaSP7jCB6CsCEiIzMaN0&e=. Please select the category 
“SI: Continual Unsupervised Sensorimotor Learning”.


IMPORTANT DATES

15th March 2019	 Paper submission deadline

1st May 2019	 Notification for authors
16th June 2019	 Deadline revised papers submission
16th July 2019	 Final notification for authors
18th August 2019 Deadline for camera-ready versions

September 2019	 Expected publication date

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=DwIDaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=oCMIckjwUAQBrllj6wmvY5ztHhP-h-MtEEUVUQBT-p0&s=pgvgviylplAIhADuco3iiazaX81SVGtqM_EHnaBTQEw&e=


GUEST EDITORS

Nicolás Navarro-Gerrero
Aarhus University, Aarhus, Denmark
nng at eng.au.dk

Sao Mai Nguyen
IMT Atlantique, France
nguyensmai at gmail.com

Erhan Oztop
Ozyeğin University, Turkey
erhan.oztop at ozyegin.edu.tr

Junpei Zhong
National Institute of Advanced Industrial Science and Technology (AIST), 
Japan
joni.zhong at aist.go.jp


With my best wishes,

Erhan

--
Erhan Oztop, PhD
ATR external collaborator,
Computer Science Dept. Ozyegin University, Istanbul, Turkey
erhan.oztop at ozyegin.edu.tr,  phone: +90 216 5649392


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