Dyna-2：具备小时级自主性的通用物理智能体——对话 Dyna Robotics 首席科学家 Jason Ma

Watch along AIE NYC and join us at AIE CODE in SF next month! This episode includes a live demo of the Taku robot that is best enjoyed on YouTube. Last month, Dyna made waves by announcing Dyna-2, a world-action model pre-trained on one million hours of human video, claiming several new scaling laws on human data across four orders of magnitude. They have now followed up the model with a physical robot agent, Dyna-2.1, that has to be seen to be believed: In his talk at AIE this year, Jason talked about how the real challenge for robotics in the real world is reliable, long-horizon autonomy, and how Dyna got its models to a 99.4% success rate folding restaurant napkins for 24 hours straight, including recovering after it pulled over the whole stack. From training a world-action model on over one million hours of human video to deploying robots that fold thousands of napkins daily at Din Tai Fung, Dyna Robotics is pushing physical AI beyond impressive demos and into real-world production. In this episode, Dyna Robotics co-founder and Chief Scientist Jason Ma joins swyx on-site to unpack how robots can learn new tasks from as little as 45 minutes of demonstrations, why predicting the future of the physical world may be the key to general-purpose robotics, and what it actually takes to build robots that work reliably for real customers. We go deep on Dyna-2, Dyna’s million-hour world-action model that combines future video prediction with robot actions, the scaling laws behind human-to-robot transfer, diffusion transformers and flow matching, and why synthetic environments and self-generated training data could unlock the next wave of robotics progress. Jason also explains why Dyna builds its own hardware instead of relying on off-the-shelf humanoids, how agentic harnesses enable robots to execute hour-long workflows, why dexterous hands and missing sensory data remain major bottlenecks, and what separates real commercial deployments from the graveyard of robotics startups. We also cover world-action models versus vision-language-action models, GPT-6 and coding agents in robotics, and Jason’s journey from AlphaGo and Penn to NVIDIA, Google DeepMind, and founding Dyna. We discuss: Why Dyna-2 was trained on over one million hours of human video World-action models: predicting future video to guide physical robot actions Diffusion transformers, flow matching, and real-time robotic control How robots learn new tasks with as little as 45 minutes of demonstrations Why robotics has no equivalent of the internet and data remains its biggest scaling challenge Human egocentric video, robot demonstrations, and cross-embodiment transfer Synthetic environments, simulation, and models that generate their own training data Why robots need on-device inference, model distillation, and custom hardware Why Dyna chose wheeled semi-humanoids instead of traditional bipedal robots The hardware bottleneck: why dexterous robot hands still overheat Best Western, Din Tai Fung, and the difference between robotics pilots and scaled deployments The Bitter Lesson of Robotics: scaling data instead of engineering specialized solutions Agentic harnesses and robots executing multi-step, hour-long physical workflows GPT-6, coding agents, and AI-generated simulation environments for robotics research World-action models vs. VLAs, and why vision alone may not be enough Jason’s journey through AlphaGo, Penn, NVIDIA, Google DeepMind, and the Eureka project Dyna’s research culture, deployment-first philosophy, and vision for millions of robots Jason Ma LinkedIn: https://www.linkedin.com/in/jason-ma-742224a2/ X: https://x.com/JasonMa2020 Dyna Robotics Website: https://www.dyna.co/ X: https://x.com/DynaRobotics Timestamps 00:00:00 Introduction and Dyna-2’s World-Action Model 00:03:08 Diffusion Transformers, Flow Matching, and Generalist Robots 00:08:19 The Robotics Data Bottleneck 00:13:50 Synthetic Data and Self-Improving World Models 00:16:51 Real-Time Inference and Model Distillation 00:19:45 Full-Stack Robotics: Hardware and Supply Chains 00:25:02 Why Dyna Built a Wheeled Semi-Humanoid 00:29:35 Teaching Robots New Tasks and Evaluating Performance 00:31:59 Autonomous Workflows and Hardware Limitations 00:36:06 Real-World Deployments at Best Western and Din Tai Fung 00:41:07 The Bitter Lesson of Robotics 00:46:13 Agentic Harnesses for Physical AI 00:49:07 GPT-6, Coding Agents, and AI-Native Robotics 00:52:02 World-Action Models vs. VLAs 00:59:17 From AlphaGo to Founding Dyna Robotics 01:04:20 Dyna’s Research Culture and the Future of Robotics Transcript Introduction: Jason Ma and Dyna Robotics Swyx [00:00:00]: All right, we’re here on site with Jason Ma, the co-founder, chief scientist of Dyna Robotics. Thanks for having us in your office. Jason Ma [00:00:06]: Of course. Thank you for coming. Vibhu [00:00:07]: It’s been a blast. We got to see—you’ll see plenty of B-roll—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? Dyna-2: A World Action Model That Predicts the Future Jason Ma [00:00:22]: So last month, we released our world action model called Dyna-2. 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 are 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. World Action Models: Inputs, Outputs, and Visual Reasoning Vibhu [00:01:17]: I wanna 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 are the inputs and outputs? What’s the training data? What is the architecture? Give us all the details. Jason Ma [00:01:39]: So, for inputs, the most important input is video, right? 