Speaker 1We built a robot that can lift up to 110 pounds, work in hot environments. The tasks we're looking at doing early on are not tasks that are easy to hire for. They cannot find enough people to work in these jobs. I'm a product person. I care about delivering real value to customers. It provides positive ROI because I don't believe that humanoids will ever become ubiquitous in society if we can't prove that. Our plan for deployment of Atlas is to start deploying actual pilots out in the world with customers in 2028.
Speaker 2It's kind of wild to be at this stage where we're talking about early deployments of the system and some of the really deep, important things still haven't been cracked.
Speaker 3Yes. As a pragmatic product, it must keep you up at night sometimes.
Speaker 1It definitely does. There are parts of the problem that haven't been solved yet.
Speaker 2There's so many moving parts. There's so many variables here. There's got to be at least some acceptance that things are going to shift, things are going to move around a bit.
Speaker 1I mean, they could, but they could also...
Speaker 2Hello, and welcome to Automated. I'm Brian Heeter, the Managing Editor at the Association for Advancing Automation. Very excited to bring you this conversation with Aya Durbin, the Director of Product for Atlas. This was recorded at Boston Dynamics HQ. Aya was previously at Six Riverside. She has a long history of productizing industrial robots, and I think that is very much reflected in this pragmatic conversation about what it will ultimately take to get humanoids out into the world. If you're enjoying the show, don't forget to like and subscribe. Check out our newsletter over at automated.fm. And with that, here is Aya Durbin of Boston Dynamics. You know, we talk a lot about what's coming up next in automation on the show, but if you really want to see the show, you've got to be there in person. Automate 2026 is where the world's leading innovators, builders, and dreamers come together to show you what's possible. Robots, AI, machine vision, motion control, you name it, all automation under one roof. And as part of Automate this year, the Humanoid Robot Forum brings together leaders, engineers, and researchers for a two-day deep dive into the real-world development, deployment, and commercialization of humanoid robotics. Register for free today. Free at automateshow.com to join us in Chicago June 22nd through the 25th. We will see you there. You're a pragmatist who has been converted into a dreamer.
Speaker 1I am. I would say I'm a dreamer that still is a pragmatist at heart, but working at Boston Dynamics has definitely proven to me that the dreams can become valuable customer solutions. Like, ultimately, that's why I'm a pragmatist, because I'm a pragmatist. I'm a product person. I care about delivering real value to customers that provides positive ROI, because I don't believe that humanoids will ever become ubiquitous in society if we can't prove that. I think the way in which I've become a dreamer is seeing here at Boston Dynamics what it looks like to transform a whole robotics solution to an AI-based approach and to have the team so focused on proving that that AI-based approach can actually provide value to customers has made me think more outside of the box of how we take a fully AI-based system and translate that into something that customers can train, instruct, and get to do what they want it to do in their workforce. Like, ultimately, humanoids have to be a trusted and reliable member of the workforce, and the team here has made me a dreamer because they've proven to me over and over and over again that it's possible to do that in new ways that we've never seen before in robotics. So it's been a very fun few years here. I see that kind of come to life.
Speaker 2We're not going to... You know, we're not going to, like, obviously, like, you know, beat the dead horse of legs versus wheels because that's been spoken about a lot. And you can... People can listen to you talk about that on one of the webinars that you did. But I think that that is an interesting distinction here because you do have to be a bit of a dreamer in order to be on team leg because that is much more of a long-term solution, right? I mean, if the goal is to, hey, let's get to ROI, and let's get these things out in the field as quickly as possible, like, you're going to put wheels on them, right?
Speaker 1I think my answer might be different if I was somewhere else. But at Boston Dynamics, this company has been focused on mobility for 30 years. And so the one thing walking in the front door on my first day at this company three years ago that I noticed was how incredibly these robots moved. Like, I worked at a company that had a wheel-based robot. And the way that Spot moves around the world... And we have a wheel-based robot here. The way that Spot moves around the world is like nothing you've ever seen before if you haven't seen it. And it moves about the world reliably. And it can walk up and down stairs. And it can walk on graded floors that, in my previous life, when I was at a wheel-based company, we couldn't imagine working on. So I think walking in the front door here at Boston Dynamics three years ago made me realize, again, like, I'm slowly getting converted into this dreamer mentality. Like, we really can move about the world differently with legs than you can with wheels on a robot. And we've done it with Spot. We've been working on humanoids for years. And so, to me, Atlas having legs is a no-brainer for us at Boston Dynamics because that's something that we've already been working on for years and years. And so we do have the bandwidth to focus more on manipulation, which I think is the hardest challenge in bringing humanoids into the world and don't have to spend as much time on, like, the leg part of the problem or falling or whatever.
