Using a world model trained on real robot data to generate synthetic transitions—combined with careful sample selection—lets robots learn manipulation tasks with 59% fewer real interactions while improving success rates by 28%.
WorldSample combines real robot interactions with a world model to generate synthetic training data for reinforcement learning. By closing a loop between physical rollouts, synthetic data generation, and policy improvement, it reduces the number of costly real-world interactions needed while maintaining high-quality learning.