Latest / Automated with Brian Heater / Rob Cochran on Shipping a Developer-First Humanoid at Fauna Robotics

Transcript

Rob Cochran on Shipping a Developer-First Humanoid at Fauna Robotics · Automated with Brian Heater

• 0:00
  SPEAKER_01: We're doing something a little unconventional in that we have taken on the humanoid form, but it is lightweight.
• 0:07
  SPEAKER_01: It's 45 pounds.
• 0:09
  SPEAKER_01: It is three and a half feet tall, so it's not a full-size humanoid.
• 0:12
  SPEAKER_01: We went through many iterations, things that hewed closer to kind of anthropomorphic design.
• 0:17
  SPEAKER_01: We ended up feeling like a much reduced, simplified, more abstract form allowed for kind of clear expression of ideas without overloading with the kind of things that you know might cause Uncanny Valley sort of experiences.
• 0:36
  SPEAKER_00: Welcome to Automated.
• 0:38
  SPEAKER_00: My name is Brian Heater.
• 0:39
  SPEAKER_00: I am the managing editor at the Association for Advancing Automation.
• 0:44
  SPEAKER_00: We have a very fun episode for you today.
• 0:47
  SPEAKER_00: Rob Cochran is a founder who I spoke with actually a few years back in the very early days of what would ultimately become Fauna Robotics.
• 0:58
  SPEAKER_00: We also caught up with him uh about a month or so ago as the startup officially came out of stealth and launched its developer robot, Sprout.
• 1:07
  SPEAKER_00: Lots of uh interesting insight into the company that's doing uh something a little bit different in the humanoid space.
• 1:14
  SPEAKER_00: If you're joining the show, don't forget to like and subscribe and rate and review us wherever you do the two aforementioned things.
• 1:23
  SPEAKER_00: And now, please enjoy this conversation with Fauna Robotics.
• 1:27
  SPEAKER_00: Not all robots work in predictable environments, but every robot depends on predictable motion.
• 1:33
  SPEAKER_00: In robotics, motor level precision drives long-term reliability.
• 1:37
  SPEAKER_00: Sand Motion from San Yodenkey delivers deterministic multi-axis control with synchronized ethercat communication, stable one millisecond cycles, and motors, drives, and absolute encoders engineered to perform as one system.
• 1:51
  SPEAKER_00: Advanced robotics needs motion you can trust.
• 1:54
  SPEAKER_00: Sand motion from Sanyodenkey, precision in motion.
• 1:58
  SPEAKER_00: Learn more at sannyodenke.com slash America.
• 2:02
  SPEAKER_00: I don't know if you're aware of the fact that uh Pitchbook seems to think that you're an ag tech firm.
• 2:07
  SPEAKER_01: A misnomer.
• 2:08
  SPEAKER_01: Somehow Flora and Fauna frequently get confused, but we are fauna robotic, so uh animal and robotic life.
• 2:16
  SPEAKER_00: Well, first of all, I've never launched a startup.
• 2:18
  SPEAKER_00: Obviously, you know, I've been on the um I was at TechCrunch for a long time and I was on the journal side of things, but I'm really curious what that period is like, um, the stealth period, especially when people are actually like actively looking at you, watching you, following you, and trying to figure out what it is you're doing.
• 2:38
  SPEAKER_01: You may or may not remember.
• 2:39
  SPEAKER_01: You and I spoke, I want to say in March of 2024, really when we were just getting out of the gate.
• 2:45
  SPEAKER_01: It was a brief conversation.
• 2:46
  SPEAKER_01: And you said, you know, even in these early days, it might be worth, you know, talking about it, doing some press.
• 2:51
  SPEAKER_01: Yeah, yeah, yeah.
• 2:53
  SPEAKER_01: We weren't really sure exactly the story we wanted to tell yet, but you know, as things evolved, you know, we built what we felt was a really great team.
• 3:00
  SPEAKER_01: We had a very clear perspective on the products we wanted to build.
• 3:04
  SPEAKER_01: And we knew that it would take time to actually get there.
• 3:08
  SPEAKER_01: And we felt there was frankly no advantage in starting to share that story ahead of actually doing the work and being ready to go.
• 3:15
  SPEAKER_01: So we're now fortunate to be in the position where we have paying customers, we're shipping robots, and and now this exciting, exciting launch coming up, and it feels like the right moment to really share, you know, our vision and and what we're up to.
• 3:28
  SPEAKER_00: I do remember that.
• 3:29
  SPEAKER_00: And I remember that on the strength of just um, you know, like, oh, these, you know, the control labs, you know, ex meta guys are launching uh robotics.
• 3:38
  SPEAKER_00: And I I and I think at that time it was even clear that it was like a humanoid robotics company.
• 3:43
  SPEAKER_00: So clearly this is something you know that TechCrunch is going to want to cover.
• 3:47
  SPEAKER_00: But yeah, again, it was still too early for you to really want to talk about it any kind of detail.
• 3:53
  SPEAKER_00: But at that point, so uh I guess now what two years ago um in two months, uh, what what was the vision for the company?
• 4:02
  SPEAKER_01: Josh and I had crossed path at control labs, which you know, uh wearable neurotech company that I I led product at, and he did some early research.
• 4:10
  SPEAKER_01: And I think shared this idea that some of the technology around general purpose robotics was was converging to a point where you could build something really interesting and exciting, but it felt like there was no way to really express that on a platform that you could put into human environments, deploy safely among people.
• 4:28
  SPEAKER_01: And and you know, we were really excited about the consumer space and remain quite excited about that uh long term, but felt like there wasn't really a straight line path to get there without a chance to really get to shipping products.
• 4:41
  SPEAKER_01: So the the way we evolved over time was to say, you know, how do we get to the north star of a you know mass market consumer humanoid product?
• 4:51
  SPEAKER_01: And it felt like if you look at personal computers, smartphone, a lot of it comes through developer programs and the ability to kind of unlock the innovation of you know thousands of people building you know many, many concepts.
• 5:02
  SPEAKER_01: And so we thought you start with a platform that's that's safe, engaging, affordable enough to be deployed at scale.
• 5:09
  SPEAKER_01: And that could be the right starting point for kind of igniting an industry and moving towards that ultimate goal.
• 5:14
  SPEAKER_00: As as we're recording this, um, I I just spoke to to Josh, God, I guess it was yesterday.
• 5:20
  SPEAKER_00: Time time is um, I don't know.
• 5:22
  SPEAKER_00: You know, you say March 2024, and I I couldn't even I actually had to do the math on how long ago that was.
• 5:29
  SPEAKER_00: Um but you know, he does have that background.
• 5:32
  SPEAKER_00: I think he was at you know deep mind.
• 5:34
  SPEAKER_00: So, you know, it's it's clear how you get from deep mind to humanoid robots, but your path is, I guess, a little less clear, you know, going up and down your CV.
