Semantic Role-Structured World Model for Physical Interaction Planning

World models for robotic manipulation must capture how actions transform a scene in ways that are meaningful for task execution. Existing object-centric approaches preserve the transient identity of entities, while leaving the evolving task structure that gives those dynamics meaning largely implicit. We introduce SimRole, a world model that represents scene evolution through semantic roles: a task-conditioned structure explicitly encoding the functional status of scene entities, their task-relevant relations, and the current phase of execution. Conditioned on language, visual observations, and proprioception, SimRole uses this structured state to govern action generation and action-conditioned dynamics, predicting interaction events, relation satisfaction, and phase progression. At inference time, these predictions guide phase-aware action selection and local repair of invalid rollouts. We instantiate SimRole on both lightweight LeWM and pretrained COSMOS backbones, improving closed-loop success from 45.5% to 83.0% on LIBERO, 42.9% to 55.2% on RoboCasa, and 55.0% to 81.0% on real-robot Galbot transfer. Across backbones and domains, explicitly modeling task-semantic structure yields substantially stronger world-model-based manipulation.