[robotics-worldwide] [Meetings] Call for Papers: RSS 2020 WS on Structured Approaches to Robot Learning

Markus Wulfmeier m.wulfmeier at gmail.com
Fri Mar 13 09:26:32 PDT 2020


*RSS 2020 Workshop on Structured Approaches to Robot Learning for Improved
Generalization*

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Recent advances in machine learning techniques, the emergence of deep
learning, access to big data and powerful computing hardware has led to
great strides in the state-of-the-art in robotics and artificial
intelligence. Many of these learning based methods tend to be black-box,
eschewing much of the careful state estimation, algorithm design and
modular structuring of traditional robotics pipelines in favor of general
function approximators that rely primarily on big data and near infinite
computation. While this has led to great successes in learning and solving
complex tasks directly from raw sensory information (e.g. autonomous
driving), we are still struggling to replicate the in-domain
generalization, knowledge transfer, interpretability and safety
capabilities inherent in traditional robotic systems.

Can we bridge this gap between traditional robotics pipelines and modern
learning based methods? Can we combine these paradigms in a way that we
retain the strengths of both? These are some of the questions we want to
explore in this workshop. We plan to bring together researchers in
robotics, computer vision and machine learning to investigate, at the
intersection of these paradigms, structured approaches to robot learning
and how they can enable us to generalize knowledge across tasks. A special
emphasis will be on methods that tightly integrate insights from both
paradigms and are demonstrably applicable in the real-world.

Topics of interest include, but are not limited to:

- Structured inference and learning for robotics

- Deep learning with structure and priors

- Learning structured representations for perception, planning and control

- Integrating learning and model-based robotics

- Structured losses and semi/self-supervised learning

- Transfer and multi-task learning

- Reinforcement/Imitation learning using domain knowledge

- Autonomous navigation, mobile manipulation with structured learning

- Structured optimization with deep learning and automatic differentiation

- Deep learning with graphical models


*Information*

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Location: Corvallis, Oregon, USA

Date: July 13, 2020

URL: https://urldefense.com/v3/__https://sites.google.com/view/rss20-sarl__;!!LIr3w8kk_Xxm!601t2lBZHtfd6X6mYY9LqJRzGWmPh7wawinezIHZIdBk0yu85qfgRLDDnuVQgkzQKJRz0EmO$ 


*Important Dates*

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April 9   - Submission deadline (AoE time)

April 16 - Notification of acceptance

April 30 - Camera ready deadline

July 13  - Workshop

*Submission*

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We solicit up to 4 pages extended abstracts (excluding citations and
supplemental material) conforming to the official RSS style guidelines.
Submissions can include archived or previously accepted work (please make a
note of this in the submission; if necessary we may take this into
consideration for the acceptance decision). Reviewing will be single blind.
All accepted contributions will be presented in interactive poster
sessions. A subset of accepted contributions will be featured in the
workshop as spotlight presentations.

Submission link: https://urldefense.com/v3/__https://cmt3.research.microsoft.com/RLWSRSS2020__;!!LIr3w8kk_Xxm!601t2lBZHtfd6X6mYY9LqJRzGWmPh7wawinezIHZIdBk0yu85qfgRLDDnuVQgkzQKGsFvAi9$ 

*Organizers*

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Arunkumar Byravan (DeepMind)

Markus Wulfmeier (DeepMind)

Franziska Meier (FAIR)

Mustafa Mukadam (FAIR)

Nicolas Heess (DeepMind)

Angela Schoellig (UToronto)

Dieter Fox (UW / NVIDIA)


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