Predicting Positions of People in Human-Robot Conversational Groups
Hooman Hedayati, Daniel Szafir
HRI 2022
Session: Sensing and Control
Abstract
Robots that operate in social settings must be able to recognize, understand, and reason about human conversational groups (i.e., F-formations). While several algorithms have been developed for identifying such groups, there has been little research on how robots might reason about inaccuracies following group classification (e.g., recognizing only 4 of 5 group members), even though such inaccuracies may lead to unacceptable robot responses, such as a robot ignoring members of the group. We address this gap through a data-driven approach that builds knowledge of human group positioning. By analyzing multiple conversational group data sets, we have developed a system for identifying high probability "regions" that indicate areas where people are likely to stand in a group relative to a single "anchor" participant. We use knowledge of these regions to train two models, which we implement on a social robot. The first model can estimate the true size of a partially observed conversational group (i.e., a group where only some of the participants were detected). Our second model can predict the locations where any undetected participants are likely to reside. Together, these models form a system that can improve F-formation detection algorithms by increasing robustness to noisy input data.
WEB:: https://humanrobotinteraction.org/2022/
Videos for HRI 2022