# Dyna Robotics: Scaling General Purpose Robots

**Podcast:** Latent Space: The AI Engineer Podcast
**Published:** 2026-10-14

## Transcript

All right, we're here on site with Jason Ma, co-founder and chief scientist of Dyna Robotics.
Thanks for having us in your studio.
Of course.
Thank you for coming.
Yeah, it's been a blast.
You'll see a lot of V-roll, but we got to see drinks being pulled, a lot of evals, robots with knives for the first time.
So under the hood of all this, you have a world action model.
What is that?
Yeah, so last month, we released our world action model called Dyna2.
So it's the first world action model trained on more than one million hours of data.
So going back to your question first, world 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 yeah i want to prompt this question a bit i think a lot of people hear world model and they think you're outputting video or world or 3d state you're also doing action i think most people don't understand what models are under the hood of robotics so what is input output what's the training data what is the architecture Give us all the details.
Yeah.
So for inputs, the most important input is video, right?
You know, you can think about videos that the robot is capturing with its cameras, right?
So video or images of the 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 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 world 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 kind of 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.
You put out a good blog post around your model.
I think a lot of people understand language models or transformers, next token predictors.
In this case, you shared quite a bit.
There's like diffusion flow matching.
You're predicting next world state plus action.
One thing you say is a million hours.
How do you determine the architecture?
Like what are the choices there?
What is the architecture?
I feel like in language models, there's a lot of take something off the shelf.
Take trillions of tokens, do prediction.
SFT, RL, and you have a model.
What about this?
How do you go from nothing to your model?
Is there vanilla architecture off the shelf?
Do you determine all this?
And 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 a 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, it's a diffusion transformer.
So transformer is essentially like the language model, you know, sequence to sequence, match, decoder only.
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 or that's something really highly defined and have very fine-grained feature.
If you think about generating images, you want sharp edges, sharp corners, color contrast.
And for robotics, even though you're not generating an image, you're generating actions that needs to be very precise, very smooth.
So it has kind of the same kind of consideration of desiderata as generating an image.
So that's why from first principle, 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.
So that's the architecture that goes behind a lot of our recent breakthroughs.
And we've covered flow matching before on the podcast.
It is mostly an efficiency speed up.
Like let's say normally you would take 40 to 100 diffusion steps.
You can reduce it to four.
Yeah, that's right.
So with the diffusion model, one thing that's really 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 a special case of diffusion that makes the transformation much simpler.
In practice, you 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.
But the concept is the same and flow matching is just a special, simpler to tune variant of diffusion.
It actually makes me think that, so flow matching obviously has trade-offs in quality, but actually it's very suited for action diffusion and robotics because you get to re-evaluate in real time.
Whereas in images, it's basically one shot.
That's right.
Which is pretty fun.
We got to mess with the laundry folding robot.
throw a random towel in, the models adapt, right?
Yeah.
I think one key thing about your models is that they're made to be generalists.
So one foundation model and you adapt them a lot.
Yeah.
How does that work?
Basically in 45 minutes of mapped data, you can generalize towards, we saw an example of pulling drinks out of a fridge.
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 the 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 at any given task, there is some amount of training that needs to happen.
And I think to train 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 on as much data as we can find.
So that is when you're dying to 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 um we actually have quite a few people who deal in data who sell data yeah um what is good data what is bad data what do you want to buy uh yeah i think these are all open questions like obviously i think the the top important thing is egocentric data of humans doing tasks.
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 a 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, 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 an individual company can collect by themselves, it's 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.
There are great websites, 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 where 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 non-stationary problem in the sense that what is high quality data for your model?
changes over time 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 In the case of egocentric data, I think it's actually a very good way of pre-training robots because human data is so diverse.
It covers every single physical activity you can possibly imagine.
And that gives robots 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 going to be some gap between human actions and the robot actions, right?
So just by training on human data today, it's not very likely you can just execute robot actions directly.
So you still need some amount of adaptation using robots own data, right?
