Abstract
The addition of a robot to a human team can be beneficial if the robot can perform important tasks, provide additional skills, or otherwise help the team achieve its goals. However, if the human team members do not trust the robot they may underutilize it or excessively monitor its behavior. We present an algorithm that allows a robot to estimate its trustworthiness based on interactions with a team member and adapt its behavior in an attempt to increase its trustworthiness. The robot is able to learn as it performs behavior adaptation and increase the efficiency of future adaptation. We compare our approach for inverse trust estimation and behavior adaptation to a variant that does not learn. Our results, in a simulated robotics environment, show that both approaches can identify trustworthy behaviors but the learning approach does so significantly faster.
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Notes
- 1.
An interruption could also be a result of the operator identifying a more important task for the robot to perform or failures could be the result of unachievable tasks. The robot works under the assumption that those situations occur rarely and most failures/interruptions are a result of poor performance.
- 2.
We plan to validate these findings in a series of user studies.
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Thanks to the United States Naval Research Laboratory and the Office of Naval Research for supporting this research.
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Floyd, M.W., Drinkwater, M., Aha, D.W. (2016). Learning Trustworthy Behaviors Using an Inverse Trust Metric. In: Mittu, R., Sofge, D., Wagner, A., Lawless, W. (eds) Robust Intelligence and Trust in Autonomous Systems. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7668-0_3
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