Physical-AI world models need more than realistic video
Video can teach appearance, motion, and spatial priors. A system that must act also needs the control signal, the state before and after execution, and evidence of what the action achieved.
The data contract should identify whether each signal was measured on hardware, exposed by a simulator, inferred by a model, or generated synthetically. Those sources carry different uncertainty and evaluation value.
- Synchronized cameras, depth, audio, force, or proprioception
- Commanded controls and measured execution
- Robot, vehicle, or agent configuration
- Contacts, events, progress, failures, and terminal state
Real, simulated, and generated trajectories play different roles
Simulation can expand coverage and expose state that is expensive or impossible to measure in the real world. Real trajectories reveal sensor noise, latency, wear, contact, and environmental variation that a simulator can miss.
Generated futures can support augmentation and evaluation design, but they should remain distinguishable from measured experience. A release should preserve source type, simulator version, generation model, prompts or controls, and validation status.
Gameplay is a testbed, not a robot-data substitute
Instrumented games can provide dense state, repeatable physics, human intent, and large action spaces. They are useful for learned simulation, planning research, and controlled transition coverage.
They do not automatically reproduce robot morphology, actuator limits, sensor noise, contact dynamics, or deployment risk. Transfer claims need task-specific evidence rather than a broad physical-AI label.