FINAL CALL FOR PAPERS
Third Machine Learning in Planning and Control of Robot Motion Workshop at
IEEE International Conference on Robotics and Automation (ICRA) 2018
Monday, 21-May-2018
Brisbane, Australia
http://www.cs.unm.edu/amprg/Workshops/MLPC18/index.html
<http://www.cs.unm.edu/amprg/Workshops/MLPC18/index.html>
Paper submission deadline: *** March 21st, 2018 ***
=====================================================================
ABSTRACT:
Modern robots are expected to perform complex tasks in changing environments.
Nonlinear dynamic, model uncertainty, and high-dimensional configuration spaces
make planning and executing the motions required for these tasks is difficult.
Recent success has been made through the integration of planning and control
methods with tools from machine learning. For example, clustering,
reinforcement learning, and intelligent heuristics have adaptively solved
planning problems in complex spaces, have automatically identified appropriate
trajectories for robots with complex dynamics, and have reduced the amount of
time required for planning motions.
After the success of the First Workshop
(http://www.cs.unm.edu/amprg/mlpc14Workshop
<http://www.cs.unm.edu/amprg/mlpc14Workshop>) and Second Workshop
(http://kormushev.com/MLPC-2015 <http://kormushev.com/MLPC-2015>) in Machine
Learning in the Planning and Control of Robot Motion at IROS 2014 in Chicago
and IROS 2015 in Hamburg, it is the goal of this workshop to continue to
explore methods and advancements afforded by the integration of machine
learning for the planning and control of robot motion. The objectives of this
workshop are to:
- Develop a community of researchers working on machine learning methods in
complementary fields of motion planning and controls
- Discuss current state of the art and future directions of intelligent motion
planning and controls
- Provide for collaboration opportunities
MOTIVATION AND OBJECTIVES:
To meet the objectives, the workshop will:
- Include high-quality keynote talks by the leaders of the fields.
- Solicit extended abstracts and short papers (2-4 pages) contributions of the
state of the art, and preliminary research results. We aim at accepting
approximately 12 short papers and several extended abstracts.
- Organize the workshop with ample time for informal meeting and networking,
including group lunch and dinner.
- Conclude the day with an interactive panel discussion and dialogue between
the invited speakers, contributed authors, and audience on the next challenges
in the field on machine learning for motion planning and control.
LIST OF TOPICS (included, but not limited to):
Because machine learning methods are often heuristic, issues such as safety and
performance are critical. Also, learning-based questions such as problem
learnability, knowledge transfer among robots, knowledge generalization,
long-term autonomy, task formulation, demonstration, role of simulation, and
methods for feature selection define problem solvability. The topics include:
- Task representation and classification
- Planning for complex and high dimensional environments
- Smart sampling techniques for motion planning
- Learning feature selection
- Methods for incorporating learning into planning
- Reinforcement learning for robotics and dynamical systems
- Transfer of learning and motion plans, knowledge and experience sharing among
the agents
- Policy selection: exploration versus exploitation, methods for safe
exploration
- Methods for creating motion plans that meet dynamical constraints
- Task planning and learning under uncertainty and disturbance
- Motion planning for system stability
- Adaptable heuristics for efficient motion plans
- Motion generalization - methods that learn subset of motion and produce plans
with higher range of motions
- Motion planning for multi-agent systems and fleets
INTENDED AUDIENCE:
- Motion planners with interests in learning and planning for changing agents,
environment, or both
- Reinforcement learning and machine learning communities that develop novel
learning methods for autonomous agents
- Multi-agent researchers
- Controls community focused on controlling physical systems
- Robotics community
=====================================================================
SOCIAL MEDIA:
Like us on Facebook: https://www.facebook.com/mlpcworkshop
<https://www.facebook.com/mlpcworkshop>
Follow us on Twitter: https://twitter.com/MLPC18 <https://twitter.com/MLPC18>
Please feel free to contact the workshop committee at
mlpc18\AT\googlegroups.com <http://googlegroups.com/>
=====================================================================
ORGANIZERS:
Aleksandra Faust, Google Brain, faust\AT\google.com <http://google.com/>
Tsz-Chiu Au, Ulsan National Institute of Science and Technology,
chiu\AT\unist.ac.kr <http://unist.ac.kr/>
Hao-Tien (Lewis) Chiang, University of New Mexico, lewispro\AT\unm.edu
<http://unm.edu/>
James Davidson, Google Brain, jcdavidson\AT\google.com <http://google.com/>
Hanna Kurniawati, University of Queensland, hannakur\AT\uq.edu.au
<http://uq.edu.au/>
Lydia Tapia, University of New Mexico, tapia\AT\cs.unm.edu <http://cs.unm.edu/>
--
You received this message because you are subscribed to the Google Groups
"ICAPS Conference" group.
To post to this group, send email to icaps-confere...@googlegroups.com
<mailto:icaps-confere...@googlegroups.com>
To unsubscribe from this group, send email to
icaps-conference-unsubscr...@googlegroups.com
<mailto:icaps-conference-unsubscr...@googlegroups.com>
For more options, visit this group at
http://groups.google.com/group/icaps-conference
<http://groups.google.com/group/icaps-conference>
---
You received this message because you are subscribed to the Google Groups
"ICAPS Conference" group.
To unsubscribe from this group and stop receiving emails from it, send an email
to icaps-conference+unsubscr...@googlegroups.com
<mailto:icaps-conference+unsubscr...@googlegroups.com>.
For more options, visit https://groups.google.com/d/optout
<https://groups.google.com/d/optout>.
_______________________________________________
uai mailing list
uai@ENGR.ORST.EDU
https://secure.engr.oregonstate.edu/mailman/listinfo/uai
