LLM agents can autonomously generate high-quality robot training data by building simulations and rendering photorealistic videos, achieving better generalization than human-collected data at a fraction of the cost.
A system where an LLM agent acts as a virtual teleoperator to generate robot training data. The agent receives a real robot scene, writes simulation code and controllers, then uses a video model to render photorealistic training episodes. The resulting synthetic data trains policies that match or exceed those trained on real teleoperated data, while costing 60-70% less than human teleoperation.