  Achieving safe control under uncertainty is a key problem that needs to be
tackled for enabling real-world autonomous robots and cyber-physical systems.
This paper introduces Probabilistic Safety Programs (PSP) that embed both the
uncertainty in the environment as well as invariants that determine safety
parameters. The goal of these PSPs is to evaluate future actions or
trajectories and determine how likely it is that the system will stay safe
under uncertainty. We propose to perform these evaluations by first compiling
the PSP to a graphical model then using a fast variational inference algorithm.
We highlight the efficacy of the framework on the task of safe control of
quadrotors and autonomous vehicles in dynamic environments.