• 5:43
  SPEAKER_00: Um how how how do you eventually get from electromagnetic materials and electromagnetic theory and numerical methods to humanoid, well, to neural interfaces to humanoid robots, to Goldman Sachs to humanoid robots.
• 6:02
  SPEAKER_01: Yeah, it's uh, you know, I've really followed followed my interest, which has not always been in a straight line, but the the two-through lines to my career have been uh interest in frontier technologies.
• 6:13
  SPEAKER_01: So whether that was working on metamaterials research uh when I was, you know, in in in undergrad, frankly, uh doing some work in in self-driving cars early on as a researcher, and then really more moving to the business side, but uh Amazon Web Services worked in Internet of Things technology and enabling software for companies that themselves built connected devices.
• 6:35
  SPEAKER_01: In that capacity, I met a guy named Thomas Reardon who was starting control labs and got fascinated with neurotechnology and and the sort of interactions that that might allow for.
• 6:46
  SPEAKER_01: And so, and then even at Goldman Sachs, you know, the chance to work on what we were calling the financial cloud, but actually externalizing much of the infrastructure and moving to this kind of modern platform for API development, felt like at each stage getting to touch on something that was transformational to transformational to how businesses and industries could work.
• 7:08
  SPEAKER_01: And then the other piece, so that that's kind of the frontier technology side, and the other piece is developer platforms.
• 7:14
  SPEAKER_01: We built Control Kit at Control Lab, so it was actually a developer.
• 7:17
  SPEAKER_01: Our our very first product before becoming a part of Meta was a developer platform that allowed you to integrate neural control into any computer application.
• 7:27
  SPEAKER_01: And that's was kind of my key responsibility when I first onboarded there.
• 7:31
  SPEAKER_01: Um at Amazon Web Services, we built the Internet of Things platform for developers.
• 7:38
  SPEAKER_01: Goldman Sachs, we built the the Financial Cloud, which was again an uh externalized API interface to much of the financial institution.
• 7:46
  SPEAKER_01: And so coming here, it felt very natural to then have having seen many of these cases where you could unlock the creativity of many people by opening and extending the platform felt like a very natural fit.
• 8:00
  SPEAKER_01: And then tied into my interest in frontier technologies, humanoids was a really exciting space.
• 8:05
  SPEAKER_00: I'm sure I'm not the first person.
• 8:06
  SPEAKER_00: I'm definitely not going to be the last person to bring this up, but you know, Meta has um and and you know, somewhat publicly has been doing work in in robotics.
• 8:16
  SPEAKER_00: You know, we've seen a lot of the research come up.
• 8:19
  SPEAKER_00: Um and and you were there for a while through the acquisition.
• 8:23
  SPEAKER_00: Uh at the time though, did that did it feel like that wasn't the place for you to pursue an interest in robotics?
• 8:31
  SPEAKER_01: Well, the thing that united Josh and I was shipping a product.
• 8:37
  SPEAKER_01: And it felt like uh given how the space is evolving, the best way to do that quickly and in a highly opinionated way was in a startup, right?
• 8:48
  SPEAKER_01: It gave us a chance to really put on paper uh together an idea we had for a product and in you know two years get it out the door.
• 8:56
  SPEAKER_01: Big companies can put a lot of resources behind something once something's kind of working.
• 9:00
  SPEAKER_01: But I think the the flexibility and and kind of opinionation that a startup allows for was the right form for us.
• 9:05
  SPEAKER_00: Yeah, what what is what does a highly opinionated mean in that context?
• 9:10
  SPEAKER_01: Well, we're doing something a little unconventional in that we have taken on the humanoid form, but it is lightweight.
• 9:18
  SPEAKER_01: It's 45 pounds, it is three and a half feet tall, so it's not a full-size humanoid.
• 9:23
  SPEAKER_01: It's physically soft to the touch, so it's covered in foam-like materials.
• 9:26
  SPEAKER_01: It's focused on a lot of really interactive features, so it has articulated eyebrows and an LED array around the face.
• 9:32
  SPEAKER_01: So we've taken a real eye for design and in how we express this idea.
• 9:37
  SPEAKER_01: And what that means is it's limited in some capacities.
• 9:41
  SPEAKER_01: It's not appropriate to factory work.
• 9:42
  SPEAKER_01: It's not going to lift a 50-pound tote, but it does allow for something that you could put in an office space and have work right by your desk and be able to deploy a novel motor control policy onto it and not worry too much about putting a cage around it for safe operation.
• 9:56
  SPEAKER_01: And so that felt like something that would really unlock a lot of uh really exciting work from us and others, but it was and is quite different from what is available today.
• 10:07
  SPEAKER_00: It's unconventional, sort of like in the broader industrial sense.
• 10:10
  SPEAKER_00: So it's unconventional, like certainly like compared to like any like, you know, like a like a big fanic arm or something that we that we're used to seeing in in factories.
• 10:18
  SPEAKER_00: But it's also it's interesting to be at a point where we feel comfortable suggesting that there is a convention for humanoids that this runs counter to.
• 10:30
  SPEAKER_01: It is the earliest innings of uh of an industry that has yet to, I would say, broadly find product market fit.
• 10:37
  SPEAKER_01: Right.
• 10:38
  SPEAKER_01: There are a number of use cases in in industry, in in commercial applications.
• 10:43
  SPEAKER_01: You know, I think many have ambitions for the home, but none of this is yet realized.
• 10:48
  SPEAKER_01: Right.
• 10:49
  SPEAKER_01: And so what we said is what is the market that actually exists today?
• 10:52
  SPEAKER_01: And I think there are some proof points for this, but there are many developers who are excited about robotics and looking to extend them into human spaces and are excited about that.
• 11:02
  SPEAKER_01: And so that felt like something where we could actually get the experience of building and shipping something at some scale, and and that's the chance to then start iterating and building from there.
• 11:12
  SPEAKER_00: Yeah, I I want to talk about the mark is really interesting.
• 11:15
  SPEAKER_00: And I know that the part of the answer to this is that, you know, that that this is the first of what I expect will be many different robots, you know, even like looking at there's different iterations that I can see behind you right now.
• 11:27
  SPEAKER_00: But the robot that we're specifically here to talk about is I would say like a research robot, right?
• 11:34
  SPEAKER_00: It's a is a developer platform robot.
• 11:36
  SPEAKER_00: A few analogies come to mind both on market now and from the past.
• 11:40
  SPEAKER_00: Pollen's got the robot, you know, works with Hugging Face, explain specifically for developers.
• 11:46
  SPEAKER_00: In the past, we've seen robots specifically for research, also other ones not for research that have become research robots, you know, very famously by us.
• 11:56
  SPEAKER_00: If you go to NYU or Columbia, you'll see, you know, probably one of the PR2s from Willow Garage still over there.
