Training reinforcement learning (RL) policies directly on physical robots is often slow, costly, and risky. World models can learn environment dynamics from collected data, allowing safe policy training in generated simulations.
This project will develop a vision-language-action model that learns within a world-model-generated environment using modern RL algorithms such as GRPO. The trained policy will then be deployed on a physical robot for direct evaluation.