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In this study, we present a novel paradigm for industrial robotic embodiedagents, encapsulating an 'agent as cerebrum, controller as cerebellum'architecture. Our approach harnesses the power of Large Multimodal Models(LMMs) within an agent framework known as AeroAgent, tailored for dronetechnology in industrial settings. To facilitate seamless integration withrobotic systems, we introduce ROSchain, a bespoke linkage framework connectingLMM-based agents to the Robot Operating System (ROS). We report findings fromextensive empirical research, including simulated experiments on the Airgen andreal-world case study, particularly in individual search and rescue operations.The results demonstrate AeroAgent's superior performance in comparison toexisting Deep Reinforcement Learning (DRL)-based agents, highlighting theadvantages of the embodied LMM in complex, real-world scenarios.

Agent as Cerebrum, Controller as Cerebellum: Implementing an Embodied LMM-based Agent on Drones