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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Each hand is a different sensorimotor interface by which GEN-1 experiences the physical world. Scaling pretraining across thousands of these interfaces teaches GEN-1 a universal physical commonsense that transfers to new hands and new ways to grasp, push, pull, twist, and more.
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Every hand is a different language for physical interaction. Just as training on multiple languages produces more capable LLMs, a model trained across embodiments can gather shared knowledge across instances and more readily separate what is specific to a hand from what is universal about the world.
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Unlike humans, robots aren’t locked into the hands they’re born with. Machines can easily swap mechanical hands, and tool changers are common in automation. Switching between different end effectors to reach a goal can be viewed as a form of physical reasoning: using the right tool for the right job, much as multilingual chain-of-thought can improve LLM reasoning for downstream RL.
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What happens when you change the hand mid-task?
We tested this by modifying the hands mid-rollout and letting the same model keep running. It perceives the new tool, and finds a new trajectory and contact strategy to complete the task.
This works because training on mixed data forces the model to condition its behavior on the hand in front of it. This brings it closer to a general understanding of how shapes and contact surfaces interact with the physical world – and which actuation strategy should follow.
Jul 23, 2026 · 2:05 PM UTC
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Nature didn’t converge on just a single solution for manipulating the physical world. It exploded into millions. You can see this everywhere in the diversity of life around us: from the beak of a bird, to the trunk of an elephant, from the suction pads of an octopus, to the pollen baskets of a honeybee.
The human hand is only a single point in a design space so large we’ve barely begun to map. For machines, five-fingered hands will just be one tool among many; limiting robots to only that would be a failure of imagination.
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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: generalistai.com/blog/toward…
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