Dyna-2: General Purpose Physical Agents with Hour-Long Autonomy — Jason Ma, Dyna Robotics

What is that? Yeah. So last month, we released our world action model called Dyna two.

So it's the first word action model trained on more than one million hours of data. So going back to your question first, word action model is a model that's able to predict the future video, the future state of the world. So hence the name world model.

But in addition, you can also use that future generation to also produce actions robots can execute in the physical world to control the robot. So why do we do that? It's a very powerful architecture.

Because if you think about how humans act in the physical world, in our brain, we're implicitly thinking about how the world will evolve based on our actions. So now we have a model architecture that's able to do that to guide robot actions. So what we find is that by doing so, the model is much more dexterous, much more robust, and much more capable of generalizing to environments, to scenarios it's never seen before.

Yeah.

for inputs, the most important input is video. Right? You know, you can think about videos that a robot is capturing with its cameras.

Right? So video or images of the, you know, past many seconds, that's the input. But for robots, what's really interesting is that robot also has a lot of actuator, a lot of joint encoders on itself.

So the robot also knows, you know, the angles of its joints. So those can also be inputs to this world model. That's something that's very different from just a pure video model that's only looking at videos.

Right? So that's the input. And additionally, for robots, you want to be able to talk to it, to instruct it to do different tasks.

Right? So you can also feed it, like, language instruction. So those are what I consider the basic set of inputs that go into any robotics models, including word action models.

And once we have that, the output at the basic level, it needs to output actions the robots can execute on the hardware. But in the case of word action model, what's really interesting is that in addition to the actions, you can also output future video. Right?

So, basically, you can almost output the future video or some representation of it first and then use that to guide robot actions. So the way I think about it is, like, this is kinda analogous to, you know, chain of thought. You know?

Basically, the robots... The models are reasoning in the visual, the physics space, and then outputting actions. So that's the key difference between world action models outputs versus other kind of models...

Robot models outputs.

vanilla architecture off the shelf? Do you determine all this? And what's...

What is the current state? Yeah. I think...

Yeah. So the mental framework to think about how do we build models for robotics is that there are essentially couple of things to look at. One is data.

And the second is, like, how do we make a model learn as much as possible from that data? So let's first talk about the model actually. Right?

So the blog post we released is our Dyna2 word action model. So under the hood is a diffusion transformer. So transformer is essentially like the language model, you know, each sequence is sequence math.

Hence, decoder only. Right. Yeah.

But what's really interesting is that in visual domains, right, if you think about generating images, generating videos, and in this case, robots looking at images to generate actions, people have found the diffusion kind of architecture to work really well. So what is a diffusion model? A diffusion model is a model that takes some random noise and through iterative computation can go from noise to something that's really high dimensional, that's something really highly, like, defined and have very fine grained feature.

If you think about generating images, right, you want sharp edges, sharp corners, color contrast. And for robotics, even though you're not generating image, you're generating actions that needs to be very precise, very, like, smooth. Right?

So it has kind of the same kind of consideration of the Siderata as generating image. So that's why from first principle, you know, the whole field, not just us, have been using this kind of diffusion transformer architecture to train robotics foundation models. You need the transformer aspect because transformer is the thing that can just ingest the most amount of data, can learn.

Then diffusion helps you to refine the outcome to get to the smooth, precise, fine grained motion you need to actually execute on the robot.

model, one thing that's very hard in practice is that you need to tune the scheduler, like by how much you're tuning or converting the initial noise input into the final output. So flow matching is kind of like a special case of diffusion that makes the transformation much simpler. In practice, don't have to tune as much, and it works just as well.

That is why I think about two to three years ago, most generative modeling for image and video has shifted from a diffusion model to a flow matching model.

pulling drinks out of a fridge. Yeah. What's the what's the mindset there of one general model, and then how do you adapt to different tasks?

Yeah. So the way we think about it is that for robotics, we need to build a very general, strong base model. Right?

And then... Because if you think about how a human interacts and behaves in a physical world, right, We have very strong base knowledge. We can just readily do a lot of tasks.

But if you want to become an expert in any given task, there is some amount of training that needs to happen. And I think to train a general purpose robots like the one we're building here, the same principle has to apply. So our approach is to first train a general model that's training as much data as we can find.

So that is when your Dyna2 report, our model is trained over a million hours of human data as a starting point. And then given that strong foundation, what we found is we can quickly make the robot or make the model do many, many different tasks with a very minimal amount of fine tuning to just tell the model, here's the task I wanted to do, and here's very few amount of demonstrations, and the model can readily do a bunch of the tasks that you have seen in the office. Yeah.

What else would you modify on that basic statement? Yeah. Yeah.

So I think data is the most important thing in robotics today because unlike language model, there is no Internet. Right? So every company that's building model, building hardware has to think about data.

So there's roughly couple of different ways of operating here. One is that you can collect a lot of data yourself, which is what we are doing as well. You have employees here doing that.

I mean, the robots everywhere in the office. Right? But if you think about how much data you need for robotic foundation model, I think the amount of data any individual company can collect by themselves is just nowhere near sufficient.

Right? It's kind of like, you know, you're trying to build an Internet by just having Reddit or just having Facebook. The...

They're a great website, a lot of users, but it's not enough. Right? So when we think about data, there has to be some sort of data acquisition happening.

Right? You buy data, you look at data that's already out there. And then there's many different tiers.

Right? You mentioned data quality. I think data quality in robotics is a nonstationary problem in the sense that what is high quality data for your model changes over time.

