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Dyna Robotics

@DynaRobotics • 14,296 subscribers

General and robust AI robots that power the future of the physical economy.

Shorts

Made for the details and the demands of real work. Built for craft and mastery. A first look at our new robot. Coming soon.

Made for the details and the demands of real work. Built for craft and mastery. A first look at our new robot. Coming soon.

208,471 views

Using large-scale GPU parallelized reinforcement learning in Isaac Sim, we are able to obtain a robust whole-body controller for Taku optimized for precise loco-manipulation.

Using large-scale GPU parallelized reinforcement learning in Isaac Sim, we are able to obtain a robust whole-body controller for Taku optimized for precise loco-manipulation.

85,440 views

We have started taking DYNA-1, our dexterous robust VLA model, to conferences and showcasing it for hours on end! The model run for 3 days, 8 hours each day at #HITEC2025 3 weeks ago with 99.9% overall success rate (dropped 1 towel in day 2). No intervention, it just works :)

We have started taking DYNA-1, our dexterous robust VLA model, to conferences and showcasing it for hours on end! The model run for 3 days, 8 hours each day at #HITEC2025 3 weeks ago with 99.9% overall success rate (dropped 1 towel in day 2). No intervention, it just works :)

55,777 views

Videos

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Taku takes its name from Takumi, a master craftsman. Behind it is a team united by a love for what they do and a relentless pursuit of excellence. Meet the people and the craftsmanship behind robots built to last. Come build with us:

Taku takes its name from Takumi, a master craftsman. Behind it is a team united by a love for what they do and a relentless pursuit of excellence. Meet the people and the craftsmanship behind robots built to last. Come build with us:

1,034,266 views • 2 days ago

We are releasing Dyna-2.1, the first Physical Agent that achieves reliable super long-horizon whole-body autonomy. It combines our brand-new semi-humanoid hardware with an agentic system built around Dyna-2 to handle ultra-long real-world workflows. Here is an uncut footage of Dyna-2.1 completing an entire hour-long laundry room workflow, just like a human does.

3,135,502 views • 1 month ago

Today we are introducing Dyna-2, a world-action model pre-trained on one million hours of human video. At this scale, for the first time, we discovered several new scaling laws: • world-action models exhibit scaling law on human data across four orders of magnitude, from 1000 to 1,000,000 hours, • this human data scaling law implied a scaling law on never seen robot data, • both data and objective matter; world modeling and scaling on video data are essential for cross-embodiment scaling transfer to emerge 🧵

Today we are introducing Dyna-2, a world-action model pre-trained on one million hours of human video. At this scale, for the first time, we discovered several new scaling laws: • world-action models exhibit scaling law on human data across four orders of magnitude, from 1000 to 1,000,000 hours, • this human data scaling law implied a scaling law on never seen robot data, • both data and objective matter; world modeling and scaling on video data are essential for cross-embodiment scaling transfer to emerge 🧵

1,821,546 views • 2 months ago

After so many demos, models, pilots, robots are still struggling to land real deployments with real customers. Until now. Today we’re excited to share that our robots have successfully crossed the ROI threshold, and Din Tai Fung, one of the highest revenue per location restaurant chain in the US, is rolling out Dyna robots across its extensive restaurant network. This brings our rollouts across hotels, logistics, data centers, and many other use cases to a fleet that reaches hundreds of robots by the first half of 2027. And we’re just getting started. It’s been a wild year, and today we’re double clicking on the battlefield stories and sharing a few learnings about scaling robot deployments. We are just scratching the surface. Read the full blog post:

After so many demos, models, pilots, robots are still struggling to land real deployments with real customers. Until now. Today we’re excited to share that our robots have successfully crossed the ROI threshold, and Din Tai Fung, one of the highest revenue per location restaurant chain in the US, is rolling out Dyna robots across its extensive restaurant network. This brings our rollouts across hotels, logistics, data centers, and many other use cases to a fleet that reaches hundreds of robots by the first half of 2027. And we’re just getting started. It’s been a wild year, and today we’re double clicking on the battlefield stories and sharing a few learnings about scaling robot deployments. We are just scratching the surface. Read the full blog post:

345,546 views • 2 months ago

A new breakthrough is coming. Stay tuned.

A new breakthrough is coming. Stay tuned.

