World Model for Robot Learning: A Comprehensive Survey

ArXi:2605.00080v1 Announce Type: cross World models, which are predictive representations of how environments evolve under actions, have become a central component of robot learning. They policy learning, planning, simulation, evaluation, data generation, and have advanced rapidly with the rise of foundation models and large-scale video generation. However, the literature remains fragmented across architectures, functional roles, and embodied application domains. To address this gap, we present a comprehensive review of world models from a robot-learning perspective.