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arXiv · 2601.03470

Toward Maturity-Based Certification of Embodied AI: Quantifying Trustworthiness Through Measurement Mechanisms

Michael C. Darling·Alan H. Hesu·Michael A. Mardikes·Brian C. McGuigan·Reed M. Milewicz
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Abstract

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.

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Michael C. Darling, Alan H. Hesu, Michael A. Mardikes, Brian C. McGuigan, Reed M. Milewicz. 2026-01-06. Toward Maturity-Based Certification of Embodied AI: Quantifying Trustworthiness Through Measurement Mechanisms. https://arxiv.org/abs/2601.03470

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