  Despite recent advancements in human-robot interaction (HRI), there is still
limited knowledge about how humans interact and behave in the presence of small
service indoor robots and, subsequently, about the human-centered behavior of
such robots. This also raises concerns about the applicability of current
trajectory prediction methods to indoor HRI settings as well as the accuracy of
existing crowd simulation models in shared environments. To address these
issues, we introduce a new HRI dataset focusing on interactions between humans
and small differential drive robots running different types of controllers. Our
analysis shows that anticipatory and non-anticipatory 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 to small differential drives than to other humans when collisions
are imminent, since interacting with small robots can only cause a finite level
of social discomfort as compared to human-human interactions. Our dataset and
related code and models are available at:
https://motion-lab.github.io/ZuckerDataset.
