ManiGaze: a Dataset for Evaluating Remote Gaze Estimator in Object Manipulation Situations

Gaze estimation allows robots to better understand users and thus to more precisely meet their needs. In this paper, we are interested in gaze sensing for analyzing collaborative tasks and manipulation behaviors in human-robot interactions (HRI), which differs from screen gazing and other communicative HRI settings. Our goal is to study the accuracy that remote vision gaze estimators can provide, as they are a promising alternative to current accurate but intrusive wearable sensors. In this view, our contributions are: 1) we collected and make public a labeled dataset involving manipulation tasks and gazing behaviors in an HRI context; 2) we evaluate the performance of a state-of-the-art gaze estimation system on this dataset. Our results show a low default accuracy, which is improved by calibration, but that more research is needed if one wishes to distinguish gazing at one object amongst a dozen on a table.