Moderating multi-party conversations with social robots

Social robotics is a multidisciplinary field focused on designing and implementing robots capable of interacting with humans in social environments. However, group conversations challenge robots in interpreting social signals for effective participation. This study evaluates control policies for moderating multi-party conversation dynamics using a humanoid robot. The system employs a cloud-based framework to calculate speaker dominance as a weighted combination of speaking time and word count, while the Louvain algorithm identifies subgroups among participants. Control policies aim to minimize dominance disparities and subgroup formation, fostering balanced participation and group cohesion. A study with 300 middle school students compared these policies to a baseline in which the robot did not address individuals directly. The results demonstrated that the proposed policies reduced dominance gaps and subgroup formation, promoting more balanced interactions. These findings highlight the potential applicability of the approach across education, healthcare, and entertainment.