Learning over Subgoals for Efficient Navigation of Structured, Unknown Environments
Abstract
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.
Cite
Text
Stein et al. "Learning over Subgoals for Efficient Navigation of Structured, Unknown Environments." Proceedings of The 2nd Conference on Robot Learning, 2018.Markdown
[Stein et al. "Learning over Subgoals for Efficient Navigation of Structured, Unknown Environments." Proceedings of The 2nd Conference on Robot Learning, 2018.](https://mlanthology.org/corl/2018/stein2018corl-learning/)BibTeX
@inproceedings{stein2018corl-learning,
title = {{Learning over Subgoals for Efficient Navigation of Structured, Unknown Environments}},
author = {Stein, Gregory J. and Bradley, Christopher and Roy, Nicholas},
booktitle = {Proceedings of The 2nd Conference on Robot Learning},
year = {2018},
pages = {213-222},
volume = {87},
url = {https://mlanthology.org/corl/2018/stein2018corl-learning/}
}