2018-10-23-stein18a.md

title	abstract	keywords	layout	series	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	publisher	container-title	volume	genre	issued	pdf	extras
Learning over Subgoals for Efficient Navigation of Structured, Unknown Environments	We propose a novel technique for efficiently navigating unknown environments over long horizons by learning to predict properties of unknown space. We generate a dynamic action set defined by the current map, factor the Bellman Equation in terms of these actions, and estimate terms, such as the probability that navigating beyond a particular subgoal will lead to a dead-end, that are otherwise difficult to compute. Simulated agents navigating with our Learned Subgoal Planner in real-world floor plans demonstrate a 21% expected decrease in cost-to-go compared to standard optimistic planning techniques that rely on Dijkstra’s algorithm, and real-world agents show promising navigation performance as well.	Navigation, POMDP, Deep Learning	inproceedings	Proceedings of Machine Learning Research	stein18a	0	Learning over Subgoals for Efficient Navigation of Structured, Unknown Environments	213	222	213-222	213	false	Stein, Gregory J. and Bradley, Christopher and Roy, Nicholas	given family Gregory J. Stein given family Christopher Bradley given family Nicholas Roy	2018-10-23		PMLR	Proceedings of The 2nd Conference on Robot Learning	87	inproceedings	date-parts 2018 10 23	http://proceedings.mlr.press/v87/stein18a/stein18a.pdf	label link Supplementary video https://youtu.be/hmJ-LON5mQc