Traditional safety-critical control methods, such as control barrier functions, suffer from semantic blindness, exhibiting the same behavior around obstacles regardless of contextual significance. This limitation leads to the uniform treatment of all obstacles, despite their differing semantic meanings. We present Safe-SAGE (Social-Semantic Adaptive Guidance for Safe Engagement), a unified framework that bridges the gap between high-level semantic understanding and low-level safety-critical control through a Poisson safety function (PSF) modulated using a Laplace guidance field. Our approach perceives the environment by fusing multi-sensor point clouds with vision-based instance segmentation and persistent object tracking to maintain up-to-date semantics beyond the camera's field of view. A multi-layer safety filter is then used to modulate system inputs to achieve safe navigation using this semantic understanding of the environment. This safety filter consists of both a model predictive control layer and a control barrier function layer. Both layers utilize the PSF and flux modulation of the guidance field to introduce varying levels of conservatism and multi-agent passing norms for different obstacles in the environment. Our framework enables legged robots to navigate semantically rich, dynamic environments with context-dependent safety margins while maintaining rigorous safety guarantees.

翻译：传统安全关键控制方法（如控制屏障函数）存在语义盲区，对障碍物表现出与上下文重要性无关的相同行为。这一局限导致所有障碍物被统一对待，尽管它们具有不同的语义含义。本文提出Safe-SAGE（面向安全交互的社会语义自适应引导），这是一个通过拉普拉斯引导场调制的泊松安全函数（PSF）来弥合高层语义理解与低层安全关键控制之间差距的统一框架。我们的方法通过融合多传感器点云与基于视觉的实例分割及持续目标跟踪来感知环境，从而维持超出摄像机视野的最新语义信息。随后采用多层安全滤波器调制系统输入，利用对环境的语义理解实现安全导航。该安全滤波器包含模型预测控制层和控制屏障函数层，两层均利用PSF及引导场的通量调制机制，为环境中不同障碍物引入差异化的保守度层级与多智能体通行规范。我们的框架使足式机器人能够在保持严格安全保证的同时，在语义丰富的动态环境中依据上下文相关安全边界进行导航。