Research Posts
GEN-1.5 / Embodied Foundation Models are One-Shot Learners
GEN-1.5 can in-context learn a new task from as little as 12 seconds of demonstration data — or adapt with 1 to 10 gradient steps on minutes of data. These capabilities emerge from pretraining on large-scale physical experience.
Towards Machines with a Thousand Hands
Robots aren't locked into the hands they're born with. GEN-1 now supports a broad range of end effectors, showing how a single model can transfer across radically different ways of interacting with the physical world.
GEN-1 / Scaling Embodied Foundation Models to Mastery
We've created GEN-1, our latest milestone in scaling robot learning — the first general-purpose AI model that crosses a new performance threshold: mastery of simple physical tasks.
GEN-0 / Embodied Foundation Models That Scale with Physical Interaction
We're introducing GEN-0, a new class of embodied foundation models built for multimodal training directly on high-fidelity raw physical interaction.
The Robots Build Now, Too
One-shot assembly is one of our new internal evaluation tasks: you build a small Lego structure, place it in front of the robot, and the robot builds copies of it.
Research Preview
A glimpse of what we're building at Generalist — end-to-end neural networks for dexterous sensorimotor policies across different embodiments, environments, and physical interactions.