TinyRL: Can AI Learn to Swing Up a Real Pendulum? | DigiKey

[@MeanGeneHacks]

Very cool you were able to get these results. Getting RL to work on real hardware is notoriously difficult.

[@TonyHammitt]

Back in the dark ages (early 90s), we'd use much smaller neural networks because our computers were about what you have there as the microcontroller. It's a whole new game now with all of these tools. Will be fun to play with, that's for sure.

[@Hellboy-ce9tm]

Is the current limit on the stepper driver set too high?

[@fdavidcamaya]

They potentiometer of the driver of the steper motor

[@AdityaMehendale]

I can imagine that the allowable latency is a function of the natural time-constant of the system. If you are having issues with fine-tuning the latency, would you consider (as a proof-of-concept) to slow-down the natural system? To achieve this, you could, for example, reduce the eccentricity of the pendulum, while keeping the moment-of-inertia constant. To slow it down 4x, you might need to increase the inertia 16x . A pragmatic way to achieve this would be to add a counterweight against the pendulum, so the mass (inertia) increases, but the restoring-force decreases. It would still be an "inverted" pendulum, only far less aggressive.

[@eCMastermind]

🎉

[@chrisBruner]

Wow, fantastic project. I wonder if a NN training a fuzzy logic controller might work better.

[@mostafanfs]

Shawn how do you know everything?! Its very cool to be this master and not fair at the same time