[robotics-worldwide] [journals] CfP IEEE RAM Special Issue "Deep Learning and Machine Learning in Robotics"

Fabio Bonsignorio fabio.bonsignorio at gmail.com
Thu Jul 18 05:42:07 PDT 2019

Dear Colleagues,

We invite contributions to a IEEE Robotics & Automation Magazine Special
Issue on "Deep Learning and Machine Learning in Robotics". Sorry for
multiple postings.
In short: Deadline 1 August 2019, publication 10 June 2020

For details see below or

Enquiry are welcome!
All the best

Jens Kober, Fabio Bonsignorio, David Hsu, Matthew Johnson-Roberson (guest

**Special Issue on Deep Learning and Machine Learning in Robotics**
*Call for Papers*

Deep learning and Machine Learning have gone through a massive growth in
the past several years. In many domains, such as perception, vision, image
recognition, image captioning, speech recognition, machine translation, and
board games, in particular, deep learning has drastically outperformed
traditional methods and overtaken them to become the method of choice. Will
the same happen to robotics and automation? These approaches typically
require massive amounts of labeled data, i.e., big data, and massive
amounts of compute. In many real robotics and automation applications data
is abundant but labeling sparse and expensive. (Deep) reinforcement
learning often requires significantly more iterations than are feasible on
real systems. Hence collecting sufficient amounts of data is impractical at
best. Therefore, a lot of work is done in purely digital or virtual
environments. In this special issue we will focus on approaches that have
been validated on real world robots, scenarios, and
  automation problems. While a lot of progress has been achieved on this
front in robotic and automation applications, still a lot of progress needs
to be made in order to render deep learning approaches directly applicable.
Robots and automation systems are interacting with the real world. Hence
mistakes that might be costly in terms of lost revenue in approaches that
operate in a purely digital world, can cause significant damage and loss of
human lives. Therefore, safe learning becomes paramount. A related issue is
interpretable learning, i.e. the capability to interpret learning
processes, moving towards approaches where humans have the option to be in
control and understand with sufficient human-readable details the decision
processes of the machine. Successful applications in 'neighboring' fields
characterized by limited amounts of sparse, labeled data coming from
physical systems will also be considered.

Papers should follow the standard RAM guidelines. A full peer-review
process will be utilized to select papers for the special issue.
Submissions should be made through the RAM submission website by August 1,

Contributions are expected to present original research on deep learning
and machine learning with real world applications in robotics and

*Topics of Interest*

--- deep/machine learning
        -- supervised
        -- unsupervised
        -- reinforcement
--- sample efficient learning
        -- new methods
        -- use of models
        -- simulation to real transfer
        -- data augmentation
        -- embedding prior knowledge
        -- ...
--- safe learning
        -- confidence estimates
        -- guarantees
        -- verification
        -- interpretable learning
        -- ...
--- real applications and use case scenarios of deep/machine learning
        -- robotics
              - perception
              - control
              - planning
              - navigation
              - manipulation and grasping
              - ...
        - automation
              - maintenance and inspection
              - production
              - quality management and assurance
              - product tracking
              - ...
        -- success stories of deep/machine learning technologies in
robotics and automation
        -- common issues and solutions in deep/machine learning
applications in robotics and automation and neighboring fields such as:
              - gravitational waves detection
              - geophysics
              - high energy physics
              - ...

*Important Dates*

1 August 2019 - Submission deadline
1 November 2019 - First decision communicated to authors
15 December 2019 - Revised paper submitted
20 February 2020 - Final acceptance decision communicated to authors
10 March 2020 - Final manuscripts uploaded by authors
10 June 2020 - Special issue

*Guest Editors*

Fabio Bonsignorio
Heron Robots
RAS Geographic Region 2
fabio.bonsignorio at gmail.com

David Hsu
National University of Singapore
RAS Geographic Region 3
dyhsu at comp.nus.edu.sg

Matthew Johnson-Roberson
University of Michigan
Ann Arbor, Michigan, USA
RAS Geographic Region 1
mattjr at umich.edu

Jens Kober
TU Delft
Delft, Netherlands
RAS Geographic Region 2
J.Kober at tudelft.nl


Fabio P. Bonsignorio

IEEE Senior Member

Reproducibility Editor IEEE Robotics & Automation Magazine

Heron Robots s.r.l.
Via Malta, 3/7
I-16121 Genova

The ShanghAILectures

Past Research Board of Directors Member
Founding Past Director
euRobotics aisbl

Visiting Professor 2014-2019
Scuola Superiore Sant'Anna, Pisa, Italy
The BioRobotics Institute
viale Rinaldo Piaggio 34, 56025 Pontedera (Pisa)

Banco de Santander-Universidad Carlos III de Madrid  Chair of Excellence in

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