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We study two approaches for predicting an appropriate pose for a robot to take part in group formations typical of social human conversations subject to the physical layout of the surrounding environment. One method is model-based and explicitly encodes key geometric aspects of conversational formations. The other method is data-driven. It implicitly models key properties of spatial arrangements using graph neural networks and an adversarial training regimen. We evaluate the proposed approaches through quantitative metrics designed for this problem domain and via a human experiment. Our results suggest that the proposed methods are effective at reasoning about the environment layout and conversational group formations. They can also be used repeatedly to simulate conversational spatial arrangements despite being designed to output a single pose at a time. However, the methods showed different strengths. For example, the geometric approach was more successful at avoiding poses generated in nonfree areas of the environment, but the data-driven method was better at capturing the variability of conversational spatial formations. We discuss ways to address open challenges for the pose generation problem and other interesting avenues for future work. 

Robots' spatial positioning is a useful communication modality in social interactions. For example, in the context of group conversations, certain types of positioning signal membership to the group interaction. How does robot embodiment influence these perceptions? To investigate this question, we conducted an online study in which participants observed renderings of several robots in a social environment, and judged whether the robots were positioned to take part in a group conversation with other humans in the scene. Our results suggest that robot embodiment can influence perceptions of conversational group membership. An important factor to consider in this regard is whether robot embodiment leads to a discernible orientation for the agent. 

The practice of social distancing during the COVID-19 pandemic resulted in billions of people quarantined in their homes. In response, we designed and deployed VectorConnect, a robot teleoperation system intended to help combat the effects of social distancing in children during the pandemic. VectorConnect uses the off-the-shelf Vector robot to allow its users to engage in physical play while being geographically separated. We distributed the system to hundreds of users in a matter of weeks. This paper details the development and deployment of the system, our accomplishments, and the obstacles encountered throughout this process. Also, it provides recommendations to best facilitate similar deployments in the future. We hope that this case study about Human-Robot Interaction practice serves as an inspiration to innovate in times of global crises. 

Many robot applications being explored involve robots leading humans during navigation. Developing effective robots for this task requires a way for robots to understand and model a human's following behavior. In this paper, we present results from a user study of how humans follow a guide robot in the halls of an office building. We then present a data-driven Markovian model of this following behavior, and demonstrate its generalizability across time interval and trajectory length. Finally, we integrate the model into a global planner and run a simulation experiment to investigate the benefits of coupled human-robot planning. Our results suggest that the proposed model effectively predicts how humans follow a robot, and that the coupled planner, while taking longer, leads the human significantly closer to the target position.