When May I Help You? On The Effect of Proactivity on Group Human-Robot Collaboration

2026-06-26

AI Summary

This study investigates the balance between robotic proactivity and reactive responsiveness in multi-user human-robot collaboration to avoid disrupting coordination or missing timely assistance opportunities. Through a comparative evaluation of proactive versus reactive interaction models in an escape-room task, the research integrates a humanoid robot, continuous speech monitoring, autonomous contribution mechanisms, and periodic interaction resets. User experience was assessed using the Godspeed and RoSAS scales. Results indicate that the reactive model achieved higher overall task success rates (92.86% vs. 71.42%), whereas the proactive model significantly increased interaction frequency. Notably, the effectiveness of robotic proactivity was significantly moderated by users’ prior experience and personality traits. This work transcends the limitations of static interaction strategies by demonstrating the critical importance of dynamically adapting robot initiative to contextual and individual factors in group collaboration.

Abstract

Robot initiative is a central challenge in multi-party human-robot collaboration. A robot that contributes without being addressed may provide timely support, but it may also disrupt coordination, divide attention, or interrupt turn-taking; a robot that waits to be addressed may preserve human control, but it may also miss opportunities to assist. We investigate this design challenge in a collaborative escape room in which pairs of participants work with a humanoid robot under either a reactive interaction model, where the robot responds only when addressed, or a proactive model, where it listens continuously, contributes autonomously, and periodically re-initiates interaction. We evaluate both models using puzzle-solving performance, interaction frequency, and participant ratings on the Godspeed and RoSAS scales. The proactive model substantially increases interaction frequency, whereas the reactive model shows a descriptively higher overall success rate (92.86% vs. 71.42%). The strongest differences emerge when prior experience and personality are taken into account: participants with LLM experience solve the early puzzles faster in the reactive condition, and participants with prior robot experience show modified evaluations of proactive and reactive interaction as do introverted participants. These findings demonstrate that the effects of robot initiative are simultaneously shaped by users' prior experience, personality traits and more generally by the needs of the group.

Problem

• human-robot collaboration
• proactivity
• robot initiative
• group interaction
• interaction design

Innovation

• proactive interaction
• human-robot collaboration
• robot initiative
• personality traits
• user experience