[robotics-worldwide] [meetings] [cfp] RSS 2020 Workshop on Advancing the State of Machine Learning for Manufacturing Robotics

Schlenoff, Craig I. (Fed) craig.schlenoff at nist.gov
Mon Mar 16 06:54:06 PDT 2020


Call for Posters / Extended Abstracts
==============================

Workshop on Advancing the State of Machine Learning for Manufacturing Robotics
Robotics: Science and Systems (RSS)
July 12, 2020
Corvallis, Oregon, U.S.A.

Website:  https://urldefense.com/v3/__https://www.nist.gov/el/intelligent-systems-division-73500/advancing-state-machine-learning-manufacturing-robotics__;!!LIr3w8kk_Xxm!5kg2Lev4qtaHLGQ9v9fn8gYVXPYiFwsuYaNKvp3HGEPiJ2tZuqUMRqoxETIjBLrOmtvfFG12$ 


Important Dates
==============

- Submission deadline: April 9, 2020
- Notification of acceptance: April 16, 2020
- Workshop date: July 12, 2020

Overview
========

Machine learning approaches are fostering impressive new capabilities for robots. The number of
research projects and publications are growing quite rapidly, and ML-based product spending is increasing at a compound annual growth rate of 25%. It is an exciting time, but this rapid expansion
is outpacing the definition of consensus and science-based methods of assessing approaches and best
practices for applying these technologies. Supporting tools, such as datasets for training and bench-
marking, are becoming widely available to assist in the development of ML-based systems, but there
is a severe lack of such tools for manufacturing robotics applications.

This workshop will focus on addressing the needs of this important application domain that is significantly under-represented in research publications and support infrastructure. The goals of this workshop are:

  *   Raise awareness of the need for ML metrics, evaluations, benchmarks, especially for manufacturing-relevant parts, operations, and environments.
  *   Convene stakeholders to define common language for discussing ML performance, characteristics, applicability and/or tools and measurement science necessary to advance the state of ML in manufacturing robotics and reduce the risk of adopting ML-based technologies and solutions.
  *   Produce initial document articulating challenges and gaps, ideas for directions to go for defining metrics and other measurement science to bring more rigor to the field.
  *   Form an ongoing community to develop, review, try out, mature, and contribute to the concepts and tools that can help mature the field and foster well-informed, successful adoption and implementation of ML-based manufacturing robotics capabilities.





Workshop Structure and Invited Speakers

==================================


The workshop will consist of a combination of invited talks that present user and developer perspectives, a panel discussion to bring out major themes or areas of need, a poster session, and a structured discussion with general participation intended to identify the priorities going forward for forming a community to define protocols, guidelines, metrics, test methods, datasets, and tools that will be useful for maturing the application of ML to manufacturing robotics.



This workshop will feature invited presentations by:



  *   Adam Norton, University of Massachusetts Lowell, New England Robotics Validation and Experimentation (NERVE) Center
  *   Dragos Margineantu, Boeing Research & Technology
  *   Berk Calli, Worchester Polytechnic Institute
  *   Megan Zimmerman, National Institute of Standards and Technology (NIST)
  *   Nathan Ratliff, NVIDIA



Submission Instructions
====================


We invite submissions of extended abstracts (no more than three pages single-spaced) in the RSS conference template by April 9, 2020 on topics related to the workshop focus, including but not limited to:



  *   Industry perspectives on requirements to assist in evaluation and matching of solutions to implementations
  *   Available resources and lessons-learned that may be applicable to manufacturing robotics
  *   Available tools to automate dataset collection and curation
  *   How to assess the quality, applicability, and transferability of datasets or learned models


All extended abstracts will be reviewed by the members of the organizing committee and notification of acceptance will be provided by April 16, 2020.  All accepted contributions will be presented as posters during the interactive sessions.



It is the organizers' intention to guest edit a special issue of a journal based on the output of this workshop. Contributors may be asked to submit an extended version of their submission for inclusion in the special issue.

All submissions should be sent in PDF format to the email: elena.messina at nist.gov<mailto:elena.messina at nist.gov>.

For each accepted paper/poster, one author is required to register for the RSS workshops to present the contribution. IMPORTANT: If your paper/poster's representative is from a country that requires a visa to enter the United States, the RSS organizers are able to provide a visa support letter. If you require such a letter, **please confirm the full name and country of citizenship of the representative before April 23rd, 2020**. Due to expected long visa processing times in the US, we urge you to start the visa application process as soon as possible. Please note that the RSS organizers are only able to provide one such support letter per contribution.

Organizing Committee
===================

- Elena Messina, National Institute of Standards and Technology
- Holly Yanco,  University of Massachusetts Lowell
- Megan Zimmerman, National Institute of Standards and Technology
- Craig Schlenoff, National Institute of Standards and Technology
- Dragos Margineantu, Boeing

For further information please contact the organizing committee at elena.messina at nist.gov<mailto:elena.messina at nist.gov>.




Craig Schlenoff, PhD
Group Leader, Cognition and Collaboration Systems Group
Associate Program Manager, Measurement Science for Manufacturing Robotics
Associate Vice President for Standardization, IEEE Robotics and Automation Society
Project Leader, Agility Performance of Robotics Systems
Co-Project Leader, Embodied AI and Data Generation for Manufacturing
National Institute of Standard and Technology (NIST)
100 Bureau Drive, Stop 8230
Gaithersburg, MD 20899
craig.schlenoff at nist.gov<mailto:craig.schlenoff at nist.gov>
P:  301-975-3456




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