Sergey Levine

Like the fundamental research is really important, but it's not enough by itself.

You need the fundamental research and you also need the impetus to make it real.

And make it real means like actually put the robots out there.

data that is representative of the kind of tasks that they need to do in the real world, get that data at scale, build out the systems, get all that stuff right.

And that requires a degree of focus, a singular focus on really nailing the robotic foundation model for its own sake, not just as a way to do more science, not just as a way to publish a paper, and not just as a way to have a research lab.

Yeah, that's a really good question.

The challenge here is in understanding which axis of scale contributes to which axis of capability.

So if we want to expand capability horizontally, meaning like the robot knows how to do 10 things now and I'd like it to do 100 things later, that can be addressed by just directly horizontally scaling what we already have.

But we want to get robots to a level of capability where they can do practical useful things in the real world, and that requires expanding

Along other axes too, it requires, for example, getting to very high robustness.

It requires getting them to perform tasks very efficiently, quickly.

It requires them to recognize edge cases and respond intelligently.

And those things, I think, can also be addressed with scaling.

But we have to identify the right axes for that, which means figuring out what kind of data to collect, what settings to collect it in, what kind of methods consume that data, how those methods work.

So answering those questions more thoroughly will give us greater clarity on the axes, on those dependent variables, on the things that we need to scale.

And

We don't fully know right now what that will look like.

I think we'll figure it out pretty soon.

It's something we're working on actively.

But we want to really get that right so that when we do scale it up, it'll directly translate into capabilities that are very relevant to practical use.