World Models for Robotics

World models learn to predict what will happen when a robot acts, giving it a way to plan ahead, learn from imagined experience, and reduce costly or risky real-world trials. Approaches range from latent-dynamics models such as JEPA and Dreamer, often paired with model-predictive control or reinforcement learning, to video-based world-action models that predict future observations alongside executable actions. Researchers also add structure—3D geometry, object-centric states, physics, memory, and tactile or force signals—to handle contact, navigation, and long-horizon tasks. A central challenge is that visually convincing predictions may not follow actions faithfully or support reliable control; balancing physical consistency, robustness, evaluation quality, and inference speed remains open.