Capturing Dynamic Trust Metrics during Shared Space Human Robot Collaboration: An eye-tracking approach

Zhang, Yinsu; Hopko, Sarah; Aakash, Y; Mehta, Ranjana K.

doi:10.1177/1071181322661296

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…A possible explanation is that after learning to minimize errors during the first half of the LD condition, participants may have shifted to more frequent visual sampling of the plate’s orientation to ensure more precise placement, or tracking the ball, or improving other (secondary) aspects of performance. Fixations have been found to be not only sensitive to workload but also be task-specific and increase due to visual monitoring strategies in prior work (Rivecourt et al, 2008; Zhang et al, 2022), which may be a reason for the dissociation of FC with collisions and NASA-TLX ratings in our study. It should be noted that although all our workload metrics showed significant differences across task conditions and most measures showed an effect of learning trial, all effect sizes were small (Cohen, 1988), with GTE and FC effects likely being too small to be practically important.…”

Section: Discussionmentioning

confidence: 53%

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

Upasani,

Srinivasan,

Zhu

et al. 2023

Hum Factors

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Background In Physical Human–Robot Interaction (pHRI), the need to learn the robot’s motor-control dynamics is associated with increased cognitive load. Eye-tracking metrics can help understand the dynamics of fluctuating mental workload over the course of learning. Objective The aim of this study was to test eye-tracking measures’ sensitivity and reliability to variations in task difficulty, as well as their performance-prediction capability, in physical human–robot collaboration tasks involving an industrial robot for object comanipulation. Methods Participants (9M, 9F) learned to coperform a virtual pick-and-place task with a bimanual robot over multiple trials. Joint stiffness of the robot was manipulated to increase motor-coordination demands. The psychometric properties of eye-tracking measures and their ability to predict performance was investigated. Results Stationary Gaze Entropy and pupil diameter were the most reliable and sensitive measures of workload associated with changes in task difficulty and learning. Increased task difficulty was more likely to result in a robot-monitoring strategy. Eye-tracking measures were able to predict the occurrence of success or failure in each trial with 70% sensitivity and 71% accuracy. Conclusion The sensitivity and reliability of eye-tracking measures was acceptable, although values were lower than those observed in cognitive domains. Measures of gaze behaviors indicative of visual monitoring strategies were most sensitive to task difficulty manipulations, and should be explored further for the pHRI domain where motor-control and internal-model formation will likely be strong contributors to workload. Application Future collaborative robots can adapt to human cognitive state and skill-level measured using eye-tracking measures of workload and visual attention.

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Section: Discussionmentioning

confidence: 53%

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

Upasani,

Srinivasan,

Zhu

et al. 2023

Hum Factors

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“…Trust, a critical factor in HRI, influences decision-making and the efficiency of joint operations [6,7]. While existing research in HRI trust spans sociological, psychological, and computer science domains, it often overlooks specific aspects such as the nuanced nature of trust, its quantifiable measurement, and the impact of trust dynamics on HRI [8]. This gap highlights the need for empirical research focused on trust in HRI, particularly in the context of AI-enhanced systems.…”

Section: Introductionmentioning

confidence: 99%

Complexity-Driven Trust Dynamics in Human–Robot Interactions: Insights from AI-Enhanced Collaborative Engagements

Zhu,

Wang,

Wang

et al. 2023

Applied Sciences

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This study explores the intricate dynamics of trust in human–robot interaction (HRI), particularly in the context of modern robotic systems enhanced by artificial intelligence (AI). By grounding our investigation in the principles of interpersonal trust, we identify and analyze both similarities and differences between trust in human–human interactions and human–robot scenarios. A key aspect of our research is the clear definition and characterization of trust in HRI, including the identification of factors influencing its development. Our empirical findings reveal that trust in HRI is not static but varies dynamically with the complexity of the tasks involved. Notably, we observe a stronger tendency to trust robots in tasks that are either very straightforward or highly complex. In contrast, for tasks of intermediate complexity, there is a noticeable decline in trust. This pattern of trust challenges conventional perceptions and emphasizes the need for nuanced understanding and design in HRI. Our study provides new insights into the nature of trust in HRI, highlighting its dynamic nature and the influence of task complexity, thereby offering a valuable reference for future research in the field.

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A 3D Vector Field and Gaze Data Fusion Framework for Hand Motion Intention Prediction in Human-Robot Collaboration

Jayasuriya,

Hu,

et al. 2024

2024 IEEE International Conference on Robotics and Automation (ICRA)

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Capturing Dynamic Trust Metrics during Shared Space Human Robot Collaboration: An eye-tracking approach

Cited by 3 publications

References 8 publications

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

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

Complexity-Driven Trust Dynamics in Human–Robot Interactions: Insights from AI-Enhanced Collaborative Engagements

A 3D Vector Field and Gaze Data Fusion Framework for Hand Motion Intention Prediction in Human-Robot Collaboration

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