[robotics-worldwide] [meetings] Last CfP for Second Annual Workshop on Machine Learning in Planning and Control of Robot Motion (MLPC) at IROS 15

Aleksandra Faust sandraorion at yahoo.com
Wed Jul 15 21:09:45 PDT 2015


The submission deadline for the Second Annual Workshop on Machine Learning in 

Planning and Control of Robot Motion (MLPC) at IROS 15 is Friday, July 17th 2015 PDT.
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Second Machine Learning in Planning and Control of Robot Motion Workshop at
IEEE/RSJ International Conference on Intelligent Robots and Systems
(IROS) 2015
October 02, 2015
http://kormushev.com/MLPC-2015/
Hamburg, Germany
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IMPORTANT DATES:

Paper submission deadline extended: **17-Jul-15 PDT**
Notification of acceptance:         21-Aug-15
Camera-ready paper submission:      1-Sep-15
Workshop at IROS 2015:              02-Oct-15

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SUBMISSION:
Please submit a PDF document in IEEE format via EasyChair at
https://easychair.org/conferences/?conf=mlpc15

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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 in Machine Learning in the Planning and
Control of Robot Motion at IROS 2014 in Chicago, 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. Because these 
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 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:
The workshop aims to spark vibrant discussion with talks from invited speakers,
presentations from authors of accepted papers, and a poster session. We are
soliciting two types of contributions:

- Papers (4-6 pages) for oral or  interactive poster presentation
- Extended abstracts (2 pages) for interactive poster presentation


LIST OF TOPICS (included, but not limited to):
- 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

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CONFIRMED GUEST SPEAKERS:
- Lucian Busoniu, Technical University of Cluj-Napoca

- Danica Kragic, Royal Institute of Technology, KTH
- Matteo Leonetti, University of Texes, Austin
- Jan Peters, Technischen Universität Darmstadt

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SOCIAL MEDIA:
Like us on Facebook:  https://www.facebook.com/mlpc2015
Follow us on Twitter: https://twitter.com/MLPC2015
Email:                mlpc15\AT\cs.unm.edu
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ORGANIZERS:
Aleksandra Faust, Lead Organizer, Sandia National Laboratories, afaust\AT\cs.unm.edu
Maria Gini, University of Minnesota, gini\AT\cs.umn.edu
Petar Kormushev, Italian Institute of Technology (IIT), petar\AT\kormushev.com
Marco Morales, Instituto Tecnologico Autonomo de Mexico, marco.morales\AT\itam.mx
Ivana Palunko, University of Dubrovnik, ivana.palunko\AT\unidu.hr
Angela P. Schoellig, University of Toronto, schoellig\AT\utias.utoronto.ca
Lydia Tapia, University of New Mexico, tapia\AT\cs.unm.edu


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