2024-06-11-jeong24a.md

title	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
Parameterized fast and safe tracking (FaSTrack) using DeepReach	Fast and Safe Tracking (FaSTrack) is a modular framework that provides safety guarantees while planning and executing trajectories in real time via value functions of Hamilton-Jacobi (HJ) reachability. These value functions are computed through dynamic programming, which is notorious for being computationally inefficient. Moreover, the resulting trajectory does not adapt online to the environment, such as sudden disturbances or obstacles. DeepReach is a scalable deep learning method to HJ reachability that allows parameterization of states, which opens up possibilities for online adaptation to various controls and disturbances. In this paper, we propose Parametric FaSTrack, which uses DeepReach to approximate a value function that parameterizes the control bounds of the planning model. The new framework can smoothly trade off between the navigation speed and the tracking error (therefore maneuverability) while guaranteeing obstacle avoidance in a priori unknown environments. We demonstrate our method through two examples and a benchmark comparison with existing methods, showing the safety, efficiency, and faster solution times of the framework.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	jeong24a	0	Parameterized fast and safe tracking ({FaSTrack}) using {DeepReach}	1006	1017	1006-1017	1006	false	Jeong, Hyun Joe and Gong, Zheng and Bansal, Somil and Herbert, Sylvia	given family Hyun Joe Jeong given family Zheng Gong given family Somil Bansal given family Sylvia Herbert	2024-06-11		Proceedings of the 6th Annual Learning for Dynamics & Control Conference	242	inproceedings	date-parts 2024 6 11	https://proceedings.mlr.press/v242/jeong24a/jeong24a.pdf