In principle a thousand different deep learning models could all train simultaneously on a thousand different robot experience feeds.. but not 1 to 1, but instead 1 to many.. each neural net training on data from dozens or hundreds of the robots at the same time, and different neural nets sharing those feeds for their own rounds of training.
Then of course all of the input data paired with outputs tested and further inputs as ground truth to predictions can be recorded for continued training sessions after the fact.
Perhaps the least applicable part is that "robot hurting itself" has the liability of some cost to replace the broken robot part, vs the potentially immeasurable cost of a human infant injuring themselves.
If it's not a good idea to put a "glass vessel" in a human crib (strictly from an "I don't want the glass vessel to be damaged" sense) then it's not a good idea to put that in the robot-infant crib either.
Give them something less expensive to repair, like a stack of blocks instead. :P
Here is an informal talk I gave on my work. Let me know if you want the thesis
https://www.youtube.com/live/ZXlQ3ppHi-E?si=MKcRqoxmEra7Zrt5
Why not have the AI train on a simulation of the real world? We can build those pretty easily using traditional software and run them at any speed we want.