Human-robot collaboration has been studied primarily in dyadic or sequential settings. However, real homes require multiadic collaboration, where multiple humans and robots share a workspace, acting concurrently on interleaved subtasks with tight spatial and temporal coupling. This regime remains underexplored because close-proximity interaction between humans, robots, and objects creates persistent occlusion and rapid state changes, making reliable real-time 3D tracking the central bottleneck. No existing platform provides the real-time, occlusion-robust, room-scale perception needed to make this regime experimentally tractable. We present OmniRobotHome, the first room-scale residential platform that unifies wide-area real-time 3D human and object perception with coordinated multi-robot actuation in a shared world frame. The system instruments a natural home environment with 48 hardware-synchronized RGB cameras for markerless, occlusion-robust tracking of multiple humans and objects, temporally aligned with two Franka arms that act on live scene state. Continuous capture within this consistent frame further supports long-horizon human behavior modeling from accumulated trajectories. The platform makes the multiadic collaboration regime experimentally tractable. We focus on two central problems: safety in shared human-robot environments and human-anticipatory robotic assistance, and show that real-time perception and accumulated behavior memory each yield measurable gains in both.

翻译：人机协作研究主要在二元或序列化场景中开展。然而真实家庭环境需要多对多协作——多个人类与多台机器人在共享工作空间中同时操作，以紧密的空间与时间耦合方式处理交错子任务。这一模式尚未得到充分探索，因为人类、机器人与物体之间的近距离交互会产生持续遮挡与快速状态变化，使得可靠的实时三维追踪成为核心瓶颈。现有平台均无法提供使该模式具备实验可行性的实时、抗遮挡、房间级感知能力。我们提出OmniRobotHome——首个在共享世界坐标系下统一大范围实时三维人体与物体感知及多机器人协调驱动的房间级住宅实验平台。该系统在自然家居环境中部署48台硬件同步RGB相机，实现无标记、抗遮挡的多人与多物体追踪，并与两台Franka机械臂基于实时场景状态进行时间对齐操作。在此一致坐标系下的连续数据采集，进一步支持从累积轨迹中进行长时程人类行为建模。该平台使多对多协作模式具备实验可行性。我们聚焦两大核心问题：人机共享空间的安全性与人类预判型机器人辅助，实验表明实时感知与累积行为记忆均在此两项任务中带来可量化的性能提升。