Conditional Invertible Neural Networks for Data-Driven UAV Control: A 2-D Proof of Concept
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Abstract (arXiv)
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 st, ct) from an incremental nonlinear dynamic inversion (INDI) teacher using rational-quadratic spline coupling and invertible linear mixing. Open-loop reproduction reaches R2 = 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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References · 11
- 2016Density estimation using Real NVPcited by 2,042
- 2018Glow: Generative Flow with Invertible 1x1 Convolutionscited by 1,545
- 2019Neural Spline Flowscited by 596
- 2018Analyzing Inverse Problems with Invertible Neural Networkscited by 242
- 2023Reaching the Limit in Autonomous Racing: Optimal Control versus Reinforcement Learningcited by 111
- 2020Deep Drone Acrobaticscited by 75
- 2021Time-Optimal Planning for Quadrotor Waypoint Flightcited by 23
- 2021Model Predictive Contouring Control for Time-Optimal Quadrotor Flightcited by 21
- 2021Conditional Invertible Neural Networks for Medical Imagingcited by 19
- 2021Inverse design of two-dimensional materials with invertible neural networkscited by 15
- 2020Invertible Neural Networks for Uncertainty Quantification in Photoacoustic Imagingcited by 2
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