Anti-misinterpretation registry
A VLA or robot foundation model can be described as a complete robot brain.
It hides the rest of the stack: embodiment, actuation, sensing, safety, latency, control, data, deployment environment, and maintenance.
Call it a model or policy component inside a robot stack, unless a source explicitly describes the shipped system boundary.
Performance in simulation proves real-world readiness.
Simulation is useful for data, validation, and stress testing, but real friction, contact, sensor noise, wear, and human environments can break assumptions.
Simulation can reduce iteration cost and expose candidate failures; real deployment still needs measured validation.
A high-degree PhysDB node is the most important concept in Physical AI.
Graph degree reflects current corpus coverage, not intrinsic importance or causal priority.
High-degree nodes are graph leads or bridge candidates within the current source set.
A public robot demo proves broad capability.
Demos often use curated scenes, known objects, controlled lighting, limited task distributions, or operator recovery.
Treat demos as evidence of a tested scenario, not as a general capability claim.
Physical AI means humanoid robots only.
Autonomous vehicles, manipulators, drones, warehouse systems, surgical and inspection platforms, and embodied perception systems share parts of the same stack.
Humanoids are one embodiment class inside the broader Physical AI map.