[robotics-worldwide] [meetings] Deadline Extended, CfP ICRA 2020 Workshop on Machine Learning in Planning and Control of Robot Motion (MLPC)

Brian Ichter ichter at google.com
Wed Mar 18 12:08:16 PDT 2020


The deadline to submit works to the Fourth Machine Learning in Planning and
Control of Robot Motion Workshop (MLPC) at ICRA 2020 has been extended to
April 8th.

Website: https://urldefense.com/v3/__https://sites.google.com/view/mlpc-icra2020__;!!LIr3w8kk_Xxm!84cVXPCItim9owT7JeVxI7_X8je5OaCs5Ifoa13uMYBwAp0pqZy5Gs4R1KHgxIcyRHltC77K$ 

We solicit submissions of:


   Papers (up to 6 pages)- to be presented as posters with a selected few
   spotlight talks

   Demos / Interactive Exhibits (1 page extended abstract)- e.g., robot
   demonstrations, software demonstrations

In IEEE format (LaTeX <https://urldefense.com/v3/__http://ras.papercept.net/conferences/support/tex.php__;!!LIr3w8kk_Xxm!84cVXPCItim9owT7JeVxI7_X8je5OaCs5Ifoa13uMYBwAp0pqZy5Gs4R1KHgxIcyRNw2P-jl$ >
or MS Word <https://urldefense.com/v3/__http://ras.papercept.net/conferences/support/word.php__;!!LIr3w8kk_Xxm!84cVXPCItim9owT7JeVxI7_X8je5OaCs5Ifoa13uMYBwAp0pqZy5Gs4R1KHgxIcyRNGZrwAD$ >) to be
submitted via Microsoft CMT at https://urldefense.com/v3/__https://cmt3.research.microsoft.com/MLPC2020/__;!!LIr3w8kk_Xxm!84cVXPCItim9owT7JeVxI7_X8je5OaCs5Ifoa13uMYBwAp0pqZy5Gs4R1KHgxIcyRHBeOTjl$ 
by April 8, 2020 AOE (Deadline Extended From March 18, 2020).



Modern robots are increasingly expected to perform complex tasks in the
real world. These tasks are complicated by high-dimensional state spaces,
changing environments, nonlinear dynamics, and significant uncertainty
throughout the robotic stack. The ability to plan and control robotic
systems lies at the core of addressing these challenges. Recent successes
in machine learning offer promising steps forward towards addressing these

Following previous workshops on Machine Learning in the Planning and
Control of Robot Motion (2014, 2015, 2018), this workshop seeks to continue
to explore methods and directions for integrating machine learning with
robotic planning and control. This workshop will feature talks from leading
experts, panel discussions, solicited papers, and poster presentations in
an effort 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

This workshop aims to build off previous MLPC workshops held in 2014
<https://urldefense.com/v3/__http://www.cs.unm.edu/amprg/mlpc14Workshop/__;!!LIr3w8kk_Xxm!84cVXPCItim9owT7JeVxI7_X8je5OaCs5Ifoa13uMYBwAp0pqZy5Gs4R1KHgxIcyRI8azsne$ >, 2015
<https://urldefense.com/v3/__http://kormushev.com/MLPC-2015/__;!!LIr3w8kk_Xxm!84cVXPCItim9owT7JeVxI7_X8je5OaCs5Ifoa13uMYBwAp0pqZy5Gs4R1KHgxIcyRJDtG1hL$ >, and 2018
<https://urldefense.com/v3/__https://www.cs.unm.edu/amprg/Workshops/MLPC18/index.html__;!!LIr3w8kk_Xxm!84cVXPCItim9owT7JeVxI7_X8je5OaCs5Ifoa13uMYBwAp0pqZy5Gs4R1KHgxIcyRLY64EGo$ >.


To meet the objectives, the workshop will:


   Include high-quality keynote talks by the leaders of the fields.

   Solicit papers (< 6 pages) with contributions of the state of the art,
   and preliminary research results.

   Solicit interactive demos (1 page extended abstracts), such as robot and
   software demos.

   Conclude the day with an interactive panel discussion and dialogue
   between the invited speakers, contributing authors, and the audience on the
   next challenges in the field of machine learning for motion planning and

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.  Other potential topics include:


   Reinforcement learning for control and planning of robotic systems

   Latent-space learning planning and control

   Task representation and classification for task and motion planning

   Learning feature selection for motion-based tasks

   Planning and learning under uncertainty and disturbances

   Methods for incorporating learning into algorithmic subroutines

   Intelligent planning for complex and high dimensional environments

   Experience transfer for planning and control among the agents

   Smart sampling techniques for motion planning

   Learning within kinodynamic motion planning

   Adaptive motion planning for system stability

   Adaptable heuristics for efficient motion plans

   Intelligent motion planning for multi-agent systems and fleets

   Learning planning with limited data

   Sim2real transfer for planning and control




   Nathan Ratliff, NVIDIA

   Leslie Kaelbling, MIT

   Aude Billard, EPFL

   Marc Toussaint, University of Stuttgart

   Michael Beetz, University of Bremen

   Emre Ugur, Bogazici University



Like us on Facebook: https://urldefense.com/v3/__https://www.facebook.com/mlpcworkshop__;!!LIr3w8kk_Xxm!84cVXPCItim9owT7JeVxI7_X8je5OaCs5Ifoa13uMYBwAp0pqZy5Gs4R1KHgxIcyRIDgtaZ7$ 

Please feel free to contact the workshop committee at
mlpc_icra2020 at googlegroups.com



Brian Ichter <https://urldefense.com/v3/__http://brianichter.com__;!!LIr3w8kk_Xxm!84cVXPCItim9owT7JeVxI7_X8je5OaCs5Ifoa13uMYBwAp0pqZy5Gs4R1KHgxIcyRHraI1Kg$ > (Robotics at Google)

Aleksandra Faust <https://urldefense.com/v3/__https://www.afaust.info/__;!!LIr3w8kk_Xxm!84cVXPCItim9owT7JeVxI7_X8je5OaCs5Ifoa13uMYBwAp0pqZy5Gs4R1KHgxIcyRPkdoa1t$ > (Google Research)

Lydia Tapia <https://urldefense.com/v3/__https://www.cs.unm.edu/amprg/People/tapia/index.php__;!!LIr3w8kk_Xxm!84cVXPCItim9owT7JeVxI7_X8je5OaCs5Ifoa13uMYBwAp0pqZy5Gs4R1KHgxIcyRPz86AFV$ >
(University of New Mexico)

Marco Pavone <https://urldefense.com/v3/__https://web.stanford.edu/*pavone/__;fg!!LIr3w8kk_Xxm!84cVXPCItim9owT7JeVxI7_X8je5OaCs5Ifoa13uMYBwAp0pqZy5Gs4R1KHgxIcyRMniCXm1$ > (Stanford University)

Tamim Asfour <https://urldefense.com/v3/__https://h2t.anthropomatik.kit.edu/english/21_66.php__;!!LIr3w8kk_Xxm!84cVXPCItim9owT7JeVxI7_X8je5OaCs5Ifoa13uMYBwAp0pqZy5Gs4R1KHgxIcyRAR6ZmdF$ >
(Karlsruhe Institute of Technology)

Jan Peters <https://urldefense.com/v3/__http://www.jan-peters.net__;!!LIr3w8kk_Xxm!84cVXPCItim9owT7JeVxI7_X8je5OaCs5Ifoa13uMYBwAp0pqZy5Gs4R1KHgxIcyRFGKxqXX$ > (Technische Universität Darmstadt)

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