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

Christian Wittke; Stephan Myschik; Oliver Niggemann

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

Machine Learning 2026-07-15 v1 Systems and Control

Authors: Christian Wittke , Stephan Myschik , Oliver Niggemann

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.

Keywords

conditional invertible neural networks probabilistic inverse dynamics multirotor control INDI uncertainty quantification UAV

Cite

@article{arxiv.2607.13703,
  title  = {Conditional Invertible Neural Networks for Data-Driven UAV Control: A 2-D Proof of Concept},
  author = {Christian Wittke and Stephan Myschik and Oliver Niggemann},
  journal= {arXiv preprint arXiv:2607.13703},
  year   = {2026}
}