Drones that Think on their Feet: Sudden Landing Decisions with Embodied AI
This work addresses safety and resilience challenges in autonomous aerial systems, representing an incremental improvement over traditional hand-coded methods.
The paper tackles the problem of autonomous drones needing to respond to sudden events like alarms or environmental changes by introducing an embodied AI approach using large visual language models for real-time decision-making, demonstrating its capability in a simulated urban benchmark to enable adaptive recovery pipelines that were previously infeasible.
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.