Workshop on Self-Supervised Robot Learning

Robotics: Science and Systems
Oregon State University, Corvallis OR, USA

 

 

Overview

Self-supervised learning is an exciting research direction that aims to learn representations from the data itself without explicit and potentially even manual supervision. One of the major benefits of self-supervised learning is the ability to scale to large amounts of unlabelled data in a lifelong learning manner and to improve performance by reducing the effect of dataset bias. Recent development in self-supervised learning has resulted in achieving comparable or better performance than fully-supervised models. However, many of these methods are developed in domain-specific communities such as robotics, computer vision or reinforcement learning. The aim of this workshop is to bring together researchers from different communities to discuss opportunities, challenges and explore new directions. 


Topics

The focus topics of our workshop include, but are not restricted to:

  • Self-supervised learning for robotics, robot vision, reinforcement learning….
  • Self-supervised domain adaptation
  • Meta-learning of self-supervised tasks
  • Large-scale self-supervised learning
  • Learning of generalizable pretext-tasks
  • Loss functions for self-supervised learning
  • Learning from auxiliary/multiple tasks
  • Multimodal and cross-modal learning

Invited Speakers

Dieter Fox
U Washington & NVIDIA
Pieter Abbeel
UC Berkeley
Abhinav Gupta
CMU & Facebook AI Research
Roberto Calandra
Facebook AI Research
Chelsea Finn
Stanford University
Pierre Sermanet
Google Brain
Andy Zeng
Google Brain
 

 


Call for Contributions

We encourage participants to submit their research in the form of a single PDF. Submissions may be up to 4 pages in length, including figures, excluding references and any supplementary material. Please use the RSS conference template. Accepted papers will be presented in a poster session and selected awards papers as spotlight talks. All submitted contributions will go through a single blind review process. The contributed papers will be made available on the workshop’s website and selected papers will be invited for a special issue of a major robotics journal.

Submission Website: https://easychair.org/conferences/?conf=ssrl20

LaTeX Template: https://roboticsconference.org/docs/paper-template-latex.tar.gz

In order to make acceptance decisions early, we request interested researchers to submit a single page extended abstract by 19th April as an expression of interest. The authors would then have time 2nd July to include new results and submit the full 4 page paper. We also welcome submissions that have already been accepted to RSS or other major conferences and journal papers that have not been discussed in a conference.

There is currently some uncertainty around travel due to COVID-19, however, there is no cause for concern in the region where RSS is taking place. Nevertheless, we plan to give participants and presenters teleconferencing options.


Important Dates

April 19th, 2020 (AOE) One page submission deadline
April 24th, 2020 Notification of acceptance
July 2nd, 2020 (AOE) Full paper submission
July 13th, 2020 Workshop
Oregon State University, Corvallis OR, USA

Tentative Schedule

Time
Talks
08:00-08:15 Welcome and Introductory Remarks (Organizers)
08:15-08:45 Invited Talk 1
08:45-09:15 Invited Talk 2
09:15-09:45 Invited Talk 3
09:45-10:15 Poster Spotlights
10:15-10:45 Refreshment Break
10:45-12:00 Poster Session
12:00-13:30 Lunch break
13:30-14:00 Invited Talk 4
14:00-14:30 Invited Talk 5
14:30-15:00 Invited Talk 6
15:00-15:30 Invited Talk 7
15:30-16:00 Refreshment Break
16:00-16:30 Invited Talk 8
16:30-17:00 Invited Talk 9
17:00-17:45 Panel Discussion
17:45-18:00 Concluding Remarks

Organizers

Abhinav Valada
University of Freiburg
Anelia Angelova
Google Brain
Joschka Boedecker
University of Freiburg
Oier Mees
University of Freiburg
Wolfram Burgard
Uni Freiburg & TRI