Abstract
Social robots have varied effectiveness when interacting with humans in different interaction contexts. A robot programmed to escort individuals to a different location, for instance, may behave more appropriately in a crowded airport than a quiet library, or vice versa. To address these issues, we exploit ideas from program synthesis and propose an approach to transforming the structure of hand-crafted interaction programs that uses user-scored execution traces as input, in which end users score their paths through the interaction based on their experience. Additionally, our approach guarantees that transformations to a program will not violate task and social expectations that must be maintained across contexts. We evaluated our approach by adapting a robot program to both real-world and simulated contexts and found evidence that making informed edits to the robot’s program improves user experience.
DOI: 10.1145/3313831.3376355
Bibtex
@inproceedings{Porfirio_2020,
doi = {10.1145/3313831.3376355},
url = {https://doi.org/10.1145%2F3313831.3376355},
year = 2020,
month = {apr},
publisher = {{ACM}},
author = {David Porfirio and Allison Saupp{\'{e}} and Aws Albarghouthi and Bilge Mutlu},
title = {Transforming Robot Programs Based on Social Context},
booktitle = {Proceedings of the 2020 {CHI} Conference on Human Factors in Computing Systems}
}