00:19:48 · Jim Fan
[00:19:48] S1: That's a great question, Molly. Well, first, to... For the audience who's not familiar, Project GR00T is NVIDIA's moonshot initiative to solve general purpose robot AI. And I think to solve this, first, we need to define a milestone. Right? Like, what is this one challenge? So here's my proposal. Suppose you, you know, on on a Sunday on a Sunday night, you hosted a big hackathon party, and you wrecked a house, right, like pizza everywhere. It's a complete mess. Now you go to work on Monday, and you come back home to a clean house and a candlelit dinner, and you couldn't tell if a human or robot had been there. And I'm calling this the physical Turing test, and it's really a shout out to the actual Turing test, right, like that Turing proposed, and we as a community more or less solved the Turing test. When you're chatting to a chatbot, you can tell. It's very easy to fool a human now, But what about the physical Turing test? I think it's deceptively simple. Right? It sounds so simple, so intuitive, but it's perhaps the next or the last grand challenge in AI. And, Molly, as you hinted, right, like, it's so easy for humans, but, really, we're at the sake of the more of its paradox where since... Things that are easy for humans are actually incredibly hard for the machine. And why is that? I think the main problem is the data problem. You know, I I listened to Ilya talk, and he famously said that ROMs pre training is hitting a wall because we're running out of data. Right? Like, the Internet, we only have one Internet, and the Internet is the fossil fuel of AI. But I will say that you guys, LLM researchers, are so spoiled because you... At least you have the Internet to train on, and we roboticists, unfortunately, we don't even have that. So let me me share a screen. I would love to show the audience. Robotics is a very visual thing. I think one video is worth 10,000 words, So let me share. And, Molly, tell me if you can see it.
00:22:07 · Molly Welch
[00:22:07] S0: We can see it. We see the grocery in NVIDIA HQ.
00:22:11 · Jim Fan
[00:22:11] S1: Grocery in NVIDIA HQ. So this is, like, a typical data episode that we collect for for robotics, and this is the input to the robot. And, actually, this is the output. That's what the robot joint control or the or the motor control looks like. It's a high degree of freedom, high dimensional continuous signal, and unfortunately, you cannot scrape this signal from Wikipedia. So we have to collect the data in in some ways. So what we do is through a process that we call teleoperation. So here, this researcher is wearing a VR device where it's streaming her hand pose in real time to the actual robot. So the robot is physically mirroring her movements. And then in this way, we can control the robot to do a lot of tasks, like here to pour honey over over a toast, and the researcher basically sees the the robot actions, right, through this egocentric camera. So unfortunately, you know, this process is very manual. Right? You can see it's very tedious, and it hits the physical limit of twenty four hours per robot per day. But actually that's the upper bound. The robots are fragile. We babysit them all the time. They throw a tantrum, and if the robot god is merciful on that day, we may collect up to four hours of high quality data, Right? So it never hits twenty four hours. So really, it's it's a kind of human fuel that we are collecting for the for the robot, and then that's compared to the fossil fuel, right, that that Ilya said, you know, all the ROMs are pretraining on. So the question is, how can we get the nuclear fuel for robotics? Right? What is the clean energy? What's a way to generate almost infinite amount of data, right, to feed our our pipelines. So so here is a is a quick pander... Pyramid. Right? The real data is on top. It's the smallest amount. The web data is huge, and then synthetic data, right, will be the will be the future. So it's infinite in principle, but it's limited by PhD brain cycles because we need to come up with novel methods to actually generate the data, and also by the compute. So the more you buy, the more you save. This message has been approved by my boss. And I want to show some new research that we're doing on synthetic data. So, Molly, at the beginning, you mentioned Eureka. Yeah, I want to show this.
