安全フィルタのアンサンブルと共形予測による制御システムの安全性の統計的保証
Statistically Assuring Safety of Control Systems using Ensembles of Safety Filters and Conformal Prediction
学習したハミルトン・ヤコビ値関数と安全政策の不確実性を共形予測で境界付け、確率的な安全保証を提供するフレームワークを提案。独立に訓練した値関数のアンサンブルを安全フィルタとして用いる手法も検討。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
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著者: Ihab Tabbara, Yuxuan Yang, Hussein Sibai
分類: cs.LG, cs.AI, cs.RO, cs.SY, eess.SY
原文アブストラクト
Safety assurance is a fundamental requirement for deploying learning-enabled autonomous systems. Hamilton-Jacobi (HJ) reachability analysis is a fundamental method for formally verifying safety and generating safe controllers. However, computing the HJ value function that characterizes the backward reachable set (BRS) of a set of user-defined failure states is computationally expensive, especially for high-dimensional systems, motivating the use of reinforcement learning approaches to approximate the value function. Unfortunately, a learned value function and its corresponding safe policy are not guaranteed to be correct. The learned value function evaluated at a given state may not be equal to the actual safety return achieved by following the learned safe policy. To address this challenge, we introduce a conformal prediction-based (CP) framework that bounds such uncertainty. We leverage CP to provide probabilistic safety guarantees when using learned HJ value functions and policies to prevent control systems from reaching failure states. Specifically, we use CP to calibrate the switching between the unsafe nominal controller and the learned HJ-based safe policy and to derive safety guarantees under this switched policy. We also investigate using an ensemble of independently trained HJ value functions as a safety filter and compare this ensemble approach to using individual value functions alone.
