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Safety-Critical Control under Uncertainty via Adaptive Conformal Quantile Prediction Intervals

arXiv · AI, language, vision and robotics · article · Sep 20, 2026 · UTC

Safety-critical control under uncertainty requires uncertainty representations that are both statistically valid (for certifiable performance) and compatible with enforceable safety constraints. However, existing methods often assume particular distributions of uncertainty for provable safety guarantees or establish symmetric and input-agnostic prediction intervals for robust safety, which can lead to misaligned or overly conservative safety constraints in control synthesis. In this paper, we introduce a novel safe control framework with adaptive uncertainty quantification that constructs cali

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