• 12:02
  SPEAKER_00: You'll see some fetch robots, you'll see some now robots.
• 12:05
  SPEAKER_00: But there's a reason specifically why there aren't a lot of research robots, and it's because like there just hasn't been a market fit for them in the past.
• 12:16
  SPEAKER_01: I think this time is different.
• 12:18
  SPEAKER_01: The space has evolved meaningfully over the years.
• 12:21
  SPEAKER_01: And so, you know, if you take some of the work Josh did at DeepMind, you know, they they were using the OP3 platform, which is very much a small research device.
• 12:30
  SPEAKER_01: And there wasn't anything really hardened for continuous operation in a research setting that you could then train to do useful things and actually test those deployments in in real spaces.
• 12:41
  SPEAKER_00: They actually built their own in-house too, right?
• 12:43
  SPEAKER_00: I mean, they had everyday robots, so they actually just went ahead and made their own.
• 12:47
  SPEAKER_01: Yeah, and I think that was maybe a separate, separate division.
• 12:50
  SPEAKER_01: And you know, there are different teams doing different work.
• 12:53
  SPEAKER_01: Uh, but you know, I think the the point is, you know, there are a bunch of different questions you ask from the outset, right?
• 12:59
  SPEAKER_01: There are tabletop options, there are mobile options.
• 13:03
  SPEAKER_01: Uh, we've chosen a form that we think is quite flexible to deployment in human environments.
• 13:09
  SPEAKER_01: So it can operate across dynamic and unstructured settings.
• 13:13
  SPEAKER_01: It has capable locomotion, it has grippers out of the box, so it can open a door, grab something out of the fridge.
• 13:18
  SPEAKER_01: It's physically capable of doing many useful things.
• 13:22
  SPEAKER_01: And it's also small enough that in a lot of settings you can actually safely try those things out and deploy them at scale.
• 13:28
  SPEAKER_01: And so what we started with was the right form factor allows for many, many ideas to be expressed on the platform.
• 13:37
  SPEAKER_01: But you pair that with an SDK, a set of AI capabilities out of the box that give developers of all types, whether that's a deep research group doing motor control or someone interested in character design and expression, a range of tools that let them get started right out of the box.
• 13:54
  SPEAKER_01: Right.
• 13:54
  SPEAKER_01: So if you just want to change the voice or change the kind of character that sits behind that voice, you can do that and still rely on mapping and navigation, capable locomotion that lets the robot operate.
• 14:06
  SPEAKER_01: But if you want to dig into how your mapping and navigation system operates, you can you can deploy that on our robot as well.
• 14:12
  SPEAKER_01: So we wanted to give that flexibility, and I think that's a a key difference to what has existed previously.
• 14:18
  SPEAKER_00: So I have all these um I have these robots behind me.
• 14:21
  SPEAKER_00: You can kind of see them like blurry, um maybe a few of them out.
• 14:24
  SPEAKER_00: But one of them um in the front right there, he he he stopped working a long time ago.
• 14:29
  SPEAKER_00: But if it uh the Anki Cosmo, do you remember Anki?
• 14:32
  SPEAKER_00: Yeah, yeah, of course.
• 14:33
  SPEAKER_00: I remember meeting with them when they launched, and one of the really sort of I would say innovative but prohibitively expensive ideas that they had at the time was hey, let's hire a bunch of X Pixar, X DreamWorks animators to create these facial expressions.
• 14:50
  SPEAKER_00: And they were able to create this like really expressive, interesting robot.
• 14:55
  SPEAKER_00: And that was probably the highlight of it.
• 14:57
  SPEAKER_00: And then looking behind you, looking at all these iterations and designs, you know, it strikes me that you probably maybe taken a similar route.
• 15:03
  SPEAKER_00: I mean, you brought creatives in to help you make this like expressive system.
• 15:09
  SPEAKER_01: Yeah, well, we're we're lucky to have a very talented industrial designer and a and a team of folks working on this.
• 15:15
  SPEAKER_01: But I would say from the outset, we we set out to build the kind of characters from science fiction that we liked and grew up with.
• 15:21
  SPEAKER_01: You know, Wally, Baymax, Rosie Jetson, these were kind of inspirational designs that felt comfortable to be around, felt engaging, endearing, something that you could connect with uh on an emotional level.
• 15:35
  SPEAKER_01: And I think, yeah, Anki, you mentioned did a did a wonderful job of that.
• 15:38
  SPEAKER_01: And so we took cues from much of robot history as as well as you know, science fiction and other kind of storytelling capacities.
• 15:46
  SPEAKER_01: But yeah, you can see on the wall behind me, we went through many iterations, you know, things that hewed closer to to kind of anthropomorphic design.
• 15:54
  SPEAKER_01: We ended up feeling like a much reduced, simplified, more abstract form allowed for kind of clear expression of ideas without overloading with the kind of things that you know might cause Uncanny Valley sort of experiences.
• 16:07
  SPEAKER_00: And the head is a router, right?
• 16:09
  SPEAKER_00: Am I am I off on that?
• 16:12
  SPEAKER_01: Uh a router wasn't the wasn't the motivation, but but we pulled from a lot of yeah, other other design ideas.
• 16:21
  SPEAKER_00: The marketing material is interesting specifically for this product in that it is a developer product, that it is a research product, but obviously you are showing that you're showing it with kids as this sort of like family-friendly system.
• 16:34
  SPEAKER_00: I know that you have, again, you were at Meta, you were at Goldman Sachs, you know, you've got Kleiner Perkins and Lux and these big investors involved.
• 16:42
  SPEAKER_00: So I'm really just I'm curious, like from the outset, like what is the marketing strategy and and like who who are you going after with this first system?
• 16:53
  SPEAKER_01: We have a a really broad range of customers who are developers.
• 16:57
  SPEAKER_01: So they are businesses and individuals with technical skills who have ideas that they want to express on a robotics platform.
• 17:04
  SPEAKER_01: So we have, you know, Disney and Parks and Entertainment looking at accelerating and doing a lot of iteration on character-based experiences in in parks.
• 17:14
  SPEAKER_01: Uh, we've got uh Boston Dynamics uh that is interested, you know, obviously they have deep history and industrial robotics, but is also interested in more human interactive, human spaces type of work.
• 17:25
  SPEAKER_01: And so they're a customer.
• 17:26
  SPEAKER_01: We've got a number of NYU, UCSD, we've got academic research labs uh building and extending on the platform.
• 17:34
  SPEAKER_01: We've got AI world model labs building on the platform.
• 17:38
  SPEAKER_01: So a really broad range of use cases.
• 17:39
  SPEAKER_01: And again, I think part of the reason for a humanoid form is to allow for a highly generalized set of use cases.
