We're introducing a new class of foundation models built for high-fidelity raw physical interaction – designed to capture human-level reflexes and physical commonsense. Today we are sharing that these capabilities are scaling.
One-shot assembly is a new addition to our internal evaluation tasks: you build small Lego structure, place it in front of the robot, and the robot builds copies of it. This requires visual understanding, next-level precision, and sequential reasoning.
To push the frontiers of what end-to-end AI models can achieve in the real world, we've been training models and evaluating their capabilities for dexterous sensorimotor policies across different embodiments, environments, and physical interactions.
At Generalist, we are on a mission to make general-purpose robots a reality. We believe the industries and homes of the future will depend on humans and machines working together in new ways. Robots can help us build more and get more done.
We build embodied foundation models, starting with a focus on dexterity. This requires advancing the frontiers of data, models, and hardware, to enable robots to intelligently interact with the physical world.
The company embraces both large-scale AI and robotics as core to its DNA. Our team of researchers, roboticists, and company builders come from OpenAI, Boston Dynamics, Google DeepMind, and other frontier labs—with a track record of shipping AI breakthroughs. Before Generalist, we pioneered large embodied multimodal models (PaLM-E) and vision-language-action models (RT-2), scaled ChatGPT and GPT-4 to hundreds of millions of users, engineered the foundations of autonomous driving, built next-generation robots (Atlas, Spot, Stretch) and pushed the limits of what they can do (from parkour to manipulation, and testing robustness). We are located in the Bay Area (CA) and Boston (MA).
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