The GPT Moment for Robotics Is Here

16:24Quan Vuong:So let me talk about WeFus. What you're seeing in this video is a system that we built together, folding really diverse item of laundry in a real laundromat in the mission. You can see, you know, people walking outside. And why this task is difficult is because there's just infinite possibility of observation space. Like, you know, clothings are deformable and no two items of clothing here are the same. And these are also unseen. These are not clothing items that are seen in the training data.

16:58Jared:Yeah, I love this team. They are some of the most cracked people out of Apple I've ever met.

17:03Diana:Gary was the partner for Weave. Maybe you want to explain what Weave is and what their company is.

17:09Jared:Yeah, I mean, they're actually shipping their first robots into the home. We sort of talked about it as you know, being able to do household tasks like this. And I think they were very inspired by physical intelligence's first demos with laundry folding. So it's actually a total trip to hear about it, you know, a year ago, we were talking about them doing it. And then now to see them do it working in hand in hand with you is really awesome. I think this is a great example of like, you know, you need the model smarts, you need the data collection, and then the hardware and And the sort of system integration all working together is just hard to nail.

17:47Jared:Yeah.

17:47Quan Vuong:And to get back to your question about why robotic is hard, it is a really hard system problem. You need everything to work well and work well together to get this result. And we've been such an incredible team for us to work with to get this result. And it actually didn't even take us that long to get this result. It was roughly, well, we set a goal and maybe it was like two weeks afterwards where we got a model and a system that was good enough at performing this task.

18:18Diana:It still like blows my mind to see a robot actually folding laundry because I remember until basically until ChatGPT, I didn't know if this would exist even in my entire lifetime. because folding laundry, I mean, it's always been the Turing test for robotics because there's no way to deterministically program a system the way that you did pre-AI to do this because the space is so infinite. And we've shown that it's possible for us to do, basically if everyone can do this, robots will be able to do everything. It's only a matter of improving it from here.

18:47Quan Vuong:There was a funny story where when we first published PyZero, people thought of us as the laundry company because the demo was just focused on laundry. And actually picking home tasks, especially tasks that has to do with deformable objects, is a very intentional choice on our end. We're not just after the home. We really want to make it broadly applicable. But picking home tasks for us to start with has a few benefits. One is relatable. You can see the laundry folding demo and you can kind of grog how this is going to be useful. And you can get a sense of why it's hard. And the second is that it's really easy to set up to test generalization.

19:26Harj:You can talk about Ultra, which is your company, Jared, a demo of it.

19:30Quan Vuong:Yeah, this is Ultra. The thing that I love about this video is you see, you know, it's bright outside. And you see this is 4x speed and it's 100 minutes. If I scroll to the end, the sun has set.

19:42Harj:Oh, wow. That was one of the big problems in robotics where it would be so sensitive to the environment in lighting and mess up the vision system, the semantics part of it.

19:54Quan Vuong:Yeah. And the interesting thing here is that it is possible to get to the level of autonomy that the robot is just performing the task. This is autonomy at scale. Like this is ready to be scaled.

20:09Diana:Quan, because this task is less familiar than laundry folding, do you want to explain what the robot is doing here and what Ultra is like doing as a company?

20:16Quan Vuong:Ultra is a company that want to make it really easy to adapt robot to, you know, new tasks. And right now they're focusing on logistics space, which is really important because there's lots of labor shortage in logistics. And the task that we focus on together here is, you know, if you order an item from Amazon, you sometimes get this soft pouch that item gets shipped from. And the task here is you have a tray of these items here and the robot is supposed to pick one of them at the time and place it inside this pouch. The machine would then close it and then pick up the pouch and put it on the left here to be ready for shipping.

20:55Quan Vuong:Now, this heart is hard because there are many different types of objects that can be in this tray. And the opening here is actually very narrow. So you see this interesting example of the robot kind of nudging the item to go into the pouch. And that's really hard. That requires a very good understanding of the scene and very precise motion to nudge the object into the pouch. The other thing that's hard about this task is the level of autonomy that's required. This is running for an entire day. There is still human intervention, I want to say, in this full day operation. But the level of intervention is actually quite minimal.

21:39Diana:This is not just like some demo station, right? But this is actually recorded in an actual e-commerce warehouse where they're actually shipping real products to real customers. This isn't just like a lab.