And when your model doesn't know anything, like when it's zero, any data is useful. Right? You can use a lot of video data.

You know, in the case of egocentric data, think it's actually a very good way of pretraining robots because human data is so diverse. Like, it covers every single physical activity you can possibly imagine. And that gives robot, the model, such a strong prior on how things...

How the world can evolve around a embodied actor, human. But the issue with human data is that before your robot looks exactly like a human, there's gonna be some gap between human actions and the robot actions. Right?

So just by training a human data, today, it's not very likely you can just execute the robot actions directly. So you still need some amount of adaptation using robots' own data. Right?

But the robot's own data collected on a robot is gonna be much more expensive and much harder to collect. So the kind of recipe we're thinking about is, like, how do we take the largest source of data we have today, egocentric data, to give the model a strong player over everything physical manipulation can cover and then combine that with a small amount of robotics data to make the robot, you know, master the... Whatever task we're interested in.

So data quality and getting data in robotics is really... I think it's a fascinating multifaceted problem. But the reality is that there's not a single winning strategy like, oh, here's one kind of data.

I'm only gonna collect on that, which will give you a strong foundation model. You really need think about, this human data covers this part of robot cognition I need. Robot data covers low level reflex dexterity the robot also needs and put them into one model.

And I think that's what gives a really adaptable, really strong model at the end. Yeah. I'll I'll I'll prompt you just for...

Again, for the people listening who want to work with you. Yeah.

This is your call for data. What... Yeah.

No. That's a great question. And...

Yeah. So in our Dyna2 blog at the end, we actually have a email address called datapartnerships@dyna.co.

K. So if you're a data vendor that has a lot of high quality, led to be egocentric data, robot data, video data, or anything you think could help robots, you know, send it to that email. Our team will take a look, and it will get back to you.

Yeah. But, like, at your stage, are you... You're you're probably not buying any more egocentric data off the shelf.

Right? You probably want to be like, here's what I need. You Is there a niche that's lacking?

You need a scale AI. You need a macore for, like, doing this. I see.

The The custom data don't work in robotics are probably thinking like, okay. Some niche factory, like, you know, you showed putting tubes in. There's only so much data you'll find about that, but, like, would a lot of that help you?

Yeah. No. I mean, if you really want to nail a task, having data from that task will certainly help you a lot.

Right? So for us, it's like, we still think, you know, just a million hours of data is is not enough. It's just the beginning.

But we showed the first model on a million hour scale, but we need more than that. Right? So we are still looking at a lot of just raw, diverse data that we can get, but we're also thinking about how do we get more custom data, either collected in house or by our partner, for, like, really, you know, specific niche tasks?

Because I think you still need that to get to, you know, excellence that, you know, any model needs to actually be useful in the physical world. Right? It's kinda like humans.

You know? You know, if, you know, Sean or me, you know, we go work at Rosewood Hotel tomorrow, we're not gonna be able to make the bed the way they want. Right?

We need a lot of training. So that kind of data, like, special data, it's kinda like what Mark Core is doing today for language model, I think, will be very, very valuable for robotics as well. Yeah.

So hotels, you know, strap a GoPro on your employees' heads.

I think generating synthetic environments... Synthetic data, simulation data for robotics, is going to be one of the next frontiers. Right?

Because today, people have looked at egocentric data, robot data. You know, those are scaling, But I think synthetic data will be a new dimension of scaling. So going back to your question, like, yeah, we have been exploring, like, using frontier models to generate a synthetic environments for us on our data, on our environments that we care about.

And surprisingly, I think now it's at a point where this technology is actually very much worth, like, going really, really deep into. So for some context, so in my PhD, I primarily focused on simulation based training for robotics. Back then, simulation wasn't ready.

But we had this paper called Eureka, which was the first paper taking, like, a large language model. At the time, it's g p d four, which just had to, like, self generate environment, generate reward functions or reinforcement learning, and just, like, do things in simulation. And at the end, I think, we had this really nice demo of the robot spinning pencils.

Right? That was really cool at the time. But that kind of thing was still very, very limited at the time because back then, the model didn't really have good vision capability, didn't have an innate three d understanding.

Right? So it was basically just generating code and, like, using very limited feedback from text to try to come up with new simulation environments. But today, like, the vision capability of these frontier models are crazy.

They can do, like, real to sim, do CADing, there's three d reconstruction, all of that. So I think now it's at a tipping point where this technology could really accelerate robotics. And for us, right, we think about that as, like, a way to accelerate our internal progress.

Right? Because we're building models. We're building hardware.

So if a frontier model can give us more data that way, like, generate really tailored data for ourselves. That's extremely useful for us. And another thing we've been thinking about is instead of using these models to generate, like, three d environments, we have also used our own world model, right, the world action model I was telling you about.

Because the model not only generates action, can also generate videos. We... The model can also generate its own training data.

So that becomes a, you know, self fulfilling, like, flywheel that I think will also be powerful. So, overall, I'm just really excited about this direction because I think even though we have scaled to a million hours of data, we are deploying a bunch of robots. The robots act...

You know, already starting to do very cool things. We're just at the beginning, just the tip of iceberg for, like, what is here to come. Yeah.

For those on the research side, we recently had runway on the podcast. One of their things is robotic world models.

Yeah. No. That's a great question.

So I think what's really different about robots versus language model and other applications is that for coding agents, language model, it's okay if it's... It has latency. Right?

Yeah. But, like, you know, today, whenever I ask ChatGPT, it takes, like, couple seconds before it spits out anything. But for robots, like, the inference latency has to be, like, very short.

