Jacob T. Cassady
ABSTRACT
Recently, there has been an increase use of collaborative robots in manufacturing, healthcare, military, and personal use scenarios. Such robots operate under shared or traded control paradigms with their human operators or users. Therefore, it is important to understand how to address and improve issues of trust between the humans and collaborative robots. In this paper, we investigate the impact of robotic agent transparency to their subjective trust level by a human operator. Several experiments were conceived with the help of a fetching mobile robot under traded control, and data such as subjective trust level was collected during experimentation. Results indicate that trust is easier to lose than it is to gain. Furthermore, results also indicate that agent transparency's effect on operator trust is more significant in tasks of increasing complexity.
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Index Terms
- Increasing user trust in a fetching robot using explainable AI in a traded control paradigm
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