But the robots own data collected on a robot is going to 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, like egocentric data, to give the model a strong prior over everything physical manipulation can cover, and then combine that with a small amount of robotics data to make the robot 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 going to collect on that, which will give you a strong foundation model.
You really need to 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.
I'll prompt you, just for, again, for the people listening who want to work with you, what data vendors do you want?
This is your call for data.
Yeah, no, that's a great question.
And yeah, so in our Dyna2 blog at the end, we actually have an email address called datapartnerships at dyna.co.
So if you're a data vendor that has a lot of high quality, let it be egocentric data, robot data, video data or anything you think could help robots, send it to that email, our team will take a look and it will get back to you.
Yeah, but at your stage, 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 macor for like doing this i see the the customers that 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.
So for us, it's like we still think just a million hours of data is not enough.
It's just the beginning.
We showed the first model on a million-hour scale, but we need more than that.
So we're 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 kind of like humans, you know, you know, if, you know, Sean or me, you know, we go work at Rosewood Hotel tomorrow, we're not going to 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 kind of like what Mark Rowe 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 and then there's probably more about there's a you know put themselves in their in their way yeah uh so so one thing that we discussed is like the the closing the scene to real uh and uh what's very interesting is like now you can basically fabricate environments on the fly yes yeah i uh have you tried that with the the frontier models um what's your experience are they good enough are you using any world model robotic like world model companies to generate these and train on them?
Yeah, so I think generating synthetic environments, synthetic data, simulation data for robotics, it 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 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 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 a large language model at the time, it's GBD-4, which is how 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 very nice demo of the robots 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 3D 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, the vision capability of these frontier models are crazy.
They can do real-to-sim, do catting, there's 3D reconstruction, all of that.
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 3D 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 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 it has scaled to a million hours of data we are deploying a bunch of robots the world was actually you know already starting to do very cool things we're just at the beginning just the tip of iceberg for like what is there to come yeah For those on the research side, we recently had Runway on the podcast.
One of their things is robotic world models.
They're shifting a little towards there.
So, you know, nice people to look at it.
One of the questions I have about...
you making your own models is what is the constraints you operate in?
So if you draw a parallel to language models, you see like GPT-OSS 20B is very fit for your average MacBook.
Then there's a larger one that's fit for a single GPU.
Do you work in the constraints of robot model on device, in cloud, a hybrid?
And then how does that affect your...
scaling law of data.
Yeah.
You know, a million hours is great, but like, do we just scale up parameters, data?
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 has latency, right?
Well, like, you know, today, whenever I ask ChatGPT, it takes like a couple of seconds before it spits out anything.
But for robots, like the inference latency has to be like very short.
The robot needs to act.
You can't just hold a knife and not do anything for three seconds.
That's going to freak people out.
We saw someone put their real hand under a knife.
Our model's pretty smart, so that's okay.
But in general, if you think about very dynamic tasks, for example, you want your robot to hold a tennis racket and juggle the ball.
It's very dynamic, very reactive.
The physics, gravity doesn't wait for you.
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...
Networked entity.
There's no guarantee.
There's no guarantee, right?
So for that reason, the model that gets deployed eventually has to be of 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 have a large...
People do distillation, right?
Like the best way to make a small model very smart is by doing distillation, right?
You're training the biggest model that you distill, right?
So I think in robotics, we're getting there, but I think that's what people are going to converge on.
Yeah.
How about like inference?
You have your own inference stack or a lot of this is architecture.
You've pulled diffusion and whatnot.
I'm guessing you can pull off of something.
Anything you want to share?
Yeah.
So we...
build our own inference stack for whatever model we deploy because like I was saying, latency is so important.
So only the whole stack is extremely important for robotics.
You want to optimize 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.
Everything from the actuators all the way to the industrial design ID, which I...
didn't really know as an acronym.
Obviously to the models and data as well.
I'm curious, are these your own actuators?
How much of this is your own?
Of course the design, it's not like humanoid, but actuators, how far down the robotics stack?
