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

arXiv:2607.13703v1 Announce Type: new 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 rho = -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.

Source: arXiv cs.LG | 2026-07-16