arXiv:2505.18201v2 Announce Type: replace-cross Abstract: Controlling flapping-wing drones requires controllers that handle time-varying, nonlinear, underactuated dynamics from incomplete, noisy sensor data. Recent advances in artificial intelligence (AI), particularly reinforcement learning (RL), have opened new perspectives for addressing such complex control problems through data-driven policy optimization from interaction with the environment. Yet purely data-driven methods are sample-inefficient, demanding extensive, sometimes unsafe exploration, especially without guiding physical models
Advances in AI, particularly reinforcement learning, are reaching a maturity where they can begin to address complex real-world control problems previously intractable for traditional methods, spurred by computational improvements and algorithm refinement.
This work demonstrates a crucial step towards robust autonomous control of highly dynamic systems, expanding the operational capabilities of drones and potentially other robotic platforms for both commercial and defence applications.
The ability to integrate reinforcement learning with physical models offers a more sample-efficient and safer path to deploying AI-driven control in complex, real-world environments, accelerating autonomous system development.
- · Defence contractors
- · Logistics and delivery companies
- · Robotics research institutions
- · AI software developers
- · Manufacturers of legacy drone control systems
- · Companies reliant on human-operated drone inspection services
More sophisticated and autonomous drones will become available for various applications.
Reduced operational costs and increased efficiency across sectors adopting these advanced drone technologies.
Enhanced AI control capabilities could migrate to other complex robotic systems, accelerating general robotics development.
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Read at arXiv cs.LG