Hi, thanks! Love your product, it's been incredible for debugging simulations as well as looking at individual policy rollouts (instead of e.g. static videos) to design better rewards/environments
Yeah, I did. I didn't implement disturbances like wind, so a very simple PD position controller was enough for stabilization or simple trajectory tracking. I won't focus too much on (position) control as my controller will be RL-based (with a policy network outputting thrust and body rates) and coupled with a PD rate controller (very simple as it's 1st-order).
You are technically correct - the best kind of correct. But yeah, think of it as a "slice" of a quadcopter along one of its principal axes. Writing the 3D blog post right now.
Good question, haven't really thought about modeling complex effects besides prop aerodynamic drag. If I were to start, I'd probably look at the model described in the "Aerodynamic forces and torques" section of "Champion-level drone racing using deep reinforcement learning" (Kaufmann et al. 2023).
I've spent the last six months replicating the paper "Champion-level drone racing using deep reinforcement learning" and now I'm writing down the blog posts I wish I had along the way.
Any feedback is welcome, especially as I'm a bit unsure if I struck the right balance between being concise and not requiring too many prerequisites.
Also if you're working on RL and robotics (especially aerial), let's connect!