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 actuators and 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 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 world action model, what’s really interesting is that in addition to the actions, it 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 this is kind of analogous to chain of thought, you know. And basically, 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. Diffusion Transformers, Flow Matching, and Smooth Actions Vibhu [00:03:08]: 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 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’s, what is the current state? Jason Ma [00:03:49]: 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 things to look at. One is data, and the second is 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 Dyna-2 world action model. So under the hood is a diffusion transformer. The model uses an encoder-only transformer architecture. Vibhu [00:04:20]: Right, yeah. Jason Ma [00:04:20]: 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 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 smooth, right? So it has kind of the same kind of consideration and desiderata as generating image. So that’s why from first principles, 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. Swyx [00:05:37]: And we’ve covered flow matching before on the podcast. It is mostly an efficiency speed-up. Like, let’s say you normally would take forty to a hundred diffusion steps. You can reduce it to four. Jason Ma [00:05:48]: Yeah, that’s right. So with the diffusion 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, 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. Yeah. Swyx [00:06:26]: 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 reevaluate in real time. Whereas in images, you are— it’s basically one shot. Jason Ma [00:06:40]: That’s right. Pretraining a Generalist Robot and Adapting to New Tasks Vibhu [00:06:41]: Which is pretty fun. We got to mess with the laundry folding robot … throw a random towel in. The models adapt, right? I think one key thing about your models is that they’re tr— they’re made to be generalists, so one foundation model and you adapt them a lot. 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 what’s the mindset there of one general model, and then how do you adapt to different tasks? Jason Ma [00:07:14]: 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 a general-purpose robot 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 why in our Dyna-2 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 different tasks with a very minimal amount of fine-tuning to just tell the model, “Here’s the task I want it to do, and here’s very few amount of demonstrations,” and the model can readily do bunch of the tasks that you have seen in office. Yeah. A Million Hours of Data: Egocentric Video and Robot Demonstrations Swyx [00:08:19]: We actually have quite a few people who deal in data, who sell data. What is good data? What is bad data? What do you want to buy? I think that these are all open questions. Like, obviously, I think the top, important thing is egocentric data of humans doing tasks. What else would you modify on that basic statement? Jason Ma [00:08:42]: 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. Swyx [00:09:02]: You have employees here doing that. Jason Ma [00:09:04]: There are 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’re trying to build an internet by just having Reddit or just having Facebook. They’re 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, 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 non-stationary problem in the sense that what is high-quality data for your model changes over time. Like, 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. 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 until 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 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 the robot’s 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, 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 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,” Jason Ma [00:11:25]: 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. Yeah. Data Partnerships and Specialized Training Examples Swyx [00:11:44]: I’ll prompt you, just for again, for the people listening who want to work with you. What, d— what data vendors do you want. This is your call for data. What- Jason Ma [00:11:55]: Yeah, no, that’s a great question. And yeah, so in our Dyna-2 blog, at the end, we actually have an email address called datapartnerships@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 we’ll get back to you. Swyx [00:12:16]: But at your stage, are you. You’re probably not buying any more egocentric data off the shelf, right? That you probably want to be like, “Here’s what I need. You go-” Vibhu [00:12:26]: Is there a niche that’s lacking? Swyx [00:12:28]: You need a Scale AI, you need a Mercor for doing this. I see. The custom data- Vibhu [00:12:32]: The folks that don’t work in robotics are probably thinking like, “Okay, some niche factory.” Like … 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? Jason Ma [00:12:43]: Yeah, no. 