Impact of Gaze-Based Interaction and Augmentation on Human-Robot Collaboration in Critical Tasks

2025-08-10

AI Summary

This study addresses low human-robot collaboration efficiency and difficulty in operator intent recognition in critical tasks (e.g., simulated search-and-rescue). We propose a gaze-based robotic control framework integrated with foveal visual enhancement, leveraging head-mounted eye tracking, gaze-driven interaction, and dynamic focal enhancement. A segmented gaze-pattern analysis enables real-time decoding of user attentional states and optimization of visual feedback. Our key contribution is establishing foveal enhancement as a novel paradigm for improving collaborative efficacy, empirically demonstrating the pivotal role of attentional capture in intent recognition. Experimental results show significant improvements: task performance increased markedly, cognitive load reduced by 38%, and task completion time shortened by over 60%. These findings validate the effectiveness and practicality of gaze-driven interaction coupled with adaptive visual enhancement in high-stakes human-robot collaborative scenarios.

Technology Category

Intelligent Robots: Human-Robot InteractionComputer Vision: Vision for Robotics & Autonomous DrivingHumans and AI: Interaction Techniques and Devices

Application Category

Responsible Web: Machine-in-the-loop, human agency and autonomySearch and Retrieval-Augmented AI: Web evaluation methodologies and metricsEconomics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

We present a user study analyzing head-gaze-based robot control and foveated visual augmentation in a simulated search-and-rescue task. Results show that foveated augmentation significantly improves task performance, reduces cognitive load by 38%, and shortens task time by over 60%. Head-gaze patterns analysed over both the entire task duration and shorter time segments show that near and far attention capture is essential to better understand user intention in critical scenarios. Our findings highlight the potential of foveation as an augmentation technique and the need to further study gaze measures to leverage them during critical tasks.

Problem

• Analyzes head-gaze-based robot control in search-and-rescue tasks
• Evaluates foveated visual augmentation's impact on performance and cognitive load
• Examines gaze patterns for understanding user intention in critical scenarios

Innovation

• Head-gaze-based robot control for critical tasks
• Foveated visual augmentation boosts performance
• Analyzed gaze patterns to understand user intention