[robotics-worldwide] [meetings]Updated CFP IROS 2018 Workshop on Machine Learning in Robot Motion Planning

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[robotics-worldwide] [meetings]Updated CFP IROS 2018 Workshop on Machine Learning in Robot Motion Planning

Sanjiban Choudhury
Dear colleagues,

We have updated speaker list and submission details for our IROS workshop on
"Machine Learning in Robot Motion Planning" to be held during IROS 2018 in
Madrid, Spain. Please see below for details.

----------------------------- -----------------------------
** Workshop on Machine Learning in Robot Motion Planning **

Website: https://urldefense.proofpoint.com/v2/url?u=https-3A__personalrobotics.cs.washington.edu_workshops_mlmp2018_&d=DwIBaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=mJx2QjzHzWj68f-HJOeo7ZKsa1rV5Nc-IqyPxY_WZX8&s=ZtFSKaLpfgnDjmySJzJE6QuGNia77CWDd_nmxU47WAU&e=
When & where: Friday October 05 (full day), IROS 2018, Madrid, Spain

Important Dates
August 15th: Submission deadline
August 31st: Notification of acceptance
September 20th: Camera ready paper
October 5th: Workshop (full day)

Motion planning has a rich and varied history. The bulk of the research in
planning has focused on the development of tractable algorithms with
provable worst-case performance guarantees. In contrast, well-understood
theory and practice in machine learning is concerned with expected
performance (e.g. supervised learning). As affordable sensors, actuators
and robots that navigate, interact and collect data proliferate, we are
motivated to examine new algorithmic questions such as " What roles can
statistical techniques play in overcoming traditional bottlenecks in
planning?", " How do we maintain worst-case performance guarantees while
leveraging learning to improve expected performance?" and " How can common
limitations inherited from data-driven methods (e.g. covariate shift) be
mitigated while combining with traditional planning methods? "

Both areas have much to contribute to each other in terms of methods,
approaches, and insights, and yet motion planning and machine learning
communities remain largely disjoint groups. There are four technical goals
for this workshop in addition to encouraging dialogue between both
* Formalize paradigms in motion planning where statistical methods can play
an essential role.
* Identify learning algorithms that can alleviate planning bottlenecks.
* Better understand common pitfalls of naively combining learning and
planning and explore strategies for mitigation.
* Arrive at a set of critical open questions that are at the intersection
of the two fields.

Invited speakers
* Maxim Likhachev, Carnegie Mellon University
* David Hsu, National University of Singapore
* Aleksandra Faust, Google Brain
* Jeannette Bohg, Stanford University
* Marc Touissant, University of Stuttgart
* Byron Boots, Georgia Institute of Technology
* Andrey Kolobov, Microsoft Research

Submissions & formats

We solicit 3 page extended abstracts (page counts do not include
references). On acceptance, the camera ready version can be a full paper
upto 6 pages (excluding references). Submissions can include original
research, position papers, and literature reviews that bridge the research
areas for this workshop. Submissions will be externally reviewed, and
selected based on technical content and ability to positively contribute to
the workshop. All accepted contributions will be presented in interactive
poster sessions. A subset of accepted contributions will be featured in the
workshop as spotlight presentations.

The following list contains some areas of interest, but work in other areas
is also welcomed:

* machine learning in planning and related topics
* learning representations for planning
* planning with learnt models
* learning heuristics in search
* learning sampling techniques
* pre-processing techniques to speed up planning
* resource allocation in planning
* learning in sequential decision-making settings
* sample efficient learning
* learning robust models to deal with distribution shifts
* bayesian models and novelty detection in decision making
* online learning in decision making
* learning applied to task and motion planning

We will accept papers in the official IEEE templates (LaTeX and Word).
Submissions must meet page restrictions (maximum of 3 pages for extended
abstracts and 6 pages for full papers), but can include additional pages as
long as those pages only contain references. Reviewing will not be double
blind. Please do not anonymize the submission.

Papers and abstracts should be submitted through the following link:

Organizing committee
* Sanjiban Choudhury, University of Washington (PoC: sanjibac AT cs.uw.edu)
* Debadeepta Dey, Microsoft Research
* Siddhartha Srinivasa, University of Washington
* Marc Toussaint, University of Stuttgart
* Byron Boots, Georgia Tech

This workshop is endorsed by the IEEE Technical Committee on Robot Learning
and the IEEE Technical Committee on Algorithms for Planning and Control of
Robot Motion.

Organizing committee, MLMP 2018.
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