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, just a million hours of data is not enough. It’s just the beginning. Like, 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 really, specific niche tasks? Because I think you still need that to get to excellence that any model needs to actually be useful in the physical world, right? It’s kind of like humans, you know. If, Sean or me, we go work at Rosewood Hotel tomorrow, we are 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, that’s kind of like what Mercor is doing today for language model, I think will be very valuable for robotics as well. Vibhu [00:13:42]: So hotels, strap a GoPro on your employees’ heads and. Jason Ma [00:13:48]: And then there’s probably more of that. Vibhu [00:13:49]: There’s the Synthetic Data, Simulation, and the Eureka Project Swyx [00:13:50]: Put themselves in their way. Yeah. So one thing that we discussed is the closing this interview, and what’s very interesting is now you can basically fabricate environments on the fly. Have you tried that with the frontier models? What’s your experience? Are they good enough? Vibhu [00:14:06]: Are you using any world model robotic. Like, world model companies to generate these and train on them? Jason Ma [00:14:13]: So, I think generating synthetic environments, synthetic data, simulation data for robotics, it is going to be one of the next frontiers, right? ‘Cause today people have looked at egocentric data, robot data. Those are scaling, but I think synthetic data will be a new dimension of scaling. So going back to your question yeah, we have been exploring 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 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 GPT-4, which is how to self-generate environment, generate reward functions or reinforcement learning, and just 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 limited at the time, because back then the model didn’t really have good vision capability, didn’t have a innate 3D understanding, right? So it was basically just generating code and 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 CADing, there’s 3D reconstruction and all of that. So I think now it’s at a tipping point where Jason Ma [00:15:52]: This technology could really accelerate robotics. And for us, right, we think about that as 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 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, it can also generate videos. We— The model can also generate its own training data. So that becomes a self-fulfilling 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 are act— already starting to do very cool things. We’re just at the beginning, just the tip of iceberg for what is there to come. Yeah. On-Device Inference, Latency, and Model Distillation Vibhu [00:16:51]: 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 … nudge 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 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, of model on on device, in cloud, a hybrid? And then how does that affect your scaling law of data? A million hours is great, but like do we just scale up parameters, data? Jason Ma [00:17:38]: 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? Well today, whenever I ask ChatGPT, it takes like couple of seconds before it spits out anything. But for robots the inference latency has to be very short, right? The robot needs to act. Like, it can’t just hold a knife and not do anything for three seconds. That’s gonna freak people out, right? Vibhu [00:18:07]: Yeah, we saw someone put their real hand under a knife. Jason Ma [00:18:10]: Our model’s, pretty smart, so that’s okay. But in general if you think about very dynamic tasks, like for example, you want your robot to hold a I don’t know, a tennis racket and juggle the ball, right? It’s just very dynamic, real reactive. You can’t just like. The 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- Swyx [00:18:39]: Network latency. Jason Ma [00:18:40]: There’s no guarantee- Vibhu [00:18:41]: Self-drive a car. Jason Ma [00:18:42]: There’s no guarantee, right? Like, so for that reason, the model that gets deployed eventually has to be of reasonable size 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- Vibhu [00:18:57]: You can distill. Jason Ma [00:18:58]: 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? So I think in robotics, we’re getting there, but I think that’s what people are going to converge on. Yeah. Vibhu [00:19:14]: How about 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 wanna share? Jason Ma [00:19:26]: So we build our own inference stack for whatever model we deploy, because that’s— like I was saying latency is so important. So owning the whole stack is extremely important for robotics, right? 