Advancing Multi-Robot Networks via MLLM-Driven Sensing, Communication, and Computation: A Comprehensive Survey

Abstract

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.

Figures (25)

• Figure 1: A scenario comparing (left) individually planned routes vs. (right) communication-based collaboration. The lower timeline highlights that the gain is conditional: "with communications" outperforms when $T_{\mathrm{uplink}}+T_{\mathrm{edge}}+T_{\mathrm{downlink}} < T_{\mathrm{wait}}$, where $T_{\mathrm{wait}}$ is the expected collision-induced waiting time without coordination.
• Figure 2: High-level overview of this paper's key concept, four end-to-end demonstrations (Demo I--IV), and overall organization.
• Figure 3: Common block diagram of MLLMs.
• Figure 4: A taxonomy of R2X communications.
• Figure 5: End-to-end architecture of Demo I (warehouse digital twin): robots uplink semantic visual observations and positions; the server orchestrator fuses sensing and predicted link states to trigger global replanning and proactive uplink configuration, then downlinks updated commands.