Like, the robot needs to act. Like, you can't just hold a knife and not do anything for three seconds. That's gonna freak people out.

Right? Yeah. We saw someone put their real hand under and I Our model's pretty smart, so that's okay.

But in general, like, if you think about very dynamic tasks, like, example, you want your robot to, like, you know, hold a, I don't know, a tennis racket and, you know, juggle the ball. Right? It's just very dynamic, real reactive.

You can't just like the physic... Physics gravity doesn't wait for you. Right?

So for that reason, robotics model needs to be able to inference very fast. And the fastest way to do that is by serving on prem, like on device. Right?

Because if you call a model Network latency. But there's no guarantee. Drive a car.

There's no guarantee. Right? Like...

So for that reason, the model that gets deployed eventually has to be off a reasonable side that can be served locally. But that doesn't mean that the initial model you train also has to be small. Right?

Like in language model You can distill. You have a large... People do distillation.

Right? Like, the best way to make a small model very smart is by doing distillation. Right?

You train the biggest model, then you distill. Right?

Yeah. So we build our own inference stack for whatever model we deploy because I... I was saying, like, latency is so important.

So only the whole stack is extremely important for robotics. Right? You want to optimize, like, the inference speed for the kind of model you're running, and there's a lot of work in there.

So that's what we do. Yeah. You're really full stack.

what we need to own versus what we can buy is, like, if the input output and of a component is very, very clear, And, like, there's already extremely good mature solution, then there's no point of building a house. For example, on this robot, you see the LiDARs. Right?

So those are black circles. Those are LiDARs to help the robot detect obstacle, navigate surroundings. So that's a very mature technology that people have been using for now decades, is on self driving car.

So we don't need to build LiDAR from scratch. Right? Same goes with some cameras.

But for anything that we think is extremely crucial for the whole system's performance at the end, our vision is to be able to own that and build that ourselves. So one thing is, you mentioned actuator. So that's something we're working on and we're going to own ourselves.

The reason is that, like, the quality and how you design actuator really matters for how well you can model the robot. Right? Actuator is what drives the robot.

To be able to model your robot very well in simulation or to do any kind of modeling, you need to select actuators very good that, you know, can actually be easily modelable. So that's what we care about eventually, so we're gonna own that. Same thing with, you know, the robot's proportion.

You know, this robot's proportion is like, we measure against real people. Why do we do that? Because we're pretrained on human data.

Oh, yeah. That's transfer. Right?

So there's a lot of these kind of decision that goes into hardware that's very much tied to... Like, because eventually, you know, it's hardware that carries intelligence. Intelligence is the extremely important thing.

Yeah. I mean I mean, it's a good question. Right?

I think there's a lot of exciting discussions on bringing manufacturing back to The US. But, you know, for robots especially, that's actually much harder than than what it seems. And I would say, even now, there isn't a company in The US that's truly building everything from scratch in The US.

Right? Like, even when they say they assemble a robot, they build whatever they're actually doing in The US, they're still sourcing the raw materials from other places in the world. And I think that's something to really be aware of as a robot builder.

Right? Like, I think, like, as a full stack robotics company, we realize just how long the supply chain is and how long the whole problem is. It's actually extremely hard.

And I think these days, a lot of discussion centers around a robot foundation model model, data, but I think hardware and just how hard it is to build good hardware is very underemphasized.

we are currently exploring many different options. Right? Because with global supply chain, then there is some, you know, geopolitical things.

Shipping. Shipping. Also shipping delays and all that stuff.

Literally ships for robots. Yeah. And in The US, you know, it's just that going from zero to one will take some time.

Right? So we're trying to think about a solution that can both allow us to build more in The US while leveraging global supply chain. Right?

But I do think... Going back to your previous question on, like, some companies choose to just build model, we build everything, I think. I think it's very important to get this right.

I feel that for robotics, the hard thing is, like, building the whole system, or the necessary thing is to build a whole system. Like, previously, I was at DeepMind. I was at NVIDIA, primarily just building models, and I saw the upper bound of the system when you don't control the hardware, when you don't get to optimize that.

Right? Because if you think about the value creation for robots, eventually, robots are here to deliver a service to solve some physical tasks. So if you wanna solve physical tasks, you wanna be able to optimize everything, and that's important.

I mean, we have the robot. You gotta tell us about the design.

arm? Yeah. So we think of this as generally like a semi humanoid.

So basically, the upper body is like a humanoid. It's got, of course, shoulder, got arms. It also got four degrees of freedom on the waist.

So you can basically do all the upper body motion that a human can do, but it's a semi humanoid because the lower body is wheels. Alright? So why do we choose wheels?

Because I think there is a difference. There is, you know, optimal practicality versus performance trade off. I think with legs, the energy con...

The energy consumption will be a lot higher. It'll be a lot more unstable. You know, safety will be extremely, extremely hard to guarantee.

So right now for legated robots, there's actually no guarantee that the robot will stay upright, balanced the entire time. I'm sure you guys have seen a lot of those robot dancing videos.

So, yeah, how do we think about that? Right? Like...

So actually, it's very useful exercise. Like, you know, I think sometimes... A couple years ago when I was exercising, I accidentally, like, you know, like, pulled a muscle.

Right? I'm sure you guys have that experience too. Right?

Then the next few days, like, you just couldn't move, or, like, you pull it or you slept in the wrong position, so your neck is very stiff. We have all experienced that. And then whenever you experience that, you kind of realize, wow.

