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This survey explores the integration of Multimodal Large Language Models (MLLMs) into multi-robot systems to optimize sensing, communication, and computation (R2X) for enhanced coordination. It argues that MLLMs can translate high-level natural language instructions into efficient resource allocation by selectively activating sensing modalities, dynamically allocating bandwidth, and determining computation placement. The survey presents four end-to-end demonstrations, including warehouse navigation, proactive MCS control, a FollowMe robot, and trash sorting, showcasing the advantages of R2X orchestration over purely on-device approaches in terms of payload, latency, and task success.
Forget siloed robots: MLLMs can orchestrate sensing, communication, and computation across robot networks to achieve superhuman task performance.
Imagine advanced humanoid robots, powered by multimodal large language models (MLLMs), coordinating missions across industries like warehouse logistics, manufacturing, and safety rescue. While individual robots show local autonomy, realistic tasks demand coordination among multiple agents sharing vast streams of sensor data. Communication is indispensable, yet transmitting comprehensive data can overwhelm networks, especially when a system-level orchestrator or cloud-based MLLM fuses multimodal inputs for route planning or anomaly detection. These tasks are often initiated by high-level natural language instructions. This intent serves as a filter for resource optimization: by understanding the goal via MLLMs, the system can selectively activate relevant sensing modalities, dynamically allocate bandwidth, and determine computation placement. Thus, R2X is fundamentally an intent-to-resource orchestration problem where sensing, communication, and computation are jointly optimized to maximize task-level success under resource constraints. This survey examines how integrated design paves the way for multi-robot coordination under MLLM guidance. We review state-of-the-art sensing modalities, communication strategies, and computing approaches, highlighting how reasoning is split between on-device models and powerful edge/cloud servers. We present four end-to-end demonstrations (sense ->communicate ->compute ->act): (i) digital-twin warehouse navigation with predictive link context, (ii) mobility-driven proactive MCS control, (iii) a FollowMe robot with a semantic-sensing switch, and (iv) real-hardware open-vocabulary trash sorting via edge-assisted MLLM grounding. We emphasize system-level metrics -- payload, latency, and success -- to show why R2X orchestration outperforms purely on-device baselines.
