💡 The paper provides a comprehensive survey of world models for robot learning, which are predictive representations of environmental dynamics that support policy learning, planning, and simulation. The authors note that the literature on world models is fragmented across different architectures, functional roles, and application domains, making it difficult to understand the current state of the field. To address this gap, the authors present a systematic review of world models from a robot learning perspective, examining how they are coupled with robot policies, used as learned simulators for reinforcement learning and evaluation, and have progressed in terms of robotic video world models. The survey covers the progression of world models from imagination-based generation to controllable, structured, and foundation-scale formulations, and connects these ideas to navigation and autonomous driving. The authors also summarize representative datasets, benchmarks, and evaluation protocols, and highlight major challenges and future directions for predictive modeling in embodied agents. The paper aims to clarify key paradigms and applications of world models, and to facilitate continued access to newly emerging works, benchmarks, and resources, the authors will maintain and regularly update a GitHub repository accompanying the survey. Overall, the paper provides a thorough overview of the rapidly growing literature on world models for robot learning, and identifies key areas for future research and development.