So many muscles, so many degrees of freedom that we use in our daily life that we take for granted. But when it's disabled, like, it's very awkward for you to do some task that you feel are very natural. Right?

So that's also how we think about when we design robots. When you have this kind of design, which is this purely elevator that could only go up and down, we realize a lot of the human torso movement. We use our waist back a lot for day to day physical activity.

Even if I want to get this bottle, I'm not just moving my arms. It's actually very strange for me if I had to intentionally think about fixing my upper body. I would always do this.

So there's just a lot of subconscious ways that humans move their body that I think will be equally useful if a robot has a degree of freedom. And if you further think about, okay, want to learn from human data. Humans are moving their torso all the time.

You wanna be able to ingest that into the model to control the robot.

mapping it to your specific hardware. Oh, yeah. Yeah.

It generates this very well. So for all the demos that I have shown you on the new robot, because the new robot, you know, it got ready not so long ago. So we quite literally do not have a lot of data compared to the stationary arms we showed you, which had much more data.

Right? But a new robot, the model has not seen this in pre training, in some of our pre trained model. It's able to learn the task that you saw, like, very quickly.

And I credit a lot of that too, like, you know, like, even before the hardware is ready, we already committed to pretraining like humans. Because we knew at some point our robots will be like human. You can get ahead on the training side while hardware is getting ready.

And I think now we're seeing that, like, the transfer is actually there and is actually very helpful.

What does that look like? Yeah. So if we wanna learn a new task, you know, we basically do a little bit of robot data collection, then the model...

These... What is a little bit like? Yeah.

Like, couple hours couple hours. Yeah. We're...

In some of the tasks, like, just tens of minutes. Right? Yeah.

And then... The chopping one was forty five minutes. Yeah.

The open the fridge was on... And that's an interview... That's like an onboarding task for one of your engineers.

Yeah. Yeah. Yeah.

you know, collect some data, train a model, and see what happens. Tie your shoes. Julie is tying.

Yeah. That'll be cool. But...

Yeah. So, you know, a little... A small amount of data collected, train a model, then we just deploy the model in the real world.

And then we look at the performance, then... To to fill in collected means, like, you are...

when you're training the model, there's, you know, certain metrics you can look at. Right? Just like training a model, it converges nicely.

Now after that, for robotics today, we just run the model in the real world. Right? On the, you know, same setup that the data is collected to see how the model does.

Sometimes the model just works great out of the box. Right? You don't really need to do much more.

But when the model's, you know, like, maybe like 70%, 80% there, then you see how the model behaves, you figure out what it's not good at, then you can, like, collect a bit more data then iterate. Right? I think of this as the robotics version of reinforcement learning from human feedback.

The robot rolls out, you can score it. Good, bad, somewhat good, And then that data, the robot's only experience can be used to train the model, and you can also just collect more data yourself to train a model. Right?

Eventually, you know, our goal is like, you know, this robot is gonna be our platform.

I would say to get to truly general purpose robots, like like, basically a robot or a model that does anything.

Yeah. I think couple of things. One is that, like, the laundry demo that you have seen, yeah, it's gonna be, like, one hour to finish a whole group.

Right? So the longest duration that robot can do, like, a full job is already in the hour range. Nice.

Right? But, you know, to be able to do that for any task, you know, I think the bottleneck is coming back to data and hardware. Right?

So for some of the tasks, you know, for example, setting up the space, you know, you might need more than, like, you know, the parallel jaw gripper. Right? You might need something more dexterous, like a hand, like a human hand.

Right? But then if you wanna train a model on the hand, then you're bottlenecked by the hardware stability. Like hand today...

The best hands, dextrous hands, are still not durable enough. They overheat pretty quickly. Right?

So that becomes a bottleneck. Right? You're like, you...

We... You know, you quite literally had to put a, you know, ice bag.

make better model. Have we have hardware. Yeah.

So hardware limitation is a big thing. But then if hardware is limited, then that will slow down everything. Right?

Because to know that you're making progress on any task, you eventually have to eval on the hardware. Right? So then without the eval being very spontaneous, very fast, that also limits your research progress.

Right? Because then how do we collect the right data? Right?

I think how do we collect the right data to do extremely complex, like setting up this studio? Right? Because you can imagine, like today, most of the data is like vision based.

Right? It's like video streams and like, some encoding of your hand motion where the robot's joints. But humans have a lot more sensors.

You know, we hear things. We feel things. Smell.

Yeah. You smell things. Right?

So we subconsciously use all of those to help us be so good at manipulating the physical world. But today, like, if we're not capturing that at all, then even when we scale to millions of hours, you might just be missing critical dimensions of the task, so you'll never be able to solve the task. Right?

So I think those are, like, what I consider, like, you know, what's dragging on the timeline. But for tasks or for workflows that you can collect data today to high fidelity, then I don't I don't necessarily think there is a fundamental bottleneck. Right?

Just like what we are demonstrating to the public this week. You know, we have a physical agent that can do the entire laundry workflow, right, as a human would. Right?

Because for that task, we don't really have like, oh, you actually need more than a parallel jogger, but you actually need some sensing modality that's just not available today. Right?

our website that goes into much more detail. I really recommend watching the video because I think the video is Do want the Best Western one? No.

We have, like, one video for all the customers we're working with. It's a really, really nice video. It shows what we're doing, and, yeah, I think it's great.

But, yeah, we have customers, you know, Best Western that's in the hotel space. We fold a lot of laundry, like, you know, towels for them. Every hotel needs it.

