My Notes from ETH Zürich’s Robot Learning Course
Spring 2026, taught by Oier Mees.
Lecture notes
- Lecture 01
- Lecture 02
- Lecture 03
- Lecture 04
Reinforcement Learning I Coming soon
- Lecture 05
Reinforcement Learning II Coming soon
- Lecture 06
- Lecture 07
- Lecture 08
- Lecture 09
- Lecture 10
- Lecture 11
I really liked how the content flowed from one idea into the next. It made me think a bit about meta-learning, and about how much easier it is to understand a field/topic when you can see why each idea appeared in the first place.
why an approach was useful → where it fell short → how that naturally led to the next idea
I made my own notes the same way, so a quick skim helps me refresh my memory. Codex was helpful in refining them, and sometimes generating the first draft. I've gone through everything myself and fixed where it was not quite clear.