Reservoir-Based Approaches for Low-Level Quadrotor Control

Abstract

Low-level quadrotor control is challenging due to nonlinear coupled dynamics, sensor noise, and sensitivity to unmodeled effects such as battery voltage sag or ground effect [4, 5, 6]. We investigate reservoir computing (RC) as a computationally lightweight learning-based alternative that preserves real-time feasibility by training only a linear readout layer [1, 2]. We develop a Betaflight-based simulation and logging pipeline that uses Betaflight [3] as a teacher controller to generate demonstration trajectories for reservoirbased policies mapping onboard-like observations to raw motor commands. We evaluate controllers using both offline imitation error and a closed-loop stabilization protocol in simulation. Our results show that explicit short-horizon representations (via buffered reservoir states) improve imitation accuracy and can enable stable closed-loop flight in multiple trials, highlighting RC as a practical candidate for embedded lowlevel control. Our results demonstrate that reservoir-based controllers can achieve stable closed-loop flight in simulation while maintaining extremely low computational complexity, suggesting RC as a practical lightweight alternative for embedded quadrotor control.