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

TL;DR

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, is presented.

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.

Figures (7)

• Figure 1: Safe-SAGE in action: A quadruped robot navigates a hallway with humans moving both towards and away from it. The robot successfully avoids collisions with humans and maintains social norms, passing on the left side of the humans.
• Figure 2: System Architecture: The robot takes in multi-sensor point clouds and RGB images from the camera, performs semantic segmentation and object tracking to build a semantic occupancy grid, and then uses it to generate a social-semantic guidance field and Poisson safety function, then apply it in both real-time and predictive safety filters to ensure safety and social compliance.
• Figure 3: Simulation benchmark of the proposed method safety filtering the robot going to the other side of the arena with a human and a static obstacle. It can be observed that the robot would exhibit social compliance unless well away from the human and also keeps a wider berth from the human than the static obstacle.
• Figure 4: Hardware experiments on the Unitree Go2 quadruped robot. Similar to the simulation benchmark, the robot is tasked with going from one side of the area to the other side. The robot behaves similarly to the simulation, keeping a wider margin to the human and observes social norms whenever possible.
• Figure 5: An example of the biased margin induced by our proposed method. With our method enabled ($b_{\text{human}}(\mathbf{q})=-1.7$ and $b_{\text{objects}}(\mathbf{q})=-0.5$), the robot keeps a wider margin to the human than the walls. While without it ($b_{\text{human}}(\mathbf{q})=-1.0$ and $b_{\text{objects}}(\mathbf{q})=-1.0$), it keeps the same margin.

Theorems & Definitions (5)

• Definition 1: ames2019control
• Definition 2: agrawal_dtcbf_2017
• Remark 1
• Theorem 1: Forward Invariance
• proof