Speaker 2Yeah, okay. So I guess let me broaden it then because... And you're somebody who's coming from industrial robotics. You're at Six River Systems and Shopify. So, you know, in a sense, like, you are more familiar with the more, I guess, traditional systems. You're saying that your family, like, finally believes that you're in robotics because you have something that looks like a robot. But, you know, if it's a purely pragmatic goal of getting this into the market as quickly as possible, then it's going to be, like, a fixed, repetitive system. You know, like, how can we get this robot out there and then do the long term? But, like, you're focused on, with Atlas, you're focused on the long tail things at the same time.
Speaker 1Yeah, which is not something that traditional robotics has ever really been able to do successfully. But I think in a world where AI for robotics actually does rapidly expand our capability set for the robot, you have to be thinking about all things. You have to be building out... You need to have a research team that's building out the models that enable you to be a generalist robot. That enable your customers to easily train the robot to do any task in their facility. To be able to easily retask a robot to do any job in a facility easily. But also, you can't lose sight of the fact that you need to get that robot into a facility soon to start doing real work. Which is why we have been focused on sequencing and logistics tasks that are semi-repetitive, but they're not just pick and place to a conveyor over and over again. We're not doing tote movement. We're doing something where we're working with many variants of car parts. Or many variants of an item in a warehouse. And picking and placing those parts based on the instructions of an external customer system. Dealing with exceptions like no inventory. Dealing with problems like where there's no barcode on a part. And so all of those kind of nuanced, smaller problems are the questions that customers are asking us how we're going to solve. Because they've seen us solve it before with Spot. They've seen us solve it before with Stretch. And that's something that's important for us to also be working on right now. That's something that's important for us to also be working on right now. The sexiest research problem. There are problems that have been solved before. But you have to work on the application part of the problem in addition to the long-tail research generalist part of the problem. And the goal is that they come together. The goal is that we build one platform, one system, one set of foundational tools that enable this long-term vision. So that, you know, you start with that first application on top of a foundation that enables you to quickly expand within and beyond that application.
Speaker 2Yeah. Spot, obviously Spot was a big learning for the company in a lot of ways. You know, the first commercial product after like 20 to 30 years. You know, I think one of the big learnings, and I'm curious to get your take on this. One of the big learnings is definitely like, hey, maybe we can't quite do an iPhone model here. You know, maybe we can't expect the customers to maybe do all of the work of like the programming of all of the skills. And then the other is that we really need to enter the market. We need to be focused with a single or at least a few tasks that we can do really, really well.
Speaker 1Yeah, that's definitely something we learned on Spot. And we still have customers. You know, we have exposed APIs on Spot. And we have customers that build their own applications. We have customers that build their own functionality on top of Spot. But for the most part, the people that buy Spot buy it for industrial inspection to make sure that their equipment doesn't fail. They buy it for safety use cases to do nuclear decommissioning and bomb disposal. Or they buy it for academia. They build on top of it. And so there are other use cases for Spot. You know, people are using it more and more for security, for construction. But again, when we move into those use cases, we move into security use cases for Spot or into more construction use cases for Spot. We do it intentionally with partners that are good at understanding what it is they need out of a product to make it successful. So that when we deploy into that space, it's something we can copy and paste and bring to the next customer with a product that's a little bit better. Because ultimately, when you're bringing a product. into a new market to do a new task. ask or do a new job, you have to start somewhere, give them a minimum thing that we think will work and then learn from them and make it better. And that's going to be true for humanoids too. And the goal is that you can build as many tools and AI as possible to give the customer the ability to build their own features and functionality. But as we've learned from Spot and as we've learned across the robotics business, and if anyone who's deployed any industrial technology knows, you need to be thoughtful about showing improvement in your product in order for it to become a trusted and reliable member of your workforce. And so starting in target markets on target applications with customers that have safety teams, that have security and IT teams already in place that we can learn directly from gives us the best chance of making our product great as quickly as possible for customers so that it does become easier to copy and paste and redeploy. And also that they can help us build those tools and make it easier and easier for them. To build their own stuff or for them to retask the robot?
Speaker 2Yeah, this is something I've talked to a lot of people about. And I don't think anybody has a great answer is, you know, there's a data gap, right? There's not enough real world data. You know, I know that Bot Dynamics is doing a lot of different, you know, obviously like tele-op, real world simulation, video, all of these different things. But the big question right now is, once we start these early deployments, and we're trying to get the kind of and the flywheel of data going, is there a way to incentivize those companies to be a part of that data collection? Like what, effectively, like, is there something in it for the customers themselves to be an early customer, to be a pilot, to be one of those partners who is helping sort of develop the system at an early stage?
