Revisiting PGD Attacks for Stability Analysis of High-Dimensional Nonlinear Systems and Perception-Based Control

Abstract

Many existing region-of-attraction (ROA) analysis tools find difficulty in addressing feedback systems with large-scale neural network (NN) policies and/or high-dimensional sensing modalities such as cameras. In this letter, we tailor the projected gradient descent (PGD) attack method as a general-purpose ROA analysis tool for high-dimensional nonlinear systems and end-to-end perception-based control. We show that the ROA analysis can be approximated as a constrained maximization problem such that PGD-based iterative methods can be directly applied. In the model-based setting, we show that the PGD updates can be efficiently performed using back-propagation. In the model-free setting, we propose a finite-difference PGD estimate which is general and only requires a black-box simulator for generating the trajectories of the closed-loop system given any initial state. Finally, we demonstrate the scalability and generality of our analysis tool on several numerical examples with large state dimensions or complex image observations.