Abstract
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.
Abstract (translated)
传统的安全关键控制系统，例如控制屏障函数，在处理障碍物时存在语义盲点问题，无论环境上下文如何变化，它们对障碍物的反应都是相同的。这种限制导致所有障碍物被统一对待，忽视了它们在不同情境下的意义差异。为此，我们提出了Safe-SAGE（社会-语义自适应引导安全互动）框架，该框架通过使用泊松安全性函数（PSF）调节拉普拉斯指导场来弥合高层次语义理解和低层次安全关键控制之间的差距。 我们的方法通过融合多传感器点云和基于视觉的实例分割以及持续的对象跟踪技术来感知环境，保持超出摄像机视野范围内的最新环境语义信息。接着使用一个多层安全过滤器来调节系统的输入，以实现利用这种环境语义理解的安全导航。该安全过滤器包括模型预测控制层和控制屏障函数层。两层都利用PSF和指导场的通量调制，在处理不同障碍物时引入不同程度的保守性和多代理通过规范。 我们的框架使腿部机器人能够在具有上下文相关安全边距的语义丰富且动态变化的环境中导航，同时保持严格的安全保证。
URL
https://arxiv.org/abs/2603.05497