• 17:45
  SPEAKER_01: And I think the power of what we're doing is that though it is a research prototype device, it allows for the kind of user testing and experience uh work that that then gives you confidence you can actually deploy it in the real world.
• 18:01
  SPEAKER_01: And so, you know, my hope is very much that we see yes videos from labs, but also a lot of experiences of of the robot and and people very broadly getting to interact with it in the world.
• 18:14
  SPEAKER_00: Okay.
• 18:14
  SPEAKER_00: So so this this was something, you know, this is this is news to me, and this is very interesting.
• 18:18
  SPEAKER_00: And obviously, I mean, for different reasons, having Disney and Boston Dynamics as customers are are different levels of votes of confidence.
• 18:27
  SPEAKER_00: You know, Boston Dynamics 1, like clearly you've designed a capable robot if Boston Dynamics is is interested.
• 18:35
  SPEAKER_00: Um but like specifically, let's start with Boston Dynamics.
• 18:39
  SPEAKER_00: What does it mean for a robotics company to be a customer of yours?
• 18:44
  SPEAKER_01: In addition to building uh robots, they have a whole commercial go-to-market arm that helps Spot get out into the world at factories.
• 18:53
  SPEAKER_01: They have Boston Dynamics Consulting, which works with companies to provide robotic solutions.
• 18:58
  SPEAKER_01: This isn't in the experimental phase, but what they want to explore is how do they get robots more into human spaces, not just factories and warehouses, but out into the real world.
• 19:08
  SPEAKER_01: And so, you know, they're exploring what that could mean both with with their customers and and internally.
• 19:13
  SPEAKER_00: Disney, you know, I know we've been speaking with them a little bit lately.
• 19:17
  SPEAKER_00: We'd spoken with Moritz and the work that they're doing over in Zurich as far as um they're doing like really incredible work, you know, around Star Wars and around these re-roaming robots in the parks.
• 19:28
  SPEAKER_00: You know, I assume that that's something that they're probably maybe interested in the consumer space as well.
• 19:33
  SPEAKER_00: So, how does a system like yours kind of fit into the work that they're doing?
• 19:38
  SPEAKER_01: Yeah, so the imagineering team uh at Disney has done so much amazing work in in robotics, and I think has a real understanding for what it means to take something from concept, idea, IP in a movie or on a whiteboard and put it into real spaces with lots of people around.
• 19:54
  SPEAKER_01: And I think they'll pick it to it directly at some point.
• 19:57
  SPEAKER_01: There's not that much I can share, but I I think the idea is that this gives them something where they can really freely experiment on something where they can rely on mapping and navigation, on an audio system that's reproducible, on core locomotion that they can build and extend on.
• 20:13
  SPEAKER_01: And so not just the the form factor, which you know, I think from a design standpoint is is appealing, but an SDK that is well supported, well documented, and and provides a lot of features out of the box that they would otherwise have to have to build in-house means a lot more character design from their standpoint.
• 20:30
  SPEAKER_00: So commercial applications and then you know, NYU, so so research applications.
• 20:36
  SPEAKER_00: Um like I don't know.
• 20:38
  SPEAKER_00: That seems that seems pretty straightforward, right?
• 20:40
  SPEAKER_00: I mean, that that's kind of what we were talking about before as far as like what has been done on the wheel o' garage side of systems, just doing these sort of like reinforcement learning kinds of work on these robots.
• 20:51
  SPEAKER_01: From the from an academic standpoint, yeah, I think I think it's a a fairly broad range of things.
• 20:56
  SPEAKER_01: We at UCSD it's it's frankly more neuroscience research.
• 21:00
  SPEAKER_01: And uh, you know, we have manipulation research, uh, multi-robot collaboration.
• 21:07
  SPEAKER_01: So uh, you know, it's it because it is a quite a flexible system, I think it will appeal to to researchers of many kinds.
• 21:15
  SPEAKER_00: I'm curious how your robot crosses over into the neurosciences.
• 21:19
  SPEAKER_01: Well, I think it's a way to express research in motor neuroscience, right?
• 21:24
  SPEAKER_01: It's a simplified form of how you control bodies that you can simulate and and reproduce.
• 21:30
  SPEAKER_01: And so it's a another means of, you know, some of Josh's work actually was uh you know published in in Nature uh on you know motor nervous systems in insects.
• 21:40
  SPEAKER_01: And so, you know, doing work in simulation and then validating some of that work in uh in the real world, robotics provides a an interesting platform for that kind of expression.
• 21:52
  SPEAKER_00: That's really interesting.
• 21:53
  SPEAKER_00: And and and that I mean that does seem to kind of connect back to control labs.
• 21:57
  SPEAKER_00: Is that fair?
• 21:58
  SPEAKER_01: Yeah, yeah.
• 21:59
  SPEAKER_01: I think neuro.
• 22:00
  SPEAKER_01: Neuroscience, I obviously I'm not a neuroscientist, but it's been core to Josh's work and career and to my passion.
• 22:07
  SPEAKER_01: So it's fun to get to kind of connect these ideas back.
• 22:11
  SPEAKER_00: On a very abstract level, like how does that play in here as far as the robot?
• 22:15
  SPEAKER_00: I mean, are we talking around like are we talking teaching the robot to interact with the world?
• 22:19
  SPEAKER_00: Are we talking about sort of like teleoperations or just how the robot?
• 22:23
  SPEAKER_00: I mean, like with insects, I'm thinking about how the robot just functions on kind of a very basic level.
• 22:29
  SPEAKER_01: Yeah.
• 22:29
  SPEAKER_01: So from the beginning, we've taken, I would say, a fairly modular approach to our kind of AI stack, the way the robot moves in the world.
• 22:40
  SPEAKER_01: You know, motor control policies are trained at a low-level reinforcement learning, largely in simulation, some training from video and other sources.
• 22:49
  SPEAKER_01: Um, but then we've built a whole mapping and navigation stack, which also leverages models that we've trained, but is not trying to connect all of these concepts into a single uh single model that kind of rules them all.
• 23:02
  SPEAKER_01: And in part, that's a uh philosophical choice about the moment in time and how to get something deployable and working.
• 23:10
  SPEAKER_01: It's also a choice about the type of developer experience we can offer, where having modular systems then allows our customers to separate and work on the pieces that are most interesting and useful to them while relying on components that are are kind of hardened and usable off the shelf.
• 23:27
  SPEAKER_00: Yeah.
• 23:28
  SPEAKER_00: Okay, so deployable and working gets back to something that you were talking about before, because again, two years in terms of what we're talking about now is not that's not a lot of time, right?
• 23:38
  SPEAKER_00: It's it is certainly not a lot of time to like build up to scale company to get to where you are to actually like announce this thing and and I guess like I guess launch it as well.
