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AR-mediated explainability for teaching and cooperation

Anna Belardinelli, Chao Wang, Michael Gienger, Daniel Tanneberg, Stephan Hasler, Theodoros Stouraitis, "AR-mediated explainability for teaching and cooperation", Second TRR 318 Conference: Measuring Understanding, 2023.


The current spread of social and assistive robotics applications is increasingly highlighting the need for robots that can be easily taught and interacted with, even by users with no technical background. Still, it is often difficult to grasp what such robots know or to assess if a correct representation of the task is being formed. Augmented Reality (AR) has the potential to bridge this gap. We demonstrate three use cases where AR design elements enhance the explainability and efficiency of human-robot interaction: 1) a human teaching a robot some simple kitchen tasks by demonstration, 2) the robot showing its plan for solving novel tasks in AR to a human for validation, and 3) a robot communicating its intentions via AR while assisting people with limited mobility during daily activities.

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