Yeah I'm envisioning putting a thousand simplistic robotic "infants" into a vast "playpen" to gather sensor data about their environment, for some (probably smaller) number of deep learning models to ingest the input and guess at output strategies (move this servo, rotate this camshaft this far in that direction, etc) and make predictions about resulting changes to input.

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.

Never thought I’d get to do this but this was my masters research! Simulations are inherently limited and I just got tired of robotic research being done only in simulations. So I built a novel soft robot (notoriously difficult to control) and got it to learn by playing!!

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

A very interesting idea. I am curious about this sharing and blending of the various nets; I wonder if something as naive as averaging the weights (assuming the neural nets all have the same dimensions) would actually accomplish that?

But the playpen will contain objects that are inherently breakable. You cannot rough handle the glass vessel and have it too.