Abstract
Abstract
Physical AI systems must reason about real-world dynamics in order to perceive, predict, and act safely under partial observability and uncertainty. World models–learned predictive representations of environment dynamics and action consequences–have emerged as a unifying framework for integrating perception, prediction, planning, and control in embodied agents. This survey provides a comprehensive and technically grounded review of learning-based world models for Physical AI, with particular emphasis on closed-loop decision-making. We organize existing approaches along six compositional design dimensions: state abstraction, temporal dynamics, uncertainty source and treatment, structural prior, observation modality, and decision coupling. Beyond this design-oriented taxonomy, we analyze how world models interact with optimization–highlighting compounding error, planner exploitation, rollout horizon management, and uncertainty calibration as central design tensions. We further examine evaluation methodologies, benchmark ecosystems, and sim-to-real transfer challenges, and synthesize open problems in long-horizon consistency, physical constraint enforcement, data efficiency, and safety. By clarifying recurring trade-offs across robotics and model-based reinforcement learning, this survey outlines principled directions for building reliable and scalable Physical AI systems.