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The repository for making new post submissions to the SAIL Blog

License: MIT License

Shell 0.25% JavaScript 6.03% Ruby 3.20% Python 5.76% CSS 1.65% HTML 53.56% SCSS 29.55%

sail-blog-new-post's Introduction

Why write for SAIL blog

It's a great way to let a broader set of people get exposed to your work and help represent SAIL. So if you have recently published or ongoing research that you feel could benefit from being shared more widely in this format, or if one of your new year's resolution is to work on scientific communication, please reach out to the email the editors ([email protected]) and we will let you know the process from there.

How to write a new blog post

  1. Confirm your paper(s) are a good fit to write a blog post for (see requirements here). You are encouraged to email the editors ([email protected]) confirm your paper is a good fit and ask any quetsions.
  2. Get a draft of your post in Google doc, and email the editors ([email protected]) to get an editor assigned to the draft. More detailed instructions on that here. See the note below about citations; to make use of this, just include your citations as footnotes in the Google Doc.
  3. One or two editors will help you finalize the draft with feedback. Once you have a draft that is finalized, you need to create a pull request with markdown and images of your post. First, fork the repo, clone the fork and pull source branch
  4. Follow these instructions to convert your post from google doc to markdown and submit a pull request.
  5. Once you've submitted a pull request and email your editor to let them know, we'll typically merge it within a few days and publicize via our mailing list and Twitter. Feel free to request specific wording for the tweet promoting it.

Citations

We support bigfoot pop-up citations and recommend using them. In text, use [^<name>] as follows:

- **Autonomous Data Collection**: Many data collection mechanisms and algorithms such as Self-Supervised Learning[^SSL][^robonet] 

Then at bottom of file:

[^robonet]: Dasari, S., Ebert, F., Tian, S., Nair, S., Bucher, B., Schmeckpeper, K., ... & Finn, C. (2019). RoboNet: Large-Scale Multi-Robot Learning. arXiv preprint arXiv:1910.11215.
[^SSL]: Levine, S., Pastor, P., Krizhevsky, A., & Quillen, D. (2016, October). Learning hand-eye coordination for robotic grasping with large-scale data collection. In International Symposium on Experimental Robotics (pp. 173-184). Springer, Cham.

sail-blog-new-post's People

Contributors

alextamkin avatar amandlek avatar andreykurenkov avatar andyshih12 avatar ashwinparanjape avatar cgpotts avatar chrispiech avatar cwein3 avatar danielkunin avatar fereshte-khani avatar friedeggs avatar jmschrei avatar kohpangwei avatar madeleinegrunde avatar meghabyte avatar mhw32 avatar nelson-liu avatar okhat avatar sangmichaelxie avatar serinachang5 avatar seyuboglu avatar siddk avatar ssagawa avatar suraj-nair-1 avatar windweller avatar

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