Long-horizon tabletop games pose a distinct systems challenge for robotics: small perceptual or execution errors can invalidate accumulated task state, propagate across decision-making modules, and ultimately derail interaction. This paper studies how to maintain internal state consistency in turn-based, multi-human robotic tabletop games through deliberate system design rather than isolated component improvement. Using Mahjong as a representative long-horizon setting, we present an integrated architecture that explicitly maintains perceptual, execution, and interaction state, partitions high-level semantic reasoning from time-critical perception and control, and incorporates verified action primitives with tactile-triggered recovery to prevent premature state corruption. We further introduce interaction-level monitoring mechanisms to detect turn violations and hidden-information breaches that threaten execution assumptions. Beyond demonstrating complete-game operation, we provide an empirical characterization of failure modes, recovery effectiveness, cross-module error propagation, and hardware-algorithm trade-offs observed during deployment. Our results show that explicit partitioning, monitored state transitions, and recovery mechanisms are critical for sustaining executable consistency over extended play, whereas monolithic or unverified pipelines lead to measurable degradation in end-to-end reliability. The proposed system serves as an empirical platform for studying system-level design principles in long-horizon, turn-based interaction.

翻译：长时域桌面游戏对机器人系统提出了独特的挑战：微小的感知或执行误差会累积破坏已建立的任务状态，跨决策模块传播，并最终导致交互失败。本文研究如何在基于回合制的人机协作桌面游戏中，通过系统性设计而非孤立模块改进来保持内部状态一致性。以麻将作为典型的长时域场景，我们提出一种集成架构：显式维护感知、执行和交互状态，将高层语义推理与时间关键型感知控制相分离，并结合带触觉触发的可验证动作基元以防止过早的状态损坏。我们进一步引入交互级监测机制，以检测可能破坏执行假设的违规回合操作和隐藏信息泄露。除演示完整游戏操作外，我们还对部署中观察到的故障模式、恢复有效性、跨模块误差传播以及硬件算法权衡进行了实证表征。结果表明，显式状态分离、受监测的状态转换和恢复机制对于在长时间博弈中维持可执行一致性至关重要，而单一化或未经验证的流水线会导致端到端可靠性的显著下降。本系统可作为研究面向长时域、回合制交互的系统级设计原理的实证平台。