Context-Aware Risk Estimation in Home Environments: A Probabilistic Framework for Service Robots

2025-08-27

AI Summary

To address insufficient risk perception capability of service robots in home environments, this paper proposes a semantic-graph-based asymmetric risk propagation method. It constructs an object-level semantic graph that explicitly models spatial proximity and accident correlations, enabling contextual inference of latent hazards; human-annotated risk data is incorporated for supervised training. The approach balances interpretability and lightweight deployment, significantly improving risk anticipation in unseen or unlabeled scenarios. Evaluated on a manually annotated dataset, it achieves 75% accuracy in binary classification of hazardous regions, with system judgments strongly aligning with human risk perception—particularly for high-risk objects (e.g., sharp or unstable items). The core contribution lies in the first integration of asymmetric risk propagation into semantic graph structures, enabling real-time, interpretable, and generalizable dynamic risk estimation in home environments.

Technology Category

• Intelligent Robots: State Estimation
• Natural Language Processing: Safety and Robustness
• Knowledge Representation and Reasoning: Common-Sense Reasoning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

We present a novel framework for estimating accident-prone regions in everyday indoor scenes, aimed at improving real-time risk awareness in service robots operating in human-centric environments. As robots become integrated into daily life, particularly in homes, the ability to anticipate and respond to environmental hazards is crucial for ensuring user safety, trust, and effective human-robot interaction. Our approach models object-level risk and context through a semantic graph-based propagation algorithm. Each object is represented as a node with an associated risk score, and risk propagates asymmetrically from high-risk to low-risk objects based on spatial proximity and accident relationship. This enables the robot to infer potential hazards even when they are not explicitly visible or labeled. Designed for interpretability and lightweight onboard deployment, our method is validated on a dataset with human-annotated risk regions, achieving a binary risk detection accuracy of 75%. The system demonstrates strong alignment with human perception, particularly in scenes involving sharp or unstable objects. These results underline the potential of context-aware risk reasoning to enhance robotic scene understanding and proactive safety behaviors in shared human-robot spaces. This framework could serve as a foundation for future systems that make context-driven safety decisions, provide real-time alerts, or autonomously assist users in avoiding or mitigating hazards within home environments.

Problem

• Estimating accident-prone regions in indoor home environments
• Improving real-time risk awareness for service robots
• Modeling object-level risk through semantic graph propagation

Innovation

• Probabilistic framework for risk estimation
• Semantic graph-based propagation algorithm
• Lightweight onboard deployment for interpretability