To Ask or Not to Ask: A User Annoyance Aware Preference Elicitation Framework for Social Robots

Gucsi, Balint; Tarapore, Danesh; Yeoh, William; Amato, Christopher; Tran-Thanh, Long

doi:10.1109/iros45743.2020.9341607

Cited by 2 publications

(1 citation statement)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Meanwhile, implicit feedback are cues and signals that people exhibit without intending to communicate some specific information about robot performance, yet they can be used to infer such perceptions. Inferring performance from implicit feedback can reduce the chances of excessively querying users for explicit feedback in robot learning scenarios [33], [34], thereby minimizing the risk of feedback fatigue [35]. Learning from implicit feedback is not without challenges, however, as it can be difficult to interpret [2], [3].…”

Section: Related Workmentioning

confidence: 99%

Recent Advances in Mobile Robot Localization in Complex Scenarios

Zhang

Liu

et al. 2023

Lecture Notes in Electrical Engineering

View full text Add to dashboard Cite

Human impressions of robot performance are often measured through surveys. As a more scalable and cost-effective alternative, we study the possibility of predicting people's impressions of robot behavior using non-verbal behavioral cues and machine learning techniques. To this end, we first contribute the SEAN TOGETHER Dataset consisting of observations of an interaction between a person and a mobile robot in a Virtual Reality simulation, together with impressions of robot performance provided by users on a 5-point scale. Second, we contribute analyses of how well humans and supervised learning techniques can predict perceived robot performance based on different combinations of observation types (e.g., facial, spatial, and map features). Our results show that facial expressions alone provide useful information about human impressions of robot performance; but in the navigation scenarios we tested, spatial features are the most critical piece of information for this inference task. Also, when evaluating results as binary classification (rather than multiclass classification), the F1-Score of human predictions and machine learning models more than doubles, showing that both are better at telling the directionality of robot performance than predicting exact performance ratings. Based on our findings, we provide guidelines for implementing these predictions models in real-world navigation scenarios.

show abstract