Giving robots a brain | Intrinsic’s Brian Gerkey

**Andrew** (0:05)
Brian, just before we roll into the scripts, you yourself as an engineer and an engineer-minded person, what is most exciting to you right now on a personal level about how fast all the technology is moving and the things that you're experimenting with outside of Intrinsic? Are you doing anything fun outside of applying it towards robotics?

**Brian Gerkey** (0:25)
Well, I think I'm mostly focused on robotics. And for me, the exciting part is really just seeing that robotics is...
I feel like I've been waiting for robotics to have its moment for about 25 years. Been at this for a long time, mostly as an intellectual pursuit. And now it's starting to become not only a hot topic, because that wouldn't be quite enough, but it's a hot topic in part for a reason, because it's also becoming really, really useful. So that to me is just, it's very satisfying.

**Andrew** (0:56)
Absolutely. It's like transformative to see this technology that has so much potential finally be able to like manifest it. Robotics is something that I've always found personally interesting, just in my own interest with technology and experimenting with it, just as like a solo dev or just even like a teenager, just trying to figure out how a breadboard works is like really complex to get into.
So it's really amazing now to see how it can become more accessible too. And I'm excited to talk about that today.
And to everyone listening, welcome back to Dev Interrupted. Today, we're looking at the physical AI revolution. For decades, the hardware and robotics was way ahead of the software. We had the bodies, but they lacked the brains to handle the messy, unpredictable world that we all live in. But our guest today has spent his career solving that gap. Brian Gerkey is the CTO of Intrinsic. Many of you know him as a titan of the open source community. He was a co-founder and CEO of Open Robotics, and one of the primary architects of the Robot Operating System. Now at Intrinsic, which just officially moved from an alphabet moonshot to an integrated core unit within Google, he's leading the charge to make robots as easy to program as web apps. Brian, it's so great to have you here today.

**Brian Gerkey** (2:17)
Well, thank you for inviting me, Andrew. It's great to be here.

**Andrew** (2:20)
Absolutely. I want to dive right in to the breakthrough that we're all talking about and experiencing in robotics.
You feel it yourself. You said it at the top of our conversation that you feel like robotics is finally having its time in the sun, and it's going to be able to make huge strides, and you're right at the forefront of that. A lot of that is made possible by things like artificial intelligence and new developments like LLMs. But applying that to a heavy robot arm is like an entirely different beast to tackle. What was the missing link from your perspective that's finally made this software-driven robotics viable for more industrial work?

**Brian Gerkey** (2:59)
I think it's a great characterization because I think the robot hardware has been pretty capable for quite a long time. If you look at the kind of applications we're tackling at Intrinsic with our customers, we're basically using robots of a kind that have been around for a long time. They've gotten better, they've gotten lighter, they've gotten smaller, they've gotten more collaborative robots that might be safe to use without having a cage around them.
So there are some improvements along the way, but by and large, it's kind of the same hardware. But historically, those robots have only been used in really fixed rigid environments where we, in order to use the robot, you engineered out all the variants so that the robot could operate in almost a blind fashion and it just would do the same thing again and again, pick from here, put to there.
That's really useful in those high-volume manufacturing type applications. But there's so much more that could be done. Now, so we've been wanting to do that for a long time. I think what has really been the breakthrough is the availability of better software. That software goes all the way from perception, so how do we understand the world? I've got cameras, I've got lasers, I've got other sensors that are going to help me understand, see the world and then now I need to make sense of it. Then I need to decide, well, how can I act in that world in a safe way? Can I move my body so that I don't collide with the world and I go to a place where I want to get the object and I figure out where it is, I figure out how to grasp it, I pick it up and then I do whatever it is I'm supposed to do with it. So that solving all the components of that control like perception planning and control problem that took us as a field just decades to get right, it's taken, I mean, I shouldn't say it's right yet. It's always a work in progress, but we've made a ton of progress and now it's being accelerated by the advent of what I'll call modern AI, because I've been at this long enough that AI has been a term, I mean, it's been a term since the 50s. When I was in school and undergrad in the 90s, I took an AI class, right? And there are lots of techniques that are AI that now when we see AI, we mean a specific type of AI, which is these big neural networks. We still use a lot of the older AI and the things that we do, and that's also really good. But these big neural networks have now, they've allowed us to really solve some problems in ways that we didn't previously think was possible. And perception is a great example. So now we can, if we think back to like how do I get the robot to do applications where I haven't engineered out all the variants, so it's going to encounter a different situation from run to run, from job to job.