ARGOS：通过属性引导的组合推理实现具身AI的自动功能安全需求合成
ARGOS: Automated Functional Safety Requirement Synthesis for Embodied AI via Attribute-Guided Combinatorial Reasoning
AI总结：
ARGOS通过属性引导的组合推理，实现具身AI自动功能安全需求生成，提升开放环境中的安全性和可靠性。
摘要：
确保功能安全对于在复杂开放世界环境中部署具身AI至关重要。然而，传统危害分析与风险评估（HARA）方法在这一领域难以扩展。尽管HARA依赖于对有限且预定义的功能列表进行风险枚举，具身AI则基于开放性的自然语言指令进行操作，从而产生组合交互风险的挑战。尽管大语言模型（LLMs）已出现作为解决此扩展挑战的有希望的解决方案，但它们通常缺乏物理基础，导致语义表面且不连贯的风险描述。为克服这些限制，我们提出了一种新的框架ARGOS（AttRibute-Guided cOmbinatorial reaSoning），它在开放性用户指令和具体物理属性之间架起桥梁。通过动态将指令中的实体分解为这些细粒度属性，ARGOS将LLM推理扎根于因果风险因素，以生成具有物理合理性的风险场景。然后，通过将这些场景与机器人能力相结合，ARGOS将抽象的安全标准，如ISO 13482，转化为情境特定的功能安全要求（FSRs）。广泛的实验验证了ARGOS生成高质量FSRs的能力，并在识别长尾风险方面优于基线方法。总体而言，这项工作为系统且扎根的功能安全需求生成铺平了道路，这是安全工业部署具身AI的关键一步。
英文摘要：
Ensuring functional safety is essential for the deployment of Embodied AI in complex open-world environments. However, traditional Hazard Analysis and Risk Assessment (HARA) methods struggle to scale in this domain. While HARA relies on enumerating risks for finite and pre-defined function lists, Embodied AI operates on open-ended natural language instructions, creating a challenge of combinatorial interaction risks. Whereas Large Language Models (LLMs) have emerged as a promising solution to this scalability challenge, they often lack physical grounding, yielding semantically superficial and incoherent hazard descriptions. To overcome these limitations, we propose a new framework ARGOS (AttRibute-Guided cOmbinatorial reaSoning), which bridges the gap between open-ended user instructions and concrete physical attributes. By dynamically decomposing entities from instructions into these fine-grained properties, ARGOS grounds LLM reasoning in causal risk factors to generate physically plausible hazard scenarios. It then instantiates abstract safety standards, such as ISO 13482, into context-specific Functional Safety Requirements (FSRs) by integrating these scenarios with robot capabilities. Extensive experiments validate that ARGOS produces high-quality FSRs and outperforms baselines in identifying long-tail risks. Overall, this work paves the way for systematic and grounded functional safety requirement generation, a critical step toward the safe industrial deployment of Embodied AI.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
