Beyond Dense Futures: World Models as Structured Planners for Robotic Manipulation

Recent world-model-based Vision-Language-Action (VLA)architectures have improved robotic manipulation through predictive vi-sual foresight. However, dense future prediction introduces visual redun-dancy and accumulates errors, causing long-horizon plan drift. Mean-while, recent sparse methods typically represent visual foresight usinghigh-level semantic subtasks or implicit latent states. These representa-tions often lack explicit kinematic grounding, weakening the alignmentbetween planning and low-level execution. To address this, we proposeStructVLA, which reformulates a generative world model into an explicitstructured planner for reliable control. Instead of dense rollouts or seman-tic goals, StructVLA predicts sparse, physically meaningful structuredframes. Derived from intrinsic kinematic cues (e.g., gripper transitionsand kinematic turning points), these frames capture spatiotemporal mile-stones closely aligned with task progress. We implement this approachthrough a two-stage training paradigm with a unified discrete token vo-cabulary: the world model is first trained to predict structured frames andsubsequently optimized to map the structured foresight into low-level ac-tions. This approach provides clear physical guidance and bridges visualplanning and motion control. In our experiments, StructVLA achievesstrong average success rates of 75.0% on SimplerEnv-WidowX and 94.8%on LIBERO. Real-world deployments further demonstrate reliable taskcompletion and robust generalization across both basic pick-and-placeand complex long-horizon tasks.