Every gym needs it. Every fitness center needs it. What's the deployment so far?

How... Like, where can I go to? Which best person do I go to to see it?

Yeah. So if you wanna go somewhere, I actually recommend going to our other deployment, Jingtai Phong. Have you guys been to Yes.

Yes. Yeah. You know, the best Very thin rapper.

Yeah. Very We should have filmed there. Yeah.

No. We should have. We've done just film there.

A b roll. Yeah. But in Din Tai Fang, like, if you go to Din Tai Fang or other, like, you know, somewhat upscale restaurant, there's napkins on your table.

Right? So our robots our robots today fold thousands of napkins for many, many different locations, many different Din Tai Feng locations in The United States. So that's actually our biggest scaled customer.

Right? So I wanna make a distinction here. Today, there's a lot of robotics companies that's talking about deployments, talking about pilots, but what's really hard is not putting your robot in one location or doing a pilot.

What's really hard is that the customers believe or validate that your robot creates enough value that they wanna deploy your robots across many of their sites. So that's what's happened for us with JingtaiFeng, which, to the best of my knowledge, hasn't really happened for other companies doing similar, like, general purpose robots. So at Dingtaifeng, our robots fold thousands of napkins per day.

It's... The quality standard is extremely high, you know, because they want like, okay, the second fold or the seam of the napkin is only like a couple centimeters below the... I don't even know how you call it, the the pocket, so you can stick the chopsticks in.

So it's actually very fine grained, high precision mask.

Yeah. I'm sure maybe you can. Right?

But the hard thing about manipulating deformable object, right, like napkin, towels, everything is deformable. There's just infinitely many possible configuration. So that's really hard for a specialized robot.

Right? And what we find is that by developing a general purpose model on a robot and adapting it to these use cases, you can actually get to your production quality faster. And what's really nice is that now that Jingtai Feng has scaled, has signed a much bigger contract with us, they not only want our robots to fold napkins, Now they want our robots, you know, like cut.

You know? That's why we had the I mean example, to cut food to do Is that actually the second use case? That's one of the use cases they're thinking.

Oh, wow. I mean, that's to cut so many vegetables per day. They need to do the dumplings.

Exactly. Do the fold. That's the final boss.

That's the final boss.

You can't just Amazon this. Yeah. But this can do it pretty fast.

Yeah. This can do it very fast, and now it's actually... It's at human parity.

Yeah. You know? And this robot doesn't get tired.

Doesn't need to, you know... You can and I think I really like the fact that you mentioned consistency. Right?

Like, one issue with Ding Tai Feng is like, you know, their LA location with New York location, Seattle location, they fold napkin study differently, but from their perspective, they want consistency. But, you know, that's exactly what these models are good at because models are, you know, just... It's a set of weights.

You can just, like, put the weights in the different robots across the Can OTA update them as well? Yeah. You can just update all of them at once.

You know? It just... Once it works, I think it's truly, like Yeah.

Incredible.

Yeah. I think there are several bitter lessons for robotics. One is that robotics is going to scale just like any other domains that machine learning has solved.

Right? So what does that mean? That means have as much data as possible.

But in robotics, the one thing that's a lot harder is, like, what is the right data? It's much harder to figure out than other domains. Right?

In language, it's text. In video games, just video game data. But for robotics, like, what sensors you choose determine what kind of data you have.

Right? But if you have the right set of sensors and just scale that data, I think you'll get predictable return on model performance. So in that way, robotics isn't so different.

So that's the bigger lesson. As a corollary of that is that, like, there's a lot of research in robotics that's coming out with, like, very specific priors. Like, for example, we need to make the robot see objects as they are, draw some boxes that will give robot better perception.

Right? But I think the better lesson here is that just keep the algorithm simpler and scale the right kind of data, then generalization, better behavior will emerge. So that's not so different.

And the second... I think that's the main bitter lesson, which is, like, at the end of the day, scaling will work. But in robotics, to scale and how to scale is just much harder because there is no Internet.

You have to come out with your hardware, come out with the sensors, get all of those right, and then scale. Then I think...

But you have to be practical is what we have now. Right? Yeah.

Yeah. So the way I think about it is that, yes, eventually the robot should... If we wanna build a general purpose robot, right, like a machine that can be adapted to as many use cases as possible, I think human form factor will be the best one just because that's what we have been doing for ages.

Right? But I think the best way to actually build a humanoid robot is to take an iterative approach. Because robotics is so hard, the whole problem, the whole stack is so long, it's really hard to know a priori what's the right decision to make.

Right? And because hardware decision is much more expensive and much more costly just because hardware iteration is a lot slower. You actually want to take an incremental iterative approach, not going all the way to the final form factor because the chances that the thing you build actually doesn't last, it's not good enough, so you just end up scratching it anyway.

Know, like, it's public news like, you know, optimists, you know, already gone through many, many iterations, and they had to scratch their designs. Right? And I think by iterating with a semi humanoid form factor and actually putting them out to real customers or in just getting models redeployed, you get that feedback loop that you wouldn't get if you're just trying to build, like, know, warehouse, humanoid, and you spend, like, five, ten years building it.

The model is... Can can move every single motor at once. Yeah.

In more than two cohort. Yeah. Yeah.

Yeah. And that's actually a very hard problem because if you just think about, like, the model, you know, it's outputting action. There's the action's, you know, dimension.

Right? The bigger the dimension is, the harder to fit the model to the data. But here, the action space is quite large, but we did a very good job to make sure the model fits well and gives you a smooth Can you bound the order of magnitude of the action space, or is that Yeah.

