When May I Help You? On The Effect of Proactivity on Group Human-Robot Collaboration
A study on robot initiative in multi-party human-robot collaboration using an escape room experiment. The reactive model (responds only when addressed) achieved 92.86% success rate vs. 71.42% for the proactive model (listens continuously, contributes autonomously). Effects vary with prior LLM experience, robot experience, and personality traits.
[2606.28469] When May I Help You? On The Effect of Proactivity on Group Human-Robot Collaboration
[Submitted on 26 Jun 2026]
Title:When May I Help You? On The Effect of Proactivity on Group Human-Robot Collaboration
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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.
Comments: Published at the RO-MAN 2026 conference
Subjects:
Robotics (cs.RO); Human-Computer Interaction (cs.HC)
Cite as: arXiv:2606.28469 [cs.RO]
(or arXiv:2606.28469v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.28469
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Thomas Vitry [view email] [v1] Fri, 26 Jun 2026 14:21:08 UTC (5,247 KB)
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