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.
@article{arxiv.2601.03470,
title = {Toward Maturity-Based Certification of Embodied AI: Quantifying Trustworthiness Through Measurement Mechanisms},
author = {Michael C. Darling and Alan H. Hesu and Michael A. Mardikes and Brian C. McGuigan and Reed M. Milewicz},
journal= {arXiv preprint arXiv:2601.03470},
year = {2026}
}
Comments
Accepted to AAAI-26 Bridge Program B10: Making Embodied AI Reliable with Testing and Formal Verification