Quantifying human decision-making: implications for bidirectional communication in human-robot teams
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A goal for future robotic technologies is to advance autonomy capabilities for independent and collaborative decision-making with human team members during complex operations. However, if human behavior does not match the robots’ models or expectations, there can be a degradation in trust that can impede team performance and may only be mitigated through explicit communication. Therefore, the effectiveness of the team is contingent on the accuracy of the models of human behavior that can be informed by transparent bidirectional communication which are needed to develop common ground and a shared understanding. For this work, we are specifically characterizing human decision-making, especially in terms of the variability of decision-making, with the eventual goal of incorporating this model within a bidirectional communication system. Thirty participants completed an online game where they controlled a human avatar through a 14 × 14 grid room in order to move boxes to their target locations. Each level of the game increased in environmental complexity through the number of boxes. Two trials were completed to compare path planning for the condition of known versus unknown information. Path analysis techniques were used to quantify human decision-making as well as provide implications for bidirectional communication.
Paper presented at the 10th International Conference on Virtual, Augmented and Mixed Reality (VAMR 2018), Las Vegas, Nevada, July 15-20, 2018.
DepartmentMassachusetts Institute of Technology. Department of Aeronautics and Astronautics
VAMR: International Conference on Virtual, Augmented and Mixed Reality 2018
Springer Science and Business Media LLC
Schaefer, Kristin E., et al., "Quantifying human decision-making: implications for bidirectional communication in human-robot teams." Virtual, Augmented and Mixed Reality: Interaction, Navigation, Visualization, Embodiment, and Simulation: 10th International Conference, VAMR 2018, edited by Jessie Y. C. Chen and Gino Fragomeni. Lecture Notes in Computer Science 10909 (Cham, Switzerland: Springer, 2018): p. 361-79 doi 10.1007/978-3-319-91581-4_27 ©2018 Author(s)
Author's final manuscript