21:48Quan Vuong:This is packaging real order for customer to be shipped out in a real warehouse. So this is real operations.

21:56Diana:So I think this is really cool because I think when people think about robots, they tend to think of the consumer use cases like Weave because that's what we're familiar with in our daily life. What I find really interesting is that there's like a million applications like this Ultra thing that you wouldn't think of as obviously like, oh, who packs the like soft pouch of things that you get from like Amazon? Well, there's some person like who does that. And this is like a job that we could not build a robot to do.

22:18Quan Vuong:The interesting thing about the approach is that you're converting it from a very difficult engineering problem into an operation problem of how do I identify the use case and how do I collect the right data, which is in some sense more scalable because you can build a system that allows you to collect data for many different tasks. So it's now a problem of how do I scale data collection rather than for every new task, how do I design a really difficult engineering system to solve it?

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23:01Harj:I think one thing that the audience may not know is that you have a very unique technical insight that in the past, robotics folks would have kind of gasped and be shocked because robots need to run in real time. A lot of times, all of the compute runs on device, but you guys have done something very different. Can you tell us more about that so that this works in real time with large models and really well?

23:27Quan Vuong:So the context here is that we talked to many companies that would like to deploy robots. And one of the first question we get is what compute units should we get on the robot? It's expensive. It's going to increase the bomb cost. And they're worried that it's going to go out in fashion very quickly Does the model change? The model gets bigger? How do I make sure that the hardware that I'm going to commit to today is going to be viable for a couple of years? It's a very difficult question. People are often really surprised when I tell them that almost all of the robot evaluation that we run at Pi today, including the really complicated demo that we have shown, making coffee, folding laundry, mobile robots navigating around, the model actually hosted in the cloud.

24:07Quan Vuong:And, you know, this is not like a cloud as in a server in the office. It's a real cloud. The model is hosted in a data center somewhere, and within this high-frequency control loop that is controlling the robot, the robot is actually querying an API endpoint that hosts the model, sending it images and language command and getting back action that then execute it directly on the robot. And this is surprising because of precisely the reason that you mentioned, you know, how do you actually make it work? This is why it's really important for Pi to couple system, hardware, and model development and research very tightly together because it allows us to solve for this problem.

24:48Quan Vuong:So, for example, one of the insets we have here is that you can actually bury the inference time within the robot control loop. Because, you know, if I'm a robot, I have enough action for me to execute for the next 100 milliseconds. There's no reason for me to wait until I finish executing that action. to ask my model for a different action. I can do it as fast as inference, essentially. And so maybe when I only have 50 milliseconds of action worth left, I can ask for the next sets of action. And when the current 50 milliseconds is over, I have something that's ready for me to continue with my next 100 milliseconds.

25:28Quan Vuong:So that's one of the insights. The other kind of algorithmic improvement, we refer to them as real-time chunking. design inference in such a way that you know there's going to be a delay in how long it takes to query the model on the cloud, basically. Like the problem here, if I get a little bit more technical, is an action chunk is a sequence of action that I can execute on the robot. So, you know, it's not just one action. And if I have an action chunk that I can execute for 100 milliseconds and 50 milliseconds in, I want to predict another action chunk and I'm going to transition to that new action chunk after my current 50-metrasecond is over, how do I make sure the two are consistent?

26:12Quan Vuong:How do I make sure that if I'm moving this way, the next action chunk is going to allow me to continue to be smoothly moving this way?

26:20Jared:You can pre-compute.

26:21Quan Vuong:Yeah, you can pre-compute, and that's one of the algorithmic improvements that we've made to make inference using model hosted in the cloud possible.

26:30Jared:I studied computer engineering, so I'm not really an algorithms person, but when it comes to systems like that, like pipelining, like get me all over that. That sounds great. That's so interesting.

26:40Harj:I mean, this simplifies, it's a brilliant choice because it simplifies so much of the system for the robots. You don't need all these clunky, I don't know, people have two operating systems at some times for robots embedded, RTOS and then the regular one and all these complex giant compute and power. And this is what the initial versions of Waymo used to run basically a server on the trunk. And you can't afford to do that with general day robotics, which is brilliant that you figure out how to do it.

