Connectionists: [journals] [CfP] Limits and Potentials of Deep Learning in Robotics - IJRR Special Issue

[Juxi Leitner]

Dear colleagues, 

following up on our deep learning workshop at RSS (Webpage here: http://Juxi.net/workshop/deep-learning-rss-2016/ ), we are running a special issue of the International Journal of Robotics Research (IJRR) entitled "Limits and Potentials of Deep Learning in Robotics”. Attached is the call for contributions!


Call for Papers: The International Journal of Robotics Research (IJRR)
Special Issue: Limits and Potentials of Deep Learning in Robotics

This special issue invites papers that identify the limits and potentials of current deep learning techniques in robotics, and that propose directions for future research to overcome those limits and realize the promising potentials. The special issue particularly encourages contributions that analyse why deep learning has not yet had the wide impact in robotics that it had in neighboring research disciplines, especially in computer vision.

Call for Papers in PDF:
http://Juxi.net/special-issue/ijrr-deep-learning-2016/deep-learning-cfp-2016-ijrr.pdf

*Important Dates*
1 October 2016: Submission deadline
April 2017: Online publication of accepted articles
Summer 2017: Online and hard copy publication

*Motivation*
Deep learning techniques have revolutionised many aspects of computer vision over the past three years and have been tremendously successful at tasks like object recognition and detection, scene classification, action recognition, and caption generation. Despite deep learning thriving in computer vision, it has not yet been nearly as impactful in robotic vision. Although deep learning techniques are successfully applied by a few groups for tasks like visually guided robotic grasping and manipulation, they have not yet evolved into mainstream approaches that are generally adopted and applied.

In a recent workshop at the Robotics: Science and Systems Conference (RSS), the participants and invited speakers identified both advantages and shortcomings/missing features of current deep learning techniques for robotic applications. This special issue will collect contributions that identify such challenges, and propose novel methods to overcome current limits. 

The topics of interest for contributed papers comprise, but are not limited to:

* limits of deep learning for robotics
* case studies: when does state-of-the-art deep learning fail in robotics?
* success stories: where did deep learning enable breakthroughs in robotics?
* fundamental differences between typical computer vision tasks and robotic vision
* deep learning for perception, action, and control in robotics contexts
* reliable confidence measures for deep classifiers
* exploitation of semantic information and prior knowledge for deep learning
* deep learning in the context of open set classification
* incremental learning, incorporation of human feedback for classification
* utilizing robotic technology to create novel datasets comprising interaction, active vision etc.
* deep learning for embedded systems or platforms with limited computational power


*Manuscript Submission*
Please submit papers to: http://mc.manuscriptcentral.com/ijrr Under “Manuscript Type” select “Deep Learning”. Submission instructions for authors, including information on how to submit multimedia, are available online at:
http://www.sagepub.com/journals/Journal201324#tabview=manuscriptSubmission


*Guest Editors*
Niko Sünderhauf (niko.suenderhauf at qut.edu.au>) – Queensland University of Technology, ACRV
Jürgen Leitner (j.leitner at qut.edu.au – Queensland University of Technology, ACRV
Ben Upcroft (ben.upcroft at qut.edu.au) – Queensland University of Technology, ACRV
Nicholas Roy (nickroy at csail.mit.edu) – Massachusetts Institute of Technology

*Website*
http://Juxi.net/special-issue/ijrr-deep-learning-2016/



-- 
Juxi Leitner | http://Juxi.net
Research Fellow | ARC Centre of Excellence for Robotic Vision
E: j.leitner at qut.edu.au | W: www.roboticvision.org | P: +61 7 3138 2363

Science and Engineering Faculty | Queensland University of Technology
Gardens Point, S Block 11 30 | 2 George Street, Brisbane, QLD 4000 | CRICOS No. 00213J