IndoorR2X: Indoor Robot-to-Everything Coordination with LLM-Driven Planning
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arXiv
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| Autores principales: | , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866915902413340672 |
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| author | Yang, Fan Teotia, Soumya Mehta, Shaunak A. KrisshnaKumar, Prajit Xie, Quanting Liu, Jun Song, Yueqi Li, Wenkai Moteki, Atsunori Uchino, Kanji Bisk, Yonatan |
| author_facet | Yang, Fan Teotia, Soumya Mehta, Shaunak A. KrisshnaKumar, Prajit Xie, Quanting Liu, Jun Song, Yueqi Li, Wenkai Moteki, Atsunori Uchino, Kanji Bisk, Yonatan |
| contents | 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/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_20182 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | IndoorR2X: Indoor Robot-to-Everything Coordination with LLM-Driven Planning Yang, Fan Teotia, Soumya Mehta, Shaunak A. KrisshnaKumar, Prajit Xie, Quanting Liu, Jun Song, Yueqi Li, Wenkai Moteki, Atsunori Uchino, Kanji Bisk, Yonatan Robotics Multiagent Systems 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/. |
| title | IndoorR2X: Indoor Robot-to-Everything Coordination with LLM-Driven Planning |
| topic | Robotics Multiagent Systems |
| url | https://arxiv.org/abs/2603.20182 |