27:08Jared:Yeah, you don't have to. I mean, you can do things. Some of it, obviously, has to be some compute there, but a lot of the compute can happen elsewhere. And then there must be a video, like this thing that we're looking at in the top left, like how much of that is sort of like video feedback? How much of it is like local processed? I mean...

27:26Diana:Is there any compute locally on this robot or is it just like a dumb like video camera that streams data to the cloud?

27:31Quan Vuong:For this, I am not 100 % sure, but I am inclined to believe that it's just a dumb computer. For this specific video, I don't remember, but I'm just 100 % confident that we can make this work with a dumb computer and a robot. And one other interesting thing about our collaboration with Weave and Ultra is one, I've never seen their robot in person.

27:53Jared:Oh, wow.

27:55Quan Vuong:Two is I have very little idea about how their robot actually works.

28:00Diana:Interesting.

28:01Quan Vuong:And that's a very intentional choice. I want to stay away from that as far as possible. I also don't know how they collect data. I intentionally don't ask them this question to understand whether it's possible for an organization like Pi to parachute into their existing system and to work really closely with them on the thing that actually matters to get the system to work and not have to learn about how they've set up their system because in a way that's like a more scalable recipe.

28:31Harj:Yeah, you completely decouple a lot of the hardware control loop choices from the semantics and planning, which just works. Just brilliant.

28:42Quan Vuong:Yeah. I mean, I'm really surprised. It works? And when we started the company, we thought that real deployment is only going to be in a conversation like five years into the life of the company. Because the problem is it's really hard. And we're two years in, and this is the result that we have. and real deployment and scaling the number of robots is a really serious consideration today. And so the pace of progress has just been very pleasantly much faster than we expected originally.

29:12Diana:Often on this podcast, we talk about what all this means for startup founders. I think that might be an interesting question for us to explore here. So if you imagine someone was listening to this podcast, maybe they're like a college student that's studying computer science and they think robots are really cool and they want to do something like this, how should they get started and what are the skills that they need? Do they need to be a mechanical engineer to be able to build a robot like this? Can they just buy an off-the-shelf robot arm and camera system? And load pie and you're off and running in like a day?

29:42Quan Vuong:Before I actually answer your question, let me provide a few more contexts. The first is that robotic is traditionally really hard because it's an extremely vertically integrated business. You need to have your own customer relationship, your own hardware, your autonomy stack, your own safety certification, your own everything. And the barrier to entry is just really high because of that. And one of the things that we're trying to change is that we're trying to provide a foundation of physical intelligence that the community can build on top of that allow them to onboard autonomy onto their robot and their task much quicker than before.

30:20Quan Vuong:So that's the first, you know, we want to provide that kind of seat of intelligence that allow people to move much faster so that they can focus on other problems. The second thing is that I think the recipe for starting a vertical robotic business today is one, have a really good understanding of the existing workflow because the robotic system needs to fit into existing workflow. and the second is to be very meticulous about identifying where the opportunity is. If there's a workflow that needs X number of work today, where is the robot when you insert it? It's going to make the biggest difference.

31:00Quan Vuong:And two is to really be scrappy when it comes to hardware and data collections. You don't need an incredibly expensive robot that is capable of very precise motion today to be able to do these tasks. And the reason why is this model really reactive And so they can compensate for some of the inaccuracy in the actual robot movement and to ensure that you have the ability to collect data and to run evaluation, especially evaluation in real deployment. The next step after that is to get a mixed autonomy system that allow you to get to the point where it's break even. Like break even economically. Break even economically.

31:38Quan Vuong:Because the reason why that's important is because it allows you to then scale the number of robots.

31:43Diana:Because if you lose money in every robot, it's very hard to scale.

31:45Harj:That has been historically one of the biggest challenges for robotic companies that they go into growth stage. It's just the payback hack period. It just doesn't make sense.

31:53Quan Vuong:Yeah. So the equation, I think, for starting a robotic business has changed and will continue to change at an accelerating pace. Because the upfront cost is not that high anymore. And now, you know, what is the upfront cost? The upfront cost is much cheaper hardware, ability to collect data, ability to collect evaluation, and ability to kind of like understand the use case to see where they should insert the robot. You know, it's not about having incredibly expensive hardware. It's not about having your own proprietary, I think, autonomy, classical stack anymore to be able to do this task. And so it allows a company to focus on the component that will actually allow them to differentiate themselves from the rest of the space.

