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    Robots can better predict human intentions using a new action prediction (AP) system based on eye gaze. This advanced human-robot collaboration method offers more seamless robotic movement and user preference over traditional gaze triggering (GT).

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    Area of Science:

    • Robotics
    • Human-Computer Interaction
    • Artificial Intelligence

    Background:

    • Robots require real-time human intent recognition for effective collaboration in daily living tasks.
    • Eye gaze control offers a non-invasive method to infer intent and reduce operator cognitive load.
    • Traditional gaze triggering (GT) relies on sustained gaze to initiate pre-programmed robot actions.

    Purpose of the Study:

    • To introduce and evaluate a novel neural network-based action prediction (AP) mode for inferring human intent from eye gaze.
    • To compare the performance of the AP mode against the traditional GT mode in human-robot collaboration tasks.
    • To assess user preference and objective performance metrics for gaze-based control interfaces.

    Main Methods:

    • Developed a shared autonomy framework integrating 3D gaze reconstruction and real-time intent inference.
    • Implemented an AP mode using a neural network to extract gaze features for predicting operator action primitives.
    • Conducted a user study comparing AP and GT modes using subjective (Likert scales) and objective (recognition delay) metrics.

    Main Results:

    • Statistical analyses indicated that the AP mode facilitated more seamless robotic movement compared to the GT mode.
    • Participants generally expressed a preference for the AP mode over the traditional GT approach.
    • The AP mode demonstrated effective recognition and prediction of operator intended action primitives.

    Conclusions:

    • The proposed action prediction (AP) mode offers a superior alternative to gaze triggering (GT) for human-robot collaboration.
    • Gaze-based intent recognition using AP enhances robotic task performance and user experience.
    • This research advances the development of intuitive and efficient human-robot interaction systems.