Verification of Neural Reachable Tubes via Scenario Optimization and Conformal Prediction

arXiv:2312.08604v2 Announce Type: replace-cross
Abstract: Learning-based approaches for controlling safety-critical systems are rapidly growing in popularity; thus, it is important to assure their performance and safety. Hamilton-Jacobi (HJ) reachability analysis is a popular formal verification tool for providing such guarantees, since it can handle general nonlinear system dynamics, bounded adversarial system disturbances, and state and input constraints. However, its computational and memory complexity scales exponentially with the state dimension, making it intractable for large-scale systems. To overcome this challenge, neural …

abstract analysis arxiv cs.ai cs.lg cs.ro cs.sy dynamics eess.sy general hamilton optimization performance popular prediction safety safety-critical systems tool type verification via