Speaker 1Yeah, I mean, for us, we're being intentional about who we work with early on. And those customers that we work with are direct customers. And so, you know, we're trying to get them to be a part of that. And so, you know, we're trying to get them to be a part of that. And so, you know, we're trying to get them to be a part of that. And so, you know, we're trying to get them to be a part of that. And so, you know, we're trying to get them to be a part of that. are directly guiding our roadmap, which is very different than I've been in product my whole career. And that's not typical that when you bring a robot on site, that naturally through teaching the robot how to do work, you're guiding the roadmap, you're guiding the robot capability. And the customers we choose to work with are going to be the types of tasks that the robot is able to be really good at in the early days. And so that in itself, I think, is an incentive to work with the robot. in the early days. And so that in itself, I think, is an incentive to work with, work with us early on, because you are, it's not just we're saying you're guiding the roadmap, we're saying your feedback will guide us, it will, but also, the tasks we teach your robot to do first are going to be the tasks we have the most hours of training on. in the early days. And so the robot is going to be more performant, more reliable, faster, and be able to provide ROI faster. And there is, you know, for us, we built a robot that can lift up to 110 pounds, can lift up to 66 pounds continuously, work in hot environments. in the early days. And so the robot is going to be more performant, more reliable, faster, and be able to provide ROI faster. And there is, you know, for us, we built a robot that can lift up to 110 pounds, can lift up to 66 pounds continuously, work in hot environments. the tasks we're looking at doing early on are not tasks that are easy to hire for, they have high turnover, they cannot find enough people to work in these jobs. And so the incentive for companies is, I can't find people, and it's not just companies, it's operations managers on the floor that are saying, I can't find someone to do this, because it's backbreaking work, it's terrible, if I find someone, it's for two weeks, and then they quit. And so there's also incentive there that's like, I need a solution. And I need a solution now, because no one wants to do this task. And I can't find another way to automate it.
Speaker 2I'm not, I am not suggesting that the production Atlas is over engineered, because like there have been over engineered robots in Boston, IMS history, like, like handle is like over engineered, right. And that's, that's why that's why we got stretched. But it's not over engineered, but it's, it's almost like over capable, certainly for this first set of problems, or the first set of things that it's going to do on the floor. And it seems like it is. That that from a hardware perspective, that it's effectively sort of designed to be a general purpose robot, and that in terms of applications, and AI and capabilities that those things have to kind of catch up to that.
Speaker 1I don't think so the robot design and the specs that we targeted meeting this robot at were designed to actually have the robot do hard work. So yes, it's designed to do a wide variety of tasks, like the height, for example, of our robot is not required. Right. For every single task. It's really tall, it is really solid person, and it's surprisingly tall, it's really tall. But that's because if you go work in a warehouse, I don't want to have to walk up a two step ladder to go reach the top shelf, I want the robot to just be able to reach the top shelf. And so we designed the robot to be able to just easily move about the world that exists. And in terms of its capabilities to lift heavy, and move differently than a human moves, those specs were designed to actually have a robot that can do the hardest work in industry, because that's where the biggest impact is. That's where the biggest challenge is for industrial customers. And that's where the biggest challenge is for people like we aren't, we're looking to create robots that make our lives better. And so we wanted to design a robot that could actually do it. We aren't targeting doing lightweight tote movement, we're targeting moving the totes in the building that are 5060 pounds, you're supposed to be using a lift assist to move, but no one's using a lift assist to move them. We want you to remove your lift assist, we want you to save money and space on removing those lift assist. So for us, it wasn't over engineered, because we were trying to engineer a robot that could actually do the hardest work and walk into an environment and just work like I didn't want a customer calling me saying, Okay, I want to move it from, you know, the inbound process in my warehouse to go do picking. And then when you go move it to do picking, I can't reach the top shelf, or I can't reach the bottom shelf, or it can't reach into the back of a pallet, because its arms aren't long enough to balance when it's doing that. And so we designed a robot that met all of those specs. And that's ultimately why you land with something that is as tall as it is has the reach capacity that it has. But it just makes life easier for the robot to be able to do the same things that humans do.
Speaker 2Yeah, and I think I think there's always going to be, there's like, going to be an inevitable bit of confusion as far as like messaging goes, especially with something like a humanoid. You talked about this in that earlier conversation about, you know, expectations that we have going into it, because it looks like us. But then that's further clouded by, you know, we don't have to get into like, Morvec's paradox too much, but Morvec's paradox too much about, you know, like what the robot can and can't do versus people. But, you know, videos of like an engineer, like blowing off Steve and the robot can do a backflip or something. But it's, you know, it's not engineered to do a backflip, but it can do a backflip.
