Safe-SAGE: Social-Semantic Adaptive Guidance for Safe Engagement through Laplace-Modulated Poisson Safety Functions

TL;DR

Safe-SAGE introduces a novel safety framework that integrates semantic understanding with safety control, enabling robots to navigate complex environments with context-aware safety margins using a Laplace-modulated Poisson safety function.

Contribution

The paper presents a unified safety control framework that combines high-level semantic perception with low-level safety modulation through a novel Poisson safety function.

Findings

Enables safe navigation in dynamic, semantically rich environments.

Integrates multi-sensor perception with safety filtering layers.

Demonstrates improved safety margins with context-aware obstacle handling.

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 safely navigate semantically rich, dynamic environments with context-dependent safety margins.