Call For Papers-Special Issue on Deep Learning and Machine Learning in Robotics

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 15 September 2019.

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

 

*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

              - …

*Tentative Schedule/Important Dates*

15 September 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

The BioRobotics Institute, SSSA and Heron Robots

Italy

RAS Geographic Region 2

fabio.bonsignorio@gmail.com

 

David Hsu

National University of Singapore

Singapore

RAS Geographic Region 3

dyhsu@comp.nus.edu.sg

 

Matthew Johnson-Roberson

University of Michigan

Ann Arbor, Michigan, USA

RAS Geographic Region 1

mattjr@umich.edu

 

Jens Kober

TU Delft

Delft, Netherlands

RAS Geographic Region 2

J.Kober@tudelft.nl