Data-Driven Control of Insect Flapping Flight Via Deep Reinforcement Learning

Modeling and simulating realistic insect flight pose unique challenges due to the complex interaction between multi-degree-of-freedom wing kinematics and highly precise aerodynamic forces. To solve this challenge, this paper presents a bidirectional kinematics-aerodynamics coupled simulation framework for miniature insect flight. Our approach first models the kinematics of flying insects by parameterizing natural wingbeat cycles based on available real-world datasets. Subsequently, we compute aerodynamic forces utilizing an improved semi-empirical model, which extends from quasi-steady formulation by incorporating critical unsteady force components. To achieve closed-loop control for both kinematics and aerodynamics, we employ deep reinforcement learning to train a virtual insect to adaptively adjust flapping strategies in response to dynamic flight states. Finally, an integrated controller enables the simulated insect to autonomously regulate the wing motion and perform complex tasks such as visual obstacle avoidance. Extensive experiments and comparisons demonstrate that our framework can effectively generate physically plausible and autonomous insect flight across a variety of scenarios.