Robot skills learned in a world model transfer to a real robot, project says

The project describes a reinforcement-learning method that trains robot control policies from scratch entirely inside a large world model—a learned model of how the environment behaves. It says the world model is itself learned from scratch using real-world data, and the policies are then deployed “zero-shot” on a real robot, without additional training.