Call for Papers
4th Annual Conference on Robot Learning (CoRL2020)
November 16-18, 2020 | Cambridge MA, USA
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.
Considerations for CoRL2020 and the Pandemic
The CoRL 2020 organising committee and board acknowledge the challenges in producing high-quality experimental results during the social distance policies implemented in different parts of the world. Authors affected by these policies are encouraged to submit a one-page supplemental material explaining how their method will be experimentally validated with real data.
Papers with no real robot experimental results (and that could not rely on experimental datasets) should contain extensive experimentation in simulation. Those papers should explicitly detail why the authors believe their method will also work on real robots and explain the measures taken to support this claim (e.g., utilising more than one simulator). Reviewers will be requested to carefully consider empirical evidence in simulation on the basis of its generalisation properties to real scenarios.
Paper submission open: July 1, 2020
Paper submission deadline: July 28, 2020; 23:59 Pacific Time (UTC-7)
Reviews available: September 7, 2020
Rebuttals due: September 14, 2020
Paper acceptance notifications: October 14, 2020
Camera ready papers due: to be announced
Paper Submission Requirements and Instructions
Papers may be submitted through CMT, via the link at the top of this page. Submissions are due July 28, 2020 at 23:59 Pacific Time. All submissions should comply with the format and length indicated below. CoRL is double-blind, which means all papers must be anonymized. The reviewing process is strictly confidential and both accepted papers and supplemental material will not become public until a few weeks before the conference.
Submissions will consist of papers up to eight pages in length (plus up to two additional pages of references). Authors will have the option to submit a supplementary file containing further details, which the reviewers may decide to consult, as well as a supplementary video. All supplementary materials will be submitted through CMT as a single zip file. Submitted papers will be reviewed by at least two reviewers. Accepted papers will appear in the Proceedings of Machine Learning Research (formerly JMLR Workshop and Conference Proceedings).
Accepted papers will be presented in long talks, spotlights, and/or posters.
We will not accept papers that are identical or substantially similar to papers that have previously been published or accepted for publication in an archival venue, nor papers submitted in parallel to other conferences. Archival venues include conferences and journals with formally published proceedings, but do not include non-archival workshops. Submission is permitted for papers that have previously appeared only as a technical report, e.g. in arXiv.
Submissions will be evaluated based on the significance and novelty of the results, either theoretical or empirical. Results will be judged on the degree to which they have been objectively established and/or their potential for scientific and technological impact, as well as their relevance to robotic learning. Authors will have an opportunity to submit a response to reviewers during the rebuttal period. Reviews and rebuttals of accepted papers will be made publicly available.
We take a broad view of robot learning. Papers with both experimental and theoretical results relevant to robot learning are welcome. Our intent is to make CoRL a selective top-tier conference on robotic learning.
Software Submission Instructions
Authors are encouraged to submit code alongside the paper. Authors should provide a readme file explaining how to run the author's software, and, when applicable, how to use it to replicate experimental results given in the article. For code that include files not directly relevant to the scientific contribution of the paper, authors should indicate in the readme file which part of the code pertains to the scientific claims of the paper to ease the review process.
By default and unless authors specify a different license scheme, the code submitted along the paper will be protected under exclusive copyright linked to the paper ID. Reviewers will be strictly forbidden to use the code outside the review process.
Use of Code / Citation / Licensing
Be aware that you must always cite your sources, including in code you may be using for your research. Failing to do so may lead others to believe that you are the authors of the code, which would be considered as plagiarism. Authors are requested to explicit cite sources in the code header and in the readme file.
Authors must also ensure that they have a license to modify or use other people's code. See https://choosealicense.com/no-permission/ for information on how to act when you find code on the web that does not have a specific license.