Autonomous drones must often respond to sudden events, such as alarms, faults, or unexpected changes in their environment, that require immediate and adaptive decision-making. Traditional approaches rely on safety engineers hand-coding large sets of recovery rules, but this strategy cannot anticipate the vast range of real-world contingencies and quickly becomes incomplete. Recent advances in embodied AI, powered by large visual language models, provide commonsense reasoning to assess context and generate appropriate actions in real time. We demonstrate this capability in a simulated urban benchmark in the Unreal Engine, where drones dynamically interpret their surroundings and decide on sudden maneuvers for safe landings. Our results show that embodied AI makes possible a new class of adaptive recovery and decision-making pipelines that were previously infeasible to design by hand, advancing resilience and safety in autonomous aerial systems.

翻译：自主无人机常需应对突发状况，如警报、系统故障或环境突变，这些情况要求系统具备即时自适应决策能力。传统方法依赖安全工程师手动编写大量恢复规则，但该策略无法预判现实世界中可能出现的各种意外情况，且规则集会迅速变得不完备。基于大型视觉语言模型的具身AI最新进展，为实时环境评估与适应性动作生成提供了常识推理能力。我们在Unreal Engine构建的模拟城市基准测试中验证了该能力：无人机动态解析周围环境，并决策突发机动动作以实现安全着陆。实验结果表明，具身AI实现了一类全新的自适应恢复与决策流程，这类流程以往难以通过人工设计完成，从而显著提升了自主空中系统的韧性与安全性。