将特权控制屏障函数蒸馏为仅RGB的安全滤波器，用于动态视觉导航
Distilling Privileged Control Barrier Functions into RGB-Only Safety Filters for Dynamic Visual Navigation
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中文总结 AI 辅助
针对RGB视觉导航在动态环境中的碰撞风险，提出教师-学生蒸馏框架，将特权CBF安全行为迁移至仅RGB滤波器，实验证明其优于视觉CBF基线并提升安全性。
中文摘要 AI 辅助
仅依赖RGB的端到端视觉导航策略在真实世界的动态环境中仍易发生碰撞，这促使需要专门的安全层。现有的视觉控制屏障函数（CBF）方法试图从RGB观测中提供安全保障，但通常依赖于实时渲染或显式场景重建，且主要针对静态场景设计，限制了其在机载部署中的实用性。我们提出了一种教师-学生视觉蒸馏框架，将特权CBF教师的安全行为迁移到仅RGB的学生滤波器中，以适用于动态环境。学生将短时RGB历史、机器人速度和标称控制动作直接映射为安全动作，而教师则在真实到模拟的动态高斯溅射环境中使用真实机器人及障碍物状态。为缩小教师-学生信息差距，教师仅基于学生RGB历史中可观测的障碍物构建安全约束。同时，它考虑障碍物速度不确定性以提高对运动变化的鲁棒性，而动作增强则使学生接触多样化的安全与不安全标称动作，以更好地捕捉安全边界。在部署时，学生仅需RGB观测和机器人速度，无需显式3D重建或在线渲染。实验表明，所提方法优于视觉CBF基线，并在动态障碍物运动下提升了基于RGB导航策略的安全性。项目页面：此https URL。
英文摘要
RGB-only end-to-end visual navigation policies remain vulnerable to collisions in real-world dynamic environments, motivating a dedicated safety layer. Existing visual Control Barrier Function (CBF) approaches seek to provide safety from RGB observations, but often rely on real-time rendering or explicit scene reconstruction and are primarily designed for static scenes, limiting their practicality for onboard deployment. We propose a teacher-student visual distillation framework that transfers the safety behavior of a privileged CBF teacher to an RGB-only student filter for dynamic environments. The student maps a short RGB history, robot velocity, and a nominal control action directly to a safe action, while the teacher uses ground-truth robot and obstacle states in a real-to-sim dynamic Gaussian Splatting environment. To reduce the teacher-student information gap, the teacher constructs safety constraints only from obstacles observable within the student's RGB history. It also accounts for obstacle-velocity uncertainty to improve robustness to motion variations, while action augmentation exposes the student to diverse safe and unsafe nominal actions to better capture the safety boundary. At deployment, the student requires only RGB observations and robot velocity, without explicit 3D reconstruction or online rendering. Experiments show that the proposed method outperforms visual CBF baselines and improves the safety of RGB-based navigation policies under dynamic obstacle motion. Project page: https://syeon-yoo.github.io/distill-cbf-site/.
发表机构
- Seoul National University（首尔大学）
机构由 AI 辅助整理，请以论文原文为准。