Speaker 1Yep. And that doesn't mean we didn't make the robot higher cost. We didn't make the robot specifically designed to be something that can do a backflip. But the team said, hey, what if, what if it could do a backflip?
Speaker 2Yeah. And I think, I mean, and there is, I don't know, from like a messaging, not a messaging standpoint, but I mean, is there their value in showing like, hey, like, these actuators are incredibly strong, or hey, look at the sense of balance this robot has.
Speaker 1Absolutely, absolutely. And the backflip is a specific example. But a lot of the like, even the round off back handspring that we did in the lab recently, that video is using reinforcement learning to train the robot. And so a lot of the videos we show seem like they are just playing, but we're actually using the core fundamental technology that we use to train the robot in general. And so when customers come in and see the office, and you know, we show them some of the gymnastics moves we do, we show them some of the live industrial work, they, we bring them back in and explain, hey, here's some of the behind the scenes of the videos that we showed. They're, they're very fun. But 99.9% of the time, the fundamental technology that they're using to train the robot is just playing. they're using to do these things is a fundamental technology that we use to unload roof racks, or unload dishes from a dishwasher or do any other industrial task. And so again, pragmatism turned dreamer at Boston Dynamics, like when I used to see the robots doing backflips, when I used to see the robots doing round off back handsprings or doing parkour, I'd be horrified as a product person, like some engineers going out there and working on something that isn't core to the customer need. And so I So I'd run downstairs and say, why the heck are we doing this? And Alberta, who is the head of our behavior team, is one of my favorite people at this company, he's, this is how this is working. And that really starts to transform the way you think about just work in general, like, you can learn and do science and improve your product in more ways in this new world of AI than just doing the same thing over and over and over again. And specifically, when it comes to AI, the more we train the robot to do all sorts of different tasks, the more capable the robot gets over time. And so it's been fun to be at Boston Dynamics and learn kind of what the behind the scenes actually looks like and see from the engineering team, how we can improve the product in ways that are fun and are exciting to show the world.
Speaker 2You just like completely shifted the kind of the dynamic of the conversation of what I was talking about, because that is fascinating to me, like, and seeing it like through your perspective that you came in. And, you know, being a, I guess, sort of like a more traditional product manager coming in and seeing these things, you know, obviously, Boston Dynamics. This is like ephemera, you know, this is fluff. And then you came in and realize that maybe it's more essential to the actual core of the company.
Speaker 1Totally. It's essential to our research process. It's essential to the team morale in general, like applying. We spend a lot of time building core foundational technology that enables customer value. And taking that technology that you built to learn how to quickly work with all new types of automotive parts, or learn how to more dexterously manipulate something, or test whether or not we can pick up something heavy. Like, hey, when we test to pick up what our weight capacity is, we pick up a fun object instead of, you know, just a hundred pound weight. And so it's been fun to be here and see how the team, like this company didn't get good at making viral videos. This company is full of incredibly passionate people who express their passion through robotics, express their passion through staying a Friday at 7 p.m. and testing out a new thing with this technology they just built. And the marketing team got really good at saying, hey, please don't do cool stuff. Unless you've pressed record on the camera. And so a lot of those videos, even in the early days, was someone who was instructed to have at least an iPhone out and be recording when that cool thing happened. And then we just shared people's passions with the world. And I think that's why it's resonated so much with the rest of the world, because you're just seeing people's passion come to life in this robot, which is the best part about working here.
Speaker 2This is so, so, so we got the tour earlier and Nick and I were talking about the last time I was at the Boston Dynamics office. Yeah, the smaller one. And at the time, you know, one of the when you walk around Boston Dynamics, there are a lot of these tweaked out spots, I say, like customized spots. And at the time, the big one was the margarita spot, right? I mean, it was like it was a party spot. It had a blender on the back and obviously really fun. But what I didn't realize until talking today is that there was a pragmatic reason for it and it had something to do with the voltage on the back. And that was like the voltage in order to like crush ice. And that sounds like. And it's similar to what you were saying before about, you know, just the way that this Atlas gets up and down is probably because somebody thought it would be cool to do that. And then it's sort of an actual like real function. Absolutely. And it's there are so many ways,
Speaker 1especially, you know, when you're in this early stage of product development, when you look at something like Atlas, there's so many core things we're testing and core capabilities we're trying to build. And there's so many ways to build those core capabilities. And so, you know, as long as, you know, if we're working on high dexterity tasks, we can pick up something like a screw. I don't care if you're testing with something that looks like a screw, but is maybe more fun than a screw. If you can also show that we can pick up a screw. Like, I think that I love the testing the voltage on the back of spot example, because it's just taking you're solving a problem and the way you solve the problem. I don't care how as much fun as you want, as long as the problem gets solved at the end of the day. And that's what creates such a really cool, culture here.
