Everything that has been digitised has been learned. Text, code, images, speech: anything that fits on a screen now has a model that gets better at it every few months.

But the digital world is a thin film over the physical one, and by our estimate it captures less than 0.001% of it. Under the film are the atoms. Every object that has to be lifted, turned, opened, stacked or carried is still moved by a hand.

If you can build something that improves itself in the physical world, you unlock realities humans can't yet imagine. It starts with a world where you don't have to lift a finger to move anything, and it ends with every atom around you being manipulated at will.

Every leap forward has come from learning beyond the limits of reality

Hey, can you tell which ones are real?

Here are ten frames from the same robot stations. Some come from the real robot, and some from our simulation of it. Mark each one sim or real, then check your score. Every round draws a new set of ten from a pool of 98.

Learning only inside the worlds

The robot then learned from trajectories it generated in those simulated worlds. For closing the dishwasher, we collected no real-world data at all: no demonstrations, no teleoperation, no fine-tuning on the customer's site.

Then we ran the model on the real robot in the real kitchen. It closed the dishwasher. The skill it had learned in our virtual kitchens transferred directly to the physical one.

The robot learned to drop the plate

The dishwasher was not the only thing the model figured out. In simulation, a plate got stuck at the edge of the rack, in a pose the gripper couldn't grasp well. Instead of retrying the same grasp, the model pushed the plate down, let it slide, and picked it up again from a better position.

Dropping something on purpose so you can pick it up better is the kind of move people make without thinking, and the kind nobody writes into a dataset.

This discovery is not alone

There were more than 300 techniques that we traced back to sim explorations. This behaviour was not present in any data.

This loop is super useful. The virtual world proposes endless possibilities on model finding techniques to navigate the real world. Each discovery becomes something the model can build on, which is what we mean by recursive learning.

Technical breakdown

We will be releasing the complete pipeline, with the technical details of each block, in the coming days.