Yeah, so the way we think about what we need to own versus what we can buy is if the input-output and other components are very, very clear.
and like there's already extremely good mature solution then there's no point to building house for example on this robot you see the lidars right so those are black circle 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 it's 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 systems 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 actuator is very good that you know can actually be easily modelable so that's what we care about eventually so we're going to own that same thing with you know the robots proportion you know this robot's proportion is like we measure against real people why do we do that because we're pre-training on human data no 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 hardware that carries intelligence intelligence is the extremely important thing we want to make sure the hardware serves for that yeah yeah i'll just highlight i don't know if this is relevant to you but one of the most interesting robotics discussions i've had is someone who was very stressed out about actuators and specifically magnets because apparently we don't make magnets in the us no we don't and there's only two countries that make magnets uh rare earth material yeah right i don't know what the what's the chemical what's the material i don't know what the thing is but yeah uh so yeah if anyone's interested in that that's apparently a bottleneck that we have no yes so what do we do yeah i mean i mean it's a good question right i think there's a lot of uh exciting discussions on bringing manufacturing back to the u.s but you know for robots especially that's actually much harder than um 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.
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.
I think as a full-stack robotics company, we realized...
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 the discussion centers around a robot foundation model, model, data, but I think hardware and just how hard it is to build good hardware, it's very under-emphasized.
But I think that's going to be extremely important.
This is a platform, right?
Actually, there's some companies that choose to be, let's call it platform-less.
They only do the model and they'll say we'll deploy on any platform.
But they plan optimists.
Off-the-shelf robotics.
And so it's a choice, right?
Obviously, you own everything, so you can do that.
Do you see yourself building more in the US?
Or do you see building your own global supply chain?
What's the direction that is likely?
Yeah, so we are currently exploring many different options, right?
Because with global supply chain, then there is some geopolitical things.
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 building the whole system, or the necessary thing is to build a whole system.
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 service to solve some physical tasks.
So if you want to solve physical tasks, you want to be able to optimize everything.
And that's important.
I mean, we have the robot.
You got to tell us about the design.
Your own custom platform.
What do you call this?
What do we call it?
Yeah.
So it's not humanoid.
It's very smooth.
But what's the rotating arm?
Yeah.
So we think of this as generally like a semi-humanoid.
So basically the upper body is, you know, like a humanoid.
It's got, you know, of course, shoulder, got arms.
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-human way because the lower body is wheels all right so why do we choose wheels because i think there is a difference there's uh you know optimal like 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 legged 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, like the robot dancing.
Yeah, they fall over very easily.
No, but you also see the demos that you can tell are not production where you can box the robot and you can push it over and it's very good.
But that robot is just good at balance, right?
I think the more interesting thing that you guys have innovated, so we have one of the...
prototypes uh which is the Mewtwo yeah back here you can call it this is this is just like an up down uh axis and you've chosen to then move towards something like this which um has the the ability to lean and we're going to show the laundry demo where you have to lean into machines right that's another degree of freedom that i don't even have the words for it it's not pitch it's not yaw yeah yeah yeah so yeah how did we think about that right like uh so actually it's very used for exercise like you know i think Sometimes a couple years ago when I was exercising I accidentally like you know like pull 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're 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 tasks that you feel are very natural, right?
So that's also how we think about like when we design robots when you have like a This kind of design which is this purely elevator could only go up and down We realize a lot of the like human torso movement like we use our like, you know waist back a lot for like day-to-day physical activity, right?
Even like if I want to get this bottle like I'm not just moving my arms It's actually very strange for me if I I had to intentionally think about fixing my upper body.
I would always like do this, right?
So there's just a lot of like subconscious way 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, we want to learn from human data.
The humans are moving their torso all the time.
You want to be able to like ingest that into the model to control the robot.
But if you have a form factor that doesn't allow for that, then the transfer becomes less and the robot becomes less dexterous and it's just more awkward.
What's the data labeling side of this?
