Conditional Invertible Neural Networks for Data-Driven UAV Control: A 2-D Proof of Concept

• URL: http://arxiv.org/abs/2607.13703v1
• Date: Wed, 15 Jul 2026 11:07:43 GMT
• Title: Conditional Invertible Neural Networks for Data-Driven UAV Control: A 2-D Proof of Concept
• Abstract summary: conditional invertible neural networks (cINNs) as probabilistic inverse-dynamics models for multirotor control.<n>Open-loop reproduction reaches $R2 = 0.944$, mean CRPS 0.0915, and log-probability-error correlation $= -0.60$.
• Score: 0.6308539010172308
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We investigate conditional invertible neural networks (cINNs) as probabilistic inverse-dynamics models for multirotor control. For a planar X8 coaxial multicopter, we learn $p(u \mid s_t, c_t)$ from an incremental nonlinear dynamic inversion (INDI) teacher using rational-quadratic spline coupling and invertible linear mixing. Open-loop reproduction reaches $R^2 = 0.944$, mean CRPS 0.0915, and log-probability-error correlation $ρ= -0.60$. Over 15 closed-loop scenarios, position RMSE matches INDI (9.7 vs. 9.5 m), with 47 percent tracking acceptably; failures separate into attitude divergence under aggressive steps and phase lag under high-frequency references, isolating command bandwidth and data coverage as dominant failure mechanisms.

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