| title | Can Predicted Dynamics Exist in the Physical World? |
|---|---|
| authors | Barak Or |
| date | 2026-05-23 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2606.00089 |
| affiliation | STATE16 |
| upvotes | 1 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf |
| abstract | Predictive Physical AI systems output state rollouts, action chunks, and latent plans, yet a low root-mean-square error (RMSE) does not imply that a particular proposal is physically executable. We formulate physical admissibility as a prediction-control interface: before execution, a decoded proposal is treated as candidate dynamics and evaluated using kinematic, dynamic, and direct-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 the receiver operating characteristic curve (AUC) 0.982 and 0.972, kinematic-only conditions reach AUC 0.592, and the full gate reaches AUC 0.957 with condition-level attribution. In replay-based intervention experiments, residual-based filters and the full physical-admissibility gate prevent 87-$89% of invalid proposals while preserving mean progress near 0.998. |
cite
@misc{or2026can,
title={Can Predicted Dynamics Exist in the Physical World?},
author={Barak Or},
year={2026},
eprint={2606.00089},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2606.00089}
}