Like, you know, you've seen people, is this the last like post-training fine-tuning side of actually the hour of using the robot tele-operated or remote-controlled mapping motion or like, does some of it generalize over?
When you say labeling, you mean?
Like taking the model you have that predicts state to 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, we, 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 to like, you know, like even before the hardware is ready, we're already committed to pre-training on 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.
Talk about the eval side of this.
you can have predictions you can have it do stuff and you know every now and then we see it miss stuff you test on very niche things as well so like filling a pill bottle yeah um how long does that process take how do you think about the aspect of actually deploying on a new task evaling it iterating what does that look like yeah so if we want to learn a new task uh you know we basically do a little bit of robot data collection then the model these what is a little bit like Yeah, like a 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 45 minutes.
Yeah.
Open the fridge was on.
And that's an interview.
That's like an onboarding task for one of your engineers.
Yeah, yeah, yeah.
So fun thing if you want to work here, you know, find the task, have the robot do it.
Yeah, yeah, yeah.
Pitch me a new task and I'll come to the office, you know, collect some data, try 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.
To fill in collected means like you are, you have a person actually operating remote control, doing the task for a bit.
Yeah.
And then you trained.
Yeah.
And it does okay.
You improve.
Yeah.
How do you measure?
Yeah.
Yeah.
So 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 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 to iterate, right?
I think of this as like the robotics version of reinforcement learning from human feedback, right?
The robot rolls out, you can score it, good, bad, somewhat good.
And then that data, the robot's own experience can be used to train the model.
And you can also just collect more data yourself to train a model, right?
Eventually, our goal is like, this robot is gonna be our platform.
Anyone should be able to train tasks on it.
I want to get a bit of a prediction and timeline to where that goes.
Right now, the tasks are somewhat simple, like take laundry out.
It's a towel.
You can't crush it.
Chop some food.
There's also the side of like, okay, set up this filming studio, right?
Move stuff around, bring a table, set up lights.
I think manage my data center.
Yeah, yeah.
Plug in stuff.
And also recharge another robot.
Yeah, yeah.
When do we see the factory or like, what do we need to scale up?
Is it model?
Is it everything?
It seems like your hardware is set for that.
But where do we see it go to a point where I can go from instead of just cut one thing to, you know, okay, here's a recipe, like full send or yeah.
Yeah, I would say to get to truly general purpose robots, like basically a robot or a model that does anything like Not that like, okay, so to keep it somewhat grounded on a technical level, like when do we start to see us pushing that feedback, right?
So instead of very simple chopping, maybe a five to 10 minute sequence, right?
Where now the eval step, it can break at many more points.
Instead of just fill one pill bottle, 10 different pills all in a sequence, right?
What's limiting us?
What are we pushing on?
Yeah, I think...
Yeah, I think a couple of things.
One is that the laundry demo that you have seen, it's going to be like one hour to finish the whole group.
So the longest duration that a robot can do a full job is already in the hour range.
But to be able to do that for any task, I think the bottleneck is coming back to data and hardware.
So for some of the tasks, for example, setting up the space, you might need more than the parallel job gripper.
right you might need something more dexterous like a hand like a human hand right but then if you want to try a model on the hand then you're bottlenecked by the hardware stability like hand today the best hands dexterous hands are still not uh durable enough they overheat pretty quickly right so that becomes a bottleneck right you like you we you know you quite literally have to put a uh you know bag too cool we're not we're not just auto researching Make better model we have, we have hardware.
Yeah, so hardware limitations 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 you know some uh encoding of your hand motion where the robots are 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 a task, right?
So I think those are what I consider what's dragging out the timeline.
But for tasks or for workflows that you can collect data today to high fidelity, then I don't necessarily think there is a fundamental bottleneck, right?
Just like what we are demonstrating to the public this week, we have a physical agent.
that can do the entire laundry workflow as a human would.
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.
So I do think the biggest bottleneck to have a much more general thing is still hardware and data.
