@inproceedings{leusmann2023understanding,
	title = {Understanding the Uncertainty Loop of Human-Robot Interaction},
	author = {Jan Leusmann and Chao Wang and Michael Gienger and Albrecht Schmidt and Sven Mayer},
	year = {2023},
	booktitle = {Proceedings of the Socially Assistive Robots as Decision Makers: Transparency, Motivations, and Intentions Workshop},
	series = {SARs: TMI'23},
	doi = {10.48550/arXiv.2303.07889},
	url = {https://sven-mayer.com/wp-content/uploads/2023/03/gruenefeld2022workshop.pdf},
	date = {2023-04-23},
	abstract = {Recently the field of Human-Robot Interaction gained popularity due to the wide range of possibilities of how robots can support humans during daily tasks. One such form is supportive robots, socially assistive robots built explicitly for communicating with humans, e.g., as service robots or personal companions. As they understand humans through artificial intelligence, these robots can sometimes make wrong assumptions about the humans' current state and give an unexpected response. In human-human conversations, unexpected responses happen frequently. However, it is currently unclear how such robots should act if they understand that human did not expect their response or even show the uncertainty of their response in the first place. For this, we explore the different forms of potential uncertainties during human-robot conversations and how humanoids can communicate these uncertainties through verbal and non-verbal cues.},
	keywords = {human-robot interaction}
}