Feedback modalities in human-cobot collaboration: experimental evaluation of performance, user experience, and physiological responses

Collaborative robots (cobots) are increasingly deployed in industrial as well as non-industrial domains to support human-centered operation. While physical safety and task efficiency have received considerable attention, less is known about how feedback modality influences operator experience and physiological responses under different collaboration demands. This study examines the effects of feedback modalities in two human–cobot collaboration scenarios representing distinct coordination structures: a low-collaboration turn-taking task and a high-collaboration synchronous shared-control task. Across these scenarios, auditory, visual, and combined auditory–visual feedback modalities were evaluated, and an additional projected assistive interface (PAI) condition, designed specifically for the given scenario, was introduced in the high-collaboration task. Outcomes included task completion time, subjective user assessments of interaction quality and comfort, and physiological responses based on heart rate variability (HRV) indicators reflecting autonomic activity. The results indicate that feedback modality significantly influences performance, subjective experience, and physiological activation within each collaboration scenario. In the low-collaboration task, the no-feedback condition yielded the shortest completion times but was associated with lower perceived interaction quality and descriptively higher autonomic activation. In contrast, multimodal feedback improved perceived comfort and task understanding while slightly increasing completion time. In the high-collaboration task, the examined PAI guidance condition was associated with the shortest task completion times and the highest subjective ratings, while also being accompanied by increased sympathetic-dominant physiological activation. These findings indicate a design trade-off in which richer guidance can enhance coordination efficiency but may be associated with increased cognitive and physiological demands. Importantly, the results suggest that physiological activation should be interpreted in relation to task demands and performance outcomes, rather than as a direct indicator of detrimental stress. More broadly, the study suggests that collaboration configuration and feedback modality should be co-designed as interdependent parameters within the specific coordination demands of collaborative human–robot interaction tasks. Although the PAI was designed for this specific scenario, future research may examine whether similarly tailored interfaces can support positive user evaluations in other complex collaborative tasks. By integrating subjective and physiological measures, this work provides evidence-informed guidance for designing feedback strategies that support efficient, safe, and sustainable human–cobot collaboration.