Probabilistic Safety Programs
This addresses safety challenges for real-world autonomous systems, though it appears incremental as it builds on existing probabilistic and control methods.
The paper tackles the problem of safe control under uncertainty for autonomous robots and cyber-physical systems by introducing Probabilistic Safety Programs (PSP), which embed environmental uncertainty and safety invariants to evaluate future actions, demonstrating efficacy in tasks like quadrotor and autonomous vehicle control.
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.