> large-scale behavior cloning (which is the technique used here), is essentially replay with a little smoothing
A definition of "replay" that involves extensive correction based on perception in the loop is really stretching it. But let me take your argument at face value. This is essentially the same argument that people use to dismiss GPT-4 as "just" a stochastic parrot. Two things about this:
One, like GPT-4, replay with generalization based on perception can be exceedingly useful by itself, far more so than strict replay, even if the generalization is limited.
Two, obviously this doesn't generalize as much as GPT-4. But the reason is that it doesn't have enough training data. With GPT-4 scale training data it would generalize amazingly well and be super useful. Collecting human demonstrations may not get us to GPT-4 scale, but it will be enough to bootstrap a robot useful enough to be deployed in the field. Once there is a commercially successful dextrous robot in the field we will be able to collect orders of magnitude more data, unsupervised data collection should start to work, and robotics will fall to the bitter lesson just as vision, ASR, TTS, translation, and NLP before.
"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.
Thank you for your rebuttal. It is good to think about the "just a stochastic parrot" thing. In many ways this is true, but it might not be bad. I'm not against replay. I'm just pointing out that I would not start with an _affordable_ 20k robot with fairly undeveloped engineering fundamentals. It's kind of like trying to dig a foundation to your house with a plastic beach shovel. Could you do it? Maybe, if you tried hard enough. Is it the best bet for success? doubtful.
A definition of "replay" that involves extensive correction based on perception in the loop is really stretching it. But let me take your argument at face value. This is essentially the same argument that people use to dismiss GPT-4 as "just" a stochastic parrot. Two things about this:
One, like GPT-4, replay with generalization based on perception can be exceedingly useful by itself, far more so than strict replay, even if the generalization is limited.
Two, obviously this doesn't generalize as much as GPT-4. But the reason is that it doesn't have enough training data. With GPT-4 scale training data it would generalize amazingly well and be super useful. Collecting human demonstrations may not get us to GPT-4 scale, but it will be enough to bootstrap a robot useful enough to be deployed in the field. Once there is a commercially successful dextrous robot in the field we will be able to collect orders of magnitude more data, unsupervised data collection should start to work, and robotics will fall to the bitter lesson just as vision, ASR, TTS, translation, and NLP before.