[robotics-worldwide] [journals] CfP: Topical Issue on Deep Learning and Neural Systems in Robotics
CALL FOR PAPERS
Deep Learning and Neural Systems in Robotics
Topical issue in PALADYN. Journal of Behavioural Robotics
Deep Learning can equip robots with a new level of skill by utilizing insights from neuroscience.
These new developments have impact on various types of robots, from companion robots to driver assistance in semi-autonomous vehicles. Deep Learning can enable unprecedented quality of results in tasks such as object detection, localization, communication and behavior learning.
Training deep neural networks typically requires GPU workstations or high-performance computing servers. It may require huge amounts of data or innovative training schemes. These issues indicate only some of the special challenges when utilizing deep networks in robotics.
Within this context the topics of this special issue include, but are not limited to:
- Applications of deep learning
- Software and hardware systems
- Data acquisition for deep learning
- Neural information processing systems
- Deep learning training schemes and paradigms
- GPU computing for deep learning
- Software engineering for deep learning
- Neural networks on low powered devices
- Human robot interaction and communication
- Cognitive, developmental and evolutionary robotics
The deadlines for the submission are as follows:
15 June 2018 (publication in 2018)
15 December 2018 (publication in 2019)
Notification of acceptance will be communicated as we progress with the review process.
Individual papers will be reviewed and published online on an ongoing basis.
HOW TO SUBMIT
Authors are requested to submit their full version of papers to the Topical Issue complying also the general scope of the journal.
The submitted papers will undergo the standard peer-review process before they can be accepted.
Contributors to the Topical Issue will benefit from:
NO submission and publication FEES
Free language assistance for authors from non-English speaking regions
Fast online publication of articles (continuous publication model)
Open Access to your article for all interested readers
Indexation in SCOPUS
Long-time preservation (articles archived in Portico)
Extensive post-publication promotion
Stephan K. Chalup, University of Newcastle, Australia
Alessandro Di Nuovo, Sheffield Hallam University, UK
Alan D. Blair, University of New South Wales, Australia
Aydan M. Erkmen, Middle East Technical University, Ankara, Turkey
Neil Vaughan, Royal Academy of Engineering / University of Chester, UK