I'll take this chance to let you brag about your customers and partners.
You have some announced relationships.
We can start with Best Western and then maybe some of the others that you're proud of.
Cool stories, anything like that.
Yeah, so we have a deployment blog post on our website that goes into much more detail.
I really recommend watching the video because I think the video was...
Do you want the Best Western one?
No, we have like a...
one video for all the customers we're working with.
It's a really really nice video, 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 want to go somewhere I actually recommend going to our other deployment, Dintai Phong.
have you guys been to yes yes yeah you know the best very thin wrappers yeah very we should have filmed there yeah no we should have just been there yeah but uh in din tai fun like if you go to din tai fun or other like uh 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 phone locations in the united states so that's actually our biggest scaled customer, right?
So I want to 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 want to deploy your robots across many of their sites.
So that's what's happened for us with Jintai Phone, which...
To the best of my knowledge, it hasn't really happened for other companies doing similar like general purpose robots.
So at Dintai Phone, our robots fold thousands of napkins per day.
The quality standard is extremely high, you know, because they want like, okay, the second fold with the seam of the napkin is only like a couple of centimeters below the, I don't even know how you call it, the pocket.
So you can stick the chopsticks in.
So it's actually very fine grain, high precision.
I can show you.
at some level that's what robotics are good for robots are good for too right um humans are not the most consistent yes especially in small details whereas a robot can somewhat do it perfectly it's interesting that's the example you bring for general purpose robotics versus uh at some level i think people would assume yeah you can have a specialized robot that will fold napkins perfectly to the exact you know millimeter as opposed to something like this but yeah i'm sure Maybe you can, right?
But the hard thing about manipulating deformable objects, right?
Like napkins, towels, everything is deformable.
There's just infinitely many possible configurations.
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 Dintai Phone has scaled, has signed a much bigger contract with us, they not only want our robots to fold napkins, now they want our robots to cut.
That's why we have the example, to cut food to the other.
Is that actually the second use case?
That's one of the use cases you're thinking.
Dintai Phone needs to cut so many vegetables per day.
They need to do the dumplings.
Exactly.
That's the final boss.
I think you can.
I think you have the dexterity and precision to do this.
You have a little pobol.
But like, you know, don't break the dumplings.
Yeah, yeah.
I mean, that's the obvious benefit is you can adapt them anymore.
I think the other thing you really touch on is the speed, right?
To make a custom napkin folding with the exact ply, whatever, you have to make the machine.
You can't just Amazon this, but this can do it pretty fast.
Yeah, this can do it very fast.
And now it's actually, it's a human parody.
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 Dintai Fun is like, you know, their LA location with New York location, Seattle location, they fold napkin slightly 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 information.
You 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.
Final boss is Cheesecake Factory.
If you know the story there, they're from scratch.
Everything is very consistent down to the measured gram.
Every sauce is made.
Anywhere you go in the world, you'll have the exact same thing.
If you don't know, you don't know.
But you know, that's the thing.
The term that comes to mind is like bitter lesson for robotics.
Instead of the specialized robots, which is a lot of industrial engineering, you build a generalized platform that can do a last mile fine tuning and do all those things.
Give the expert add-on outside of your what is more bitter lesson robotics.
Yeah, I think there's several bitter lessons for robotics.
One is that robotics is going to scale just like any other domains that machine learning has solved.
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 what is the right data is much harder to figure out than other domains.
In language, it's text.
In video games, it's 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 better lesson as the core worry 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 bitter 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 just much harder because there is no internet.
you have to come up with your hardware come up with the sensors get all of those right and then scale then i think uh i mean there's the argument that you know the world is very made for humans it should be a human right as hard of a hardware problem that is think 10 20 100 200 years down the line can we make a really efficient human but you have to be practical with what we have now right yeah Yeah, so the way I think about it is that yes, eventually the robot should, if you want to 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 human 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.
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.
It's public news.
Optimus already gone through many, many iterations and they had to scratch their designs.
