IndoorR2X: Indoor Robot-to-Everything Coordination with LLM-Driven Planning

TL;DR

IndoorR2X introduces a benchmark framework for LLM-driven multi-robot planning in indoor environments, integrating robot and IoT sensor data to enhance scene understanding and coordination.

Contribution

It is the first benchmark for LLM-based multi-robot planning that combines robot perception with static IoT sensors for indoor scene understanding.

Findings

IoT-augmented modeling improves multi-robot efficiency.

High-level LLM planning reduces exploration overhead.

Systematic evaluation reveals key insights and failure modes.

Abstract

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/.