Speaker 2Yeah. I mean, the other thing too, I mean, you know, I realize this now in hindsight. I mean, I'm glad that we got the tour, but I'm really, I was like, okay, part of the reason why we got the tour before we talked to you is to realize, is to recognize kind of the scope, I think, of what's going on. And, and, you know, that, that all of these things can be true at once, that, that all of, you know, that you can be working on things that are a little further out there and, and these very pragmatic problems at the same time, because there's a lot of teams working on a lot of different problems.
Speaker 1Yeah. And that's, that's what my job is, is to figure out how we take that long-term vision and boil it down into the set of tools and products that we need to build in order to bring that long-term vision to life. And the goal is start building that set of tools now and build all of our applications on top of that core set of tools so that we can more easily expand over time. And it's a very different type of product development. I think robotics in general is changing a lot. Like product development does not look the same as it used to. You used to not be able to focus on more than one application at once. Like my pragmatic brain would have been fried years ago, trying to think about working on something like sequencing and a task like machine tending at the same time. You just couldn't do both. Now you have to be doing both and you have to be building tools that enable you to do both. Otherwise you're not building the product that customers need or that customers expect. And so it's been fun again, to push, kind of more into that dreamer world and think about how you build tools, capabilities for us and for customers to be able to build one system that unlocks a really large potential for the robot. So yes, you're focusing on many things at once, but the goal is actually focus on one set of tools and capabilities that unlock many opportunities. And that's kind of the way we focus at Boston
Speaker 2Dynamics. Yeah, because I think it's a two-way street because like the way that I've seen things unfold, like, you know, as a member of the media is, you know, all of these humanoid are coming out and it's like, hey, general purpose robots, AGI, these things are eventually going to be able to do everything. And then, you know, things settle down a little bit. They're out in the world. And then we start having serious conversations, which is okay. But the reality of these pilots is when they're out there, like we're really going to have to focus on smallish tasks at once and then get
Speaker 1that data and then start to build up from there. I think so. Like, I think that's the best way to approach it. Not necessarily because you can't do everything at once. You could try. But as a customer, like if you think, put yourself in the shoes of any warehouse operations manager or any logistics operations manager, you need to make sure your work gets done and your throughput gets out the door. And that includes the robot being able to handle all sorts of problems that come up for your associates today. And then you have to make sure that you're able to handle all sorts of things. And for the robot to handle all the problems that it's going to inevitably cause because you're using technology now to do this thing instead of a person. And so those kind of niche problems that need to be solved in order to deliver a valuable customer solution need to be addressed. And if you're trying to hear that feedback from tons of customers in different markets and industries all at once, and that could be like, even if you're in the home use case, every single individual person in a home, it's going to use a robot, has a different perspective on how that robot should perform, work, behave, deal with exceptions, deal with problems. And the value for us of working in industrial settings is we can really listen to a core set of customers that are going to give us the best feedback on how to deal with problems appropriately, the way that they expect, and be repeatable and reliable, and then replicate that elsewhere. And so that's one of the things that's exciting about working in an industrial environment for us is just that kind of like streamlined feedback that allows us to build the tools to deal with all the problems that come up every day and build the tools to make sure the robot performs exactly the way you want it to, when you want it to, how you want it to in the environment.
Speaker 2Yeah, obviously, you know, the one of, if not the big selling point of the form factor is that it's brownfield, right? That like, theoretically, you can, you can slot it in and a big, in hindsight, obvious lesson that I've learned since starting at A3 is like, people who run factories really don't like if you have to stop the line for any reason. And, you know, you were at Sixth River for a while. And I suspect that like, even when you had a mature system, it was still really difficult to convince people to integrate that in, let alone this entirely like new form factor.
Speaker 1Yeah, it's, I mean, the amount of work, there's a lot of unsexy thing, things about integrating automation into any environment. And you need, someone needs to instruct the robot to do work. There's a whole team, an IT team that needs to build out new integration points to talk to the robot. That's months and months and months of work for a customer. That's not sexy to talk about. It's not impossible to build, but it's hard work for the customer. And it's something that we need to build easy solutions for customers to deploy with us quickly. So we're talking about massive scale for humanoids. If you want to unlock massive scale, you need to provide customers with the tools to do that. We need to provide customers with a new way of solving this really hard problem that all traditional automation has to solve. Because it is a big investment to introduce automation. What's cool about humanoids is the potential for them to make an impact on businesses is bigger than anything we've ever seen in the robotic space. And so the hope is that the investment that companies will make is, you know, a one time investment that can be replicated over and over again. So they don't need to keep doing all of the upfront integration, IT security, all of that. But yeah, it's a challenging process, not just to build the infrastructure, but to get your operations teams used to working with the robot, to get them used to the interfaces that they use to communicate with it. They can't stand downtime. Like when you first deploy a robot, you are going to have to work through some issues, especially if you're working with humanoids in the early years of our deployment process. We're working with customers to understand this is a process. That is huge, though.
