@inproceedings{Goubard:2024,
author = {Goubard, C and Demiris, Y},
publisher = {IEEE},
title = {Learning self-confidence from semantic action embeddings for improved trust in human-robot interaction},
url = {https://cgoubard.me/},
year = {2024}
}

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TY  - CPAPER
AB  - In HRI scenarios, human factors like trust can greatly impact task performance and interaction quality. Recent research has confirmed that perceived robot proficiency is a major antecedent of trust. By making robots aware of their capabilities, we can allow them to choose when to perform low-confidence actions, thus actively controlling the risk of trust reduction. In this paper, we propose SCONE, a policy to learn self-confidence from experience using semantic action embeddings. Using an assistive cooking setting, we show that the semantic aspect allows SCONE to learn self-confidence faster than existing approaches, while also achieving promising performance in simple instructions following. Finally, we share results from a pilot study with 31 participants, showing that such a self-confidence-aware policy increases capability-based human trust.
AU  - Goubard,C
AU  - Demiris,Y
PB  - IEEE
PY  - 2024///
TI  - Learning self-confidence from semantic action embeddings for improved trust in human-robot interaction
UR  - https://cgoubard.me/
UR  - http://hdl.handle.net/10044/1/112369
ER  - 

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