371,370 views • 2 months ago

Using large-scale GPU parallelized reinforcement learning in Isaac Sim, we are able to obtain a robust whole-body controller for Taku optimized for precise loco-manipulation.

Using large-scale GPU parallelized reinforcement learning in Isaac Sim, we are able to obtain a robust whole-body controller for Taku optimized for precise loco-manipulation.

85,440 views • 1 month ago

When people talk about robotics, they usually talk about models, data, or hardware. Few people talk about the infrastructure that lets you iterate on all three quickly. Today we're publishing how we trained Dyna-2 on over 1,000,000 hours of egocentric video, repeatably. At this scale, most of what worked at ten thousand hours did not hold up: • ingestion throughput was capped at 14,000 episode-hours per week — a million hours would have taken over a year • building a training manifest took 48 hours before a run could even start • reading a petabyte from cloud storage during training left GPUs exposed to latency and packet loss 🧵

When people talk about robotics, they usually talk about models, data, or hardware. Few people talk about the infrastructure that lets you iterate on all three quickly. Today we're publishing how we trained Dyna-2 on over 1,000,000 hours of egocentric video, repeatably. At this scale, most of what worked at ten thousand hours did not hold up: • ingestion throughput was capped at 14,000 episode-hours per week — a million hours would have taken over a year • building a training manifest took 48 hours before a run could even start • reading a petabyte from cloud storage during training left GPUs exposed to latency and packet loss 🧵

177,184 views • 2 months ago

Excited to announce that we have raised $120M in our Series A to advance the frontier of general-purpose high-performance robots. 🤖 The new funding will accelerate progress towards our mission of bringing foundation-model powered robots to everyone, everywhere. Read more 👇

Excited to announce that we have raised $120M in our Series A to advance the frontier of general-purpose high-performance robots. 🤖 The new funding will accelerate progress towards our mission of bringing foundation-model powered robots to everyone, everywhere. Read more 👇

822,462 views • 1 year ago

AI runs on compute. Compute needs scaled infrastructure. We are teaming up with Lambda to give AI infrastructure some helping hands.

AI runs on compute. Compute needs scaled infrastructure. We are teaming up with Lambda to give AI infrastructure some helping hands.

12,265 views • 21 days ago

With just 30 minutes to a couple hours of on-robot data, Taku can complete new whole-body tasks that require vastly different motion primitves.

With just 30 minutes to a couple hours of on-robot data, Taku can complete new whole-body tasks that require vastly different motion primitves.

16,156 views • 1 month ago

Everyone at Red Bull Mirage brought a DJ. We brought a robot. The robot worked out of the box, no additional training onsite. 8 hours. 700+ Red Bulls served. 99%+ success rate. No humans, no breaks, no resets. Shifting desert light. Cans handed in at every angle. Thousands of people are moving around it. The robot just kept serving. 🤖🥫

Everyone at Red Bull Mirage brought a DJ. We brought a robot. The robot worked out of the box, no additional training onsite. 8 hours. 700+ Red Bulls served. 99%+ success rate. No humans, no breaks, no resets. Shifting desert light. Cans handed in at every angle. Thousands of people are moving around it. The robot just kept serving. 🤖🥫

39,458 views • 5 months ago

Excited to share our latest progress on DYNA-1 pre-training! 🤖 The base model now can perform diverse, dexterous tasks (laundry folding, package sorting, …) without any post-training, even in unseen environments. This powerful base also allows extremely efficient fine-tuning to ~100% success on challenging new tasks with as little as 1 hour of data! 🤯 Watch it master two of them: cup stacking & celery chopping on repeat, no failures. 👇

Excited to share our latest progress on DYNA-1 pre-training! 🤖 The base model now can perform diverse, dexterous tasks (laundry folding, package sorting, …) without any post-training, even in unseen environments. This powerful base also allows extremely efficient fine-tuning to ~100% success on challenging new tasks with as little as 1 hour of data! 🤯 Watch it master two of them: cup stacking & celery chopping on repeat, no failures. 👇

68,198 views • 11 months ago

It also has exceptional robustness and zero-shot environment generalization; under various disturbances, the model continues to work. Dyna-2 is our first model to achieve an 87% quality and throughput rating during zero-shot deployment at novel customer sites. .

