Offline Robotic World Model: Learning Robotic Policies without a Physics Simulator

Reinforcement Learning (RL) has demonstrated impressive capabilities in robotic control but remains challenging due to high sample complexity, safety concerns, and the sim-to-real gap. While offline RL eliminates the need for risky real-world exploration by learning from pre-collected data, it suffers from distributional shift, limiting policy generalization. Model-Based RL (MBRL) addresses this by leveraging predictive models for synthetic rollouts, yet existing approaches often lack robust uncertainty estimation, leading to compounding errors in offline settings. We introduce Offline Robotic World Model (RWM-O), a model-based approach that explicitly estimates epistemic uncertainty to improve policy learning without reliance on a physics simulator. By integrating these uncertainty estimates into policy optimization, our approach penalizes unreliable transitions, reducing overfitting to model errors and enhancing stability. Experimental results show that RWM-O improves generalization and safety, enabling policy learning purely from real-world data and advancing scalable, data-efficient RL for robotics.