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 

Paper submission deadline: *** March 21st, 2018 ***



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 
<>) and Second Workshop 
( <>) 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


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 
- 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


- 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



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Please feel free to contact the workshop committee at 
mlpc18\AT\ <>



Aleksandra Faust, Google Brain, faust\AT\ <>
Tsz-Chiu Au, Ulsan National Institute of Science and Technology, 
chiu\AT\ <>      
Hao-Tien (Lewis) Chiang, University of New Mexico, lewispro\AT\ 
James Davidson, Google Brain, jcdavidson\AT\ <>
Hanna Kurniawati, University of Queensland, hannakur\AT\ 
Lydia Tapia, University of New Mexico, tapia\AT\ <>

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