• 23:49
  SPEAKER_00: Um and it sounds like part of uh both of your and Josh's desire to really to move as quickly as possible.
• 23:58
  SPEAKER_00: And and maybe some of that was a little bit of a backlash from getting acquired by like a massive company where it's a lot more difficult to be nimble.
• 24:08
  SPEAKER_01: I don't know if it's so much that, but I think the the freedom we had definitely let us let us move quickly, uh, align.
• 24:17
  SPEAKER_01: You know, we've got a really great team here that is spread across, you know, hardware development, AI model, you know, research, software development, human-robot interaction.
• 24:27
  SPEAKER_01: So we have a bunch of different disciplines and getting that group to kind of all row in the same direction and align behind a mission and and do so at at you know very rapid pace, I think is something that's definitely been special about the you know, culture world building.
• 24:43
  SPEAKER_01: It's part of what makes it fun to be here and operate how we are.
• 24:46
  SPEAKER_00: Timelines are really weird around humanoids.
• 24:49
  SPEAKER_00: Um, this is something that I've been trying to kind of wrap my brain around because at the same time, companies are pushing to be as like quick as possible, but also they're incredibly long and abstract.
• 25:02
  SPEAKER_00: From from the quick side of things, uh you know, I won't name any names, but there's a handful of companies that come to mind that have been like, how quickly can we get develop a robot that can walk, right?
• 25:15
  SPEAKER_00: Like, and then that's gonna be our press release is going to be it took us six months to build a robot that can walk, right?
• 25:22
  SPEAKER_00: Okay, you know, like that's impressive, but like when are you shipping?
• 25:26
  SPEAKER_00: When's it gonna be out there?
• 25:28
  SPEAKER_00: And and that's like much more abstract down the road.
• 25:31
  SPEAKER_00: And nobody really has an answer to that question, right?
• 25:33
  SPEAKER_00: I mean, we just again, I I just um interviewed the head of the Atlas team, and he was like very upfront about their their ML team.
• 25:44
  SPEAKER_00: And when it comes to like dexterous manipulation, there's a very broad range in their own room about how long it's gonna take to really kind of kind of crack some of these things.
• 25:54
  SPEAKER_00: So this robot that you're launching right now, this this developer robot, this this research robot, um this is a way for you to go to market.
• 26:07
  SPEAKER_01: What I'm hearing from you is two things.
• 26:09
  SPEAKER_01: One is uh there are companies that have specific research ideas that they are eager to express because they are a core innovation in the field of robotics, and getting something out there that shows they have made a meaningful contribution in the space sometimes requires building a whole robot to do that.
• 26:29
  SPEAKER_01: That's an unfortunate state of the world, and part of what we've tried to achieve is you know, we know that robotics is going to require thousands of new innovations to deliver humanoid robots into the home and everywhere people are.
• 26:42
  SPEAKER_01: How can we create a platform that allows a research team with a really brilliant insight into training world models or VLAs or whatever it is, express it on a platform that already exists, that's stable, as a is is safe to experiment on, a piece of what we are trying to accomplish with this.
• 27:03
  SPEAKER_01: Uh the second element is you know, timing of kind of announcement relative to progress.
• 27:09
  SPEAKER_01: And you know, the project for us is very far from done.
• 27:12
  SPEAKER_01: I would not say this is extremely early, early days, but it was our our goal to get robots out to customers before making the announcement, to be actively shipping robots before you know announcing and and really sharing what we're up to with the world.
• 27:28
  SPEAKER_01: So wanted to be in a place where we could actually start to scale and expand out from a commercial perspective before you know kind of uh making too much noise about it.
• 27:38
  SPEAKER_00: Yeah, I guess the the the distinction I would make like with with with timelines, like what is what does done mean, right?
• 27:45
  SPEAKER_00: I mean, hopefully it's gonna be a long time before before before you're done, but there's done and then there's like shipping product.
• 27:51
  SPEAKER_00: Like who knows like when any single company is going to have a reliable industrial robot that that's that's operating out there, right?
• 28:00
  SPEAKER_00: So quick to build these systems, you know, we see them in promotional videos, but actually like having them in the real world, like that's gonna take a lot a lot of time to scale them, right?
• 28:11
  SPEAKER_00: To make them at a point where you can like reliably manufacture them and have them out there.
• 28:16
  SPEAKER_00: And this is obviously the first stop in what are going to be, as we said before, many robots.
• 28:22
  SPEAKER_00: And this is a way to um establish yourself as a company and to ship to ship a robot, to get a robot in people's hands.
• 28:34
  SPEAKER_01: Yeah, and I think that that experience is is what we're looking for.
• 28:38
  SPEAKER_01: I think it's what matters to to robotics maturing.
• 28:42
  SPEAKER_01: You know, part of that is supply chains, manufacturing, reliability, service models.
• 28:48
  SPEAKER_01: All of those things, you know, are nascent or non-existent in in this industry.
• 28:53
  SPEAKER_01: And you know, part of the kind of early, early phases of iteration are certainly on how do we build out that muscle alongside better understanding uh of use cases, you know, and development of more advanced capabilities that you know earn our spot in new markets.
• 29:11
  SPEAKER_00: Yeah, I mean, I should say like I'm saying that I'm saying this as a positive thing that you're getting robots in people's hands.
• 29:16
  SPEAKER_00: And I'm saying this because, like, you know, again, I'm not gonna name any names, and this is partially because I don't know who is the right answer to this question, but there are almost certainly companies, rob uh humanoid companies that we're seeing right now that have announced products that we have seen that won't come out because you know it is such a hard problem to solve, right?
• 29:37
  SPEAKER_00: And you've identified a space in the market, you've gotten a robot in people's hands.
• 29:43
  SPEAKER_00: Years ago, when Melanie Weiss was still at Fetch, she was talking about their research robot.
• 29:51
  SPEAKER_00: And, you know, she was telling me at the time, like essentially, like, this thing's a loss leader, right?
• 29:55
  SPEAKER_00: I mean, we are not making money off of this, but it makes well, you do this one because Wheel Garage doesn't exist anymore.
• 30:01
  SPEAKER_00: So somebody has to do this damn thing.
• 30:03
  SPEAKER_00: And then two, we're doing this because, like, you know, you get people working on our platform, they get used to our platform, and they're gonna be really good at working on fetch robots in the future when like they actually enter the job market.
• 30:15
  SPEAKER_00: So you get your foot in the door with this system, you build more robots down the line, and people are used to programming, you know, with your AI on your platform, and you're really well positioned for the future.
• 30:29
  SPEAKER_01: Yeah, I mean, I think about how uh so many of us grew up on Apple computers in schools and the familiarity that you know our generation now has with that as a you know workplace platform and personal computing platform.
• 30:42
  SPEAKER_01: And I think the same will eventually be true in robotics.
