Marynel Vazquez (Yale) - Multi-Party Human-Robot Interaction: Towards Generalizable Data-Driven Models with Graph State Abstractions

Many real-world applications require that robots handle the complexity of multi-party social encounters, e.g., delivery robots may need to navigate through crowds, robots in manufacturing settings may need to coordinate their actions with those of human coworkers, and robots in educational environments may help multiple people practice and improve their skills. How can we enable robots to effectively take part in these social interactions? At first glance, multi-party interactions may be seen as a trivial generalization of one-on-one human-robot interactions, suggesting no special consideration. Unfortunately, this approach is limited in practice because it ignores higher-order effects, like group factors, that often drive human behavior in multi-party Human-Robot Interaction (HRI).

In this talk, I will describe two research directions that we believe are important to advance multi-party HRI. One direction focuses on understanding group dynamics and social group phenomena from an experimental perspective. The other one focuses on leveraging graph state abstractions and structured, data-driven methods for reasoning about individual, interpersonal and group-level factors relevant to these interactions. Examples of these research directions include efforts to motivate prosocial human behavior in HRI, balance human participation in conversations, and improve spatial reasoning for robots in human environments. As part of this talk, I will also describe our recent efforts to scale HRI data collection for early system development and testing via online interactive surveys. We have begun to explore this idea in the context of social robot navigation but, thanks to advances in game development engines, it could be easily applied to other HRI application domains.