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The only problem with this approach for learning is that LLMs are meant to be something that you give goals, and it solves the problem. There's no space for learning there. The challenges become stating the goal precisely, and working around the stupidity and idiosyncrasies of the LLM.

Setting tiny goals, getting to them yourself, then setting a slightly larger goal, that's much more intense.

What I mean is that I'm not sure what "success" means in this context. There are already programs that control robot arms. I would think that success would mean that you managed to write a program that would control a robot arm.

You're an experienced programmer although maybe not a physics or mechanical person. Executing this would mean that you learned the mechanics (most people trying this don't have your programming experience, and have to try to do both things at once!)

Learning the mechanics would mean that you would have a good instinct to critique the AI when success in future projects would mean manifesting something you'd never seen before (and being happy you had AI to help you punch above your weight.) This would also give you a good foothold to understand more complex movements and coordination.

At least that's my perspective. The deliverable isn't on the table, it's in your brain. Asking the LLM to do it is like asking the LLM to copy a famous painting. You copy a famous painting so you can learn the movements of the person who painted it, not for the painting itself.

by systemerror45 minutes ago|

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I think this perspective makes sense but a few points miss the mark in my opinion.. In this case, I learned a LOT about how to make an LLM operate outside of a purely virtual environment. In the meantime I also learned some robotics fundamentals that will assist me on my next part of the journey. The idea that LLMs are not a useful learning tool is something that I don't agree on, I can't imagine a better teaching tool than an LLM when used in the correct way.