So it's in the orders of, like, you know, about a 100 action dimensions. Yeah. These are all continuous.

Right? Continuous. Right?

So the model is just basically predicting at very high frequency, you know, like, tens...

to context of previous state, I guess. So context is a very important thing with robotics. Right?

Because otherwise, if you're just making decision based on the current observation, sometimes it's really hard to tell, like, what to do next. Right?

Yeah. Yeah. So, I mean, eventually, will have our own SDK, but the way the way we think about it is we want a general, like, harness layer for robotics foundation model.

Today, that is our own model. Right? The way I think about it is, like, for any, like, real task, long task in the physical world, there's a lot of high level decision making you have to do.

Right? It's not just like robot only reasoning about the physical world in the action, continuous trajectories. Right?

Because that's a very low level... The thing you actually execute in the physical world, but that's not the intent. But, you know, like, when we are doing decision making, we think about steps of a recipe.

We think about, oh, I finished this, I need to do that. While I'm waiting for this part to finish, I need to go somewhere else to do something else. There's a lot of high level cognition, decision making that's happening.

And I think for robots to handle even, you know, the laundry task we are showing you, it has to have that capability because, you know, it needs to reason that, okay, the washer is running, so I should go do something else while the washer is finishing. But now the washer is done, I should take the towels from the washer and put it in the dryer immediately, right? Because if not, then that becomes a bottleneck in the whole workflow, right?

So there's a lot of reasoning that needs to happen for robotics, and I think, in theory, you could just try to learn that with a low level model that's outputting actions, but it'll be extremely inefficient. So that is why having a hierarchical system, like putting a harness, like an agentic system around our word action model is what allow the model to compose different capabilities, different skills to solve a much longer horizon task. It's very much like in a language model.

Right? Like, two years ago, three years ago, the model was only good at auto complete. Like, humans have to, like, tap, tap, tap to get the model to keep spitting out code.

But today, it just... Like, agentic system just carries the end to end task. And that's...

And and I think that's important because I think today in robotics, people are still thinking about autonomy at the very task level, like, one minute, thirty second task. But really, the value is about tasks or, like, workflows that go on, like, unsupervised for hours, and that's what we're building towards. And that's why I think thinking about the robotics problem, not just as a modeling problem, but rather as a system problem, you need good agent, good harness engineer, good model, good hardware, everything.

It's like how we think general purpose robots ought to be solved.

being developed in language model space affect you guys? No. No.

Yeah. No. Absolutely.

Like, it affects us very deeply in many ways. One is that, like, we're extremely AI native company. Right?

Like, coding agents, all of that is a very fundamental part of our work, and we're very welcoming of that. Like, whenever there's a new model, we're the first one to test it. Right?

Like, we've been... Like, when g b d six came out, we had, like, a company wide hackathon the same week. And the thing that our engineers built was incredible, and many of them are...

Many of the features we built is already in May in production. Right? So we really like using coding agents to accelerate our work.

Just for the people interested, anything surprising in GPT six that you found? Yeah. I think it's about the ability for it to construct virtual environments.

Right? Turns them into simulation. We talked that twenty minutes ago.

I think that's really exciting. It's interesting.

Yeah. I think in terms of deployments, we're exploring many different industries. There isn't a particular task we think we have to nail.

Right? Because our goal is to build a general model or general platform. And we think of each different task as, like, validation of our platform as a way...

As a wedge for us to figure out how to build everything better. So today, we are doing a lot of that in laundry, in restaurants. We have already deployed tens of robots, but our goal next year is that overall is just deploying hundreds of robots.

Yeah. No. That's a great question.

So I think robotics is the most exciting space today, and there are just so many, you know, amazing companies that I truly respect. You know? They've been doing great frontier work like us, and it's very exciting.

And I think what we are seeing today is that there's still many different approaches to robotics. Like, we believe that hardware needs to be built in house. We need to own the whole chain.

We believe that scaling human data is extremely important. But there are companies that's betting on just scaling robots data or scaling some sort of intermediate, like, data collection devices. Right?

All of them are in play, and I think it's different approaches. We'll just see what happens. I think right now, it's still a bit early to tell, but we feel pretty good about the way we're building things just because we, as a company, culture wise, mission wise, we're not here to just build a model.

We're here to truly get robots deployed. So we deploy as much as possible, work on the hard problems, so we identify problems much earlier on. So then I think that really helps us identify what is the true bottleneck and then solve it.

And I think for folks, for people who are only working on one part of the problem, it's kind of like fog of war. You don't really see the whole picture, and you might be building something that's extremely cool technologically, but may not be what people need or want at the end. Right?

I think there's a whole graveyard of robotics company, and I think all of them had very shiny, very cool robots. But, you know, just figuring out what people actually want is actually... It's very easy to say, you know, like, build something people want.

You know? Everyone says that, but it's actually really hard to execute a robotics company. On the technical side, you have your world action model.

Is that standard across industries? Are there other directions people are taking? Yeah.

I would say that's also not standard. So we are building world action model on top of world model. So there's many companies building world model.

You know, you mentioned runway. They're also getting to adapt...

are also companies building a vision language action model. Really? So that's like taking vision language models and adapting them to robotics.

So I think there's many different ways to go about it. But I think at the end of the day, what we call the models is less important than what we believe the model needs to be able to do at a first principle level. So I want to explain that a bit.

