Objective: This study investigated the effects of feedback modality, robot speed, and inspection interval regularity on operators' mental workload, trust, task performance, and gaze behavior during human–robot collaborative visual inspection under a dualtask environment.

Background: In human–robot collaboration (HRC), operators often perform visual inspection while simultaneously processing additional information. Under such dualtask conditions, robot state feedback may reduce cognitive burden and improve collaboration efficiency. However, the combined effects of feedback modality, robot speed, and inspection interval regularity have not been sufficiently investigated.

Method: A 3 × 2 × 2 within-subject experiment was conducted with 20 participants using a UR3 collaborative robot. Feedback modality (none, visual, auditory), robot speed (low, high), and inspection interval regularity (regular, irregular) were manipulated. Mental workload, trust, task performance, and eye-tracking measures were analyzed using Aligned Rank Transform ANOVA, followed by pairwise comparisons with Bonferroni adjustment.

Results: Feedback modality significantly affected mental workload, trust, visual inspection response time, and gaze behavior. Participants showed the highest mental workload, the lowest trust, and increased visual monitoring in the no-feedback condition. Auditory feedback generally resulted in lower mental workload than visual feedback under specific task conditions, supporting the Multiple Resource Theory. In addition, interaction effects between robot speed and inspection interval regularity were observed for 1-back task response time and gaze behavior, indicating that the influence of robot speed depended on the temporal predictability of the task.

Conclusion: Providing robot state feedback reduced operators' mental workload and visual monitoring demands while improving trust in HRC visual inspection tasks. These findings suggest that auditory feedback can be an effective interface visually demanding dual-task environments.

Application: The findings provide practical guidelines for designing robot state feedback in industrial HRC environments by considering feedback modality together with robot speed and inspection interval regularity.