Transforming Robot Programs Based on Social Context

Porfirio, D., A. Sauppé, A. Albarghouthi, and B. Mutlu. “Transforming Robot Programs Based on Social Context”. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 2020, pp. 1-12.


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


	doi = {10.1145/3313831.3376355},
	url = {},
	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}