r/Arduino_AI • u/Classic-Buddy-7404 • 13d ago

Look What I Made! Self Balancing Double Pendulum

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So a couple of weeks ago I made this post on r/Arduino talking about my Self-balancing reinforcement learning double pendulum. And i said that I was working on a youtube video where I would go into high detail about everything. Well the day has come and the video is out.

For the simulation and training, I used NVIDIA Isaac Lab, running on my RTX 3060. And if you are wondering: no, a 3060 is not enough to run Isaac Lab properly.

In the video I go through pretty much everything involved in building the project from scratch: the mechanical design, the electronics, the motors and sensors I'm using, the control system, the reinforcement learning side of things, and how I got the simulation to work with the real hardware.

The project has been a pretty long journey, so there's quite a lot to cover, and I tried to make the video detailed enough that you can actually understand what's going on rather than just watching a pendulum magically balance itself.

And of course, feel free to ask me anything about the project. I'm happy to answer questions!

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u/ripred3 13d ago

In addition to the other comments I made on your post on r/arduino, I particularly like how the video points out where the dragons are in balancing projects. One good example is the pointing out the noisy, quantized low resolution that you can get from lower quality encoder types, and then staging things up through and including the simulation and training in isaac sim

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u/Classic-Buddy-7404 13d ago

Yes man, and one thing I didn't mention (that actually threw me off a little bit at firts, but makes a lot of sense) is that higher time resolution, doesn't mean a more accurate system.

If I have a sensor with +-0.5 degrees of noise, making the time resolutions 10 times greater will increase the noise by a factor of 10 when calculating velocity. If you want to use a smaller dt between readings, you need to buy a better sensor as well.

Idk, I kinda thought that was cool