I think for robotics, the most important thing is that you need to be able to predict the future. Right? Like robotics, any physical system is fundamentally about based on the information you have at the present moment and the before, what should you do next?

So it's really about predicting what comes next. So predicting robot actions is like predicting what the robot should do next. But if you think about it, robot is just a part of the world.

Right? Robot influences the world, but the world also influences how the robot should behave in that environment. So if you think about it this way, then the maximal...

The most first principle thing you can do is just predict as much of the world they can possibly predict. Right? So that's why we believe predicting the future is extremely important.

And today, the instantiation is world action model. And VLA only predicts robot actions where some more abstract representation of the world. But whenever you're predicting abstract representation of the world, you're encoding your own bias into what's important to predict.

And the bitter lesson tells you, let's remove human biases. Let's just take the most raw representation, the camera inputs, and just predict all of that. Right?

So that's how I think about modeling for robotics today.

Yeah. So, yeah, there's a caveat in what I've said before, and I think that's where I could be wrong. Right?

I said that you need to predict the world as much as possible. Right? But what that doesn't say is that, does your sensor have everything about the world that you need to predict?

I think I touched on this a little bit. Right? Like, if you're trying a world model that's purely based on videos, I think it will be a very powerful world model.

But it may not be a sufficiently rich enough world model because you're missing critical aspects of the world, sound, like touch. Right? So I think what...

Where we could be wrong is like, okay. I have the best world model on vision... Vision based world model.

But tomorrow, someone else comes out with a vision plus tactile world model that's just like a 100 x more data efficient just because... You know, vision is a very high information or, like, a highly redundant, you know, input because, you know, every other pixel, don't change that much. You can see...

You can understand the world without perfect vision. But tactile, you know, other modality might be much denser information. Right?

So maybe having that, predicting that, would just allow you to use much less vision. So you can get to the same performance with much less compute, with much less data. Right?

But... I think you will be able to adapt very easily. No.

Exactly. There's always a lot of how much data... Right.

So the reason why I bring this up is because I think if vision tactile world model or vision tactile audio, whatever world model, is the final thing that solves robotics. And I think robotics companies will be able to do that better than video generation companies that's trying to pivot to robotics. The reason is, like, to understand sensing is actually very complicated.

Like, we've been building that since the company started, so we know the bottleneck. We know how to combine them. But for...

If you're just trying to adapt the video model to robotics action, they're like, you know, if we were wrong... If, you know, video model is not enough, they're like, you don't really have the know how to get tactile to work very well, but we have that. This is a side question, but I I think it's a quick one.

Yeah. It could be another color. Could be another color, another input Yeah.

To the model. It's... This...

It could be used in cooking scenario, actually. Like, I know companies that use infrared to, like, know how cooked the food is. Yeah.

Because that's really hard to tell from RGB camera. Right?

yeah, maybe it's useful to talk about how I got into AI in the beginning. Right? So as a kid, so I spent time in Japan and China.

I grew up playing Go, you know, the going alpha Go. So when alpha Go came out, I was a senior in high school, and I was mind blown because I've been playing this game for many, many years. It's so complicated.

Like, I think before AlphaGo came out, people were like, okay. Go is one of game that AI will never solve. The search space is, like, larger than the total number of atoms in the world.

How could you possibly search for solutions in that? They're like, know, RL reinforcement learning just cracked it. Right?

And I think that was how I got into AI initially. I was just really interested in models or systems, agents that can self improve. Right?

So when I got to Penn as a PhD student, I just really wanted to work on reinforcement learning. So I'm co advised. Both my advisors were interested in Oriole at the time.

One of my advisors was more in robotics. He did a postdoc at Berkeley robotics lab. So that's how I got into robots at Penn.

You know? We have Grasp lab, I think one of the best places to do robotics. And so that's how I got into doing RL for robotics in my PhD.

And then, yeah, I did a bunch of research thinking about how to connect reinforcement learning with human data, how to do reinforcement learning theory in a very rigorous way. And then I went to NVIDIA. That's when g p d four came out.

At the time, we were very interested in just solving pen spinning tasks. I don't know why. Like...

Right? I mean, I think... That you can solve Rubik's cubes and then...

The time I was working with, you know, Jim Fan and Yuko, which you guys probably also know. Like... Yeah.

We were just like, okay. Let's try to do dexterous pen spinning. The initial idea was, like, learning it from human videos.

Right? We were trying to watch humans do it, but then I realized, okay, today we don't have a dexterous hand that can spin pencil at all, so how would I even be able to evaluate the algorithm even if learning from a human worked at all? So I said, okay, we need to first try this in simulation.

Then as soon as I try to set up a simulated environment for a robot hand with a pen, I immediately saw what the issue is. The issue is like, if I had to come up with this simulation environment, the reward function for reinforcement learning to solve pen spinning, that's impossible. Like, I wouldn't be able to come up with all the terms that you need to get RL to learn the right behavior.

And then I I was just thinking, what if a model that can write code can do that? That's what became the Eureka project. Right?

We basically... Like, in my opinion, we had the first, like, recursive self improving robotic agent in simulation, because we really, like, used that to solve a new task that no one has demonstrated before. And that that that allowed me to see the potential of using language models, using coding models to really accelerate robotics.

So then I went to Google DeepMind because at the time, I mean, even today, they have all of the frontier models in Gemini. Right? So I was looking at how to use Gemini for robotics.

But eventually I realized... Like, I had a lot of fun doing academic research, but there was always a question in the back of my mind, like, does any of this matter? You know?

