CALL FOR PAPERS: 3rd ANNUAL CONFERENCE ON ROBOT LEARNING
The 3rd Annual Conference on Robot Learning (CoRL 2019) is soliciting contributions at the intersection of robotics and machine learning. CoRL aims at being a selective, top-tier venue for robot learning research, covering a broad range of topics spanning robotics and ML, including both theory and practice.
We invite papers offering new advances on any of the following topics:
- Imitation learning, bayesian/probabilistic learning, neural networks
- (Inverse) reinforcement learning, model-free learning, etc.
- Machine learning and control
- Bio-inspired learning and control
- State estimation, mapping, and computer vision
- Multimodal perception and sensor fusion
- Learning-based human robot interaction, natural language instruction processing
- Applications in manipulation, mobility, driving, flight, and other areas of robotics
Accepted papers will be published via the JMLR Workshop & Conference Proceedings series and will be presented either as posters or oral presentations in the plenary session. CoRL is a single track venue
All papers go to the archival track. There is not the possibility to submit for non-archival track this year.
Authors are strongly encouraged to submit their code as supplementary material to the paper. Availability of code will be viewed as a plus. Your code should be uploaded on the CMT system at the same time as you submit your paper and other supplementary material (videos, proofs, etc).
Paper submission deadline: July 7, 2019; 23:59 Pacific Time (UTC-7)
Paper acceptance notifications: September 7, 2019
Camera ready papers due: October 7, 2019
PAPER SUBMISSION REQUIREMENTS & INSTRUCTIONS
Papers may be submitted through CMT, via the link at the top of this page. Submissions are due July 7, 2019 at 23:59 Pacific Time. All submissions should comply to the format and length indicated below. CoRL is double-blind, which means all papers must be anonymized.
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. Like submissions, the supplementary materials must be anonymized. To submit the supplementary file, first upload your submission. You will then be able to upload the supplementary file from the author console.
Submitted papers will be reviewed by two reviewers. Accepted papers will appear in the JMLR Workshop & Conference Proceedings as officially published CoRL papers. Accepted papers will be presented in long talks, spotlights, and/or posters.
Submission Policy :
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.
Preprints: Non-anonymous preprints (on arXiv, social media, websites, etc.) are permitted, though preprints in the CoRL style must use the new "preprint" option, rather than the "final" option. Reviewers will be instructed not to actively look for such preprints, but encountering them will not constitute a conflict of interest. Authors may submit work to CoRL that is already available as a preprint (e.g., on arXiv) without citing it; however, previously published papers by the authors on related topics must be cited (with adequate anonymization to preserve double-blind reviewing). This policy is derived from the NeurIPS policy.
Reviewing Criteria :
Submissions will be evaluated based significant novel results. Results may be 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. We take a broad view of robotic learning. Papers with both experimental and theoretical results relevant to robotic learning are welcome. Our intent is to make CoRL into a selective top-tier conference on robotic learning.
Q: Can we include videos, technical appendices with proofs and implementation details or additional results, or code?
A: Yes, you may upload them onto CMT. However, please note that the reviewers are under no obligation to read or view these materials and that you should make your 8 page paper self-contained and reviewable as a stand-alone document. The file type of the supplementary materials should be doc/docx/pdf/zip. The maximum number of files are three. The file size should not exceed 50MB.
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 computational 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 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. They must also ensure that, when they modify or use other people's code, they are allowed to do so. 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.