Dream to Remember: Continual Steering of Generalist Robot Policies with World Models

Generalist robot policies are trained with imitation learning and are bounded by the quality and coverage of their demonstrations. Reinforcement learning can improve and extend them, either by fine-tuning the policy weights or by training a small module to steer the input noise of a frozen generative policy.

From the abstract. Our summary is in progress.