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

TL;DR

The paper addresses the challenge of certifying trustworthiness for embodied AI by proposing a maturity-based assessment framework that translates abstract trustworthiness concepts into measurable evidence across the development lifecycle. It centers on measurement mechanisms, using uncertainty quantification (UQ) as a concrete exemplar, and maps these mechanisms to NIST trustworthiness characteristics to enable holistic certification. A motivating UAS detection case study demonstrates how a closed-loop synthetic data generation pipeline guided by uncertainty can improve robustness and provide actionable runtime safeguards. The work outlines a research agenda and highlights open questions on mechanism design, maturity mapping, and integration with formal methods, aiming to enable safe and certifiable deployment of embodied AI systems.

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.