CADRI: an Adaptive Multimodal Interface for Reducing Cognitive Load in Robot-Assisted Disaster Response

Effective human-robot collaboration in disaster response is often hindered by the high cognitive demands placed on operators, who must process large volumes of multimodal data under time-critical conditions. We present CADRI, an adaptive multimedia interface that dynamically adjusts the modality and priority of information delivery based on real-time estimates of operator cognitive load. The system integrates visual dashboards, mixed reality overlays, haptic feedback, and auditory cues, enabling context-driven adaptation that reduces information overload and enhances situational awareness. A semantic debriefing module further filters and prioritizes mission-critical messages using caption generation and sentiment-based relevance scoring. We evaluate CADRI in simulated disaster scenarios involving victim search, hazard detection, and equipment inspection. Results show that, compared to a non-adaptive baseline, CADRI improves task completion speed, reduces perceived workload, and enhances interface usability, highlighting its potential for improving decision-making efficiency in high-risk environments.