訓練データを超えた安全性：Conformal予測とシステムレベル合成によるロバストな分布外MPC
Safety Beyond the Training Data: Robust Out-of-Distribution MPC via Conformalized System Level Synthesis
学習済みダイナミクスモデルの分布外領域での安全性を確保するため、重み付きConformal予測でモデル誤差の高信頼度上界を導出し、システムレベル合成ベースのロバスト非線形MPCに統合する枠組みを提案した。
詳しい要約
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著者: Anutam Srinivasan, Antoine Leeman, Glen Chou
分類: cs.RO, cs.LG, cs.SY, eess.SY, math.OC
原文アブストラクト
We present a novel framework for robust out-of-distribution planning and control using conformal prediction (CP) and system level synthesis (SLS), addressing the challenge of ensuring safety and robustness when using learned dynamics models beyond the training data distribution. We first derive high-confidence model error bounds using weighted CP with a learned, state-control-dependent covariance model. These bounds are integrated into an SLS-based robust nonlinear model predictive control (MPC) formulation, which performs constraint tightening over the prediction horizon via volume-optimized forward reachable sets. We provide theoretical guarantees on coverage and robustness under distributional drift, and analyze the impact of data density and trajectory tube size on prediction coverage. Empirically, we demonstrate our method on nonlinear systems of increasing complexity, including a 4D car and a {12D} quadcopter, improving safety and robustness compared to fixed-bound and non-robust baselines, especially outside of the data distribution.
