They do not replay recorded motor signals. They use recorded motor signals only to train neural policies, which then run autonomously on the robot and can generalize to new instances of a task (such as the above video generalizing to an adult size sweater when it was only ever trained on child size polo shirts).
Obviously some amount of generalization is required to fold a shirt, as no two shirts will ever be in precisely the same configuration after being dropped on a table by a human. Playback of recorded motor signals could never solve this task.
> recorded motor signals only to train neural policies
Is interesting that they are using "Leader Arms" [0] to encode tasks instead of motion capture. Is it just a matter of reduced complexity to get off the ground? I suppose the task of mapping human arm motion to what a robot can do is tough.
I appreciate that going from polo shirts to sweaters is a form of "generalisation" but that's only interesting because of the extremely limited capability for generalisation that systems have when they're trained by imitation learning, as ALOHA.
Note for example that all the shirts in the videos are oriented in the same direction, with the neck facing to the top of the video. Even then, the system can only straighten a shirt that lands with one corner folded under it after many failed attempts, and if you turned a shirt so that the neck faced downwards, it wouldn't be able to straighten it and hang it no matter how many times it tried. Let's not even talk about getting a shirt tangled in the arms themselves (in the videos, a human intervenes to free the shirt and start again). It's trained to straighten a shirt on the table, with the neck facing one way [1].
So the OP is very right. We're no nearer to real-world autonomy than we were in the '50s. The behaviours of the systems you see in the videos are still hard-coded, only they're hard-coded by demonstration, with extremely low tolerance for variation in tasks or environments, and they still can't do anything they haven't been painstakingly and explicitly shown how to do. This is a sever limitation and without a clear solution to it there's no autonomy.
On the other hand, ιδού πεδίον δόξης λαμπρόν, as we say in Greek. This is a wide open field full of hills to plant one's flag on. There's so much that robotic autonomy can't yet do that you can get Google to fund you if you can show a robot tying half a knot.
__________________
[1] Note btw that straightening the shirt is pointless: it will straighten up when you hang it. That's just to show the robot can do some random moves and arrive at a result that maybe looks meaningful to a human, but there's no way to tell whether the robot is sensing that it achieved a goal, or not. The straightening part is just a gimmick.
Obviously some amount of generalization is required to fold a shirt, as no two shirts will ever be in precisely the same configuration after being dropped on a table by a human. Playback of recorded motor signals could never solve this task.