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Eye-Tracking in Physical Human-Robot Interaction: Mental Workload and Performance Prediction.

Satyajit Upasani1, Divya Srinivasan2, Qi Zhu3

  • 1Virginia Tech, Blacksburg, VA, USA.

Human Factors
|October 4, 2023
PubMed
Summary

Eye-tracking measures reliably assess cognitive load in physical human-robot interaction (pHRI). These metrics can predict task success and adapt robots to human skill levels during collaboration.

Keywords:
motor learningpsychometricsreliabilitystrategiesvirtual environments

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Area of Science:

  • Human-Robot Interaction
  • Cognitive Science
  • Robotics

Background:

  • Physical Human-Robot Interaction (pHRI) requires learning robot dynamics, increasing cognitive load.
  • Eye-tracking metrics offer insights into mental workload fluctuations during learning.

Purpose of the Study:

  • Assess sensitivity and reliability of eye-tracking for task difficulty variations in pHRI.
  • Evaluate eye-tracking's capability to predict performance in human-robot collaboration.

Main Methods:

  • Participants performed a virtual pick-and-place task with a bimanual robot.
  • Robot joint stiffness was manipulated to alter motor-coordination demands.
  • Investigated psychometric properties of eye-tracking measures and performance prediction.

Main Results:

  • Stationary Gaze Entropy and pupil diameter showed high reliability and sensitivity to workload changes.
  • Increased task difficulty led to more robot-monitoring strategies.
  • Eye-tracking predicted trial success/failure with 70% sensitivity and 71% accuracy.

Conclusions:

  • Eye-tracking measures demonstrate acceptable sensitivity and reliability for pHRI workload.
  • Gaze behaviors indicating visual monitoring are sensitive to task difficulty.
  • Further exploration of eye-tracking in pHRI is warranted for understanding workload and internal-model formation.