New arXiv method slashes control tracking error 69% in deployment

A new arXiv paper (2608.10453) from Xitong Niu and colleagues tackles a core control problem: training a controller using privileged simulation data (known gains and disturbances) but deploying it only with reference and measured states. Direct imitation is unsafe because identical student observations can map to different expert actions across regimes. The authors propose a mechanism-guided transfer route that fuses physical insight with learned behavior, avoiding new neural architectures entirely.

They derive an exact sampled-data identity to eliminate additive disturbance, reducing the learning task to recovering a task-relevant inverse input gain from causal state history. The latent target is reconstructed from expert actions and deployment-visible trajectories, so no ground-truth plant parameter is needed as a student label. In 60-second unseen trials, this structured student cuts tracking RMSE by ~69% relative to a tuned observer, while direct action networks fail in closed loop despite moderate offline error. The work provides an interpretable design perspective for converting privileged control knowledge into deployable adaptive controllers, with conditions on when the transfer is meaningful.