That's one of the coolest robotics demos! 😮‍💨 @GeneralistAI showed GEN-1 handling box folding and screw packing during @AutomateShow. The boxes are cardboard with real variability: creasing, deformation, different configurations. GEN-1 retries when things go wrong. It adapts mid-task. This is a jump from GEN-0, shown 3 months ago during GTC. GEN-0 handled rigid, predictable boxes. GEN-1 handles deformable ones with chaotic behavior. The scaling is straightforward. GEN-1 trained on 500,000 hours of proprietary, real-world dexterous manipulation data. Scaling laws are working. More high-quality real-world data leads to better physics understanding, better retry behavior, better generalization. And the time to get a working demo is shrinking drastically. They're not even at scale yet. They're just starting to see what happens when you pre-train on massive amounts of actual robot data. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news → ziegler.substack.com

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."

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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.

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!

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!

GEN-1 plays the 🐚 shell game, trained on just 1 hr of robot data. It also generalizes to unseen objects, like @BerkayAntmen 's car keys. Physical AI models should be capable of benchmark tasks like this one. It's interesting for the all the reasons @RhodaAI calls out -- requires visual memory, and the model must track the cups from the very start, at high frame rates. Interestingly, GEN-1 appears to exhibit a degree of "active perception." It's subtle; the hands can sometimes appear to "follow" the cups, using its own movements to help attend to where it thinks the object should be. Read more about GEN-1 in our blog post in the comments below ↓

Here’s something we’ve never seen done before. Real-world tasks are long and ambiguous. Solving them requires visual memory and state tracking. Most robot policies only see the last few frames. Ours doesn't. We put our DVA, FutureVision, to the perfect testbed: the shell game 🐚. The DVA nails it.