Indoor service robots carry medication, lab samples, and supplies through hospital corridors and move goods through warehouse aisles. They share this space with people who are not trained operators and who are often not paying attention to the robot. Navigation policies for such robots are increasingly learned through reinforcement learning or imitation. When a learned policy meets a situation outside its training data, it still produces an action, and nothing in that action tells the robot or its operator that the policy may be wrong.