This paper introduces an interactive calibration system for color vision assessment utilizing augmented reality (AR) glasses, and investigates its potential relevance as a perceptual calibration stage for human-robot systems. The system re-imagines the traditional Farnsworth-Munsell 100 Hue (FM 100 Hue) test–a benchmark in the textile industry for evaluating the color discrimination of quality control personnel-as an interactive AR-based task, enabling portable color discrimination assessment under ordinary ambient lighting conditions. We quantitatively evaluate the correlation between this AR-implemented assessment and the standard physical test through a comparative user study. Results demonstrate a high degree of diagnostic consistency, indicating that the AR platform can reproduce key characteristics of the conventional assessment while reducing part of the variability associated with display and ambient conditions. Beyond clinical assessment, this work positions AR glasses as a bidirectional perceptual interface, enabling individualized modeling of human color perception for robotic systems. The proposed method facilitates robust, color-aware perception, decision-making, and task execution in human-centered robotics, with potential applications in automated textile quality assurance and assistive robotics. This calibration framework is a promising step toward achieving seamless and adaptive visual perception alignment between humans and humanoid or other robotic platforms.

color vision deficiency, augmented reality, human-robot interaction, fm 100 hue test, textile color discrimination