32:41Diana:Now that you sort of unbundled it and you no longer need to build this fully vertically integrated company in order to build a robotics company, are we on the precipice of a Cambrian explosion of vertical robotics companies where there's going to be like a thousand companies like Ultra going after, you know, every menial job in the economy and getting a deep understanding of the customer, building a robot that can solve that problem, doing a mixed human machine deployment until it can run fully autonomously and building a company in every sector. Is that the future that you see people building on top of Pi?

33:13Quan Vuong:It's funny that you mentioned Cambrian Explosion because when we wrote this blog post, there was that term that was very kind of like hotly debated. We are, I think, academics at Hurt and we want to be kind of very major. when we communicate. But, you know, myself personally, I believe there's going to be a Cambrian explosion of robotic company across the entire world and across many, many different verticals just because it's just so much cheaper to build. And it doesn't require, you know, someone with 20 years of experience in robotic to start anymore. You know, it requires someone that is really scrappy, that can move really quickly, can do the system integration, can understand customer what they want to start the deployment.

33:59Jared:I mean, what's coming up for me is, obviously, we work with a lot of robotics companies and meet a lot of founders, and it feels like there's this continuum. One is, to use an analogy to personal computing, you could argue that industrial robotics today is basically like mainframe for mini-computer level. Like, if you look back in the 70s, huge public companies like Digital Computer that, you know, just did like these sort of very, very expensive deployments, but like they were very, very specialized. And it was all extreme enterprise. Like, you know, the idea of a personal computer was ridiculous, right?

34:37Jared:You know, it took the Altair and then Apple One and Apple Two and then IBM PCXT to like create personal computing. And then like the traditional advice for robotics for many years is like, go after like dirty and dangerous and then of course those are sort of the industrial cases like you know you have these giant tesla robots in the gigafactory and things like that it feels like what you said around profitability is really really big so you know does that mean that the people who do the vertical robot cambering explosion sort of moment uh the people who are a sort of first in that, like, it sounds like they would be the first to be profitable and not dirty and dangerous.

35:21Quan Vuong:I think this is already happening today. I think we have the fortune of having lots of visibility into the robotic community because, you know, people would like to talk to us, people would like to learn, you know, what it's like to build a foundation model for robotic and people would like to know, how do I get the same level of autonomy? And there's so many companies and businesses that we talk to that would love to put a robot into that space that, you know, it's okay for the robot to make a mistake. And they just need it so much. I really believe that the recipe that I mentioned earlier of identify where the robot can fit in, focus on cheaper hardware, collect data, run evaluation, mix autonomy, break even scale robots will work across many different vertical.

36:07Quan Vuong:And I'm seeing it play out today. And it's just incredibly exciting to see.

36:11Diana:And this is pretty cool that you literally just gave people the playbook for how to build a vertical robotics company. Like this is a playbook that could possibly be followed successfully hundreds or thousands of times.

36:21Quan Vuong:And the reason why I want to mention it is because I do want to see that Cumbrian explosions and we want to help enable it. You know, for Pi, if we talk about why Pi is going to fail. It's probably going to be because the problem is just way too hard. You know, maybe it takes 50 more years to solve the robotic problem and, you know, not a couple of years, 5, 10. And so we want to enable the community. We want to accelerate progress. And that's why we're very open. We publish our research. We open source Pi 0 and Pi 05. And people are sort of shocked when they ask me, you know, is there any difference between Pi 0 and Pi 05 that you open source versus the model that we use internally, Pi Zero and Pi 05?

37:03Quan Vuong:And the answer was, actually, no, it's the same model. The pre-trained model weights that you're using that we open source is also the pre-trained model weights that our researchers internally use for Pi Zero and Pi 05. And so we really want to help accelerate progress in the community and to create that Cumbrian Explosions.

37:22Jared:Yeah, that's very inspiring. I mean, I feel like everyone's sort of spending a lot of time in the digital world. And it feels like now is the time to start thinking about the world of atoms. And this is sort of the perfect mix of actually, how do you take electrons and turn it into abundance in the atoms world? And I think about Dario Amadei's essay, All Watched Over by Machines of Loving Grace. And when you really think about the perfect manifestation of that. It's not like, you know, perfect agents that look over you just like in the electronic world. It's, you know, actually something a little bit more akin to what we're seeing here.