• 30:45
  SPEAKER_01: And so our hope is to make this at first, you know, university and PhD level research, then undergrad, then high school, and eventually, you know, this will be a key part of how anyone learns computer science and and you know how to work in the technology world.
• 31:01
  SPEAKER_00: One of the things that's been really incredible about covering the space, you know, I mean, one, like it's still a relatively small world, you know, and and you still like like you said, you know, you and I talked uh not too long ago, and I end up just and I love it, but I ended up interviewing like some of the same people like over and over again in you know in different companies and they and they make those moves.
• 31:23
  SPEAKER_00: And I used, you know, I always said this about uh tech journalism, like be nice to people because you're gonna end up working with them again, you know, your your competitors are gonna end up working with them.
• 31:33
  SPEAKER_00: And and the the the same is definitely true in robotics.
• 31:36
  SPEAKER_00: And I found that like on a whole, everybody seems pretty supportive.
• 31:42
  SPEAKER_00: You know, there is some competition, but I think everybody sees there's enough space in the market um and there are enough places where companies can kind of like work together to build things up.
• 31:56
  SPEAKER_00: And you know, I'm looking at a product like this and I'm thinking about again, hugging faces of the robot as like an open source repository like GitHub.
• 32:04
  SPEAKER_00: You know, there's some NVIDIA hardware on here.
• 32:06
  SPEAKER_00: So like Jetson as a developer platform, um, their simulation.
• 32:12
  SPEAKER_00: I know that you're working with Ross here as well.
• 32:14
  SPEAKER_00: Obviously, like you need to know Ross, right?
• 32:17
  SPEAKER_00: You need to know Ross to work in robotics.
• 32:20
  SPEAKER_00: I guess this is a way in which it's not like Apple like you can't right now operate in a fully closed ecosystem in robotics.
• 32:27
  SPEAKER_00: That's right.
• 32:28
  SPEAKER_01: Yeah.
• 32:28
  SPEAKER_01: And so I mean I think there are a few things there.
• 32:31
  SPEAKER_01: One is from an ecosystem perspective, you know, a rising tide lifts all ships, and we are in a phase of the industry where innovations anywhere help everyone, right?
• 32:44
  SPEAKER_01: Seeing what's possible, uh, you know, that sort of collaboration across industry, I think is really important because we all want to see a world where robots can help us in our in our factories and warehouses, but also in our daily lives.
• 32:57
  SPEAKER_01: And and nobody's solved that yet.
• 32:59
  SPEAKER_01: So, you know, collectively starting to solve that problem, and we're making our contribution to that, I think is a uh a key piece of it.
• 33:08
  SPEAKER_00: Aaron Powell What does it mean to make an accessible robot?
• 33:10
  SPEAKER_00: Um, you know, there's like there's accessible for UCSD and NYU, and they but they I assume have deeper pockets than you know somebody who's like a recent graduate from those schools who wants to sort of just go out and start their own startup.
• 33:24
  SPEAKER_01: Right now the robot costs about$50,000.
• 33:27
  SPEAKER_01: So, you know, not inexpensive for an individual, certainly, but for an institution that's that's focused on this sort of work and relative to to other platforms that are available, it's a you know a very, very achievable price point.
• 33:40
  SPEAKER_01: But uh another piece of that is you know, we layer a lot onto it so it's not just hardware you're getting out of the box.
• 33:47
  SPEAKER_01: It's a a developer experience that includes you know uh infrastructure that supports simulation work, that supports you know training models, that you know, mapping and navigation stack, voice interaction, all these pieces get get layered into a kind of a single system, and we keep updating that software, and you always get the the software updates from there.
• 34:07
  SPEAKER_01: What I would say is we're operating at at low volumes today, and and our ambition is to ship many more.
• 34:14
  SPEAKER_01: And I think you know, driving cost curves down to where this is well under$10,000 is is very achievable in the fairly near term.
• 34:22
  SPEAKER_01: And so that's very much our goal.
• 34:24
  SPEAKER_01: And and I think we will continue to drive the product in a direction that is lower cost and more available uh and more accessible uh to a broad audience of developers.
• 34:35
  SPEAKER_00: So, so you know, if I if I'm looking to launch a robotics startup, may or may not be, you know, um and and and I want to like, you know, uh say like uh deploy an LLM on a system for like voice commands, or you know, I want to start building out like VLAs or or VLMs.
• 34:56
  SPEAKER_00: Is is that that's who you're going after with a system like this?
• 34:59
  SPEAKER_01: There are a range of customers that is uh among the set of use cases that would be a great fit for this platform.
• 35:05
  SPEAKER_01: Yeah, we have AI research labs that have a commercial product in world models or or other uh other things and are looking to start experimenting in robotics.
• 35:16
  SPEAKER_01: And this is a very, again, accessible platform, not just from a cost perspective, but from an ease of use perspective.
• 35:22
  SPEAKER_01: It's something that you could have in an office.
• 35:24
  SPEAKER_01: It sits on a little chair, it's you know, can sit right by your desk, and it's fairly approachable to get started with, even if you are new to robotics.
• 35:32
  SPEAKER_01: But then we have people at the more at the application layer who are thinking about, you know, uh a retail experience, or like you said, changing, changing the voice models and doing more character-based experiences.
• 35:45
  SPEAKER_01: And so some are doing that sort of deep VLA type research, and others are are more experimenting with the experiential aspect of robotics.
• 35:54
  SPEAKER_00: So when you talk about retail, are we talking about like Alderbon was attempting to go after with Pepper, like that kind of thing, as far as like greeting people?
• 36:03
  SPEAKER_01: Yeah, I think the the kind of uh I guess industry term would be greeter and guide.
• 36:07
  SPEAKER_01: Someone who welcomes you in, shows you around, has awareness of of space, so it can actually move around with you, can connect to a CRM system so it kind of understands the customers and their needs, can understand the space and what's available and and connect the dots on a lot of those things.
• 36:24
  SPEAKER_00: I start to develop these models, I start to develop AI, I start to collect data, and then I then I build my hardware based on those models.
• 36:32
  SPEAKER_00: Is that kind of the rough idea?
• 36:36
  SPEAKER_01: Yeah, so I I think it actually would be you know even more straightforward than that.
• 36:40
  SPEAKER_01: Out of the box, you get you get the hardware, you get uh you know, voice interaction, uh reasoning capabilities, mapping and navigation.
• 36:49
  SPEAKER_01: So if you were deploying it in a in a store, you could build a map of the space by driving the robot around, uh, you know, give it various locations that it can show customers uh objects, build that into the context window of the AI model you're working with, and very quickly be off to the races in terms of you know building out a proof of concept experience at least, and then you know, kind of maturing it from there.
• 37:16
  SPEAKER_00: Yeah, I guess that's kind of what I'm getting at is like, you know, uh are you eventually white labeling this product?
