In this section
@inproceedings{Goubard:2024:10.1109/ICRA57147.2024.10611445,
author = {Goubard, C and Demiris, Y},
doi = {10.1109/ICRA57147.2024.10611445},
publisher = {IEEE},
title = {Learning self-confidence from semantic action embeddings for improved trust in human-robot interaction},
url = {http://dx.doi.org/10.1109/ICRA57147.2024.10611445},
year = {2024}
}
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
DO - 10.1109/ICRA57147.2024.10611445
PB - IEEE
PY - 2024///
TI - Learning self-confidence from semantic action embeddings for improved trust in human-robot interaction
UR - http://dx.doi.org/10.1109/ICRA57147.2024.10611445
UR - https://cgoubard.me/
UR - http://hdl.handle.net/10044/1/112369
ER -