38:04Quan Vuong:Yeah. And this has really been our mission from the start is to create that Cumbrian explosion. And, you know, this is why we choose to focus on the model because we believe that is the bottleneck to just really make robot useful across many different tasks in the world. and that's why we also focus on cross embodiment you know success for us is not defined as only our model on our robot performing tasks that is useful the the surface area for success is actually much larger which is our model performing really useful tasks on somebody else robot out there maybe that we don't even know what that robot is like in a way that's like useful to the end consumer.

38:46Garry:Could we maybe talk a little bit about the humans behind the robots here? How did the company get started? Who are your co-founders? How do you all get together? And what skills do you each bring to such a complex problem?

38:59Quan Vuong:Sometimes the joke I make here is that the human behind the robots are also robots. Not really. Yeah. So Pi is a very, I would say, entraditional company. We have larger than average founding teams. And some of us work really closely together when we were at the robotic team at Google. And the robotics team at Google was, I think, a really, really great environment for seeing the sign of life and creating the relationships and the community that allow the robotic community and like these advances to flourish. There is Lockie, which we met when we were thinking about starting the company and it's just been really instrumental in making sure that we're a good business.

39:41Quan Vuong:And there is Adnan, our hardware lead that came over from Android. And Adnan has a really difficult job because if you want to work on cross embodiment, you know, remember my joke about how if you want to add two years to your grad school, you bring on one more robots. The hardware problem and the operational problem for us is how do we build, improve, and scale a fleet of Hedder Joe Genius robot. It's just not one robot platform. And because we built the organization from scratch in the beginning to support that, I think we're able to do it, but it's just a really hard problem because there's just like no two different robots in the fleet.

40:24Quan Vuong:How do you make sure that everything runs smoothly? We're really good at Divi and Conquer, if you ask.

40:30Garry:How many co-founders are there in total?

40:32Quan Vuong:We have Brian, we have Chelsea, Sergey, myself, Laki, and Adnan.

40:37Garry:Is it just necessary to have that many co-founders to solve a problem as big as this or was it a case like you were already sort of like a unit together you'd already work together and you just whatever you started you would all have

40:48Quan Vuong:yeah one common question that we have is you know why band together and you know the first is that we really enjoy each other company we spend a lot of time at work and it's you know in some sense give meaning to life. And so we really want to enjoy the relationship we have at work. And the second is that, you know, any one of us could have started a company and be successful. But the problem is just so incredibly hard. And the chances of success is just so much higher that we band together and we can divide and conquer the problems. And, you know, that's, I think, one of the main reasons why the progress has been much faster than we expected.

41:30Harj:What were the differences of you working before in either academia or a big industry, big company like Google and as opposed to now in a startup? This is the first time for a lot of you doing a startup, right?

41:46Quan Vuong:Yeah, this is the first time for a lot of us. One of the really surprising thing that we learned when we started the company is that the infrastructure for supporting large scale general purpose robot, which is not there. And, you know, this starts from the software itself. How do you collect data? What device do you use to collect data? How do you manage the data? How do you annotate the data? How do you get visibility into the data? How do you run evaluation? How do you build operational process? Like there wasn't a company that offered this kind of services, which is very different from software.

42:18Quan Vuong:And we were really surprised to find out. And so we ended up writing a lot of the software at Py ourself. but I think this is another area of incredible opportunity of kind of building services for a robot company. Like, you know, if you can offer remote tele-op, for example, if you can offer data collections, if you can offer annotation service, because, you know, these are functions that doesn't need to be repeated from one company to the next. So I think there's lots of opportunity to build kind of support for growing robotic business. So that's one thing, like one surprising thing that I learned.

42:53Quan Vuong:And the second is I think one of the reasons why we have managed to achieve such progress is that there is a really tight loop of collaboration in the entire lifecycle of model development. Going from what task do you collect data for? You collect data for the task. How do you do it? What hardware do you use? Once after you collect the data, how do you get visibility? How do you ensure data quality? How do you then make sure that you can easily train on that data? After you train on that, how do you run evaluation? Evaluation is a really hard problem in robotic because it scales super linearly to model capability.

