Although robot-to-robot (R2R) communication improves indoor scene understanding beyond what a single robot can achieve, R2R alone cannot overcome partial observability without substantial exploration overhead or scaling team size. In contrast, many indoor environments already include low-cost Internet of Things (IoT) sensors (e.g., cameras) that provide persistent, building-wide context beyond onboard perception. We therefore introduce IndoorR2X, the first benchmark and simulation framework for Large Language Model (LLM)-driven multi-robot task planning with Robot-to-Everything (R2X) perception and communication in indoor environments. IndoorR2X integrates observations from mobile robots and static IoT devices to construct a global semantic state that supports scalable scene understanding, reduces redundant exploration, and enables high-level coordination through LLM-based planning. IndoorR2X provides configurable simulation environments, sensor layouts, robot teams, and task suites to systematically evaluate high-level semantic coordination strategies. Extensive experiments across diverse settings demonstrate that IoT-augmented world modeling improves multi-robot efficiency and reliability, and we highlight key insights and failure modes for advancing LLM-based collaboration between robot teams and indoor IoT sensors. See our project website: https://fandulu.github.io/IndoorR2X_project_page/.

翻译：尽管机器人到机器人（R2R）通信能提升单台机器人难以实现的室内场景理解能力，但若缺乏大量探索开销或扩大团队规模，仅靠R2R仍无法克服部分可观测性困境。相比之下，许多室内环境已部署低成本物联网传感器（如摄像头），能提供超越机载感知的持续性全局建筑上下文。为此，我们提出IndoorR2X——首个面向室内环境中基于大语言模型驱动的多机器人任务规划、融合机器人到万物（R2X）感知与通信的基准测试与仿真框架。IndoorR2X整合移动机器人观测与静态物联网设备数据，构建全局语义状态，以支持可扩展的场景理解、减少冗余探索，并通过LLM规划实现高层级协同。该框架提供可配置的仿真环境、传感器布局、机器人团队及任务套件，用于系统评估高层级语义协同策略。跨多场景的广泛实验表明，物联网增强的世界建模能提升多机器人效率与可靠性，我们同时揭示了推进基于LLM的机器人团队与室内物联网传感器协作的关键洞见与典型失效模式。项目网站见：https://fandulu.github.io/IndoorR2X_project_page/。