"Limited generalisation" in the real world means you're dead in the water. Like the Greek philosopher Heraclitus pointed out 2000+ years go, the real world is never the same environment and any task you want to carry out is not the same task the second time you attempt it (I'm paraphrasing). The systems in the videos can't deal with that. They work very similar to industrial robots: everything has to be placed just so with only centimeters of tolerance in the initial placement of objects, and tiny variations in the initial setup throw the system out of whack. As the OP points out, you're only seeing the successful attempts in carefully selected videos.
That's not something that you can solve with learning from data, alone. A real-world autonomous system must be able to deal with situations that it has no experience with, it has to be able to deal with them as they unfold, and it has to learn from them general strategies that it can apply to more novel situations. That is a problem that, by definition, cannot be solved by any approach that must be trained offline on many examples of specific situations.
That's not something that you can solve with learning from data, alone. A real-world autonomous system must be able to deal with situations that it has no experience with, it has to be able to deal with them as they unfold, and it has to learn from them general strategies that it can apply to more novel situations. That is a problem that, by definition, cannot be solved by any approach that must be trained offline on many examples of specific situations.