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.

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. Two editors will help you finalize the draft with feedback - the first primary editor, and another for a secondary pass. Please allow for a week turnaround from the editors. Once you have a draft that is finalized, you need to create a pull request with markdown and images of your post.
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.

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.