The research and development of socially interactive robots is a complex challenge because of the wide variety of capabilities needed for effective social human-robot interactions (HRI). Many of these capabilities, including perception, dialog, and control, have state of the art methods and solutions, but combining those into a comprehensive and seamless interaction is still an open challenge. We describe HARMONI, a multi-modal, open-source tool for rapid social HRI development and deployment. HARMONI is centered around a ROS package for interaction development, including decision management and node orchestration. HARMONI systematically integrates with disparate functionalities needed to conduct a meaningful social human-robot interaction such as external cloud services, AI models, and modules for sensing, planning, and acting on a variety of platforms. HARMONI was applied to the QT robot platform and usability tests were conducted to evaluate the ease and speed of development and deployment. This paper describes the architecture and design of HARMONI and reports the results of a pilot study with novice users.

In this work we address the Next Speaker Prediction sub challenge of the ACM '21 MultiMediate Grand Challenge. This challenge poses the problem of turn taking prediction in physically situated multiparty interaction. Solving this problem is essential for enabling fluent real-time multiparty human-machine interaction. This problem is made more difficult by the need for a robust solution that can perform effectively across a wide variety of settings and contexts. Prior work has shown that current state-of-the-art methods rely on machine learning approaches that do not generalize well to new settings and feature distributions. To address this problem, we propose the use of group-level focus of visual attention as additional information. We show that a simple combination of group-level focus of visual attention features and publicly available audio-video synchronizer models is competitive with state-of-the-art methods fine-tuned for the challenge dataset.

Socially assistive robots have the potential to improve group dynamics when interacting with groups of people in social settings. This work contributes to the understanding of those dynamics through a user study of trust dynamics in the novel context of a robot mediated support group. For this study, a novel framework for robot mediation of a support group was developed and validated. To evaluate interpersonal trust in the multi-party setting, a dyadic trust scale was implemented and found to be uni-factorial, validating it as an appropriate measure of general trust. The results of this study demonstrate a significant increase in average interpersonal trust after the group interaction session, and qualitative post-session interview data report that participants found the interaction helpful and successfully supported and learned from one other. The results of the study validate that a robot-mediated support group can improve trust among strangers and allow them to share and receive support for their academic stress.