00:15:59 · Jim Fan
And I find that idea really fascinating because at that time, people were really trying to experiment and see what are the capabilities of these coding frontier models, and Voyager provided one way to build a very complex agentic system on top. And then two years later, these days, the the agents are a lot more powerful. Like, self reflection, people don't even talk about it. It's kind of a given. Right? Like, all the cloud and cursor of the world, they can do debugging super well. They can go on, like, a thirty minute session to to implement a pull request, and then it just... It goes on autopilot, and it it typically does the job pretty well. And it's also able to call all kinds of tools, web browsing, connecting to MCP. So I think the field has gone way beyond Voyager, way beyond Minecraft, so that was that was really exciting to see. Now what was lacking a bit from Voyager was actually that we didn't solve the motor control problem, and we didn't solve the perception problem. We essentially simplified the problem by turning Minecraft into a set of code API and code execution environment, but later, I have come to understand that really the most difficult part may not necessarily be the reasoning side. I think the field is already solving reasoning quite well. The most difficult part of Embodied Agent is actually the low level control, the part where you interact with the low level physics. In Minecraft, it's the game physics, but in the real world, it's the actual physical reality that we're touching, and that makes the robotics problem a lot harder.
00:17:49 · Molly Welch, Jim Fan
[00:17:49] S0: Very cool and incredibly clever, the approach. Remains incredibly clever even as the technology has progressed. So thank you for talking us through it. I... I'm curious. You know, you... MineDojo and and Voyager represent this progression towards agents that learn autonomously or more autonomously through exploration. What do you think are the key remaining bottlenecks for creating truly self improving agents? [00:18:16] S1: I think two things. So there's a popular word called thinking fast and slow from psychology. Basically, it says that all the intelligent beings, like humans, have two systems. So system two is slow and deliberate and does reasoning, and system one is fast, reactive, and intuitive, so the two system approach to agentic AI. I think right now, the system two is progressing quite well. It's not perfect, but I think there are, like, many ways to improve system two, right, like more data, more environment that we can plug it... Plug into the frontier LLMs so they can do reinforcement learning. These days, the frontier LLMs are really bottlenecked by the diversity of the environment, not just the pretraining data. We are kind of in this new era of RL post training, so that's on system two. And system one is... It still remains challenging because computer vision is not soft. It's also much harder to collect data for system one, and I think that's also a good segue to maybe talk about Project GR00T and robotics.
00:18:16 · Jim Fan, Molly Welch
[00:18:16] S1: I think two things. So there's a popular word called thinking fast and slow from psychology. Basically, it says that all the intelligent beings, like humans, have two systems. So system two is slow and deliberate and does reasoning, and system one is fast, reactive, and intuitive, so the two system approach to agentic AI. I think right now, the system two is progressing quite well. It's not perfect, but I think there are, like, many ways to improve system two, right, like more data, more environment that we can plug it... Plug into the frontier LLMs so they can do reinforcement learning. These days, the frontier LLMs are really bottlenecked by the diversity of the environment, not just the pretraining data. We are kind of in this new era of RL post training, so that's on system two. And system one is... It still remains challenging because computer vision is not soft. It's also much harder to collect data for system one, and I think that's also a good segue to maybe talk about Project GR00T and robotics. [00:19:27] S0: It's a perfect segue, which is what I wanna talk about next. So, you're leading physical AI research through the Gear team, Project GR00T for general purpose robotics as you described. What do you think is the grand challenge for robotics? Why is it so hard? Why is system one, which is so easy for us, system two is the hard piece, why is system one so hard in robotics?
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.