Designing Control Barrier Function via Probabilistic Enumeration for Safe Reinforcement Learning Navigation

2025-04-30

AI Summary

Addressing the challenge of ensuring safety for reinforcement learning (RL)-based navigation policies in dynamic and uncertain environments, this paper proposes a hierarchical safety-aware navigation framework. First, unsafe regions are identified via probabilistic enumeration. Second, neural network verification is integrated with probabilistic sampling—novelly enabling automatic synthesis of generalizable control barrier functions (CBFs) without requiring prior knowledge of the system dynamics. Third, the synthesized CBFs are embedded into a real-time safety layer that corrects arbitrary end-to-end RL policies. The resulting approach provides plug-and-play safety augmentation. Evaluated in both simulation and on a real-world autonomous underwater vehicle platform, it reduces safety violations by 92%, achieves a 98.7% task completion rate, and preserves over 95% of the original policy’s efficiency.

Technology Category

• Natural Language Processing: Safety and Robustness
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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.

Problem

• Ensuring safe reinforcement learning navigation in dynamic environments
• Designing control barrier functions via probabilistic enumeration
• Correcting unsafe actions while maintaining efficient navigation

Innovation

• Hierarchical control framework with neural verification
• Probabilistic enumeration for unsafe region identification
• CBF-based safe layer for arbitrary policies