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. Building the Full Robotics Stack: Sensors and Actuators Swyx [00:19:45]: You’re really full stack, like- Everything from the actuators all the way to in the industrial design ID, which I- Didn’t really know as a acronym. Obviously to the models and data as well. Vibhu [00:19:57]: I’m curious are these your own actuators, or are they. How much of this is your own? Like, of course, the design, it’s not like humanoid- Actuators, like how far down the robotics stack? Jason Ma [00:20:08]: So the way we think about, what we need to own versus what we can buy is like if the Input-output and of a component is very clear, and like there’s already extremely good mature solution, then there’s no point of building in-house. For example, on this robot, you see the LiDARs, right? So those, 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 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 the 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 can actually be easily modelable. So that’s what we care about eventually. So we’re gonna own that. Same thing with the robot’s proportion. This robot’s proportion is we measure it against real people. Why do we do that? ‘Cause we’re pre-training on human data. Swyx [00:21:36]: Oh, yeah. Jason Ma [00:21:36]: Best 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, it’s hardware that carries intelligence. Intelligence is the extremely important thing. We want to make sure the hardware serves for that. Swyx [00:21:53]: I would 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, ‘cause apparently we don’t make magnets in the US. Manufacturing, Rare-Earth Magnets, and Global Supply Chains Jason Ma [00:22:06]: No, we don’t. Swyx [00:22:07]: And there’s only two countries that make magnets. Jason Ma [00:22:09]: Rare earth material. Swyx [00:22:10]: Rare earth, right? I don’t know what the. Wh-what’s the chemi— what’s the material? I don’t know what the thing is, but- so yeah, if anyone’s interested in that’s, apparently a bottleneck that we have. Jason Ma [00:22:20]: No, not bottleneck. Swyx [00:22:21]: It’s a bottleneck. So what do we do? Jason Ma [00:22:23]: It’s a good question, right? I think, there’s a lot of exciting discussions on bringing manufacturing back to the US, but for robots especially, that’s actually much harder 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 robot, they build whatever they’re actually doing in the US, they’re still sourcing their 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 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 data, but I think hardware and just how hard it is to build good hardware is very under-emphasized. But I think that’s gonna be extremely important. Swyx [00:23:27]: This is a platform, right? Why Dyna Co-Designs Its Hardware and Models Jason Ma [00:23:29]: Platform, yeah. Swyx [00:23:30]: There are some companies that choose to be, let’s call it platform-less. They only do the model, and they will say, “We deploy on any platform.” We deploy on Optimus. Vibhu [00:23:37]: Off-the-shelf robotics. Swyx [00:23:38]: And so, it’s a choice, right? Like, 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? Jason Ma [00:23:52]: So we are currently exploring many different options, right? Because with global supply chain, then there is some, geopolitical things, right? Swyx [00:24:02]: Shipping. Jason Ma [00:24:02]: Shipping. Also shipping delays and all that stuff. Vibhu [00:24:04]: Literal ships for robots. Jason Ma [00:24:06]: And in the US, 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 some companies choose to just build model, we build everything, I think it’s very important to get this right. I feel that for robotics, the hard thing is. Like building the whole system. What are the necessary things 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. The Semi-Humanoid Robot: Wheels, Torso Movement, and Dexterity Vibhu [00:25:02]: We have the robot. You gotta tell us about the design. Your own custom platform. What do you call this? Jason Ma [00:25:11]: What do we call it? Vibhu [00:25:12]: Yeah, so it’s not humanoid. It’s very smooth, but what’s the what’s the rotating arm? Jason Ma [00:25:16]: 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. All right, so why do we choose wheels? Because I think there is a difference. There’s, optimal practicality versus performance trade-off. I think with legs, the energy con— the energy consumption would be a lot higher. It would be a lot more unstable. Safety will be 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 thing. Swyx [00:26:08]: Seeing dancing, yeah. They all, they fall over very easily. Vibhu [00:26:10]: 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? Like- there’s a. Swyx [00:26:21]: I think the more interesting thing that you guys have innovated. So we have, one of the prototypes, which is the Mewtwo back here. Jason Ma [00:26:29]: You can call it that. Swyx [00:26:30]: This is just like an up-down, axis, and you’ve chosen to then move towards something like this which, has the ability to lean. And we’re gonna 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. It’s something else. Jason Ma [00:26:47]: So yeah, how did we think about that right? Like,. So actually, it’s very used for exercise. Like, I think sometimes, a couple years ago when I was exercising, I accidentally like, pulled a muscle. Right? I’m sure you guys have that experience too, right? Then the next few days you just couldn’t move, or like you pull it where 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 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 when we design robots. When you have a— this kind of design, which is this purely elevator, could only go up and down, we realize a lot of the human torso movement, like we use our waist, back a lot for day-to-day physical activity, right? Even if I wanna get this bottle I’m not just moving my arms. It’s actually very strange for me if I have to intentionally