RoboGate: Adaptive Failure Discovery for Safe Robot Policy Deployment via Two-Stage Boundary-Focused Sampling

Deploying learned robot manipulation policies in industrial settings requires rigorous pre-deployment validation, yet exhaustive testing across high-dimensional parameter spaces is intractable. We present RoboGate, a deployment risk management framework that combines physics-based simulation with a two-stage adaptive sampling strategy to efficiently discover failure boundaries in the operational parameter space. Using NVIDIA Isaac Sim with Newton physics, we evaluate a scripted pick-and-place controller on two robot embodiments—Franka Panda (7-DOF) and UR5e (6-DOF)—across 30,000 total experiments. Our logistic regression risk model achieves an AUC of 0.780, identifies a closed-form failure boundary equation, and reveals four universal danger zones affecting both robot platforms. All data, code, and trained models are publicly available under MIT license.