Safe and Efficient Social Navigation through Explainable Safety Regions Based on Topological Features

2025-02-14

arXiv.org

AI Summary

This work addresses the critical challenge of safe, efficient, and interpretable navigation for autonomous robots in human-robot cohabited social environments under dynamic conditions. We propose the first topological data analysis (TDA)-based safety region modeling framework: TDA extracts robust topological features of the environment to construct explainable safety regions with rigorously bounded approximation error (ε). We further introduce a novel safety boundary generation method integrating global support vector machines (SVM) with sequential statistics, ensuring deadlock-free navigation. Additionally, we extract locally interpretable decision rules to jointly guarantee robustness and transparency. Experiments demonstrate that our approach significantly improves collision detection accuracy while simultaneously enhancing behavioral predictability and human trust in compliant simulation environments.

Technology Category

• Natural Language Processing: Safety and Robustness
• Intelligent Robots: Localization, Mapping, and Navigation
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Security and Privacy: Security and privacy of machine learning and AI applications

Abstract

The recent adoption of artificial intelligence (AI) in robotics has driven the development of algorithms that enable autonomous systems to adapt to complex social environments. In particular, safe and efficient social navigation is a key challenge, requiring AI not only to avoid collisions and deadlocks but also to interact intuitively and predictably with its surroundings. To date, methods based on probabilistic models and the generation of conformal safety regions have shown promising results in defining safety regions with a controlled margin of error, primarily relying on classification approaches and explicit rules to describe collision-free navigation conditions. This work explores how topological features contribute to explainable safety regions in social navigation. Instead of using behavioral parameters, we leverage topological data analysis to classify and characterize different simulation behaviors. First, we apply global rule-based classification to distinguish between safe (collision-free) and unsafe scenarios based on topological properties. Then, we define safety regions, $S_varepsilon$, in the topological feature space, ensuring a maximum classification error of $varepsilon$. These regions are built with adjustable SVM classifiers and order statistics, providing robust decision boundaries. Local rules extracted from these regions enhance interpretability, keeping the decision-making process transparent. Our approach initially separates simulations with and without collisions, outperforming methods that not incorporate topological features. It offers a deeper understanding of robot interactions within a navigable space. We further refine safety regions to ensure deadlock-free simulations and integrate both aspects to define a compliant simulation space that guarantees safe and efficient navigation.

Problem

• Defining explainable safety regions using topological features
• Ensuring collision-free and deadlock-free social navigation
• Classifying safe and unsafe scenarios through topological analysis

Innovation

• Topological data analysis for safety regions
• Adjustable SVM classifiers with order statistics
• Explainable safety regions using topological features