用概率枚举找危险区，构建通用安全控制层。
Designing Control Barrier Function via Probabilistic Enumeration for Safe Reinforcement Learning Navigation
- 通过概率枚举识别不安全区域，生成可适配任意策略的安全控制层。
- 在仿真与真实机器人上验证，能纠正危险动作且保持高效导航。
- 适合需要高安全性的自主导航场景，如动态环境中的机器人任务。
实现安全的自主导航系统对于机器人在动态不确定的真实环境中部署至关重要。本文提出一种分层控制框架，利用神经网络验证技术设计控制屏障函数（CBF）及策略修正机制，确保强化学习导航策略的安全性。该方法基于概率枚举识别运行中的不安全区域，并据此构建适用于任意策略的安全CBF控制层。我们在标准移动机器人基准和高度动态的水下环境监测任务中进行了仿真与真实机器人验证。实验表明，所提方案能在保持高效导航行为的同时纠正危险动作。结果证明了基于分层验证系统的安全鲁棒导航在复杂场景中的潜力。
原文摘要 · Abstract (English)
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.
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