[robotics-worldwide] [meetings] CFP Workshop at IROS'19: Learning Representations for Planning and Control

Ahmed Qureshi a1quresh at eng.ucsd.edu
Sat Jul 6 15:50:28 PDT 2019

Dear Colleagues,

We invite submissions for the workshop on “Learning Representations for
Planning and Control” to take place at IROS, Macau on 8 November 2019.

Website: https://urldefense.proofpoint.com/v2/url?u=https-3A__sites.google.com_view_iros-2D2019-2Dworkshop-2Dlrpc_home&d=DwIFaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=3n3BesUNkEsaI0FEqmIv77rrH1ikVAVnIHefdGfe9Zg&s=FqwtWTJDImsrwBd6nJMSblnjdUG61GJ8AcNgL0czhIo&e= 

Paper submission deadline (Pacific Standard Time):


   August 1, 2019, 6 pm: Extended Abstract Submission deadline

   September 20, 2019, 6 pm: Acceptance Notification

   October 5, 2019, 6pm: Camera-ready submission deadline

   November 8, 2019 (Full day): Workshop

Submission should be made via:



Algorithms for control and planning have deep roots in artificial
intelligence. It has a long history ranging from methods from receding
horizon optimal control to sample based planners with probabilistic
guarantees. The community continues to develop new strategies to overcome
challenges associated with classical methods, which include planning
representation, computational and memory burdens and the curse of
dimensionality. Orthogonally, machine learning advancements have led toward
the systems that can perform complex decision-making by directly using the
raw sensory information, thanks to the advancement in function
approximation. This workshop aims to bring these two long-lived research
communities under one forum to share insights towards building
computationally tractable planning methods while retaining the theoretical

Topics of Interests:


   Data-driven approaches to motion planning.

   Learning-based adaptive sampling methods.

   Learning models for planning and control.

   Imitation learning for planning and control.

   Learning generalizable and transferable planning models.

   Representation learning for planning.

   Learning-based collision detection, edge selection, and pruning
   techniques, and related topics.

   Data-efficiency in data-driven techniques to planning

   Formal guarantees to machine learning based planning methods.

   Learning methods for Hierarchical planning such task and motion
   planning, and related topics.

   Active learning methods for planning and related topics.

Paper Submission

We invite extended abstracts (2-3 pages excluding references) followed by
camera-ready submission of accepted papers (up to 3-6 pages excluding
references). All papers should follow the IEEE Conference Templates [Latex
<https://urldefense.proofpoint.com/v2/url?u=http-3A__ras.papercept.net_conferences_support_tex.php&d=DwIFaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=3n3BesUNkEsaI0FEqmIv77rrH1ikVAVnIHefdGfe9Zg&s=Ewzk-9jAxBXPA_QRRivkaCmDRZQTQaiZkUjrEWRlinc&e= >, MS Word
<https://urldefense.proofpoint.com/v2/url?u=http-3A__ras.papercept.net_conferences_support_word.php&d=DwIFaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=3n3BesUNkEsaI0FEqmIv77rrH1ikVAVnIHefdGfe9Zg&s=LpdhJYbP1bGUjiG1MW-YbZU09bw2PKcDD00jvQA3KBo&e= >]. Submissions can
be original research, late-breaking results, or a literature review that
fall under the scope of the workshop. All submissions should be made
through the following link: https://urldefense.proofpoint.com/v2/url?u=https-3A__cmt3.research.microsoft.com_LRPC2019&d=DwIFaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=3n3BesUNkEsaI0FEqmIv77rrH1ikVAVnIHefdGfe9Zg&s=IqjFxg3a3eX_vLgGjTLKrNS0G1qQjzZ0_Uv4Apyn0UA&e= .

Review Process:

All papers will be reviewed via single-blind review process: authors
declare their names and affiliations in the manuscript for the reviewers to
see, but reviewers do not know each other's identities, nor do the authors
receive information about who has reviewed their manuscript. The papers
acceptance decision will be based on contribution, novelty, and overall

Paper presentation:

Authors of accepted papers are required to present their paper in an
interactive poster session whereas a subset of accepted papers will also be
considered for a spotlight presentation.


Ahmed H. Qureshi (University of California San Diego)

Michael C. Yip (University of California San Diego)

Byron Boots (Georgia Institute of Technology)

Dmitry Berenson (University of Michigan)

Jan Peters (Technische Universitaet Darmstadt)

Marco Pavone (Stanford University)

Dorsa Sadigh (Stanford University)

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