00:37:06 · Jonathan Stephens
[00:37:06] S1: So I'll make sure I will I will link the, you know, those... At IsaacLab Arena and, the the, sorry, the LW-BenchHub as well in here because I don't think people are necessarily aware that both of those even exist at this point. Maybe maybe it's IsaacLab-Arena, but I think people get confused because they hear IsaacLab-Arena thinking, oh, I already know IsaacLab. Well, that's... It's different. Or there's IsaacArena in the past. It's kind of a new thing. Yeah. I see that. I like the the fact that we're all working on the same boat, like bench... Benchmark vocabulary, if that's the right term, but saying, okay. If if you understand the benchmark and I've been using the benchmark, there's no... Well, how do I know yours is better than mine if you use a different benchmark system? So it kind of evens the field of where you where you land. So within... Let's say I am a large company. Maybe I I don't know. Maybe I'm Google's labs who've been building the frontier model VLAs and they are using, we'll say they want to use RoboFinals with their benchmarks. So they just keep them internally or is that someone who's just saying, I want to benchmark against what we deployed three months ago or are are they trying to create like a leaderboard so other companies can compete against them? How does that work?
00:38:20 · Steve Xie, Jonathan Stephens
[00:38:20] S0: Benchmarks are still kinda new to me. Yeah. Totally. So basically, we are also providing a leaderboard to the whole industry. So, basically, we are working on it right now. So now we have the benchmark. We should be able to help to... Everyone to benchmark where they are. Right? Mhmm. And also to showcase a leaderboard. So it's definitely also coming. It's definitely also coming. Yeah. Okay. So just not a... [00:38:45] S1: Yeah. And that makes sense. But as people adopt it and get more use, then we'll have a good leaderboard because I think that's something people like to know. Like, I have another I have a new frontier model. Where does it land amongst others? There's something beyond that one benchmark to use one or the other, but it it it even it's a playing field of where everyone lines up.
00:38:45 · Jonathan Stephens, Steve Xie
[00:38:45] S1: Yeah. And that makes sense. But as people adopt it and get more use, then we'll have a good leaderboard because I think that's something people like to know. Like, I have another I have a new frontier model. Where does it land amongst others? There's something beyond that one benchmark to use one or the other, but it it it even it's a playing field of where everyone lines up. [00:39:07] S0: Totally. Right. Yeah. It it is extremely important to let everyone understand where they are, not only to help them benchmark against themselves, say, maybe a day ago, a month ago, but also help them to, benchmark against, you know, the industry, especially the top players in the world. Right? So that's, in my opinion, the most important thing. And, you know, it's it's a lot of work. We are working on, I would say, scaling the benchmark because, basically, we are working on a lot... We are working with a lot of industrial partners. Right? And, also, we are working, we are getting a lot of data in order to build the most realistic, diverse, physically accurate, scalable benchmark in the world, but also at the same time, leveraging that benchmark to provide a little bit to help everyone understand where they are.
00:39:07 · Steve Xie, Jonathan Stephens
[00:39:07] S0: Totally. Right. Yeah. It it is extremely important to let everyone understand where they are, not only to help them benchmark against themselves, say, maybe a day ago, a month ago, but also help them to, benchmark against, you know, the industry, especially the top players in the world. Right? So that's, in my opinion, the most important thing. And, you know, it's it's a lot of work. We are working on, I would say, scaling the benchmark because, basically, we are working on a lot... We are working with a lot of industrial partners. Right? And, also, we are working, we are getting a lot of data in order to build the most realistic, diverse, physically accurate, scalable benchmark in the world, but also at the same time, leveraging that benchmark to provide a little bit to help everyone understand where they are. [00:39:59] S1: Yeah. Makes sense. Okay. So... Okay. So we have the the kind of the three different portions of infrastructure. We have a world, we have behavior of the evaluation. You kind of need all three. You can't you can't run simulation without doing all three. Right? You need to build a world, do some actions, train a model and evaluate how well it does. So you know where to improve it, or to create, get more assets, create more assets, try new behaviors. It's just almost like a flywheel or, you know, an ecosystem there. One thing that I've been diving into is world models and how how... Where does that fit in that? Is that something that we're gonna be using for multiple parts of that ecosystem or is that more for just world generation?
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.