{
  "title": "Prediction before a decision",
  "caption": "A world model receives context and a candidate robot action. Its predicted results enter a planner, evaluator, filter or policy learner. The executed action returns new observations. Author-created explanatory diagram. Playback time is illustrative and is not measured runtime.",
  "alt": "Flow diagram: robot actions condition predictions for planning, evaluation, filtering or learning, and execution returns observations.",
  "sources": [
    {
      "path": "content/blog/2026-06-09_world-models-for-embodied-ai/post.md",
      "description": "Article mechanism and retained citations"
    },
    "https://liyang.page/wm-tutorial/",
    "https://arxiv.org/abs/2605.00080",
    "https://arxiv.org/abs/2607.00836"
  ],
  "limitations": [
    "This playback is an example. Playback duration is not measured runtime.",
    "The lower path is for deployed closed-loop operation. This path is not necessary for training-only models.",
    "Different methods can give different state, observation and optional reward/value outputs. These are not mandatory outputs for all world models."
  ]
}