It also has exceptional robustness and zero-shot environment generalization; under various disturbances, the model continues to work. Dyna-2 is our first model to achieve an 87% quality and throughput rating during zero-shot deployment at novel customer sites. .

19,621 views • 2 months ago

Waymo was founded in 2009. Their demo took 18 months. Their first driverless rides opened to the public in 2020. It's easy to show a demo. What comes after are the edge cases, recovery, uptime, integrations, and the small failures that only show up once a robot has done the job every day for months. In the field. We spent the past year in that "after" with our customers, running their napkin operations day in and day out. Twelve months ago, Dyna-1 was the most reliable robot foundation model DEMO published at the time. Today the comparison with Dyna-2 is stark.

Waymo was founded in 2009. Their demo took 18 months. Their first driverless rides opened to the public in 2020. It's easy to show a demo. What comes after are the edge cases, recovery, uptime, integrations, and the small failures that only show up once a robot has done the job every day for months. In the field. We spent the past year in that "after" with our customers, running their napkin operations day in and day out. Twelve months ago, Dyna-1 was the most reliable robot foundation model DEMO published at the time. Today the comparison with Dyna-2 is stark.

15,885 views • 2 months ago

Wrapped Day-1 at Conference on Robot Learning - our booth was buzzing all day, and this is why. ⬇️ Here’s a timelapse of Dynasaur folding continuously, shrugging off every interruption researchers threw at it. It’s thrilling to see the crowd erupt in applause after the robot nails a flawless fold Don't miss it! We're here all week - booth 28 #CoRL2025 #Robotics #AI

Wrapped Day-1 at Conference on Robot Learning - our booth was buzzing all day, and this is why. ⬇️ Here’s a timelapse of Dynasaur folding continuously, shrugging off every interruption researchers threw at it. It’s thrilling to see the crowd erupt in applause after the robot nails a flawless fold Don't miss it! We're here all week - booth 28 #CoRL2025 #Robotics #AI

65,096 views • 1 year ago

An hour of lab evals catches a model that doesn't work. It won't catch one that fails once every two hundred trials, or degrades over a week, or runs fine on this robot and badly on the one beside it. So the eval moved to where the work is. Every episode, every site, graded on the customer's definition of good. Over a terabyte a day, autolabelled into SOP steps, outcomes, and failure modes. Today, a new Dyna deployment goes from setup to production ROI in as little as three days. This is just scratching the surface. Also - we're hiring across deployment research:

An hour of lab evals catches a model that doesn't work. It won't catch one that fails once every two hundred trials, or degrades over a week, or runs fine on this robot and badly on the one beside it. So the eval moved to where the work is. Every episode, every site, graded on the customer's definition of good. Over a terabyte a day, autolabelled into SOP steps, outcomes, and failure modes. Today, a new Dyna deployment goes from setup to production ROI in as little as three days. This is just scratching the surface. Also - we're hiring across deployment research:

11,841 views • 2 months ago

🎃 Halloween is coming. Our hardworking team is lining up for sweet treats, of course, served by Dynasaur! DYNA VLA model now has robust agentic reasoning capability, allowing it to serve arbitrary combinations and counts of candies! Pure imitation learning can’t work given the combinatorially many possibilities. No video edits. Uninterrupted, real-life, as always 🤖 Happy Halloween from DYNA!🍬

🎃 Halloween is coming. Our hardworking team is lining up for sweet treats, of course, served by Dynasaur! DYNA VLA model now has robust agentic reasoning capability, allowing it to serve arbitrary combinations and counts of candies! Pure imitation learning can’t work given the combinatorially many possibilities. No video edits. Uninterrupted, real-life, as always 🤖 Happy Halloween from DYNA!🍬

26,296 views • 1 year ago

We brought Dynasaur to The Clean Show and ran it live, 8 hours a day. Each deployment fuels our robotics foundation model — scaling data, accelerating iteration, and enabling real-world generalization. Embodied intelligence won’t come from lab demos, but from deployment-first robotics running continuously in the wild.

We brought Dynasaur to The Clean Show and ran it live, 8 hours a day. Each deployment fuels our robotics foundation model — scaling data, accelerating iteration, and enabling real-world generalization. Embodied intelligence won’t come from lab demos, but from deployment-first robotics running continuously in the wild.

25,153 views • 1 year ago

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