Procedurally generated simulation data can replace expensive human teleoperation for training humanoid robots, enabling zero-shot transfer to real-world tasks without paired real-world supervision.
HALO is a humanoid robot system trained entirely in simulation using procedurally generated homes to learn vision-language-motion skills. By synthesizing training data computationally rather than through human teleoperation, it avoids the data bottleneck that typically limits robot learning.