Dream Machines: Physical AI, Robotics, Being Human
00:23:53 · Evan Helda, Charles Wong
[00:23:53] S1: That's awesome. [00:23:54] S2: Then when we got there, you know, we did the we did the call. He was so impressed. He then said, I'm gonna organize a seminar for all my fellow planetary scientists to come in and listen to you guys. [00:24:04] S1: Wow. That's cool. [00:24:05] S2: They're like, well, shit. Shit just hit them. [00:24:08] S1: Talk about a tail, as they say. [00:24:10] S2: Exactly. We're still faking it till we make it. Right? And so we got in the room there and, like, I like, this was like, you know, 3AM in Singapore. Right? We were pitching these NASA scientists, you know, in our parents' homes. Right? And I remember like, at some point during the the meeting, like, my mom, like, came into the room and wanted to give me, like, a drink. And I was like, no, mom. I'm on the I'm on the phone with NASA right now. Casual. What did come out of that was that we... NASA became our very first pilot project. Right? And it was for validating martian landing systems. So, like, the train you see behind me is from from one of those, you know, like, terrain generations. Right? Nice. Essentially, that the new the new Mars helicopter at the time, they were trying to validate, you know, where it's safe to land. Right? You know, regular debris, all that stuff. And they used our data to validate those systems. And I think that gave us a lot of street cred when later on we talked to more customers and, you know, the pitch ball from there.
00:24:10 · Charles Wong, Evan Helda
[00:24:10] S2: Exactly. We're still faking it till we make it. Right? And so we got in the room there and, like, I like, this was like, you know, 3AM in Singapore. Right? We were pitching these NASA scientists, you know, in our parents' homes. Right? And I remember like, at some point during the the meeting, like, my mom, like, came into the room and wanted to give me, like, a drink. And I was like, no, mom. I'm on the I'm on the phone with NASA right now. Casual. What did come out of that was that we... NASA became our very first pilot project. Right? And it was for validating martian landing systems. So, like, the train you see behind me is from from one of those, you know, like, terrain generations. Right? Nice. Essentially, that the new the new Mars helicopter at the time, they were trying to validate, you know, where it's safe to land. Right? You know, regular debris, all that stuff. And they used our data to validate those systems. And I think that gave us a lot of street cred when later on we talked to more customers and, you know, the pitch ball from there. [00:25:10] S1: That's amazing. So you have this Lighthouse customer. What did... So how did you go from creating some data and something in a very custom way? What came out of that? And then what was required to turn it into a product? And, like, what what does the actual product look like today? And from, like, a user experience perspective for someone who's just getting started using using Bifrost?
00:25:10 · Evan Helda, Charles Wong, Aravind Kandiah
[00:25:10] S1: That's amazing. So you have this Lighthouse customer. What did... So how did you go from creating some data and something in a very custom way? What came out of that? And then what was required to turn it into a product? And, like, what what does the actual product look like today? And from, like, a user experience perspective for someone who's just getting started using using Bifrost? [00:25:38] S2: Yeah. I think, you know, the the thing that people underestimate significantly about simulations in the data is how much setup is required. Right? Mhmm. Like, people think you can just download Omniverse or download Unreal Engine, chuck stuff in, and boom, you got simulation. Right? But, no, there's so much support required. Right? There's three d assets and environments. There's rendering pipelines. There's scaling up. There's compute as well if you wanna support such a heavy workload across such a large dataset. [00:26:04] S1: Mhmm. [00:26:05] S2: All of that has to come come into an experience that, you know, an AI developer can use. Right? Yep. Like, an AI developer isn't gonna wade into the Unreal Engine docs and try and figure it out. Right? They have... It's a totally different, you know, vernacular. They just... You know, their time should be spent building robots. It shouldn't be spent figuring out quaternions and materials and textures. [00:26:27] S0: Right?
00:26:27 · Aravind Kandiah, Charles Wong
[00:26:27] S0: Right? [00:26:27] S2: So a lot of our work was how do we abstract that away, present it in a way that an AI developer can understand. So today, our product is a web app. Right? It's a it's a platform called Stardust, where on one side is it's like a three d preview of what you're building being streamed into you on your browser, so you don't need compute. And on the right is a Python notebook. Right? Where you can... That's how you spec your scenarios and build your generations and all that whole. Right? So the idea here is that if you're a company and you wanna adopt simulation in your company, you're... Instead of spinning up a 30, 50 man simulation team, you know, with, like, three d artists, technical artists, three d developers, just use Bifrost. It's just one person, one seat, one experience as an AI developer, and you don't need all that additional support. Right? We are currently working on a new product that's going up to market soon. Nice. Product is specifically focused on robotics evaluation. Got Where the previous product start us was more around, like, focused on perception, general autonomy. This one is hyper focused on solving what we feel now is the hardest problem, which is evaluation. Right? How as a robotics developer do I go from my initial policy next to, like, a parameter sweep across all the things that I care about, doing it in a scalable way, doing it in an easy way that I don't have to, you know, again, wade into documentation, figure out Docker containers, and stuff like that.
00:27:56 · Evan Helda, Aravind Kandiah, Charles Wong
[00:27:56] S1: Wow. I have a lot of questions about that product. I'm seeing a lot of people go off that same space and interesting innovation happening there. We can get into that. But before, first one comment that you'll find fun. I was at AWS for six years, and my whole mission was to build a solution for three d content creation, three d content management, three d asset pipelines, three d asset configuration, trying to automate all that. We did launch it. It launched as a solution called spatial data management, or Amazon Spatial Data Management. It's a much more primitive version of what I was trying to get to before before leaving, but the code name was Nebula. And so you've mentioned Stardust. I'm like, I think you have similar ideas of, like, three d data, spatial data simulation type worlds. And Great minds. Great minds. Exactly. I I... I'm determined to make Nebula be a prog name some... At some point in my career because I think it's a great a great name. [00:28:54] S0: Very close to name he is as well. [00:28:56] S2: This is [00:28:56] S1: Yeah. Exactly. Maybe it's destiny. [00:28:59] S0: Who knows?
00:28:59 · Aravind Kandiah, Evan Helda, Charles Wong
[00:28:59] S0: Who knows? [00:29:01] S1: You mentioned earlier this idea of making surmountable. And people will toss Cindereal around, it's become a buzzword. People oftentimes will use that excuse and even try because it is such a daunting thing. And there are some use cases where it's very, very real, and you're not gonna overcome it anytime soon. The most common example is like deformation or interaction with certain types of cloth or liquids or things like this where like the physics is just hard. How do you guys convince people that sim to real is surmountable? And then where is your focus and where are you guys nailing it and make... And and it truly is surmountable for you guys? It's repeatable and you're... That you're not even having to struggle with it versus, like, where is that sort of middle ground where you're close? And then where is that edge where you're just... It's in the future and you you know it, you admit it, you're not gonna get there yet, but it's on your mind. I guess we'll start with giving confidence and explain to them why it's even worth trying to tackle in the first place. Yeah. [00:30:07] S0: So maybe you can, like, break it down into what does, like, what is it... What does simp to real actually mean? Right? And Mhmm. Simp to real, all it really means is just, you know, if you train something in simulation, does it actually translate to actual performance in the real world? [00:30:23] S1: Mhmm. [00:30:24] S2: And