[2508.04691] Before Humans Join the Team: Diagnosing Coordination Failures in Healthcare Robot Team Simulation

Computer Science > Robotics arXiv:2508.04691 (cs) [Submitted on 6 Aug 2025 (v1), last revised 7 Apr 2026 (this version, v2)] Title:Before Humans Join the Team: Diagnosing Coordination Failures in Healthcare Robot Team Simulation Authors:Yuanchen Bai, Zijian Ding, Shaoyue Wen, Xiang Chang, Angelique Taylor View a PDF of the paper titled Before Humans Join the Team: Diagnosing Coordination Failures in Healthcare Robot Team Simulation, by Yuanchen Bai and 4 other authors View PDF HTML (experimental) Abstract:As humans move toward collaborating with coordinated robot teams, understanding how these teams coordinate and fail is essential for building trust and ensuring safety. However, exposing human collaborators to coordination failures during early-stage development is costly and risky, particularly in high-stakes domains such as healthcare. We adopt an agent-simulation approach in which all team roles, including the supervisory manager, are instantiated as LLM agents, allowing us to diagnose coordination failures before humans join the team. Using a controllable healthcare scenario, we conduct two studies with different hierarchical configurations to analyze coordination behaviors and failure patterns. Our findings reveal that team structure, rather than contextual knowledge or model capability, constitutes the primary bottleneck for coordination, and expose a tension between reasoning autonomy and system stability. By surfacing these failures in simulation, we prepare the g...