This research paper introduces a new theory for learning to control nonlinear deterministic systems using geometrically supervised latent models. The proposed method establishes a finite-sample learning-to-control framework that separates approximation, sampling, and optimization errors. It introduces an encoder-only local-global metric hinge to enforce directional resolution and state discrimination, ensuring that approximate empirical minimizers are co-Lipschitz and semiconjugate to the controlled dynamics. The paper demonstrates that this approach can lead to reliable planning and improved control by restoring metric resolution, with constructive realization of approximation hypotheses using tensor-product B-spline classes. AI
IMPACT Introduces a novel theoretical framework for control systems, potentially advancing research in AI-driven control and optimization.
RANK_REASON Research paper published on arXiv detailing a new theoretical framework for control systems. [lever_c_demoted from research: ic=1 ai=1.0]
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