And I think by iterating with a semi-humanoid form factor and actually putting them out to real customers or getting models redeployed, you get that feedback loop that you wouldn't get if you're just trying to build a warehouse, humanoid, and you spend like five, 10 years building it.
I think the iterative approach will actually get us closer to the final robot faster.
And I will say, you know, for those listening and not watching, it's probably the smoothest robot we've seen.
Like every humanoid you've seen is jerky and even humans don't walk as smooth as this.
Because, yeah, the model can move every single motor at once.
Yeah.
In more than two cohorts.
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, you know, dimension, right?
The bigger the dimension is, the harder to fit.
uh the model to the data but here the action space is quite large but uh uh we did 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 uh you know about 100 action dimensions yeah and these are all continuous continuous right continuous right so the model is basically predicting at very high frequency you know like tens like at least 50 dimensional vector like 10 20 times a second to get that continuous motion.
And potentially predicting one to two seconds out so that is another 100 frames or whatever.
There's a bit of a history similar to context of previous state, I guess.
So context is a very important thing about it, right?
Because otherwise, if you're just making a decision based on the current observation, sometimes it's really hard to tell what to do next, right?
For example, if you think about opening a microwave, like in the middle part, it's unclear whether or not you're closing holes or trying to open it right so like context is also really important yeah and i have a screenshot of the the fridge demo where it is there's agentic planning and tooling and all those things so we can show that i mean for listeners i think that's an interesting side after the model yeah how do you wrap it around into agentic harness like what is final deployment clock code you have a lot of cool uis But, you know, what goes into wrapping model output towards final thing?
Is this custom per use case?
Is it you have your own SDK?
Yeah, yeah.
So, I mean, eventually we will have our own SDK.
But the way we think about it is we want a general 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 is a lot of like a high level decision making you have to do, right?
It's not just like robot, you know, only reasoning about the physical world in the action, continuous trajectories, right?
Because that's not a very like low level, the thing you're actually executing in the physical world, but that's not the intent that, you know, like when we are doing decision making, we think about steps of a recipe.
We think about, oh, like...
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.
There's a lot of high-level cognition, decision-making that's happening.
And I think for robots to handle, even the laundry task we are showing you, it has to have that capability because it needs to reason, 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 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, you know, try to learn that with a, you know, low level model that's outputting actions, but it'd be extremely inefficient.
So that is why having a hierarchical system, like putting a harness, like agentic system around our world 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 language model, right?
Like two years ago, three years ago, the model was only good at autocomplete.
Like humans have to like tap, tap, tap to get the model to keep spitting out code.
But today, just like a agentic system just carries the end-to-end task.
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, 30 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 do you guys see benefits from language model coding tools getting better so new Opus 5.5, new Astra.
Do you have language models in your agent harness?
How does AGI 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.
When GPT-6 came out, we had a company-wide hackathon the same week.
And the thing that our engineers built was incredible.
And many of the features we built is already in main, in production.
So we really like using coding agents to accelerate our work.
Just for the people interested, anything surprising in GPT-6 that you found?
Yeah, I think it's about the ability for it to construct virtual environments.
right turns into simulation we talked that uh 20 minutes ago i think that's really exciting it's interesting there were so many like cad zillow to 3d he lost the hackathon to the cad uh i won i did the cad bullshit other way other way no oh that was i won a gpt hackathon just for fun i was like i know nothing cad yeah um might as well play around with it because every demo is doing it i'm like it's gonna be garbage i'm like It's kind of grounded.
It's kind of good, but it's just hackathon stuff.
Yeah.
Cool.
I was going to round out some of the use cases and then go into a little bit of company culture and your personal background.
Yeah.
I have two technical questions.
Yeah.
One is, are there any goals you want to set for yourself?
Like six months, one year, two year timeline that you see robots should be able to do fill in the blank?
that you're kind of working towards?
Yeah, I mean, in the six months to one year frame, our goal is definitely like scaling up our robots and our model and truly getting them to deploy it.