• 37:22
  SPEAKER_01: I think we're certainly open to it and spoken to a number of kind of uh what you would call channel partners who are interested in applications in various industries and would look at commercializing them directly and running the operations of those deployments in in various spaces, like retail could be an example of that, or the kind of greener and guide uh use case.
• 37:46
  SPEAKER_01: You know, I think eventually we are particularly interested in the home and doing that uh directly ourselves.
• 37:52
  SPEAKER_01: But the, you know, in in many industries, and again, starting with a general purpose platform should allow for broader deployments.
• 37:59
  SPEAKER_01: And I think we'd be excited to work with partners uh across a range of those industries.
• 38:03
  SPEAKER_00: Yeah, I'm I'm glad you use home and eventually in close proximity to one another.
• 38:08
  SPEAKER_00: Like that's a very honest way of honest way of putting it.
• 38:11
  SPEAKER_00: Um because you know, it is we we talk about like scaling, like that's what's gonna bring the price down, and and you know, and this is why people are going after industrial before the home, right?
• 38:24
  SPEAKER_00: Like these are relatively easier problems to solve.
• 38:28
  SPEAKER_00: When when you talk about going after the home, are you talking about like a like a companion robot?
• 38:35
  SPEAKER_00: Are you talking about you know, a like a useful robot doing, you know, sweeping up around the house?
• 38:43
  SPEAKER_01: Yeah, sorry.
• 38:44
  SPEAKER_01: I mean, I I guess a few points there.
• 38:46
  SPEAKER_01: I I would challenge that industry is an easy use easier set of use cases.
• 38:51
  SPEAKER_01: I think the level of of precision, operational resilience required in those settings is is extremely challenging.
• 38:58
  SPEAKER_01: Um, you know, you don't have to deal with some of the unstructured environment and and human interactive elements, but you do have to deal with high precision, high repeatability, and a bunch of other challenges.
• 39:09
  SPEAKER_01: So I wouldn't minimize that, but I would say that the opportunity is 10% of labor is in in manufacturing, 80% exist in in service sector jobs, jobs that interact in, you know, people in human environments.
• 39:23
  SPEAKER_01: And so, you know, there's a tremendous opportunity, you know, with aging workforces and and kind of specific sector sector level shortages in in ability to hire.
• 39:34
  SPEAKER_01: So I think there's a there is a massive opportunity.
• 39:36
  SPEAKER_01: Um in the home, I do think that there's a great opportunity for a companion and light utility robot, something that can grab you a drink from the fridge, tidy up, help out around the house, uh, and and be a you know cheerful and enthusiastic companion.
• 39:54
  SPEAKER_01: Uh I think it'll be some time before we get to the thing we all want, which is, you know, cleans, does the dishes, cleans out the gutter.
• 40:01
  SPEAKER_01: These are hard, hard challenges, but part of what we're trying to build is a platform that allows you know others to start solving that and and we'll solve some of that ourselves as well.
• 40:10
  SPEAKER_00: Yeah, I I like it.
• 40:11
  SPEAKER_00: You're kind of um, it's kind of like a Tom Sawyer, like getting the neighborhood kids to to paint the fence, sort of like almost like crowdsourcing to like let's solve some of these bigger, these bigger problems together.
• 40:23
  SPEAKER_00: You've created this developer platform, you're getting your system out there, you're having people like collect data and solve problems like specifically for this robot.
• 40:32
  SPEAKER_00: And in a way, you know, as they're working to as they're working on their own things, they're also kind of like working to maybe help you out down the road.
• 40:41
  SPEAKER_01: It's very hard to say what this system looks like long term, but obviously the idea of a skills or an app store where you can actually engage a developer community in the way a product gets expressed out to the world would be would be really exciting, and I think is definitely possible.
• 40:57
  SPEAKER_01: You know, it it shifts the the kind of responsibility of what works and what doesn't work uh around in a in a way that needs to be figured out.
• 41:05
  SPEAKER_01: But you know, I think that would be uh very much an exciting idea for for us to pursue.
• 41:10
  SPEAKER_00: What is your relationship with data, right?
• 41:12
  SPEAKER_00: Because that that's like the big, that's the big question mark in a lot of this, right?
• 41:16
  SPEAKER_00: That's that's one of the biggest problems with dexterous manipulation right now, right?
• 41:21
  SPEAKER_00: And that's the big problem with these robots actually operating the real world, is there just isn't that much data out there.
• 41:26
  SPEAKER_00: If you get, you know, the more robots you have out in the world, the more data you're collecting.
• 41:30
  SPEAKER_00: Are you actively or will you be actively collecting that data from these robots?
• 41:36
  SPEAKER_01: We don't proactively collect data from our customers' robots.
• 41:39
  SPEAKER_01: Uh, you know, our customers' data is their own, uh, and and that's a key piece of the value prop.
• 41:45
  SPEAKER_01: There's no kind of Faustian bargain for returning data back to us.
• 41:49
  SPEAKER_01: We have a number of partners we work with also for data collection that's kind of uh a dedicated line, and we have a lot of data on our platform that will be useful to us.
• 42:00
  SPEAKER_01: Um, but data comes in a lot of forums, and I think a key piece of data we'll get over the next year from everybody is the is the kind of experience and product data of how this robot gets used, what what works, what doesn't, et cetera, what use cases catch on, which don't.
• 42:14
  SPEAKER_01: And I think that can then guide more focused application of large-scale data collection in a way that could be quite useful.
• 42:22
  SPEAKER_00: How's how's your team doing on the dexterous manipulation front?
• 42:25
  SPEAKER_01: I would say very early days.
• 42:27
  SPEAKER_01: It it hasn't been our uh core focus.
• 42:31
  SPEAKER_01: Um, again, we in getting to a platform that was was broadly useful, you know, making sure it could effectively get around core locomotion capabilities.
• 42:39
  SPEAKER_01: It can sit in a chair, it can get back up, it can get down to the ground, get back up.
• 42:44
  SPEAKER_01: Uh, it has full-body teleoperation out of the box.
• 42:46
  SPEAKER_01: So it comes with uh an app for a quest headset, and you can go in and pilot the robot directly.
• 42:52
  SPEAKER_01: It has a full mapping and navigation stack, it supports voice interaction, uh, long horizon reasoning and tool calling.
• 42:58
  SPEAKER_01: So we've built all of this in as kind of a developer framework.
• 43:02
  SPEAKER_01: And we'll do some of the manipulation work in-house, uh, but we also are excited about our customers who are who are building that sort of work.
• 43:10
  SPEAKER_01: I think we'll continue to focus on things like you know opening and closing doors, interacting with elevators, you know, basic object manipulation, which we can provide again at a platform level, such that our customers can then integrate it into broader workflows.