Speaker 2one on the hands, everything else is basically like two actuators.
Speaker 1- Yeah, so we've really, reduced the complexity in the robot and made it easy to service in the field to help with that problem, to help make sure that customers don't have downtime. We've made sure that our batteries are swappable in less than five minutes so that customers have their robots when they need it. Swappable by the robot. Swappable by the robot autonomously. So they run for four hours and then the robot can swap within five minutes all on its own. And then there's all sorts of creative things you can do if you need even more uptime than that. Like robots should be able to help each other and jump in and cover work if there is a problem. And so we've really intentionally designed the system to be there when our customers need it most. And that's another kind of part of the design of this robot that we thought through quite a bit before we started building. Okay, so let's get really
Speaker 2unsexy. Okay. You've got a great owner slash partner in Hyundai in that a huge part of the deployment is going to be them rolling out in their factories. But you move beyond that and then you start talking about system integration. What role does Boston Dynamics play in that integration? How do these things actually get rolled out and incorporated and customized into specific settings for specific jobs?
Speaker 1That's a great question. I think it will depend on the industry and the application. You know, for us in the first few years, we have target industries we want to work in. We want to work with industrial customers doing things like automotive manufacturing, food and beverage manufacturing, semiconductor manufacturing. And then we have the industry that we want to work in. We want to work in warehouses. And so in those markets with those customers, the deployment process will be similar to what it looks like for spot. So you go through a sales process. You work with a solutions designer that really makes sure that the solution you're going to get in your facility is what you expect and that it performs the way you want it to and that you're really going to get ROI from that system before we even show up on site. Okay. But you are ultimately
Speaker 2pretty hands-on there when it comes to making sure that everything works as... Yes.
Speaker 1Especially for spot for our autonomous application. So if we have a spot that's going out there to do industrial inspection or if we have stretch going out to do truck unloading, someone's going to come make sure that what we deliver to you is what you expected and then it works the way you expect it to. Over time, there are so many ways that we could make this process more customer-based. Like if customers want to build their own tools and deploy their own robots, absolutely in the future, that's something that we could enable. So in the early days of deployment, we want to make sure that customers get the exact value that they expect out of the system, get the ROI they expect out of the system, get the throughput and performance and reliability they need out of the system so that we can make sure that the business itself is sustainable. We want to make sure that customers love the product and going on site and hearing from them about what they love and don't love about it will make the product better in those first few years. So a lot of opportunity in the future to open it up and give customers the ability to change what the robot does. And we will need to enable that to enable massive scale. But in those first few years, we definitely will be hands-on in the process because we want to make sure customers get the value that they expected.
Speaker 2So something I wanted to loop back around, something that we were actually talking about before the camera started was reinforcement learning. And I think this, well, this certainly relates to the conversation around Spot and the App Store, how much has to be pre-programmed into the system. Obviously, a lot of these conversations around physical AI right now are, you know, what needs to be hard coded versus, you know, what can be done in AI. I mean, you know, looking at the system going forward, how much, how many of these at least initial applications do you think ultimately are going to have to be hard coded onto Atlas?
Speaker 1None of them. So part of, when we talk about building fundamental tools that make it easy to deploy the robot, easy to train the robot, those are all AI-based tools. So just because we're building foundational technology doesn't mean we're building a hard coded version of sequencing. We are building all of our applications on an AI-based system. But we're making sure that AI-based system can be directly instructed by an external customer system. So if a customer says, I want you to pick this variant that's in this location on the floor and put it into this slot on a dolly, or they say, I want you to find this part in a warehouse and go put it onto this jig at this machine in this other area of my building, we can do that. We can execute the exact way the customer wants. And so it's layering those two things together that is what's going to be crucial to having a good deployment. But it's all an AI-based system that we're building.
Speaker 2Yeah, something that's just really incredible, you know, talking to you, talking to people at this company, talking to people in physical AI, just generally sort of at the forefront. Yeah, I think part of the interesting thing of all this is like, how much of it hasn't been figured out, right? And, you know, I'm saying some offhanded things, people are like, Oh, yeah, we found that too. Or, you know, we're trying to do that. And it's kind of wild to be at this stage where we're talking about early deployments of the system and some of the like, really like deep, important things still haven't been cracked.
