CoRL 2020 : Conference on Robot Learning

in Conferences   Posted on October 29, 2020 

Conference Information

Submission Deadline Tuesday 28 Jul 2020 Proceedings indexed by :
Conference Dates Nov 16, 2020 - Nov 18, 2020
Conference Address Online, Online
Conference & Submission Link
Conference Organizers : ( Deadline extended ? Click here to edit )

Conference Ranking & Metrics (This is a TOP Conference)

Impact Score 4.79
#Contributing Top Scientists 23
#Papers published by Top Scientists 23
Google Scholar H5-index 33
Guide2Research Overall Ranking: 118
Category Rankings
Hardware, Robotics & Electronics 20
Machine Learning & Artificial Intelligence 38

Conference Call for Papers

The 4th Annual Conference on Robot Learning (CoRL 2020) is soliciting contributions at the intersection of robotics and machine learning. CoRL is a selective, single-track conference for robot learning research, covering a broad range of topics spanning robotics, ML and control, and including theory and applications.

Papers offering new advances in robot learning are invited. Topics include:

Imitation learning and (inverse) reinforcement learning

Probabilistic learning and representation of uncertainty in robotics

Model-free learning for decision-making

Machine learning for system identification and control

Bio-inspired learning and control

State estimation, localization and mapping

Multimodal perception, sensor fusion, and computer vision

Learning for human-robot interaction and natural language instruction processing

Applications of robot learning in manipulation, mobility, driving, flight, and other areas of robotics

Authors are strongly encouraged to demonstrate how their methods relate to robotics and applications. Authors are also strongly encouraged to submit their code as supplementary material to the paper.

Accepted papers will be published in the JMLR Workshop & Conference Proceedings series and presented either as posters, spotlights or long talks in the plenary session. This year reviews and rebuttals of accepted papers will be made publicly available.

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