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# Contribution guidelines
This tutorial serves two scopes: be a reference for anyone interested in the field of robot learning, and provide practical, actionable knowledge via a mix of intuition-based explanation and code examples.
That said, the audience of this tutorial is mostly researchers.
For this, the styling adopted must be somewhat academic itself. It is hard to draw a boundary of what academic writing is, but it definitely must be more on the scientific side of things!
If you have ever written a paper, think of adhering to the same registry. If you haven't: great! This is a good starting point, and you can leverage the community to learn more about how to effectively and proficiently write techinical pieces.
In general, contributing should happen:
1. **Open an issue where you detail the topic you wish to add**. In the issue description is very imporant you (1) justify why the topic you want to add is relevant to the others already in the tutorial and (2) why/to what extent that topic is not already present in the tutorial's contents.
2. Then, in the same issue you should **add a small structured summary** of the content you wish to adapt. Think of this as a way to gauge right away what you want to add, and how you want to add it. This helps you and whoever is going to look at your issue get on the same page.
3. **Ping @fracapuano to discuss your proposal**. We welcome contributions from all sorts of backgrounds, and a good idea is to discuss your contribution before you start writing, so that it is the most aligned with the contents presented. Then, open a PR, and ping @fracapuano for review.
Let's make the best, highest-quality robot-learning resource via open-source contributions! 😊
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