Speaker 3Yes. As a pragmatic product manager, this must keep you up at night sometimes.
Speaker 1It definitely does. Yeah. Yeah. Yeah. Yeah. Yeah. There are a lot of problems that haven't been solved. And this is, I mean, being at Boston Dynamics, I'm pulled more and more into the dreamer category every day. But because our dreaming here is really based in reality. So there are parts of the problem that haven't been solved yet. We are heavily focused on the research front on making sure that we're focused first on the core research problems that impact those early customer deployments. There are an unlimited number of problems that haven't been solved. There are an unlimited number of research problems you could solve with humanoids. Customers want this robot to do everything. And my job is to focus us first on the core research problems that actually block us from providing value to customers and ignore the ones for now that we could do. We could make our robot climb up a pole. We could make it climb up a ladder. But is that the most important research problem to be focused on right now? Probably not when it comes to those early customer deployments. So the way that we handle that is to make sure that we're focused on the core research problems. And that's what we're doing. And that's the way I handle that as a pragmatist is focusing us first on the core research problems that you can't succeed unless you solve first and then kind of layering in the longer ones, the longer term ones over time.
Speaker 2So what is I, you know, I'm trying to envision like, I hate I'm gonna say this word, but like what a flowchart, you know, or what like an org chart would look like as far as, you know, industrial customer problems here, all of this like research, you know, obviously, you and everybody else, I'm sure is keeping up with the latest research, things are changing overnight and causing everyone to rethink things, how we're solving these specific problems. I mean, that's, you know, juggling a lot of plates at once. I mean, how are you making sure that all of these sort of like disparate things are all, you know, functioning, I guess, a part of the same funnel.
Speaker 1I mean, we have an amazing leadership team. Alberto Rodriguez, who I've done interviews with before is over a large portion of the team. I mean, we have an amazing leadership team. A really strong head of behavior really helps having really strong leaders that lead each of those research projects really helps. And the people that work on those teams love keeping up with the latest research. They are some of the best researchers in reinforcement learning in behavior cloning, in dexterous manipulation in the world. And this is what they love to do. And so having them kind of all be the best in their field and be under someone who's so passionate about solving real problems for customers helps a lot. Especially as a product person, like for me, having those collaborators on the team, whether it's Alberto, Chris Pencil, we have some really, really great engineering leads on the team that really help to keep the team focused on getting this robot out into the world. Because ultimately, that's what all the engineering team wants to see, too, is all their hard work be used by people.
Speaker 2Yeah, as we were walking around, you know, Nick was talking about like scaling and production and assembly. And I think, you know, I don't know, 30,000. I think maybe that that number was tossed around as far as like how many are going to be produced. But when you specifically are thinking about three to five years, what are you thinking about?
Speaker 1So our plan for deployment of Atlas is to start deploying actual pilots out in the world with customers in 2028. So that's when our robots, we say, will be members of your team and doing the hard work that we've been talking about Atlas doing. When it comes to the scale numbers, you know, Hyundai's committed. To building 30,000 Atlas robots a year, starting in 2030. And that's really a mass production version of the robot that is designed to be really easy to build and gets to cost and gets really to customer ROI. But we'll start piloting earlier than that. And we are certainly already on the journey now of getting our robots out into the real world and getting feedback from Hyundai and other customers that we're working with.
