ニューラルフィードバックループの後方到達可能性解析：線形・非線形システムのための手法
Backward Reachability Analysis of Neural Feedback Loops: Techniques for Linear and Nonlinear Systems
ニューラルネットワーク制御ポリシーを持つ閉ループ系の安全性を検証するため、順方向解析ツールを活用して後方投影集合の過近似を効率的に求める手法を提案した。
著者: Nicholas Rober, Sydney M. Katz, Chelsea Sidrane, Esen Yel, Michael Everett, Mykel J. Kochenderfer, Jonathan P. How
分類: eess.SY, cs.LG, cs.RO, cs.SY
原文アブストラクト
As neural networks (NNs) become more prevalent in safety-critical applications such as control of vehicles, there is a growing need to certify that systems with NN components are safe. This paper presents a set of backward reachability approaches for safety certification of neural feedback loops (NFLs), i.e., closed-loop systems with NN control policies. While backward reachability strategies have been developed for systems without NN components, the nonlinearities in NN activation functions and general noninvertibility of NN weight matrices make backward reachability for NFLs a challenging problem. To avoid the difficulties associated with propagating sets backward through NNs, we introduce a framework that leverages standard forward NN analysis tools to efficiently find over-approximations to backprojection (BP) sets, i.e., sets of states for which an NN policy will lead a system to a given target set. We present frameworks for calculating BP over approximations for both linear and nonlinear systems with control policies represented by feedforward NNs and propose computationally efficient strategies. We use numerical results from a variety of models to showcase the proposed algorithms, including a demonstration of safety certification for a 6D system.
