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Title: Examining the Effects of Anticipatory Robot Assistance on Human Decision Making
Collaborative robots that provide anticipatory assistance are able to help people complete tasks more quickly. As anticipatory assistance is provided before help is explicitly requested, there is a chance that this action itself will influence the person’s future decisions in the task. In this work, we investigate whether a robot’s anticipatory assistance can drive people to make choices different from those they would otherwise make. Such a study requires measuring intent, which itself could modify intent, resulting in an observer paradox. To combat this, we carefully designed an experiment to avoid this effect. We considered several mitigations such as the careful choice of which human behavioral signals we use to measure intent and designing unobtrusive ways to obtain these signals. We conducted a user study (𝑁=99) in which participants completed a collaborative object retrieval task: users selected an object and a robot arm retrieved it for them. The robot predicted the user’s object selection from eye gaze in advance of their explicit selection, and then provided either collaborative anticipation (moving toward the predicted object), adversarial anticipation (moving away from the predicted object), or no anticipation (no movement, control condition). We found trends and participant comments suggesting people’s decision making changes in the presence of a robot anticipatory motion and this change differs depending on the robot’s anticipation strategy.  more » « less
Award ID(s):
1755823
NSF-PAR ID:
10273002
Author(s) / Creator(s):
; ; ; ; ;
Editor(s):
Wagner, A.R.; null
Date Published:
Journal Name:
International Conference on Social Robotics (ICSR)
Volume:
LNCS 12483
Page Range / eLocation ID:
590-603
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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    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.

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    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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