Makes sense. The end state appears to be that humans should only be supervising ML that generates goal and outcome based behaviors for robots, and the machines will construct tools to solve problems themselves.

The leap from an AI model learning how to replicate a behaviour (e.g. evolving walking to solve problems https://unitylist.com/p/2id/walking-ai ) to reasoning about it in terms of actuators and physical feedback, to assembling a physical model out of a relatively small list of parts seems like a solvable engineering problem when it is broken out into a pipeline.

Those robot parts are basically a version of mechano with actuators that a model would map a behavior to, and the robots in the article would assemble them. When you look at something like Lego or Mechano as an intermediate representation to construct buildings out of, where all objects made from it are essentially a directed graph of those elements, robots designing and building robots seems like less than 20 years away.

e.g. we could functionally specify to an ML model, "produce a digraph of these element parts that has these degrees of freedom, and then load or derive a model that solves for this outcome within the domain of those degrees, where outcome is 'plug cables into a board' "