GEN-1 is now available today to our Early Access Partners. Let’s build.

A jaw-on-the-floor moment for the whole team was when we had taught the robot to pick up a baggie and shake a usb-brick out with the left hand. During one rollout, it decided to do it with the right hand—and we all stopped. We were stunned, because no one taught it to do that. This was one of the first models with our pretrained base, and it was the moment we realized there was something here that was very different from what any of us had ever built before. We now know this to be part of the improvisational intelligence that emerges with large-scale pretraining on real manipulation data, and it continues to be one of the things that we believe to be worth pushing forward as capability. Our CTO and cofounder Andy Barry joined @a3automate to share more about these moments, and how his time at Boston Dynamics and the Broad Institute drove him to focus on building the 🧠 for robots at Generalist. Full ep 👇

What is the best data for training humanoid & robotics foundation models? Pete Florence @peteflorence (CEO @Generalist, ex-Google DeepMind) dropped his live data tier list in this 7-minute clip on @tbpn: - S-tier: Real-world robot experience (especially glove/sensor high-dexterity data) - A/B-tier: Internet/YouTube videos. Surprisingly powerful for transfer learning (the “web data” moment for physical AI) - B-tier: Text/common crawl (Reddit, books, etc.). Useful priors, but not enough alone - C-tier: Motion Capture. Great for whole-body motion, weak on finger dexterity - C or lower: Simulation / synthetic / world models. High potential, still waiting for strong real-world proof Generalist has collected 270,000 hours of real-world manipulation data (scaling ~10k hours/week). And Pete stressed one key point: “The quality of data is incredibly important.” It’s not just about volume. It’s developing intuition for what actually drives performance through hands-on work. As Physical AI scales, curated real-world and high-quality internet video looks like a winning combo. h/t @yuji_fujima

Generalist might not have raised the most amount of capital. And the model wars in physical AI are fast and furious. But I have been hearing very good things from folks in the robotics world about the quality of their models.

The technical velocity at @GeneralistAI is unlike anything I have seen. @peteflorence and @andyzengineer are building a special platform that will unlock robotics use cases across the economy. We're thrilled to invest in Generalist, and just as eager to spend time with the companies that will build on top of them. Big things ahead :)

Generalist CEO @peteflorence says robotics models are in a transition period similar to the step change between GPT-2 and GPT-3. They're "starting to cross over into levels of performance where these things are commercially viable for a number of different applications." "We think this is a crossover point where we have a general model starting to be able to hit levels of reliability, speed, and improvisational intelligence where we can start to get these things out there." "Very much like — you take a GPT-2-level model, you scale it to a GPT-3-level model, and certain types of commercial applications start to become viable."

We've raised $400M in new funding. This capital goes toward one mission: building general intelligence for the physical world and making it useful to everyone.

Gen-1 pours Kool-Aid Read more about GEN-1 in our blog post in the comments below ↓

Also this was a really impressive recovery we observed 🤯 The bottle started slipping and the robot adjusts its grip to stabilize the bottle and prevent a spill!!

In my first week at @GeneralistAI, I trained a robot to pour liquids using GEN-1 🤖💧 I wanted to challenge the robot with a non-rigid manipulation task, so liquid felt like the perfect choice. The task involved: - unscrewing the bottle cap - pouring liquid into espresso glasses - rebalancing uneven pours Best of all, the robot was able to complete the task fully autonomously 3 times in a row (out of 3)! Pour-fect 😉 Excited for the journey ahead and grateful to be building alongside such an incredible team!

Decades of hardware development led to strong, fast, and precise robot arms. The moment we can put in general intelligence in these things, we can leverage the full spectra of capabilities that they were always meant to capture. We’re betting on a future where robot hardware will continue to improve, and we intend to build the best models on top of the best hardware to push the frontier of capabilities and reliability. Here’s an old video from TossingBot that I think helps make this point extra clear. Industrial-grade repeatability started out as a crutch for dumb software -- but if you pair it with the right AI models, then it becomes an advantage that is superhuman. There are factories where the same UR robot arms are still being used to precisely and repeatably build the same car parts, operating for 10 yrs straight without a single failure or shutdown. Humanoids will get there too (among other form factors). Not yet today, but eventually. And our models will be ready to meet them when they do.

Robot reaches deep for screws Read more about Gen-1 in our blog posts in the comments below ↓

For the avid viewer -- there’s a brief moment when the robot loses it’s grip on the head of a ziptie, and so it decides to use the other hand to help readjust the grip for the pull. It’s gnarly passing by our robots everyday, and catching these random glimpses of improvisational intelligence in action. Instant dopamine hit.

Today marks the end of my first full week @GeneralistAI Last Monday, I was given a challenge: use our GEN-1 model to teach a robot a task of my choosing, using the same no-code platform our customers use. I picked the ball-and-vase magic trick. It was one of my favorites as a kid, and it felt like the right mix of fun and surprisingly hard. A few days later, GEN-1 pulled it off. I left Friday having watched the robot nail it 14 times in a row. What’s wild is that even 4 months ago, if you told me you could go from idea to on-robot skill in a couple of days, I probably wouldn’t have believed you. Really excited to be building with an incredible team. Can’t wait to see what week two brings 🤖

GEN-1 performs a magic trick Read more about GEN-1 in our blog post in the comments below ↓

GEN-1 cleans white board Read more about GEN-1 in our blog post in the comments below ↓

If anyone’s creating a benchmark for frontier physical AI models, this task is a great one to add to the roster. Sensorimotor end-to-end policies must exhibit the long-term visual memory to track and reason about where the object might be. It’s also harder to “cheat” on this task -- it can be difficult to do if you’ve got “gaps” in your model’s memory e.g. low-frame rate memory or coarse representations, like language. GEN-1 nails it (also on the first try with an unseen object). @BerkayAntmen was really trying hard to fool the model here. Shout out to an excellent task from @RhodaAI.

A week of contrasts... @sudo_robotics says why do IRL training? Generalist says why do sim? Very cool to see some of these examples coming together. Putting a dollar into a wallet definitely feels like a tail task I would not have expected robotics to solve for another 5(?) years at least! Kudos @GeneralistAI

Our robots have been busy lately!

At @GeneralistAI every day feels like an NBD day (never been done youtu.be/mx0nLWKeyXU?si=uCnF…). Stay tuned for a lot more.