Working at Dyna is the definition of team work. Cross functional development is core to how we build and develop, creating the perfect environment to refine your technical skill set.
Loving what you do is the norm here 🤖
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: dyna.co/careers
Margaret Meagher Berberi retweeted
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: dyna.co/careers
Margaret Meagher Berberi retweeted
When the New York Post calls your robot “the world’s most boring robot”, you know you are doing something right. 😭😂
This is actually the highest praise ever: when robots actually work, they are supposed to be boring, just calmly and reliably getting the job done.
Go robot! This hardware works hard!
👀 the towel drop recovery 👀
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.
Margaret Meagher Berberi retweeted
Made for the details and the demands of real work. Built for craft and mastery.
A first look at our new robot. Coming soon.
So proud to be part of the HW related efforts. Helping our team to land robots in the real world, and keep them there. There’s nothing better than that for a HW Engineer
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: dyna.co/news/scaling-custome…
Margaret Meagher Berberi retweeted
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: dyna.co/news/scaling-custome…
Margaret Meagher Berberi retweeted
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
🧵
Dyna-2 is out! Our world-action model was pre-trained on +1M hours of human data (that roughy 170 years of experience!). Real results on real hardware, check out the post!
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
🧵