• 43:24
  SPEAKER_00: This is an interesting thing that I've seen happen as um as as robots are getting more mainstream coverage.
• 43:30
  SPEAKER_00: Um, I saw it happening around the One X robot a lot.
• 43:35
  SPEAKER_00: And I think that there are um maybe misconceptions or misunderstandings around teleoperation, you know, and this idea that maybe teleoperation is like necessarily like cheating with a robot if it isn't like fully operating uh autonomously all the time.
• 43:51
  SPEAKER_00: Obviously, teleoperation is is going to be a big part of training data.
• 43:55
  SPEAKER_00: It's gonna be a big part of collecting data.
• 43:57
  SPEAKER_00: As we're seeing your robot in its current state in Iraq.
• 44:01
  SPEAKER_00: You know, it it to what degree is it?
• 44:05
  SPEAKER_00: Is it operating autonomously at all at this point?
• 44:08
  SPEAKER_00: You know, what degree is it teleoperated?
• 44:10
  SPEAKER_01: It comes out of the box with full teleoperation capability.
• 44:13
  SPEAKER_01: So that is one thing it supports.
• 44:15
  SPEAKER_01: It also supports uh autonomous mapping and navigation.
• 44:19
  SPEAKER_01: So you can drive the robot around, build a map of its environment.
• 44:22
  SPEAKER_01: Uh, it can build a semantic layer of of that environment.
• 44:26
  SPEAKER_01: So recognize refrigerators and couches and tables and chairs, and and you can reference those things and it can navigate among its environment to do that.
• 44:34
  SPEAKER_01: It can recognize people and remember things about them, address them by name.
• 44:38
  SPEAKER_01: And so we've built in that sort of autonomy out of the box and we'll continue to layer on things again, like, you know, grab me that drink from a fridge or something like that.
• 44:47
  SPEAKER_01: And and that's exciting to us.
• 44:49
  SPEAKER_00: We were talking a little bit about value prop before, you know, and and 50,000.
• 44:53
  SPEAKER_00: Like it's it's not, I mean, it's it's not a lot for a humanoid, you know, it's a it's a lot, you know, for maybe for the average person.
• 44:59
  SPEAKER_00: But you know, I but I guess it's all contextual.
• 45:02
  SPEAKER_00: And, you know, if your frame of reference is, for example, like a low in unitary robot, it's maybe a more difficult sell.
• 45:10
  SPEAKER_00: But if those right now are people's primary frame of reference as far as like a ro a humanoid robot that I can currently buy, what is the selling point for this system?
• 45:22
  SPEAKER_01: One, I'd say it's important to make uh an apples to apples comparison because there's a different price listed on the website than what you actually pay when it arrives at your door.
• 45:30
  SPEAKER_01: And I think from a from a price standpoint, we actually are quite quite competitive.
• 45:33
  SPEAKER_00: You're talking like shipping, tariffs, things like that, or you're talking about upgrades.
• 45:37
  SPEAKER_01: Yeah, and getting grippers, for example, included in the robot, then it's substantially more expensive.
• 45:42
  SPEAKER_01: So our robot comes gripper included.
• 45:45
  SPEAKER_01: But what I would say is that you know we offer a dramatically different developer experience.
• 45:50
  SPEAKER_01: So one is a robot that is aesthetic, that is much safer to be around, that is soft to the touch, engaging, has articulated eyebrows, articulated head, LED array around the face.
• 46:01
  SPEAKER_01: So it's something where you can prototype a categorically different set of interactions and deploy in different spaces than what's possible on other systems.
• 46:09
  SPEAKER_01: And then building in uh a set of capabilities that are available to developers out of the box.
• 46:14
  SPEAKER_01: So uh at risk of repeating myself, mapping and navigation via a teleoperation app, uh, core, you know, locomotion primitives, voice interaction.
• 46:24
  SPEAKER_01: These are things that are well documented, Dockerized, can deployable on the robot, which comes with a you know uh Jetson Orin uh NVIDIA uh computer on board.
• 46:35
  SPEAKER_01: Um, we leave plenty of headroom for the developer themselves to build and extend applications, but it comes with a lot of core software that works out of the box.
• 46:43
  SPEAKER_01: And so that's not the kind of you know uh developer experience you get uh with with other platforms.
• 46:49
  SPEAKER_01: And also the the service model is quite different because you know we sell directly in the US and around the world.
• 46:57
  SPEAKER_01: And if you have a problem, you speak to us directly.
• 47:00
  SPEAKER_01: And that's you know, uh I think categorically different.
• 47:04
  SPEAKER_00: Is that scalable?
• 47:05
  SPEAKER_01: Yeah, I think the reasons that that others work through resellers is is not necessarily scalability, but uh you know geographical restrictions.
• 47:13
  SPEAKER_00: Yeah, that's fair.
• 47:14
  SPEAKER_01: So so so for us, I think long-term having direct relationships with our customers is very core to what we do, and and over the next year as we scale, uh making sure our earliest customers are extremely successful is is the only thing I care about.
• 47:28
  SPEAKER_01: Um and so we'll be very focused on making that a great great experience as we scale.
• 47:33
  SPEAKER_00: And you're already talking about international sales?
• 47:35
  SPEAKER_00: We have made international sales.
• 47:37
  SPEAKER_00: So you're growing quickly, it sounds like already at this point.
• 47:40
  SPEAKER_01: We're moving as quickly as we can.
• 47:41
  SPEAKER_01: It's early, it's early days, but but we are are moving as quick as we can.
• 47:45
  SPEAKER_00: Great.
• 47:46
  SPEAKER_00: Well, thank you so much for taking the time.
• 47:49
  SPEAKER_01: Absolutely.
• 47:49
  SPEAKER_01: Yeah, very excited to share a little bit about Sprout and Fauna Robotics with you and and for developers to get our their hands on this.
• 47:58
  SPEAKER_00: Thank you so much to Rob and Fauna Robotics.
• 48:00
  SPEAKER_00: The company is doing some very cool things.
• 48:02
  SPEAKER_00: Highly recommend you check them out.
• 48:04
  SPEAKER_00: Uh, thanks to you so much for tuning in.
• 48:06
  SPEAKER_00: If you're enjoying the show, don't forget to like and subscribe and check out our sister newsletter over at automated.fm.
• 48:15
  SPEAKER_00: We have a bunch of very cool interviews lined up.
• 48:19
  SPEAKER_00: Uh we've got uh Gary Cohen, the CEO of iRobot.
• 48:24
  SPEAKER_00: We've got uh Ali Kushani from Serve Robotics, we've got Bosta Dynamics, physical intelligence is coming up, uh, Kenisi plus one, Matic.
• 48:35
  SPEAKER_00: Uh so stick around.
• 48:37
  SPEAKER_00: And with all of that, uh, we will see you just about this time next week for another episode of Automated.