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Guaranteed Reach-Avoid for Black-Box Systems through Narrow Gaps via Neural Network Reachability

In the classical reach-avoid problem, autonomous mobile robots are tasked to reach a goal while avoiding obstacles. However, it is difficult to provide guarantees on the robot's performance when the obstacles form a narrow gap and the robot is a black-box (i.e. the dynamics are not known analyticall