[robotics-worldwide] [meetings] Call for Posters: RSS 2017 Workshop on Learning from Demonstrations in High-Dimensional Feature Spaces (Deadline: June 8)

Arunkumar Byravan barun at uw.edu
Wed May 24 19:04:10 PDT 2017


We are pleased to announce our workshop on "Learning from
Demonstrations in High-Dimensional Feature Spaces." This full-day
workshop will take place during the 2017 Robotics: Science and Systems
conference in MIT, Cambridge, MA, USA, on Sunday July 16. The official
web page of the workshop is
https://urldefense.proofpoint.com/v2/url?u=https-3A__sites.google.com_site_rss17learningtoplan_&d=DwIBaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=zDJ8GCoiu7BkFDaOX2fCJWJxkKWmQrq6KMVKimWMm0o&s=D650KCuEAVmgCTnzNoSau51N1IvueUkYiUuZ9mChiPs&e= .

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Call for Posters
RSS 2017 Workshop on Learning from Demonstrations in High-Dimensional
Feature Spaces

Key Facts
=========
RSS 2017 Workshop, July 16, 2017
Location: Cambridge, Massachusetts, USA

Submission Deadline (Extended): June 8, 2017 (AoE)
Notification: June 15, 2017

URL: https://urldefense.proofpoint.com/v2/url?u=https-3A__sites.google.com_site_rss17learningtoplan_&d=DwIBaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=zDJ8GCoiu7BkFDaOX2fCJWJxkKWmQrq6KMVKimWMm0o&s=D650KCuEAVmgCTnzNoSau51N1IvueUkYiUuZ9mChiPs&e= 

Organizers
=========
Jim Mainprice, Arunkumar Byravan, Mathew Monfort, Roberto Calandra and
Stefan Schaal

Overview
========
As technology in autonomous robotics continues to evolve, so does the
complexity of the decision problems that we expect our systems to
solve. The resulting action policies range from low-level control of
forces to high-level selection of complex strategies.  These decision
problems are often straightforward for humans while they remain
difficult for standard robotics approaches. In this context, Learning
from Demonstrations (LfD) can reduce the difficulty of defining action
policies by providing expert knowledge in the form of examples of
near-optimal behaviors. Understanding and formalizing LfD has been the
topic of many fields of science including robotics, neuroscience,
cognitive science, psychology and anthropology. However, many LfD
problems are still intractable due to the embedding of exceedingly
high-dimensional representations that stem from their coupling to
high-dimensional observation spaces (e.g., visual, haptic or
auditory).

In this workshop, we aim to bring together experts in robotics,
machine learning and cognitive science to discuss the state of the art
in learning from demonstration and explore promising directions for
handling high-dimensional feature, state and observation spaces,
looking beyond traditional approaches to find connections from vision,
deep learning and human models of cognition. We hope to highlight
recent applications and identify tools and techniques that can enable
us to scale our methods to handle high-dimensional demonstrations.
>From this workshop, we expect participating researchers to identify
and address important challenges, techniques, and benchmarks necessary
for learning from demonstrations in high-dimensional feature spaces.

Invited Speakers
==============
Pieter Abbeel (Berkeley)
J. Andrew Bagnell (CMU)
Joshua Tenenbaum (MIT)
Stefano Ermon (Stanford)
Sam Gershman (Harvard)
Ken Goldberg (Berkeley)
Jan Peters (TU Darmstadt)
Jon Scholz (Google DeepMind)
Marc Toussaint (Univ. of Stuttgart)

Topics
======
Learning from high-dimensional demonstrations
Deep inverse optimal control/inverse reinforcement learning
Predicting behavior from high-dimensional observations
Learning from multiple sensor modalities
High-dimensional knowledge transfer for sequential planning
Cognitive models for learning from demonstration and planning
One/few-shot imitation learning
Learning by observing external demonstrations
Application domains with high-dimensional observations (autonomous
cars/agents, robotic manipulation, etc.)

Format
======
Please submit a PDF abstract, maximum length two pages, in the RSS
2017 format via email to lfdhighdim.rss17 at gmail.com by June 8, 2017.
Submitted abstracts will be reviewed by the organizers. Accepted
contributions will be notified by June 15, 2017. Each accepted
contribution will be presented at the workshop as a 3 minute spotlight
presentation as well as feature in an interactive poster session.

Travel Scholarship
===============
We are happy to announce that we can offer travel scholarships for
student authors of accepted contributions that want to attend the
workshop. The scholarship will be a maximum of 500$ per student and
will be handed out to the top 3 student submissions. Please indicate
in the email of your submission whether the first author is a student
and you want to apply for the scholarship. We may increase the
number/amount of money for travel scholarships pending availability of
funds. Selected contributions will be notified upon acceptance.

More information at:
https://urldefense.proofpoint.com/v2/url?u=https-3A__sites.google.com_site_rss17learningtoplan_&d=DwIBaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=zDJ8GCoiu7BkFDaOX2fCJWJxkKWmQrq6KMVKimWMm0o&s=D650KCuEAVmgCTnzNoSau51N1IvueUkYiUuZ9mChiPs&e= 


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