use_cases.json

[
  {
    "name": "Robot Manipulation",
    "real": "Robot demonstrations in a real workspace",
    "compiled": "Interactive manipulation scene with objects, contacts and actions",
    "downstream": "Policy training, world models, task rehearsal"
  },
  {
    "name": "Humanoid Learning",
    "real": "Human-scale environments and demonstrations",
    "compiled": "Structured scenes with body motion, object interaction and task context",
    "downstream": "Embodied learning and humanoid control"
  },
  {
    "name": "Autonomous Driving",
    "real": "Multi-sensor driving logs",
    "compiled": "Replayable traffic scenes with actors and dynamics",
    "downstream": "Scenario testing, prediction and planning"
  },
  {
    "name": "Industrial Automation",
    "real": "Factory cells and machine interactions",
    "compiled": "Simulation-ready workcells and process states",
    "downstream": "Robot planning, safety testing and optimization"
  },
  {
    "name": "Synthetic Edge Cases",
    "real": "Rare or expensive real-world events",
    "compiled": "Parameterized scenario families",
    "downstream": "Stress testing and robust policy training"
  },
  {
    "name": "World Model Training",
    "real": "Captured physical trajectories",
    "compiled": "Large action-conditioned simulated experience",
    "downstream": "Prediction, rollouts and model-based control"
  }
]