Offline robot simulations without real-world corrections mislead how well control systems actually perform.

Robots often use world models, which are internal simulations that predict the future. Usually, engineers test these models by making them predict many steps ahead without checking real sensor data. But in real life, robots constantly get new sensor measurements to correct their mistakes. Researchers tested whether judging models without sensor updates actually reflects real-world success. They tested six state estimatorsstate estimatorsAlgorithms that calculate a robot's position and orientation from sensor data., which track where a robot is, across 24 sensing conditions for a wheeled robot following a path. They found that open-loopopen-loopA system that acts based on predictions without real-time corrections from sensors. predictions without measurements gave misleading results. Testing models with recorded replays matched real driving much better than test runs that had no sensor checks. When rollouts completely skipped sensor updates, long predictions picked the wrong best model in most setups. However, predictions stayed accurate if the model received regular sensor corrections. Therefore, offline tests must include regular sensor updates to reflect real robot control accurately.

Why it matters

This matters for engineers designing robot control systems. Using the wrong offline tests can lead to choosing models that fail in real conditions, affecting safety and reliability.

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