TACO: Trajectory-Aware Controller Optimization for Quadrotors

Abstract-- Controller performance in quadrotor trajectory tracking depends heavily on parameter tuning, yet standard approaches often rely on fixed, manually tuned parameters that sacrifice task-specific performance. We present Trajectory-A ware Controller Optimization (T ACO), a framework that adapts controller parameters online based on the upcoming reference trajectory and current quadrotor state. T ACO employs a learned predictive model and a lightweight optimization scheme to optimize controller gains in real time with respect to a broad class of trajectories, and can also be used to adapt trajectories to improve dynamic feasibility while respecting smoothness constraints. T o enable large-scale training, we also introduce a parallelized quadrotor simulator supporting fast data collection on diverse trajectories. Experiments on a variety of trajectory types show that T ACO outperforms conventional, static parameter tuning while operating orders of magnitude faster than black-box optimization baselines, enabling practical real-time deployment on a physical quadrotor . Furthermore, we show that adapting trajectories using T ACO significantly reduces the tracking error obtained by the quadrotor .