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Trust Dynamics and Verbal Assurances in Human Robot Physical Collaboration
Basel Alhaji1, Michael Prilla2, Andreas Rausch3
1Simulation Science Center Clausthal-Göttingen, Clausthal University of Technology, Clausthal-Zellerfeld, Germany.
Frontiers in Artificial Intelligence
|August 6, 2021
Summary
Understanding human-robot trust dynamics is key for effective collaboration. This study reveals that trust dissipates faster than it builds, with different factors influencing trust accumulation versus dissipation.
Area of Science:
- Robotics
- Human-Computer Interaction
- Trust Dynamics
Background:
- Trust is crucial for human-robot collaboration, influencing reliance and cooperation.
- Current research lacks understanding of factors affecting trust dynamics in synchronous, co-located human-robot teamwork.
- Dynamic adaptation of robot behavior requires knowledge of trust evolvement characteristics.
Purpose of the Study:
- To explore factors influencing trust dynamics in human-robot collaboration.
- To investigate the impact of robot reliability and feedback on human trust.
- To identify differences in trust factors during trust accumulation and dissipation.
Main Methods:
- A two-phase lab experiment was conducted in a mixed-reality environment.
- Thirty-two participants collaborated with a virtual CoBot (Collaborative Robot) on a battery disassembly task.
- The study analyzed trust factors during collaboration, robot reliability, and feedback accuracy.
Main Results:
- Trust dynamics were more pronounced during dissipation than accumulation.
- Different factors influenced trust accumulation (perceived reliability, dependability, predictability, faith) compared to dissipation.
- Robot feedback accuracy conditionally affected trust, preserving it during expected failures but not during reliable operation.
Conclusions:
- The factors shaping human trust in robots vary depending on the collaboration stage and trust's directional change.
- Robot feedback's impact on trust is contingent on the robot's reliability.
- Findings offer insights for designing robots that dynamically adapt to human trust levels for improved cooperation.
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