I mean like a task, like right now it's cutting or opening a fridge.
Is there, like, do you set yourself to by this timeline we should to stay on track, be able to do whatever?
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 a 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're doing a lot of that in laundry in restaurants we have already deployed tens of robots but our goal next year is like overall just deploying hundreds of robots Part two on the last technical question is how do you see the rest of the industry?
So fill in for people.
What other approaches are people taking to world models?
Why are they wrong?
What is different but good about their approaches as well?
Just what is the landscape of robotic world models in this space and how have you come to your current?
Yeah, that's a great question.
So I think robotics is the most exciting space today.
And there are just so many amazing companies that I truly respect.
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.
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 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 to deploy.
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 a 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 industry?
Is there other directions people are taking?
Yeah, I would say that's also not standard.
So we're 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 video world models.
uh there 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 like what we call the models is less important than what we i 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 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 that you 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.
So just to press you a little bit on this, you're basically saying your approach is better than the VLA, right?
Because you're emphasizing prediction.
I fully buy that.
You've sold me on it.
If you were wrong, what is the most likely number two that someone else is doing?
Interesting question.
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 you'll 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, like sound, like touch, right?
So I think 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 100x 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, they 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 no exactly there's also a level 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 word model it's the final thing that solves robotics, then 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 to understand sensing is actually very complicated.
We've been building that since the company started, so we know the bottleneck, we know how to combine them.
But if you're just trying to adapt the video model to robotics action, then if we were wrong, if video model is not enough, then 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 think it's a quick one uh heat seems very important is infrared just another color to you or is that yeah you could be another color could be another color another input yeah to the model this could be used in the in cooking scenario actually like i know companies that's using infrared to like know how cooked food is yeah because that's really hard to tell from rgb camera right my uh very naive pushback on your last point is maybe we don't need to understand hardware cloud code or codex can just, you know, five code it in.
Yeah.
There's a lot you can do without understanding, but of course.
It's just not in the distribution.
So I wanted to bring it a little bit to your personal story or your background.
We both went to Penn.
I did not.
I just found out today.
Yes.
Yes.
I, you know, my big regret is I didn't join the DARPA challenge.
Okay.
Which then I could have been like early Waymo or something.
You were at Grasp, and then you've also worked at Google and NVIDIA.
What are the different focuses of, let's say, academia versus Google versus NVIDIA?
You've seen all these places, right?
And then that obviously caused you to say, like, screw all that, I'm going to do my own thing, right?
You could have joined OpenAI's robotics.
So let me just take us through chronologically, but also one thing you learned from each.
Yeah, so chronologically, 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, going AlphaGo.
So when AlphaGo 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 the games 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?
Then like, you know, RL.
reinforcement learning just like cracked it right and i think that was how i got into ai initially like i was just really interested in like models or systems agents that can self-improve right so when i got to pen as a phd student i just really wanted to work on reinforcement learning so i'm co-advised both my advisors were interested in RL at the time one of my advisor was more in robotics he did a postdoc at berkeley robotics doc so that's how i got into robots at Penn.
You know we have GrassLab, 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 GPT-4 came out.
At the time we were very interested in just solving a pen spinning task.
I don't know why like uh it's the dexterity limit right I mean I think you can solve Rubik's cubes and then it's time I was working with uh Jim Fan and Yuko which you guys probably also know like yeah we were just like okay let's try to do dexter's pen spinning the initial idea was like learning from human videos right we were trying to watch humans do it but then I realized okay today we don't have a dexter's hand that can spin pencil at all so how would I even be able to evaluate the algorithm even if learning from human worked at all.
So I thought, okay, we need to first try this in simulation.
Then as soon as I tried 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 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 use that to solve a new task that no one has demonstrated before.
And 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 one 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 asked 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 dynum like end of 2024 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 uh i started dynum with linden new york and uh fast forward two years we built our platform our model our robots are in jintai phone go check it out and uh go eat yeah go eat it's been a journey and uh but what still gets me really excited is you know like some of a lot of ideas that I temporarily abandoned when we started Dyna because you know to build a company 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.
