Designing Control Barrier Function via Probabilistic Enumeration for Safe Reinforcement Learning Navigation
This work addresses safety for robots in uncertain real-world navigation tasks, but it is incremental as it builds on existing verification and control barrier function methods.
The paper tackled the problem of ensuring safe autonomous navigation in dynamic environments by proposing a hierarchical control framework that uses probabilistic enumeration to design control barrier functions and correct unsafe actions, achieving safe navigation while preserving efficiency in simulation and real robot experiments.
Achieving safe autonomous navigation systems is critical for deploying robots in dynamic and uncertain real-world environments. In this paper, we propose a hierarchical control framework leveraging neural network verification techniques to design control barrier functions (CBFs) and policy correction mechanisms that ensure safe reinforcement learning navigation policies. Our approach relies on probabilistic enumeration to identify unsafe regions of operation, which are then used to construct a safe CBF-based control layer applicable to arbitrary policies. We validate our framework both in simulation and on a real robot, using a standard mobile robot benchmark and a highly dynamic aquatic environmental monitoring task. These experiments demonstrate the ability of the proposed solution to correct unsafe actions while preserving efficient navigation behavior. Our results show the promise of developing hierarchical verification-based systems to enable safe and robust navigation behaviors in complex scenarios.