Thesis
Understanding trust calibration: Insights from a simulated mixed-methods human-robot experiment
Masters by Coursework, Murdoch University
2018
Abstract
Understanding the human-systems challenges within human-robot teams is vital for the proper integration and exploitation of robotic autonomous systems. Among these challenges is the need for a deeper understanding of the trust calibration process, and the experience of transitioning between different levels of autonomy and control. An exploratory, within-subjects, and mixed-methods design was utilised to explore some of these challenges. Participants (N = 11) were sourced from internal staff members at DST Group and from the investigator’s own connections. Utilising a simulated scenario in which a robot and human were working together on a humanitarian relief mission, participants alternated between two modes of control whilst concurrently monitoring the robot while locating supply drops that were dispersed throughout the scenario. Applying a mixed-methods design consisting of semi-structured interview questions, behavioural observations, and quantitative data from a parallel study, a narrative account of the trust calibration process was extracted. Additionally, an inductive account of the experience of transitioning and themes salient to the process were unpacked. Implications for theory, practice, and the design of robotic systems are discussed.
Details
- Title
- Understanding trust calibration: Insights from a simulated mixed-methods human-robot experiment
- Authors/Creators
- Tony Nguyen
- Awarding Institution
- Murdoch University; Masters by Coursework
- Identifiers
- 991005542606307891
- Murdoch Affiliation
- School of Psychology and Exercise Science
- Language
- English
- Resource Type
- Thesis
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