And so then also let's talk about company culture.
Yeah.
What principles you're building with the company with.
Obviously, you're responsible for the science and the research agenda.
And it just seems like when we meet the engineers, they're very engaged and they have a lot of ownership.
They have a lot of motivations.
I'll say it's interesting to see how many side projects people just work on.
So some of the demos we've seen, it's just...
The engineers that work on it were like, yeah, this seems fun, might as well do it.
To your point on fun evals, right here, like two feet behind the camera.
Can't talk about that.
No, I mean, the thing is just the eval is just there's highlighters, four highlighters being rubber banded together.
It's like, who comes up with, ah, yes, we need to test putting rubber bands on highlighters.
It's just fun stuff.
People are very, you know.
creative here it's a beautiful space yeah i think culture 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 eventually like a research plus product company it's kind of open air where you do frontier research but eventually all of them make their way into codex to chat gpt 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 want to 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 robot 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 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 instruction 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 bottom-up projects to like actually drive execution to solve them and uh 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 a really fun place to work.
What's the split between like technical research software versus hardware side of the company?
Yeah.
You know, we recently grew so much that 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.
Then we have software engineers, robotics engineer.
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.
You just get so much exposure working with so many different people from many different backgrounds.
We're all very passionate about robots.
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 working retail working in some other jobs and they came here you know they also became very well trained in robotics.
And I think this is 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 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.
And then just to close off, Where's Dyna going?
Two years, five years?
What's the vision?
Yeah, I think our vision is to truly, the high-level mission is truly bring robots and intelligence into the physical world.
That means actually seeing our robots deployed thousands of customers, even more millions of robots.
That's what we want to get to.
What's the name from Dyna?
dynamic?
Is there a story?
Yeah, there's many stories.
So I named Dinah.
There was a lot of...
Take the credit.
Internal different options.
But how the name came to be was, there are two things.
One is, so Touring Award winner Richard Sutton was like the pioneer of reinforcement.
And I wrote a paper called Dinah many, many, many years ago.
It's about basically an agent that self-improves in a 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, you know, technology, technology directions that converging to that.
So I felt that was a really neat inspiration also because I've worked in reinforcement and I 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.
Of course.
Yeah, yeah.
Yeah, the Ultraman that year was called Dyna.
Oh, yeah.
So yeah, it's a very subtle thing not people know, but I guess now everyone would.
Yeah, the team is finding out too.
Yeah.
But like, I think Richard would appreciate it.
I don't know if you ever talked to him, but.
No, I've seen him at RL conferences.
You should just be like, hey, this company.
Yeah, I'll invite him.
Inspired by you.
Everyone knows Richard for Bitter Less and Baito.
Yeah, also Bitter Less.
Yeah.
Okay, any parting thoughts or calls to action?
Calls to action.
Yeah, I think I do feel like as an industry, robotics is still very demo oriented.
Like I have done a lot of demos in the past in my lab, you know, even here, like we had to show some demos, but I think we need more people that's 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're a pioneer in this regard and I want to see more in this field.
And for the audiences watching, if this mission inspires you, if solving the hardest problems today is what makes you wake up every day and you want to contribute, we're always hiring and looking forward to that.
But I think just...
Bay Area, anywhere else?
Yes, just Bay Area today.
But as company grows, never say never to other locations.
Yeah, but that's what I want to say.
I wanted to see more robots in the world.
Yeah.
Congrats on everything.
I mean, this is incredible progress for two years.
Yeah.
Amazing.
Yeah, I really appreciate it.
Thank you for visiting us.
I hope, you know, it's been fun.
Yeah, I would say, like, yeah, I don't regret doing this on-site.
Like, yeah, we do a lot in our studio.
Yeah.
Like, this was worth it.
Yeah, yeah.
I think for robotics, gotta do it on-site.
For people listening and, you know, watch the...
what's the video version we have fun b-roll