[journals] CfP : Special Issue on Continual Unsupervised Sensorimotor Learning
Dear Colleagues,
IEEE Transactions on Cognitive and Developmental Systems is currently running a Special Issue entitled " Continual Unsupervised Sensorimotor Learning". Dr. José Antonio Iglesias Martínez, Assoc. Prof. 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 http://cis.ieee.org/component/content/article/7/131-ieee-transactions-on-aut.... Submissions must be done through the IEEE TCDS Manuscript center: https://mc.manuscriptcentral.com/tcds-ieee. 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 http://projects.au.dk/socialrobotics/news-events/show/artikel/special-issue-...
GUEST EDITORS
Nicolás Navarro-Gerrero Aarhus University, Aarhus, Denmark nng@eng.au.dk
Sao Mai Nguyen IMT Atlantique, Francenguyensmai@gmail.com
Erhan Öztop Özyeğin University, Turkeyerhan.oztop@ozyegin.edu.tr
Junpei Zhong National Institute of Advanced Industrial Science and Technology (AIST), Japanjoni.zhong@aist.go.jp ----
Nguyen Sao Mai nguyensmai@gmail.com Researcher in Cognitive Developmental Robotics http://nguyensmai.free.fr
Dear Colleagues,
IEEE Transactions on Cognitive and Developmental Systems is currently running a Special Issue entitled " Continual Unsupervised Sensorimotor Learning" : http://projects.au.dk/socialrobotics/news-events/show/artikel/special-issue-...
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 http://cis.ieee.org/component/content/article/7/131-ieee-transactions-on-aut.... Submissions must be done through the IEEE TCDS Manuscript center: https://mc.manuscriptcentral.com/tcds-ieee. 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 http://projects.au.dk/socialrobotics/news-events/show/artikel/special-issue-...
GUEST EDITORS
Nicolás Navarro-Gerrero Aarhus University, Aarhus, Denmark nng@eng.au.dk
Sao Mai Nguyen IMT Atlantique, Francenguyensmai@gmail.com
Erhan Öztop Özyeğin University, Turkeyerhan.oztop@ozyegin.edu.tr
Junpei Zhong National Institute of Advanced Industrial Science and Technology (AIST), Japanjoni.zhong@aist.go.jp ----
Nguyen Sao Mai nguyensmai@gmail.com Researcher in Cognitive Developmental Robotics http://nguyensmai.free.fr
Dear Colleagues,
Happy new year 2019!
We are preparing a special issue in IEEE Transactions on Cognitive and Developmental Systems on "Continual Unsupervised Sensorimotor Learning", and would like to invite you to contribute a research article or a review for the SI. The deadline is set as February 28th, 2019. The scope, aim, submission and other details are given below.
URL: http://projects.au.dk/socialrobotics/news-events/show/artikel/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://cis.ieee.org/publications/t-cognitive-and-developmental-systems/tcds... . Submissions must be done through the IEEE TCDS Manuscript center: https://mc.manuscriptcentral.com/tcds-ieee. Please select the category “SI: Continual Unsupervised Sensorimotor Learning”.
IMPORTANT DATES
28th February 2019 – Paper submission deadline 15th April 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
http://projects.au.dk/socialrobotics/news-events/show/artikel/special-issue-...
On behalf of the guest editors:
Nicolás Navarro-Gerrero Aarhus University, Aarhus, Denmark nng@eng.au.dk
Sao Mai Nguyen IMT Atlantique, France nguyensmai@gmail.com
Erhan Oztop Ozyeğin University, Turkey erhan.oztop@ozyegin.edu.tr
Junpei Zhong National Institute of Advanced Industrial Science and Technology (AIST), Japan joni.zhong@aist.go.jp
With my best wishes for the year 2019,
Nguyen Sao Mai nguyensmai@gmail.com Researcher in Cognitive Developmental Robotics http://nguyensmai.free.fr
Dear Colleagues,
IEEE Transactions on Cognitive and Developmental Systems is currently running a Special Issue entitled " Continual Unsupervised Sensorimotor Learning" : http://projects.au.dk/socialrobotics/news-events/show/artikel/special-issue-...
Due to a number of requests, we have extended the paper submission deadline to 21 March.
IMPORTANT DATES *21st March 2019 – Extended Paper Deadline* (final) 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
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://cis.ieee.org/publications/t-cognitive-and-developmental-systems/tcds... Submissions must be done through the IEEE TCDS Manuscript center: https://mc.manuscriptcentral.com/tcds-ieee. During the submission process, please select the category “SI: Continual Unsupervised Sensorimotor Learning”.
More information on Continual Unsupervised Sensorimotor Learning http://projects.au.dk/socialrobotics/news-events/show/artikel/special-issue-...
GUEST EDITORS
Nicolás Navarro-Gerrero Aarhus University, Aarhus, Denmark nng@eng.au.dk
Sao Mai Nguyen IMT Atlantique, Francenguyensmai@gmail.com
Erhan Öztop Özyeğin University, Turkeyerhan.oztop@ozyegin.edu.tr
Junpei Zhong National Institute of Advanced Industrial Science and Technology (AIST), Japanjoni.zhong@aist.go.jp ----
Nguyen Sao Mai nguyensmai@gmail.com Researcher in Cognitive Developmental Robotics http://nguyensmai.free.fr
participants (1)
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Nguyen, Sao Mai