I built Autodrone, an LLM-driven drone agent that takes a natural-language mission and flies a simulated drone through an Unreal/AirSim environment. The core idea is that the LLM is not just giving one-off commands; it is inside a self-improvement loop. Each flight produces structured rollout data: the mission, route, drone state, actions, rewards, safety violations, and final outcome. That data is stored in MongoDB, where successful trajectories become reusable flight memory and failed attempts become lessons for future runs. The harness can then use those stored experiences to change future behavior automatically by retrieving past successful routes, adjusting the context window, generating training examples, and computing GRPO-style advantage scores across multiple attempts. In short, I made an LLM fly a drone, then gave it memory and feedback so it can improve its future flight behavior from its own simulator experience rather than relying on manual human correction.
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