00:40:47 · Steve Xie, Jonathan Stephens
[00:40:47] S0: Yeah. It's basically everywhere. We are actually leveraging world model everywhere. And I really see that, you know, world model and simulation, they go hand in hand. Right? So it's fully integrated into, you know, our pipeline. For example, in the word generation. Right? In the word generation, you know, you are getting a lot of data from EgoSuite. So EgoSuite is Lightwheel's egocentric data product. Right? And we have already delivered more than one million hours of EgoSuite to the industry already. Right? And our goal this year is to hit more than 10,000,000. So for EgoSuite, it is bringing a lot of different type of real data. Right? The real scene, the real trajectory from human data collector, and also the real task. Right? And we are leveraging world model, for example, Marble, to actually digitize and transfer those real things into USDs and put into RoboFinals. Right? So that's basically how we scale RoboFinals, leveraging our EgoSuite, our real data collection, from the real world, and also through our real two sim pipeline. [00:42:06] S1: That's been a great unlock. Right? Like, I I Yes. Our team can be spending more time creating SimReady Assets and improving those aspects and less time creating the part of the scene we're not necessarily interacting with us, but Marble cannot put the backdrop that you still need to have.
00:42:06 · Jonathan Stephens, Steve Xie
[00:42:06] S1: That's been a great unlock. Right? Like, I I Yes. Our team can be spending more time creating SimReady Assets and improving those aspects and less time creating the part of the scene we're not necessarily interacting with us, but Marble cannot put the backdrop that you still need to have. [00:42:21] S0: Exactly. You don't want time creating. Exactly. Yeah. So so I would say that that's extremely important. And also in, for example, the behavior layer, meaning the data layer, right, we have, you know, the auto data gen, which is, you know, like, leveraging our sim words, sim things, leveraging our, like, automated data collection algorithms in simulation, and also leveraging word models. For example, COSMOS to augment them, right, to help us, to provide, more data for our customers. So that's also where we would use, you know, the world models.
00:43:02 · Jonathan Stephens, Steve Xie
[00:43:02] S1: Mhmm. Yeah. I feel like I feel like there's a little bit of a misunderstanding of what all world models can and will do. I think some people are really focused on the real world real time frame generation and, think like Genie three and you're walking around, but then there's Cosmos who's saying, Hey, I gotta have a robot doing interactions in this kitchen. I want the kitchen to be completely different, but I want the exact same interaction. So we can easily redo that without having to actually go there or even setting up a new sim environment. Or we have world mar... Marble, world lives as marble where you can just say, want a scene. We don't need exact replicas most of time. We just say, I need a kitchen of this theme, or I need a warehouse that looks like this specific kind of scene, but you don't, you don't need a one for one replica of a, of a warehouse because we're trying to build models where building models are generalizing across styles and scenes, not one. I don't... You know, I I need it working more than just one kitchen. So that's the great thing. Right? Can adapt out a 100 kitchens in a day where a three d artist would take a month or more to create a 100 kitchens in in in Isaac Sim. [00:44:11] S0: Totally. Alright.
00:44:11 · Steve Xie, Jonathan Stephens
[00:44:11] S0: Totally. Alright. [00:44:12] S1: Totally. Then moving forward, just kinda wrap this up. What what part of that stack do you think will look most different in, let's say, two years from now of those three, world or the world, have the behavior, we have eval, which one do you think is gonna really like change the most? What we're gonna see a big advance in? Or or is it gonna just have to be all three?
00:44:33 · Steve Xie
[00:44:33] S0: Totally. So I would say first, you know, we introduced the three layers, the work behavior, the evolve. But, actually, there will be much more connections among those three and also between real and also seen. We are actually truly building the flywheel. For example, you know, we started as purely simulation. Right? Then we found out that, you know, we need to scale simulation. We need to get real data. Then we get into the Ego centric data collection. And now we are building a really scalable pipeline from real to seen. Right? From Ego, from EgoSuite, from the behavior layer to actually the world layer, to actually the evolved layer, which is amazing. Right? But then the other thing that we are building right now is how can we best leverage our evolve layer? Right? Our evolve layer right now is we are providing the evaluation framework, evaluation products to the whole industry, to the academia, to help them understand where they are. But, actually, can we light our eval layer, to help us understand where we are with our work, with our behavior? So that's the thing that we are building right now, which is amazing. You know? We are we are actually leveraging our evolved layer to helping us to benchmark our EgoSuite right now. So meaning, leveraging a simulation evaluation to help us now... Then, basically, we will get this flywheel. Right? Meaning, basically, from, you know, the real behavior from the real EgoSuite. Right? We are getting into simulation. Right? But also from simulation, we are actually improving our real data collection from EgoSuite. Then actually, we are building the real flywheel. So this is basically the major focus I'm with right now, to building the real flywheel, a scalable flywheel, I would say. Right? From real to scene, but also from scene to real. You say that... Yeah. Go on. Go ahead. Yeah. Go on.
00:46:31 · Jonathan Stephens
[00:46:31] S1: I was gonna say in two years, you can't say one's... Like, one thing has stood out because they kinda all need each other.