Publication
Fast or accurate? How intention-recognition models shape human perception of a mobile robot
Conference Article
Conference
ACM/IEEE International Conference on Human-Robot Interaction (HRI)
Edition
2026
Pages
502-506
Doc link
https://doi.org/10.1145/3776734.3794445
File
Abstract
Robots that operate alongside people increasingly depend on intention recognition models to anticipate human motion and adapt their behavior in socially appropriate ways. However, these models vary widely in both latency and accuracy, leading to different trade-offs between reacting quickly and correctly. Although these technical differences are well documented, it remains unclear how they shape the user's experience of interacting with a robot. To examine how these translate into human perception, we conduct a preliminary user study comparing three intention-recognition models: a fast but low-accuracy model (Geo), an intermediate model (LSTM), and a slower but highly accurate model (Fusion). Participants interacted with a mobile robot controlled by each model and rated their experience across key dimensions of social interaction. Overall, the findings suggest that socially fluent interaction does not emerge from speed or accuracy alone, but from the balance of timely, reliable, and predictable robot behavior.
Categories
emotion recognition, learning (artificial intelligence), service robots.
Author keywords
Human-Robot Interaction, Intention Prediction, Social Robotics
Scientific reference
V. Bo, A. Garrell Zulueta and A. Sanfeliu. Fast or accurate? How intention-recognition models shape human perception of a mobile robot, 2026 ACM/IEEE International Conference on Human-Robot Interaction, 2026, Edinburgh, Scotland, UK, pp. 502-506, ACM/IEEE.
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