Generalist’s GEN-1 foundation model now supports a range of robot end effectors

Generalist’s latest embodied foundation model, GEN-1, now supports a broad range of robot end effectors. The company said GEN-1 is compatible with five-fingered hands to specialized tools with new modes of actuation and everything in between.

By training GEN-1 to work with these new hands, Generalist said it is demonstrating that a single base AI model can learn sensorimotor policies on robots that transfer across radically different ways of interacting with the physical world.

Below, the company shared examples of these hands in action and where it thinks this is headed.

GEN-1 is pretrained on Generalist’s in-house robotics dataset, which now spans a wide variety of different end effectors across more than half a million hours of real interaction data. Some end effectors involve new form factors with their own actuation schemes and camera positions, a few inspired by real commercial use cases. Others are off-the-shelf tools, printed parts, or custom modifications to the company’s standard two-finger grippers — approximately 9,000 variations so far — all chosen to expose the model to a broad range of contact physics.

Each end effector is a different sensorimotor interface through which GEN-1 experiences the physical world — a way to learn about geometry, contact, friction, forces, and dynamics. Scaling pretraining across thousands of these interfaces teaches GEN-1 universal sensorimotor representations: a general physical commonsense that transfers to new hands and new ways to grasp, push, pull, twist, and more.

Each hand is its own vocabulary for acting in the world. Power screwdrivers rotate faster than fingers can match. Controlling a tape dispenser requires managing tension and placement at once. Tongs introduce compliance and spring-force dynamics that change how an object should be approached. Metal spatulas and scrapers work against a surface rather than around an object, which means reasoning about distributed contact rather than single points of contact. A box cutter or a vegetable peeler demands controlled force along a constrained path.

Just as training on multiple languages produces more capable language models (concepts learned in one language improve understanding in another), learning across many hands stands to benefit physical intelligence. A model trained across embodiments can gather shared knowledge across instances and more readily separate what is specific to a tool from what is universal about the world. Switching between end effectors to reach a goal then becomes a form of physical reasoning: using the right tool for the right job, much as multilingual chain-of-thought can improve language model reasoning for downstream reinforcement learning.