Generalist
@GeneralistAI
Generalist is an AI robotics company building general intelligence for the physical world and making it useful to everyone.
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GEN-1.5 packs a bottle into a box, then packs another bottle into a box, then another bottle, then another one, then another...
Read more about GEN-1.5 in our blog post in the comments below.
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Robot puts a ring on it
GEN-1.5 puts an O-ring on a hydraulic rod gland, then takes the ring off, then puts the ring back on, then takes the ring off, then ring on, ring off, ring on...
Read more about GEN-1.5 in our blog post in the comments below.
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We've reduced the time it takes to go from physical prompt → robot behavior.
The faster anyone can teach a robot to do something new, the easier it becomes to scale physical work.
Read more about GEN-1.5 in our blog post in the comments below.
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Experiments across 10 diverse tasks show 59% average success with one-shot physical prompting, straight from pretraining. With few-shot learning, performance rises to 83% via 10 gradient steps on 5 minutes of data per task.
Although the tasks are simple and success rates are modest, it’s the first model we know of that exhibits the general ability to learn a wide range of dexterous closed-loop physical tasks from just one or few demonstrations. This accelerates reaching a base level of competence for new skills that can be subsequently refined towards mastery.
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Introducing GEN-1.5, a one-shot learner.
It can learn new tasks in a few seconds. Show it what to do, and it generalizes.
This capability emerged from pretraining on physical data at scale, as a step towards our mission of building general intelligence for the physical world.
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We've improved how GEN-1 learns to adapt to new actuators and new robots at the lowest level, with up to 10-20x gains on internal benchmarks. This significantly boosts performance on high-precision tasks like disassembling parts from a NIST board.
Read more about GEN-1 in our blog posts in the comments below.
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If we can build general intelligence that understands the underlying physics of interaction, then the shape of the hand becomes secondary to the intelligence that drives it. A suction pad, a gripper, a brush, a plasma welding nozzle – are all just different interfaces through which the same intelligence can reshape the physical world.
The future of robot hands won’t look like ours. It will look more like a toolbox with a thousand hands: augmented, recombined, and scaled. Robots were always meant to extend what humans can do – to empower people to shape the physical world in places and at scales we never could before.
Read more about our work at:
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Why create robot intelligence for just one hand, when we could have it learn from many?
GEN-1, our latest embodied foundation model, now supports a broad range of end effectors from 5-finger hands, to specialized tools, and everything in between.
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