A Study in Zucker: Insights on Human-Robot Interactions

In recent years there has been a large focus on how robots can operate in human populated environments. In this paper, we focus on interactions between humans and small indoor robots and introduce a new human-robot interaction (HRI) dataset. The analysis of the recorded experiments shows that anticipatory and non-reactive robot controllers impose similar constraints to humans' safety and efficiency. Additionally, we found that current state-of-the-art models for human trajectory prediction can adequately extend to indoor HRI settings. Finally, we show that humans respond differently in shared and homogeneous environments when collisions are imminent, since interacting with small differential drives can only cause a finite level of social discomfort as compared to human-human interactions. The dataset used in this analysis is available at: https://github.com/AlexanderDavid/ZuckerDataset.