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

2026-03-31

AI Summary

This work addresses the challenges faced by multi-robot systems in executing high-level tasks driven by natural language instructions, which are often hindered by perceptual overload, limited communication bandwidth, and imbalanced computational resource allocation. The authors propose R2X, a novel collaborative framework that, for the first time, leverages task intent to jointly optimize perception, communication, and computation, thereby establishing an end-to-end closed loop from multimodal large language model–based semantic understanding to physical execution. Integrating edge–cloud collaboration, semantic-aware sensing selection, predictive communication, and digital twin technologies, R2X significantly outperforms purely edge-based baselines across diverse tasks—including warehouse navigation, mobile crowdsourcing, semantic following, and open-vocabulary waste sorting—demonstrating superior performance in payload efficiency, latency, and task success rate.

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.

Problem

• multi-robot coordination
• resource orchestration
• multimodal large language models
• intent-driven optimization
• R2X

Innovation

• MLLM-driven orchestration
• intent-aware resource allocation
• multi-robot coordination
• R2X optimization
• edge-cloud collaboration