We propose a maturity-based framework for certifying embodied AI systems through explicit measurement mechanisms. We argue that certifiable embodied AI requires structured assessment frameworks, quantitative scoring mechanisms, and methods for navigating multi-objective trade-offs inherent in trustworthiness evaluation. We demonstrate this approach using uncertainty quantification as an exemplar measurement mechanism and illustrate feasibility through an Uncrewed Aircraft System (UAS) detection case study.

翻译：我们提出了一种基于成熟度的框架，通过明确的测量机制对具身人工智能系统进行认证。我们认为，可认证的具身人工智能需要结构化的评估框架、定量评分机制以及处理可信度评估中固有的多目标权衡的方法。我们以不确定性量化作为示例测量机制来展示该方法，并通过一个无人驾驶航空器系统检测案例研究说明其可行性。