IROS 2018 Workshop

Machine Learning in Robot Motion Planning

October 5, 2018 | Madrid, Spain


Description

Motion planning has a rich and varied history. The bulk of the research in planning has focused on 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 communities:

  • 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.


Important Dates

(deadlines are "anywhere on earth")

Aug 15 Aug 29
Extended abstract submission deadline
Sep 12
Notification of acceptance
Sep 20
Camera ready deadline for full paper
Oct 5
Workshop

Submissions

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
  • 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: https://cmt3.research.microsoft.com/MLMP2018/ .


Program

Invited Speakers

likachev

Maxim Likhachev

Carnegie Mellon University

          hsu

David Hsu

National University of Singapore

faust

Aleksandra Faust

Google Brain

          hsu

Jeannette Bohg

Stanford University

toussaint

Marc Toussaint

University of Stuttgart

          boots

Byron Boots

Georgia Institute of Technology

kolobov

Andrey Kolobov

Microsoft Research

          srinivasa

Siddhartha Srinivasa

University of Washington



Schedule Overview

The workshop will include:

  1. seven invited keynote speakers: These talks will be given by experts whose research spans a range of disciplines and applications.
  2. a panel: Experts from each domain will lead a group discussion on open questions at the intersection of planning and learning. In the first half, panelists will be asked to present open problems and areas of contention. The second half of the panel will be in the format of a discussion, where the participants will be encouraged to engage with the presented topics or extend them by suggesting their own.
  3. spotlight talks: A selection of accepted papers will be presented in a 5-8 minute spotlight talk.
  4. poster session: Poster sessions will continue during all breaks to enable participants to engage in deep discussion about the presented work.


Organizers

Organizing Committee

choudhury

Sanjiban Choudhury

University of Washington

          dey

Debadeepta Dey

Microsoft Research

srinivasa

Siddhartha Srinivasa

University of Washington

          toussaint

Marc Toussaint

University of Stuttgart

boots

Byron Boots

Georgia Institute of Technology

         


Program Committee

Lydia Kavraki, Rice University

Geoff Hollinger, Oregon State University

Marco Pavone, Stanford University

Chinwe Ekenna, University of Albany

Aleksandra Faust, Google Brain

Oren Salzman, Carnegie Mellon University

Luigi Palmieri, Bosch

Shawna Thomas, Texas A&M University

Maxim Likhachev, Carnegie Mellon University

David Hsu, National University of Singapore

Ariel Felner, Ben-Gurion University

Jeannette Bohg, Stanford University