Modern robotics is, at its core, not a hardware problem. It's an AI problem. We have plenty of headroom in the hardware - what we don't have is an AI good enough to utilize it. We don't know the practical limits of current hardware because we can't make a robot AI that would make the hardware a meaningful bottleneck.
Today's robots don't fail at tasks because they have poor fingers. They fail because they don't know how to perform those tasks. If you put an effort into solving that? You get demos like: Gemini Robotics 2 tying a garbage bag. Take one long look at that and think of manual dexterity.
Human body is crude and suboptimal in a thousands different ways, and all of it is salvaged by advanced intelligence.
Yeah, look carefully at that demo of tying the garbage bag strings: it's done in a very peculiar style that suggests a very specific, very precise, "algorithm" taught in an imitation learning session, which has no chance to transfer to other tasks, or even other garbage bag strings.
Did the past decades of AI research teach you absolutely nothing?
Every time you see something that "suggests a very specific, very precise, "algorithm" taught in an imitation learning session"? Scale the imitation learning up x10, x100, x1000, and it suddenly generalizes!
I'll be honest: I don't see what you see. I don't see anything that would suggest this algorithm is so brittle there's zero transfer to "even other garbage bag strings". AI robotics isn't innately brittle like conventional robotics is. But even if you are, somehow, completely right on that? Teach a hundred "very specific algorithms" like this - and watch them fuse into a manifold of algorithms that can be applied to different problems as needed.
And that is what you need. If an algorithm for "tie a garbage bag with current generation robot hands" exists and can be learned by an AI, then the gains from getting better AI are far from exhausted. The limits of robotics are the limits of AI.
This is why every AI robotics company is saying "we need more data". They understand what they're dealing with. They looked at the scaling laws and went "robotics isn't magic, that curve applies to us too". I don't get what makes people see robotics as a special magic thing, that makes them look at the advances in robot AI and say "this is intractable" and not "this is hard". It's hard. We're getting through it though.
>> Did the past decades of AI research teach you absolutely nothing?
Before I put in the effort to reply in good faith I have to know: do you think we're going to have a conversation or are you going to fulminate and scold me like some kind of all-important authority (which I have to say you clearly are not)?
To clarify, I'm happy to have a curious and respectful exchange.
Modern robotics is, at its core, not a hardware problem. It's an AI problem. We have plenty of headroom in the hardware - what we don't have is an AI good enough to utilize it. We don't know the practical limits of current hardware because we can't make a robot AI that would make the hardware a meaningful bottleneck.
Today's robots don't fail at tasks because they have poor fingers. They fail because they don't know how to perform those tasks. If you put an effort into solving that? You get demos like: Gemini Robotics 2 tying a garbage bag. Take one long look at that and think of manual dexterity.
Human body is crude and suboptimal in a thousands different ways, and all of it is salvaged by advanced intelligence.