think about fixing my upper body. I would always do this right? So there’s just a lot of subconscious way that humans move their body that I think will be equally useful if robot has a degree of freedom. And if you further think about, okay, we want to learn from human data. Like, humans are moving their torso all the time. You wanna be able to 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. Transferring Human Pretraining to New Robot Hardware Vibhu [00:28:22]: What’s the data labeling side of this? Like, I— you’ve seen people,. Is this the last post-training fine-tuning side of actually the hour of using the robot tele-operated or remote-controlled mapping motion? Or does some of it generalize over? Jason Ma [00:28:41]: When you say labeling, you mean? Vibhu [00:28:43]: Like, taking the model you have that predicts state to mapping it to your specific hardware. Jason Ma [00:28:50]: Oh, yeah. It generalizes very well. So for all the demos that I have shown you on the new robot, ‘cause the new robot, we, 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 very quickly. And I credit a lot of that to like, even before the hardware is ready, we 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 the transfer is actually there and is actually very helpful. Evaluating New Skills and Learning from Real-World Feedback Vibhu [00:29:35]: Talk about the eval side of this. So you can have predictions, you can have it do stuff, and every now and then we see it miss stuff. You test on very niche things as well. It’s like filling a pill bottle. 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? Jason Ma [00:29:56]: Yeah, so if we wanna learn a new task, we basically do a little bit of robot data collection. Then the model, these- Swyx [00:30:04]: What is a little bit like? Jason Ma [00:30:05]: Yeah, like couple hours. Yeah, we’re— In some of the tasks, like just tens of minutes, right? Yeah. And then- Swyx [00:30:11]: The chopping one was 45 minutes. Vibhu [00:30:13]: The open the fridge was on- Swyx [00:30:14]: And that’s an interview— That’s like an onboarding task for one of your engineers. Jason Ma [00:30:18]: So,. Vibhu [00:30:20]: Fun thing if you wanna work here, you know. Find the task, have the robot do it. Jason Ma [00:30:24]: Pitch me a new task and I’ll come to the office, collect some data, train a model and see what happens. Vibhu [00:30:29]: Tie your shoes. Jason Ma [00:30:31]: Truly, it’s tying. Yeah, that would 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- Vibhu [00:30:41]: To fill in collected means you are— you have a person actually operating- Jason Ma [00:30:46]: Operating. Vibhu [00:30:48]: Doing the task for a bit. And then you train? And it does okay, you improve. How do you measure? Jason Ma [00:30:56]: So when you’re training the model, there’s, 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 maybe like seventy percent, eighty percent there, then you see how the model behaves. You figure out what it’s not good at. Then you can collect a bit more data, then iterate, right? I think of this as the robotics version of reinforcement learning from human feedback, right? The robot rolls out. You can score it good, bad S- 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. Long-Horizon Tasks and the Bottlenecks to General Robotics Vibhu [00:31:59]: I wanna 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- Swyx [00:32:21]: Manage my data center. And also, ref— like, recharge another robot. Vibhu [00:32:29]: When do we see the factory or 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, “Okay, here’s a recipe.” Jason Ma [00:32:49]: I would say to get to truly general-purpose robots like basically a robot or a model that does anything, like- Vibhu [00:32:58]: 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- Jason Ma [00:33:06]: I see. Vibhu [00:33:06]: Instead of very simple chopping- Maybe a five to ten-minute sequence, right? Where now the eval step, it can break at many more points. Instead of just fill one pill bottle- Ten different pills all in a sequence, right? What’s limiting us? What are we pushing on? Jason Ma [00:33:20]: Yeah, I think. Yeah, I think a couple of things. One is that the laundry demo that you have seen, yeah, it’s gonna be one hour to finish a whole loop, right? So the longest duration the robot can do a full job is already in the hour range. Right? But to be able to do that for any task, I think the bottleneck is coming back to data and hardware, right? So for some of the tasks, for example, setting up the space, you might need more than a 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, dexterous hands are still not, durable enough. They overheat pretty quickly, right? So that becomes a bottleneck, right? Like, you— We— you quite literally have to put a ice bag- Swyx [00:34:15]: Ice. To cool. Jason Ma [00:34:16]: Human. Vibhu [00:34:16]: We’re not just auto-researching, make better model we have. We have hardware limitations. Jason Ma [00:34:20]: 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 vision-based, right? It’s like video streams and some, encoding of your hand motion where the robot’s, joints. But Humans have a lot more sensors. We hear things. We feel things. Swyx [00:35:05]: Smell. Jason Ma [00:35:06]: 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 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 what I consider 