DATT: Deep Adaptive Trajectory Tracking for Quadrotor Control

Kevin Huang, Rwik Rana, Alexander Spitzer, Guanya Shi, Byron Boots

Keywords: Quadrotor, Reinforcement Learning, Adaptive Control

Abstract: Precise arbitrary trajectory tracking for quadrotors is challenging due to unknown nonlinear dynamics, trajectory infeasibility, and actuation limits. To tackle these challenges, we present 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 $\mathcal{L}_1$ 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 3.2ms, less than 1/4 of the adaptive nonlinear model predictive control baseline.

Student First Author: yes

Instructions: I have read the instructions for authors (https://corl2023.org/instructions-for-authors/)

Website: https://sites.google.com/view/deep-adaptive-traj-tracking

Code: https://github.com/KevinHuang8/DATT

TL;DR: DATT enables quadrotors to precisely track arbitrary, potentially infeasible trajectories in the presence of large disturbances.