Can Predicted Dynamics Exist in the Physical World?
Summary
This paper proposes a method for validating physical admissibility of AI predictions by using a prediction-control interface with kinematic and dynamic conditions. It demonstrates high accuracy in filtering invalid proposals on the LeRobot PushT task.
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Paper page - Can Predicted Dynamics Exist in the Physical World?
Source: https://huggingface.co/papers/2606.00089
Abstract
Physical admissibility validation for AI systems uses prediction-control interfaces with kinematic and dynamic conditions to filter invalid proposals while maintaining high performance.
Predictive Physical AIsystems outputstate rollouts,action chunks, andlatent plans, yet a lowroot-mean-square error(RMSE) does not imply that a particular proposal is physically executable. We formulatephysical admissibilityas aprediction-control interface: before execution, a decoded proposal is treated as candidate dynamics and evaluated using kinematic, dynamic, anddirect-to-composed horizon conditions. Passing is not a certificate of task success; rejection identifies violation of the specified physical envelope and gives a component-level reason. On Hugging Face LeRobot PushT, controlled falsification shows that one-step prediction-RMSE and standardized dynamics residuals reach area under thereceiver operating characteristic curve(AUC) 0.982 and 0.972, kinematic-only conditions reachAUC0.592, and the full gate reachesAUC0.957 with condition-level attribution. In replay-based intervention experiments,residual-based filtersand the full physical-admissibility gate prevent 87-$89% of invalid proposals while preserving mean progress near 0.998.
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