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Title: Transforming Robot Programs Based on Social Context
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.
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Award ID(s):
1925043 1651129
Publication Date:
Journal Name:
CHI '20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
Page Range or eLocation-ID:
1 to 12
Sponsoring Org:
National Science Foundation
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