
Generalist
@GeneralistAI • 9,872 subscribers
Generalist is an AI robotics company building general intelligence for the physical world.
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GEN-1 plays the 🐚 shell game, trained on just 1 hr of robot data. It also generalizes to unseen objects, like berkay 's car keys. Physical AI models should be capable of benchmark tasks like this one. It's interesting for the all the reasons Rhoda AI 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 ↓
Generalist858,574 просмотров • 1 месяц назад

Introducing GEN-1. Our latest milestone in scaling robot learning. We believe it to be the first general-purpose AI model to master simple physical tasks. 99% success rates, 3x faster speeds, adapts in real time to unexpected scenarios, w/ only 1 hour of robot data. More🧵👇
Generalist376,653 просмотров • 2 месяцев назад

This is GEN-1 putting paper bills into a wallet. Paper has always been deceptively hard for robots. Thin, deformable, and unforgiving—it bends, folds, and slips. Not precise, you miss. Too much force, you crumple. Easy for humans. But for robots, it’s a full-stack challenge.
Generalist218,995 просмотров • 2 месяцев назад

Introducing GEN-0, our latest 10B+ foundation model for robots ⏱️ built on Harmonic Reasoning, new architecture that can think & act seamlessly 📈 strong scaling laws: more pretraining & model size = better 🌍 unprecedented corpus of 270,000+ hrs of dexterous data Read more 👇
Generalist483,021 просмотров • 7 месяцев назад

GEN-1 puts plushies into polybags, in a warehouse outside the lab in New Hampshire.
Generalist101,456 просмотров • 1 месяц назад

GEN-1 cleans white board Read more about GEN-1 in our blog post in the comments below ↓
Generalist62,857 просмотров • 1 месяц назад

Today we're excited to share a glimpse of what we're building at Generalist. As a first step towards our mission of making general-purpose robots a reality, we're pushing the frontiers of what end-to-end AI models can achieve in the real world. Here's a preview of our early results in autonomous general-purpose dexterous capabilities – fast, reactive, smooth, precise, bi-manual coordinated sensorimotor control.
Generalist277,684 просмотров • 11 месяцев назад

GEN-1 plugging in ethernet cables to a handheld socket.
Generalist53,467 просмотров • 2 месяцев назад

Gen-1 ties zipties Read more about Gen-1 in our blog posts in the comments below ↓
Generalist35,872 просмотров • 1 месяц назад

Everyday for the past 2 weeks, we've been sharing something new from GEN-1, our latest milestone in scaling robot learning. This has never been done before. Going from ideas to skills in days (or faster) is what physical AI models should deliver. More coming. Stay tuned. Read more about it in our blog post in the comments below ↓
Generalist38,936 просмотров • 1 месяц назад

We ran a live demo NVIDIA GTC last week, but the real story is how quickly we got it running. The system was up and running in days, not weeks. This is a step toward robots that can be deployed quickly without task-by-task programming. How we made it happen👇 🧵 (1/6)
Generalist41,671 просмотров • 2 месяцев назад

GEN-1 puts pens into zipper bag. Robot’s view, pixels into the model.
Generalist27,155 просмотров • 2 месяцев назад

Happening now at #NVIDIAGTC: Generalist’s GEN-0 model autonomously packing phones on Universal Robots arms in our first public demo. To move robotics beyond the lab, systems need to operate in real time on industrial hardware. See the demo below, and stop by booth #1840 👇🤖
Generalist34,081 просмотров • 2 месяцев назад

Robot reaches deep for screws Read more about Gen-1 in our blog posts in the comments below ↓
Generalist15,031 просмотров • 1 месяц назад

GEN-1 still works with lights off, and generalizes under harsh lighting conditions. The model uses raw video pixels to make decisions, so strong lighting changes can drastically alter its input distribution. Yet performance still holds. Why? GEN-1 was pre-trained on a massive, diverse dataset of different lighting conditions—everywhere from outdoor farms, to warehouses, from grocery stores, to dimly lit homes—it's already seen it all, and transfers this knowledge to new tasks. This is a glimpse of what we call Mastery, and is part of the reason these models can cross a new performance threshold. Read more about it in our blog post in the comments below 👇
Generalist18,101 просмотров • 1 месяц назад