From sensor to model, continuously
Keeping a digital twin synchronized requires a pipeline: sensors on the physical robot generate readings, those readings are transmitted (often over a local network or industrial protocol), ingested by a backend system, and used to update the twin's internal state representation. This sounds simple as a diagram, but each stage introduces its own failure modes. A joint encoder might report a value, but if that value takes three seconds to reach the twin, the twin is now describing where the robot was three seconds ago, not where it is now. For a twin used to monitor slow wear trends, that lag is irrelevant. For a twin meant to support real-time operational decisions, it can make the twin actively misleading.
Latency isn't the only concern, data quality matters just as much. A twin is only as trustworthy as the sensor stream feeding it. Dropped packets, sensor noise, miscalibrated instruments, or a temporary network outage can all cause the twin's model to diverge from physical reality without any obvious signal that this has happened. Unlike a training simulation, where a bad data point mostly costs you one bad training example among many, a twin with corrupted incoming data can silently drift into representing a robot state that no longer exists, and every downstream decision built on that twin (a maintenance alert, a what-if test, an operator dashboard) inherits that error.