The Safety Challenge of World Models for Embodied AI Agents: A Review

2025-10-07

AI Summary

This work systematically investigates safety risks of world models in embodied intelligent agents, specifically within autonomous driving and robotics. Addressing the potential for hazardous environmental or agent-level consequences arising from inaccurate predictions and unsafe control generation, we propose the first safety risk analysis framework tailored to embodied world modeling. Our methodology integrates literature review with empirical evaluation: we collect prediction outputs from state-of-the-art world models, identify and categorize recurrent failure modes—including temporal misalignment, physical inconsistency, and boundary violation—and establish a pathology-driven taxonomy. We further design quantitative metrics for assessing predictive safety and validate their efficacy across multiple case studies. The study uncovers critical vulnerabilities in current world models, revealing fundamental trade-offs between prediction fidelity and safety guarantees. These findings provide both theoretical foundations and empirical evidence to guide the development of safety-aware world modeling techniques.

Technology Category

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

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

The rapid progress in embodied artificial intelligence has highlighted the necessity for more advanced and integrated models that can perceive, interpret, and predict environmental dynamics. In this context, World Models (WMs) have been introduced to provide embodied agents with the abilities to anticipate future environmental states and fill in knowledge gaps, thereby enhancing agents' ability to plan and execute actions. However, when dealing with embodied agents it is fundamental to ensure that predictions are safe for both the agent and the environment. In this article, we conduct a comprehensive literature review of World Models in the domains of autonomous driving and robotics, with a specific focus on the safety implications of scene and control generation tasks. Our review is complemented by an empirical analysis, wherein we collect and examine predictions from state-of-the-art models, identify and categorize common faults (herein referred to as pathologies), and provide a quantitative evaluation of the results.

Problem

• Reviewing safety implications of World Models in autonomous systems
• Identifying common prediction faults in embodied AI agents
• Evaluating safety risks in environmental prediction and control

Innovation

• Reviewing World Models in autonomous driving and robotics
• Analyzing safety implications of scene and control generation
• Identifying and categorizing prediction faults in models