审视世界模型在机器人与自动驾驶中的安全风险
The Safety Challenge of World Models for Embodied AI Agents: A Review
- 系统梳理世界模型在感知与预测中的安全问题
- 发现并分类了主流模型生成结果中的典型错误模式
- 适合关注具身智能安全性的研究者与开发者
具身人工智能的快速发展凸显了对更先进、集成化模型的需求，以实现环境感知、理解与动态预测。在此背景下，世界模型（WMs）被提出，使具身智能体能够预测未来环境状态并填补知识空白，从而提升规划与执行能力。然而，在涉及具身智能体时，确保预测结果对智能体及环境的安全至关重要。本文对自动驾驶与机器人领域的世界模型进行了全面文献综述，重点分析场景生成与控制生成任务中的安全影响。研究结合实证分析，收集并评估了前沿模型的预测结果，识别并分类常见故障（称为病理），并提供量化评估。
原文摘要 · Abstract (English)
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.
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