fasterbynight

I don't see why electric motors are not backdriveable enough. Boston Dynamics' Spot uses electric motors with harmonic drive gearboxes (or some other backdriveable transmission) as an example of this. Similarly, series elastic actuators are very capable of giving the characteristics you deem necessary to work alongside humans albeit with lower bandwidth than the harmonic drive gearboxes.

It's bonded by epoxy just like plywood is. Technically plywood is a composite (at least from a mechanical engineering perspective).

As someone with a similar background, I believe some of the confusion is because there is a lot of overlap. System identification is very similar to supervised learning, however there are other learning "methods" that still fall under the umbrella of ML/AI. For example, unsupervised learning doesn't really have a good controls analog (as far as I know). Reinforcement learning on the other hand is somewhat analogous to model predictive control.

A better way of phrasing your point is that ML/AI is "just" optimization.

I'd don't know what you mean by "performs like" but it's definitely significantly closer in runtime speed after the first run to compiled languages than interpreted (in particular Python/Matlab).

Anyways, my point was more in response to this from the person I responded to: "my instinct is to look for opportunities to vectorize by rewriting loops into matrix notation on paper and then expressing them as array calculations". In some languages (Julia in particular) that is slower than the equivalent loop based code.

I don't know about other languages, but in Julia I've heard people often say that loops end up faster than the equivalent vectorized code. So while this is true for Python/Matlab I don't think it is good universal advice.

That said, matrix notation can sometimes be the more readable way of expressing a calculation.