As the deployment of autonomous robots expands, relying on complex decision-making policies – whether handcrafted or learned – raises concerns about their reliability. These policies are susceptible to vulnerabilities and bugs that can compromise operational integrity. This case study focuses on monitoring robotic navigation tasks, where a robot, driven by a black-box controller, must navigate towards a target. We compare three progress criteria – a simple distance metric and two variants of a stability certificate – and three finite horizon roll-out failure criteria. In laboratory experiments in both a free-space and an obstacle-field environment, one criterion combination detects true failures early with a low number of false alarms and outperforms classical heuristics at minimal computational cost. Our monitor uses only the filtered state and a finite horizon roll-out, providing formal progress/failure guarantees with low computational overhead.