  Precise arbitrary trajectory tracking for quadrotors is challenging due to
unknown nonlinear dynamics, trajectory infeasibility, and actuation limits. To
tackle these challenges, we present Deep Adaptive Trajectory Tracking (DATT), a
learning-based approach that can precisely track arbitrary, potentially
infeasible trajectories in the presence of large disturbances in the real
world. DATT builds on a novel feedforward-feedback-adaptive control structure
trained in simulation using reinforcement learning. When deployed on real
hardware, DATT is augmented with a disturbance estimator using L1 adaptive
control in closed-loop, without any fine-tuning. DATT significantly outperforms
competitive adaptive nonlinear and model predictive controllers for both
feasible smooth and infeasible trajectories in unsteady wind fields, including
challenging scenarios where baselines completely fail. Moreover, DATT can
efficiently run online with an inference time less than 3.2 ms, less than 1/4
of the adaptive nonlinear model predictive control baseline