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 ne— 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, right, as a human would, right? Because for that task, we don’t really have like, oh, you actually need more than a parallel jaw gripper. You actually need some sensing modality that’s just not available today, right? So I do think the biggest bottleneck To have a much more general thing is still hardware and data. Real-World Deployments: Best Western and Din Tai Fung Swyx [00:36:06]: I want to take this chance to talk— 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, that you’re proud of. Cool stories, anything like that. Jason Ma [00:36:20]: 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 is- Swyx [00:36:29]: Which one? The Best Western one? Jason Ma [00:36:30]: No, we have one video for all the customers we’re working with. It’s a really nice video. It shows what we’re doing. And yeah, I think it’s great. But yeah, we have customers, Best Western. That’s in the hotel space. We fold a lot of laundry towels for them. Every hotel needs it, every gym needs it, every fitness center needs it. - Swyx [00:36:51]: What’s the deployment so far? How— Like, where can I go to— Which Best Western do I go to see it? Jason Ma [00:36:56]: So if you wanna go somewhere, I actually recommend going to our other deployment, Din Tai Fung. Have you guys been to- Vibhu [00:37:04]: Very thin wrapper. Swyx [00:37:05]: We should have filmed there. Jason Ma [00:37:09]: Just filmed there. Vibhu [00:37:10]: The B-roll. Jason Ma [00:37:11]: But in Din Tai Fung if you go to Din Tai Fung or other somewhat upscale restaurant, there’s napkins on your table, right? So our robots today fold thousands of napkins for many different locations, many different Din Tai Fung 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 Din Tai Fung, which, to the best of my knowledge, hasn’t really happened for other companies doing similar general purpose robots. So at Din Tai Fung, our robots fold thousands of napkins per day. It’s — The quality standard is extremely high, ‘cause 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 pocket, so you can stick the chopsticks in. So it’s actually very fine-grained, high-precision task. I can show you. Vibhu [00:38:26]: At some level, that’s what robotics are good for robots are good for too, right? Humans are not the most consistent- 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, at some level, I think people would assume- you can have a specialized robot that will fold napkins perfectly to the exact, millimeter as opposed to something like this but. Jason Ma [00:38:53]: Yeah, I’m sure, maybe you can, right? But the hard thing about manipulating deformable object, right, like napkin, towels, everything’s- Deformable. There’s just infinite- 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 in a robot and adapting it to these use cases, you can actually get to a production quality faster. And what’s really nice is that now that Din Tai Fung has scaled, has signed a much bigger contract with us, they not only want our robots to fold napkins. Now they want our robots cut. That’s why we had the- Example, to cut food to the- Swyx [00:39:31]: Is that actually the second use case? Jason Ma [00:39:33]: That’s one of the use case they are thinking. Din Tai Fung needs to cut so many vegetables per day. Swyx [00:39:38]: They need to do the dumplings. Jason Ma [00:39:39]: Exactly. To the fold. That’s the final boss. That’s- Vibhu [00:39:41]: That’s the final one. Swyx [00:39:43]: I think you can. I think you have the dexterity and the precision to do this. Vibhu [00:39:46]: You have a little robot. Swyx [00:39:47]: But no, don’t break the dumplings. Vibhu [00:39:50]: That’s the obvious benefit is you can adapt them anymore. Generalist Robotics, Speed, and Consistency at Scale Vibhu [00:39:54]: 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, right? Jason Ma [00:40:07]: Yeah, this can do it ex— very fast. And now it’s actually al— it’s at human parity. 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 Din Tai Fung is like, their LA location with New York location, Seattle location, the they fold napkins slightly differently, but from their perspective, they want consistency. But that’s exactly what these models are good at, ‘cause models are, just, it’s a set of weights. You can just put the weights in the different robots across different- Vibhu [00:40:43]: You can OTA update them as well. Jason Ma [00:40:45]: Yeah, you can just update all of them at once. It just. Once it works, I think it’s truly, like- Incredible. Vibhu [00:40:52]: Final boss is Cheesecake Factory. If the story there, they’re from scratch. Everything is very consistent, down to the measured gram. Every sauce is made. Like, anywhere you go in the world, you’ll have the exact same thing. If you don’t know, you don’t know. But that’s the that’s the final. The Bitter Lesson of Robotics: Scale the Right Data Swyx [00:41:07]: The term that comes to mind is kind of like bitter lesson for robotics, right? You, 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- Vibhu [00:41:18]: That’s right. Swyx [00:41:19]: And do all those things. Vibhu [00:41:20]: Give the expert add-on outside of your. What is more bitter lesson robotics? Jason Ma [00:41:25]: 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, right? So what does that mea