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

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  • Опубликовано: 30 окт 2024

Комментарии • 9

  • @MeanGeneHacks
    @MeanGeneHacks 10 месяцев назад +3

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

  • @TonyHammitt
    @TonyHammitt 10 месяцев назад +1

    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.

  • @mostafanfs
    @mostafanfs 10 месяцев назад +2

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

  • @AdityaMehendale
    @AdityaMehendale 10 месяцев назад +1

    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.

  • @chrisBruner
    @chrisBruner 10 месяцев назад +1

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

  • @eCMastermind
    @eCMastermind 4 месяца назад +1

    🎉

  • @Hellboy-ce9tm
    @Hellboy-ce9tm 10 месяцев назад +1

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

    • @fdavidcamaya
      @fdavidcamaya 9 месяцев назад

      They potentiometer of the driver of the steper motor