00:24:46 · Jim Fan
So it's a technique that allows us to train a high degree of freedom, robot hands, to do pen spinning at human level, and I want to admit that I am a subpar human. I can never really do pen spinning, and I gave up a long time ago in my childhood. So it's really glad to see my AI avenging my poor skills. Right? How do we get the data to train this? The answer is there is actually no supervised dataset. It's all done by reinforcement learning in massively parallel simulations on the GPU because, luckily, all the physical equations are mostly just matrix modifications. So you can run this on CUDA, simulate reality at 10,000 times faster than real time. And then you can transfer this to the real world. And how do we do that? Through a process called domain randomization, where for the 10,000 different environments, you can vary the gravity, friction, and weights, and then you follow the simulation principle. So suppose an AI model is able to master a million realities with different physical parameters, then it's highly likely that it's gonna zero shot the one million and first reality, which happens to our own physical world. And just to kind of show some quick videos, we can run like a robot parking simulation, and then it transfers to real world. We can control a four finger hand to manipulate a cube, and this one is most interesting. We have a robot dog walking on a yoga ball and trying to stay balanced, and actually, the yoga ball is very hard to simulate. It's bouncy, right? It's soft, but actually, we don't even simulate that. It's just a hard sphere that we do, but we do a lot of domain randomization, and then, you know, we're roboticists, we're weird, so we just transfer and run it in the streets. Yeah. It's an actual robot dog running a yoga ball in the streets. You know, sometimes I feel like I'm the director of a Black Mirror episode. Yeah. It's it's just so weird. Actually, my friend, after seeing this video, he told me that he tried a yoga ball on his own dog, and it couldn't do it. So in a sense, we have achieved super dog performance. And of course, we can scale this to a lot more complex embodiment, like an army of humanoid in sim, and these humanoids are going through ten years worth of intense training in only two hours of simulation time. And then with that much data, we can train a 3,000,000 parameter neural network. No.
00:27:28 · Jim Fan, Molly Welch
That's not billion, only 3,000,000 parameter to control the robot body to stay balanced, walk, run, jump, and dance. So basic... Basically, this small neural net that we train in simulation, it can capture this kind of subconscious motor coordination that we, as humans, do all the time, and it runs at millisecond scale on a GPU onboard. So this is actually shot in the NVIDIA headquarters, the Voyager Building. So if you visit Voyager, you might run into these little guests, and, you know, don't be a stranger. Wave. Say hi to the robots running around. And also don't forget to visit our robot hospital. These little guys work as hard as our researchers, and, you know, no robot harmed during filming. [00:28:19] S0: Too good. Another day in Silicon Valley where you see a robot dog on yoga ball. You see a robot getting CPR at NVIDIA HQ. Yes. We live in an amazing world. [00:28:33] S1: Only happens in Silicon Valley. We are the weirdest people. [00:28:36] S0: Exactly. We're very weird. We're very weird. Taking us in another but related direction, we talked about synthetic data. We talked about Teleop. I wanna hear your view on world models, which is this newer paradigm, pioneered by some of the folks, a few of the folks that we've talked about today and, and may have a role in the data... In helping solve the data problem for robotics. I... I'd love to maybe hear you talk a little bit about world models, where you think world models fit into this overall picture, whether they can help on the data side. I'd love to hear your views there.
00:29:15 · Jim Fan
[00:29:15] S1: Absolutely. So first, let me kind of define world models. I think the term means different things to different researchers. And to us, world model means this kind of pretrained video foundation model that can take as input actions and then predict the future in pixels. That's an operational definition of world model that I I work with on a daily basis. Now why are these models so powerful and and and so useful? I I really like to think of... Just to make analogy, I really like to think of video world models as kind of like Doctor Strange. Right? They have seen almost every possible physical phenomenon from billions of Internet videos, and the model basically learns a superposition of visual realities. It's just like LMs learns a superposition of all kinds of different personalities from all the novels and articles it read on the internet. Right? So the video models is a superposition of visual realities. And when you prompt these models, you basically collapse the infinite possibilities into one chosen future. So in this world, in this way, these video generation models are actually counterfactual simulation engines. Right? Like, they have seen all the physics, and they know how things should act given certain actions. So let's say you drop a bottle of water, you drop a cup of water, it will know that the water will spill. It has this predictive ability. And for robotics, this ability is super important because actually, for humans, when you do certain actions, right, you have this mental model of how the actions... Like, what kind of results would the action yield? And then you plan implicitly to optimize for actions. So we see this world model as kind of an alternative and a very powerful new way to either generate data or even become the backbone for a robot brain. And I would like to actually share screen again. I want to show a very recent work from our lab. So, yeah, let me let me share screen. We actually just showed this video, like, a few minutes ago, and, actually, I I I tricked all of you. There is not a real pixel in this. It's actually generated entirely by a video world model. So we call this idea and and the algorithm GR00T dreams. Right? Like, does humanoid robot dream of pixelated sheep? That's where this this name is inspired by.