Like, when you're working on this kind of thing in the lab, like, you know, you ask me how do we come up with so many interesting fun tasks? You're basically doing your own eval. You're grading your own homework.

Like, I wanted something more real. I wanted something that could actually be used by millions of people in the world. And I felt 2024 was when we started Dyna, like end of twenty twenty four.

That was the right time that we can take some of these technology to actually putting them out in the real world. So that's why I started Dino with Linden, New York. And fast forward two years, we built our platform, our model.

Our robots are in Jingtai Phong. Go check it out. Go eat.

Yeah. Go eat. It's been a journey.

And... But what still gets me really excited is, you know, like, some of... A lot of ideas that I temporarily abandoned when we started Dynam because, you know, to build a company, you have to be very practical.

Like, I think simulation wasn't ready in 2024. But now with, you know, like, the new frontier models and what we're seeing internally, like, this might change, and that's very exciting.

Yeah. So let's first talk about, you know, how we run the company. Right?

Like, we think of ourselves as like a like a like a... Eventually, a research plus product company. It's kinda like an open AI where you do frontier research, but eventually, all of them make their way into codex to charge APT.

That's how we think about it too. We're not here to just do open ended, like, whatever thing that interests us at the time. We're always, like, product driven.

Like, we wanna deploy our robots, our platform to a new customer. What are the technical problems we have to solve? Right?

We have to solve generalization. We have to solve whole body control. We have to solve how to adapt quickly.

We have to solve how to make the robots follow language. We have to solve dexterity. So once all those problems are very clear, then, like...

I mean, our engineers are very capable. They're very smart. So they just create instances of the task.

They create tasks that would stress test those capabilities. Right? Like, the the grabbing and drink task is just how we test language following.

Right? You can just specify what you want. If the model follows intent, follows instructions, you should be able to do it.

So that's how we think about, like, at the top level, we identify what are the things we have to solve. Then we have a lot of bottoms up projects to, like, actually drive execution to solve them. And, you know, you hire the right people, and everyone here is super passionate about robots.

You know, they've been doing this for a long time. They love it. And we just create a culture where it just naturally happens, and it's it's a really fun place to work.

Yeah. Side of the company? Yeah.

You know, we recently grew so much, so I don't have the perfect number. But I would say on the hardware side, we have, I would say, probably, like, 30%, 40% of hardware, just because hardware, you need a lot more people. And then, like, you know, we have, like, around 10 people working on models, and then we have, like, software engineers, robotics engineers.

We have a lot of very talented people on the business side. The most fun thing about building a robotics company is you see everything. Everything under the sun that could possibly be helpful will be helpful.

Right? And so you just get so much exposure working with so many different people from many different backgrounds who are all very passionate about robots. And I think it just it just been really eye opening for me as a founder.

Because when I was a researcher at a lab or, like, a PhD student, you know, I was talking to researchers every day, and that was great. That was really good. I still get to do that every day, but now I also get to learn, like, just from so many different people from different walks of life.

Like, some of our people who collect data, they had no robotics experience. They were work... Working in retail, working in some other jobs, they came here.

You know? They also became very well trained in robotics, and I think that's just pretty amazing to see. And I think of myself as like, I'm still learning, you know?

I think of myself as just always learning in the robotics space, and I haven't learned so much as I am now. You know, even though, you know, you think about me now, I'm not a student anymore. I'm I'm here, like, running a company managing a lot of things, but I still feel like the day to day learning is, like, just so much.

Yeah. I think our vision is to truly... The high level mission is truly bring, like, you know, robots and intelligence, right, into the physical world.

Like, action... That means, like, actually seeing our robots deployed, like, thousands of customers, even more millions of robots. That's where we wanna get to.

What's the what's the name from? Dyna?

Is there a... Is there a story? Yeah.

There's a... There's many stories. So I I I Dyna.

different options. But how the name came to be was... There are two things.

One is... So Turing Award winner, Richard Sutton, was, like, the pioneer reinforcement runner, wrote a paper called Dinah many, many, many years ago. It's about basically an agent that self improves, you know, world model.

And I felt... Even when we started the company, like, we weren't doing world model yet, but I felt that's how robotics will get solved, and now our, technology technology direction is, like, converging to that. So I felt that was a really neat inspiration also because I've worked in reinforcement learning.

Have a lot of respect for Richard. The second thing is that it's a bit more personal. Like, I grew up in Japan, and, you know, the year I was born, there was just, like, a cartoon show.

I don't know if you guys have seen, like, Ultraman. Yeah. Of course.

Yeah. Yeah. The the...

Yeah. The Ultraman that year was called Dinah. Oh.

Yeah. So, yeah, it's a very subtle thing not people know, but I guess now everyone would Yeah.

Calls to action. Yeah. I think...

I do feel like as an industry, robotics is still very demo oriented. I have done a lot of demos in the past in my lab, you know, even here, we had to show some demos, but I think we need more people that are truly mission focused on doing whatever it takes to get robots deployed, get robotic foundation models truly integrated with hardware, and seeing them at real customer sites. I think we are a pioneer in this regard, and I want to see more in this field.

For the audience that's watching, if this mission inspires you, if, you know, solving the hardest problems today is what makes you wake up every day and you wanna contribute, you know, we're always hiring and, yeah, looking forward to that. But I think just Bay Area? Anywhere else?

Yes. Just Bay Area today. But, you know, as company grows, never say never to other locations.

Yeah. But that's what I wanna say, and I wanted to see more robots in the world. Yeah.

Cool. Congrats on everything.