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Having taken Tedrake's underactuated robotics class which uses Drake's python bindings, it is an amazing collection of tools and examples of algorithms. Really great for learning.

I would say is very focused on research and quick prototyping, however.

MIT Driverless experimented with using Drake in 2018-19 race car, but we found that just implementing everything in eigen was more flexible, removed a dependency, and more transparent for tinkering.

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Ah the classic problem. Someday we'll get our shit together in Haskell land and just blow away these awful protype/production dicotomies.

Not if the smalltalkers get there first

Not if CPU's are fast enough to do it in Julia :-)

Ah, Julia shares my goals, but I really don't like it's methods.

To be we absolutely need static types and super high level stuff to both:

- keep the code closer to it's purpose

(e.g. do linear algebra and proof of why this program does the stats you want it to together)

- be super architecture agnostic

(With lambda generalized to arbitrary categorizes we can just elaborate to Vect and fuse functional pipelines with matrix multiplication).

(https://ncatlab.org/nlab/show/Vect)

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