CAREER: Modeling Group Human-Robot Interactions: Towards A Unified Data-Driven Perspective

Yale University, New Haven CT

Investigators

Abstract

The long-term research goal of this Faculty Early Career Development (CAREER) grant is to advance Human-Robot Interaction (HRI) so that robots can effectively take part in social encounters with multiple users. While social robotics research has traditionally focused on one-on-one interactions, real-world applications typically require that robots interact in multi-party settings. Examples include robots that are deployed as information providers (in public kiosks or in museums), robots that work with group of people or with individuals, and robots that assist children or elderly people (in homes or elderly care centers). Such real-world contexts led to the emergence of group Human-Robot Interaction as a new area of study. This project is for a data-driven perspective for robot-group interactions that will allow robotic systems to reason about individuals and groups. Additionally, this project includes activities in pursuit of the researcher’s long-term goals of making Computer Science a more diverse and inclusive field, and increasing engagement in science, technology, engineering, and mathematics via Artificial Intelligence technologies. For example, project activities include a partnership with the Yale Peabody Museum to help educate high-school and college students from primarily low-income communities, engage them in research, and engage the general public with Artificial Intelligence and Robotics. These synergistic activities complement the research by providing novel opportunities to study group human-robot interactions. To unify many problems in computationally understanding group HRI, this project comprises a three-pronged approach that addresses: 1) data representation via graph abstractions, 2) learning via Graph Neural Networks, and 3) data collection in a scalable manner via self-supervision. The project will demonstrate this approach in a tangible manner by improving how robots initiate and sustain interactions. First, the team will study the problem of forecasting user engagement with a public robot as an example of reasoning about individuals in consideration of social relationships. Second, it will study the problem of identifying interaction breakdowns in HRI as an example of reasoning holistically about groups. Taken together, this project will demonstrate how the approach can be integrated with robot decision making, validate experimental protocols for data collection and algorithm evaluation in public settings, and advance our understanding of HRI at a fundamental level so that robots can better take part in group social encounters. This project is supported by the cross-directorate Foundational Research in Robotics program, jointly managed and funded by the Directorates for Engineering (ENG) and Computer and Information Science and Engineering (CISE). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.