2023-12-02-huang23a.md

title	section	openreview	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
DATT: Deep Adaptive Trajectory Tracking for Quadrotor Control	Oral	XEw-cnNsr6	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.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	huang23a	0	DATT: Deep Adaptive Trajectory Tracking for Quadrotor Control	326	340	326-340	326	false	Huang, Kevin and Rana, Rwik and Spitzer, Alexander and Shi, Guanya and Boots, Byron	given family Kevin Huang given family Rwik Rana given family Alexander Spitzer given family Guanya Shi given family Byron Boots	2023-12-02		Proceedings of The 7th Conference on Robot Learning	229	inproceedings	date-parts 2023 12 2	https://proceedings.mlr.press/v229/huang23a/huang23a.pdf