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arXiv · 2511.02060

TACO: Trajectory-Aware Controller Optimization for Quadrotors

Hersh Sanghvi·Spencer Folk·Vijay Kumar·Camillo Jose Taylor
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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-Aware Controller Optimization (TACO), a framework that adapts controller parameters online based on the upcoming reference trajectory and current quadrotor state. TACO 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. To 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 TACO 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 TACO significantly reduces the tracking error obtained by the quadrotor.

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Hersh Sanghvi, Spencer Folk, Vijay Kumar, Camillo Jose Taylor. 2025-11-03. TACO: Trajectory-Aware Controller Optimization for Quadrotors. https://arxiv.org/abs/2511.02060

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