43:31Quan Vuong:Let's say you have a model that can perform a two-minute task. Running evaluation for that is very different from running evaluation for a task that's 20 minutes. It's not 10 times harder. It's more than 10 times harder. After you run evaluation, how you can distill the learning from that evaluation to know how to improve the model further. One of the really side projects I would love to take on is to build an automated robotic research scientist, which is really one of the bottlenecks we have today because this is a really difficult skill set that requires intuition about the entire stack. So I would love it if there is a model that can ingest multi-model data such as this and analyze failure modes, understanding, oh, is the robot performing this way because of the data that was collected or the way that it was annotated or the way that we train the model and then suggest idea and actually try them to figure out if those hypotheses are correct.

44:31Quan Vuong:So that's something that I would love to have and would dramatically unlock us. So sometimes I make the joke in a company that we should record all of the meetings and then train a model to basically just make prediction about what is the next set of experiment. Oh, you could.

44:44Jared:You totally could. But what if it's OpenClaw and Obsidian and Markdown files and like, you know, a brain.md with like ontology that's custom to your use case? And what if it's 100 OpenClaws in the background that you orchestrate?

45:00Quan Vuong:I think there's two sides to this. The first is that we already see a little bit of a sign of life where for simple failure modes during evaluation, if you can describe the way that the robot failed in text very precisely and very clearly, then you can ask the language model to make very reasonable recommendation about what the next step is. But the flip side is that this only works for simple cases today. And the reason why that's the case is because I think it's pretty fundamental limitation of the model that we have today, which is that they are not at the core model that take action in the world and see the consequences of its own action, especially action that changes the physical world.

45:41Quan Vuong:And so I think this kind of very fundamental understanding about how the physical world works is missing from the really large foundation model. And I think that's one of the ingredients that's missing to be able to build this automated robot research scientist.

46:00Jared:What's interesting about OpenClaw, I don't know, I mean, basically it can go and it can just do things, which is interesting. And then at that point, it's on the research lab to provide CLI, MCP endpoints to the things that might control robots or reconfigure rooms. I mean, I think Karpathy feels like he's starting to talk a bunch about this, where if you mix auto research plus what he's been talking about with markdown files, it might just happen in the open. Like, you know, there's a sort of sense that you have to make something much, much more complicated to make it work. But what if that's just wrong?

46:39Jared:What if we just have markdown files and agents and, you know, you could make it yourself with, you know, literally Claude Code and MCP today? What if it's not an algorithm problem? It's just literally an integration challenge.

46:54Quan Vuong:We have a version of this internally that I use a lot. There was a point when I was spending an embarrassingly large amount of money on API queries. Yeah, yeah. And, you know, my team was like, Quan, what are you doing?

47:09Jared:Oh, I'm that guy at Y Combinator right now.

47:12Quan Vuong:So to give you an example, we have a cloud skill that's essentially serving the role of a pre-training on-call today. So, you know, we have these pre-training runs that are really large. It's very, I think, a difficult exercise to keep them alive, for them to continue to churn, just because there's so many things that can go wrong. And we have a prototype, a pre-training on-call, that kind of babysit the run, and have the permission to take action, to remedy error that it sees. And one of the surprising outcomes of that exercise is that it leads to about 50 % improvement in compute usage, like just overall compute utilization for that large pre-training run, which is huge for us.

48:00Quan Vuong:And, you know, this is just a small, simple prototype that I built. And I think like there's a lot more to be done.

48:06Jared:Kwon, this is incredible. Thank you so much for everything. Thank you for making physical intelligence. Thank you for showing us these incredible demos. And honestly, like the thing that gives me the most hope is this idea that there's an entity, there's a research lab out there that is focused on giving this to the world, about to create this Cambrian explosion of robotic startups. So someone watching right now will be inspired by this and start playing with your models, and they might create a robot that touches billions of people's lives for the good.

48:40Quan Vuong:Thank you for having me. It's been a pleasure. To the listener, the one takeaway that I want you to have is I think robotics has changed a lot, And the cost of building in robotic has decreased and I think will continue to dramatically decrease. And it also requires a very different kind of scrappy skill set that young startup needs. We hope to enable really an explosion of many, many, many different robotic use case. And, you know, always reach out to us if you want to collaborate. Thanks, man.

49:12Jared:Thank you. Thank you.

49:23ferrant