適応的Conformal分位点予測区間による不確実性下の安全臨界制御
Safety-Critical Control under Uncertainty via Adaptive Conformal Quantile Prediction Intervals
適応的Conformal予測とConformal分位点回帰を組み合わせ、状態依存で非対称な不確実性区間を構築し、確率的制御バリア関数とMPCに統合することで、保守性を抑えつつ高確率の安全性を保証する制御フレームワークを提案した。
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著者: Hao Zhou, Yanze Zhang, Yiwei Lyu, Wenhao Luo
分類: cs.RO
原文アブストラクト
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 calibrated and state-dependent prediction intervals to enable high-probability safety guarantees, while improving constrained control performance. The framework leverages adaptive conformal prediction (ACP) and extends it with conformal quantile regression (CQR) to capture distribution-free, asymmetric uncertainty intervals with certifiable probabilistic coverage, and integrates the resulting uncertainty sets into a probabilistic control barrier function formulation to enforce robust safety with reduced conservativeness. This yields uncertainty-aware safe control constraints that can be incorporated within a model predictive control(MPC) framework to provide provably safe behaviors with high probability. Simulation and theoretical results are provided to demonstrate the effectiveness of our approach.