Speaker 2Do you think you have to give yourself some like extra buffer there? Like I know, like, you know, somebody told me years ago. That, you know, when you take a flight, they always make the landing time a little bit later just because, you know, just. to give yourselves that extra time so they can tell you that you like landed a little bit earlier. Like there's so many, there's so many moving parts. There's so many variables here. And I know that it's to a certain extent, it's like a fool's errand to try to think about three years from now in terms of AI and robotics. There's got to be at least some acceptance that things are going to
Speaker 1shift. Things are going to move around a bit. I mean, they could, but they could also move closer. Well, that's what I'm saying. Sometimes the plane gets there early. Yeah. I feel more confident than ever that that 2028 timeline is more than feasible because we're choosing to do a set of tasks that aren't the easiest tasks to do in industry. We're not choosing to do the simplest thing first. We're choosing to do something that we think requires us to build the fundamental tools that enables us to do many things in industry. So when we talk about 2028, we're not talking about doing the most simple thing in a building in 2028. We're talking about a complex, that's hard work, that's repetitive, that's hard on the body, that requires you to do real exception handling and be truly integrated into a customer system. So it's not like we couldn't do work. We can do all types of work now. We don't need to wait to 2028 to pilot. We could pilot right now, whether it be here or with a customer, but we want to work on meaningful work when we go out into the world with customers. We want to work on tasks that are challenging enough that when we say we launch, we're launching and we can then go do lots of other similar types of tasks in the world. So I'm not super worried about the timeline because we're already doing this type of work today. All we're doing right now is improving product performance or improving reliability. We're improving capabilities. So we're adding features and functionality for humans to interact with the robot in a more consistent way and a more user-friendly way. We're adding in tools to deal with exceptions. We're hearing from customers about what's not working about those tools. And so we're not doing something, we're not shooting for something that's impossible in 2028. So I think if you have me saying I feel comfortable with 2028 as a pragmatist, I feel good about 2028. Oh yeah, I know. No, of course. But I do, you know, of course. I don't think it's going to move as fast as like LLM. Like, I don't think the robotics and AI, personally. Well, yeah, hardware is hard. It's hard. The data set's different. And so I think everyone has a different expectation when you say launching, when you say piloting, when you say we're going to have these robots in the world, everyone has a different expectation of what a proof of concept is, of what a pilot is, of what a launch means. And for us, it means starting to add real value in a customer facility. And for some people, that means, you know, millions of robots in the world by 2028. And so some people it means two robots. Yeah, to some people it means two robots. And so I, I feel very comfortable with the timeline of 2028. What I'm more concerned about is what the public's perception of these timelines is. Like, do they think that, like in LLM, robots are just going to be wandering around the world in 2028, doing anything and everything like you talked about earlier? And that really isn't our first target. First target is do the hard work in industrial
Speaker 2environments first. I feel like that shit might have sailed. That messaging is going to be real hard to get the cat back in the bag on that one. But when you say meaningful, what do you mean by
Speaker 1meaningful deployments? Meaningful deployments, meaning the robots doing. Doing that hard work that has high turnover, that's hard to hire for and specifically things that they would want to get a robot for. Yes. Like I couldn't automate this and my whole team wants this task automated and we're bringing humanoid in to automate the task and the robots doing it reliably and passing your site acceptance testing and getting through the throughput it needs to do. And the team's looking around saying, I like Atlas as a part of my team.
Speaker 2That's the goal for the product. So, so, you know, you know, obviously, you know, as, as, as a product person, um, you know, you, you, you're thinking a lot about ROI. Um, where does that sort of enter the conversation and like, how quickly is that going to really, how quickly will people like expect that for these things to these things for these robots to really, uh, you know, to, uh, I guess to really be proven to be like a valuable commodity in the workplace. I mean, I think it's
Speaker 1immediate. Like if we say we're going to launch in 2028, you want to be providing positive ROI to the customer in 2028, that's the, that's my goal. Um, and that is the goal of Boston Dynamics. I don't think customers should be deploying robots without an expectation of getting value from them. And I don't think that the industry will survive if customers aren't getting positive value from the system. And so, you know, obviously if you deploy a robot in 2028, it's going to take more than a few weeks to get positive ROI, but you should be starting on that journey. Once you have that successful pass of site acceptance testing on proving that your product provides positive return on investment.
Speaker 2Yeah. I mean, it, it, it's interesting, but you know, the, the thing that I've really like realized over the past few years is that it seems like the companies that are really going to, uh, with the humanoid companies are really going to withstand the test of times. Uh, the test of time are the ones that are going to be able to kind of like wait it out until that period where they can really prove out those things.
Speaker 1Yeah. I, I totally agree. It's again, not, not a sexy conversation, but it's part of building any scalable robotics business. And if you've looked at the companies that have had success, even looking at spot or looking at stretch, it's we, we have to prove that our products at Boston dynamics provide ROI every day. And so it's not new to us to have customers expect that. And you know, the customers we talked to about Atlas, many of them are spot customers or stretch customers already, and they expect ROI in a timeframe. And that's not unreasonable to us. It's just not widely discussed right now because we are early in the process. The process of building the technology. And there are a lot of fish to fry on the research front before we get to high amounts of scale for humanoids. Well, I think we're out of time. Aya, thank you so much. Nice to meet you. Thank you so much.
Speaker 2Thanks to Aya, Nick and the rest of Boston dynamics for accommodating us. Great conversation and excellent tour of your facilities. Aya will also be appearing at our humanoid robot forum event in Chicago. That's happening June 23rd and 24th. Thanks to you as ever for watching. If you've been enjoying the show, please like, and subscribe to it and the newsletter of the same name over at automated.fm. And with that, we will see you next week for another episode of automated.
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