The Robot Report Podcast
00:59:30 · Eugene Demetri, Aaron Nathan
[00:59:30] S3: Yeah. Well, you talked about that 10x growth. I know Point One raised 35,000,000 in series c funding led by CrossLab Ventures. What does that enable as you as you continue scaling up? [00:59:42] S2: Yeah. So one of the big things that we've been doing is trying to get into more markets. So, you know, obviously, we started here in The US. That's... We're headquartered in San Francisco. And then since then, you know, we've really put a lot of effort into global expansion. So we just actually announced a large partnership with Orange, who's one of the largest telcos in Europe and, frankly, around the world. And, you know, we're also doing, you know, business in Asia and, like, trying to figure out how do we actually solve this problem not just from a US perspective, but actually have, like, a global solution because we've seen, actually, our technology is most valuable to the biggest companies who actually are thinking at a global scale. And this is really, like, I think, a great thing for us as a business because we're now able to go in and say, yes. We have something that will work in US, in Europe, in Japan, you know, in South Korea, in New Zealand, in Australia, and, you know, we're expanding even beyond just that. And I think that's, that's a really powerful value proposition when you're trying to make a product that serves all those markets.
01:00:55 · Mike Oitzman
[01:00:55] S1: Wow. So I I curious what's next? You know, we've talked about ag robots as one of the growing markets and where I've seen, as you already said, a lot of companies adopting new technology for the accuracy it gives them in the workflows that they're trying to deploy. But, you know, what's around the corner? What what excites you about this industry or other industries as well as the growth of the company?
01:01:18 · Aaron Nathan
[01:01:18] S2: Yeah. I mean, there's been amazing advancements, I think, in the overall architecture for how people are building these robotic systems. You know, foundational models are transforming how we think about, you know, just robotics in general. And, you know, several of the companies leading in that field are customers, And we are trying to figure out the right way to bring location in a, like, maximally useful way in that context. And so if you have these kind of, like, parallel systems, which is what we're kind of seeing shake out as the default today where you have some model based, you know, decision system, and then you also have, like, a more traditional stack of perception, planning, and acting. Location is ultimately, like, kind of, like, common between those two pieces of the stack. We are trying to think how do we bring Point One into that ecosystem in a way that's native, I guess you could say. The other piece is just, you know, how do we continue on our North Star vision of making... You know, positioning work everywhere with a high degree of reliability. And, you know, we have a lot of work in our lab that, you know, is not quite yet ready, but it is something that I'm very excited for the future because, yeah, robotics is not slowing down, and I still believe... What I believed in 2016, which is that we will have a demand for more robotic systems to have high precision location than we do all the humans and phones combined today.
01:02:54 · Mike Oitzman, Aaron Nathan
[01:02:54] S1: Any opportunity to bring it indoors? We think... We talk about the outdoor location problem, but we also think about warehouses and and manufacturing lines in... Under the roof. Is is there a future application where this or some other new technology provides the accuracy those robots need? [01:03:14] S2: Yeah. I mean, it's it's a very clear need, and we're studying that quite a bit. You know, the technologies that are out there today, like ultra wideband or, you know, Bluetooth beaconing, They're great for tracking, like, hey. Where's the tool or where's the package along the, you know, the the pallet line or something, but it's not sufficient to navigate a robot. And so Yeah. You know, you can imagine a world where our technology works in tandem with, you know, indoor technologies to be able to deliver that, like, common coordinate system I was talking about. [01:03:50] S1: Mhmm. [01:03:50] S2: But, yeah, I I think it's it's an absolutely exciting area, and we are definitely exploring to see, you know, the right way for Point One to, service not just the outside, but the indoor and the outside and in between. Awesome. [01:04:06] S1: Well, hey. One more time before we let you go. Fuse is coming up. November 19, tell us one more pitch on on that event and and and what folks should expect and who should come.
01:04:06 · Mike Oitzman, Aaron Nathan, Steve Crowe
[01:04:06] S1: Well, hey. One more time before we let you go. Fuse is coming up. November 19, tell us one more pitch on on that event and and and what folks should expect and who should come. [01:04:16] S2: If you are building robotic systems and you use location in any way, which basically, unless you have a statically mounted arm, like, you probably had experienced a lot of these problems, you should be at this event. So November 19 in San Francisco at the foundry, We have... Our website is fuse.0.1nav.com. And any listeners here, they can get a 30% discount. The code is Fuse Robot Report, and I'm sure we could put a link in the bio or something. Absolutely. [01:04:53] S1: Yeah. Fuse Robot Report, all one word. So thanks for that. Appreciate you extending that to our listeners, and I hope folks... If you're listening, go sign up. Be there. It's... It'll be the place to be if you wanna learn about the latest and location. So, Aaron Nathan, thank you so much for for joining us and sharing an update on where you guys are, and and look forward to following your guys' success in the future. [01:05:17] S2: No. I really appreciate it. Thanks, Mike. Thanks, Gene. [01:05:24] S0: Great stuff there. Thanks again to Aaron for for joining us there. I liked where you said, Mike, location. This is a a quote quote. Location is one of those things you really have to get right early in the design process of your robotic system. Otherwise, everything else downstream kind of falls apart. I think that's a good model for folks listening to this to make sure they follow.
01:05:43 · Mike Oitzman
[01:05:43] S1: Yeah. It's true. And, again, like, we we talked about, you know, what happens to to robots when they're in... Like an orchard, you know, when, you... The robots are are occluded from seeing the sky and, you know, GPS becomes spotty. You know, what was the robots need to do? It's it's one of those things, Steve, when I was managing the mobile robots, the indoor mobile robots, you know, lost, needing to be relocalized was a frequent and still is for for many indoor robot systems, and it's even a bigger challenge when that's outdoor. So, you know, again, it was interesting to to dive deep on that, it was a great conversations. Same thing, inspection drones, another use case. They always need to know where they are. So if you're looking at something, you want to know where you are in space so you can relate that. And the more accurate you are, the the better that data is going to be. I know, Steve, one of the last things I want to point out Point One Nav is that remember, we've been covering the Indy Autonomous Challenge since its inception, and they've used Point One Nav since the beginning of the event five years ago to keep those cars on track. That was one of the, you know, great stress cases for this concept of of knowing where you are. And even in those race cars, they have two receivers, one in the front of the vehicle and one in the back of the vehicle because, you know, that centimeter accuracy was important to know how the vehicle was turning, rate of turn, all that kind of stuff. I remember one of the biggest crashes, so to speak, one of the vehicles went off the track because it lost... There was some problem on board. It lost the GPS signal and the robot was moving, you know, I don't 150 miles an hour or something like that. And so it sort of spun off the track in the corner. It was because it lost... The GPS signal didn't know where it was anymore, and that caused the whole system to become confused. So position is important.