You can formally verify neural safety filters for interactive robots by focusing verification on regions where the robot's inference is reliable, enabling safer and more efficient human-robot interaction.
This paper solves a key problem in safe robotics: how to verify that neural safety filters work correctly when robots learn about human behavior in real-time. The authors use conformal prediction to mathematically guarantee safety while accounting for inference errors, enabling robots to be less overly-cautious around people.