This is a repository for the following paper:
Ryo Yonetani*, Tatsunori Taniai*, Mohammadamin Barekatain, Mai Nishimura, Asako Kanezaki, "Path Planning using Neural A* Search", ICML, 2021 [paper] [project page]
Neural A* is a novel data-driven search-based planner that consists of a trainable encoder and a differentiable version of A* search algorithm called differentiable A* module. Neural A* learns from demonstrations to improve the trade-off between search optimality and efficiency in path planning and also to enable the planning directly on raw image inputs.
A* search | Neural A* search |
---|---|
- This branch presents a minimal example for training and evaluating Neural A* on shortest path problems.
- For reproducing experiments in our ICML'21 paper, please refer to icml2021 branch.
- For creating datasets used in our experiments, please visit planning datasets repository.
- The code has been tested on Ubuntu 18.04.5 LTS.
- Try Neural A* on Google Colab!
- See also
docker-compose.yml
anddocker/Dockerfile
to reproduce our environment.
# ICML2021 version
@InProceedings{pmlr-v139-yonetani21a,
title = {Path Planning using Neural A* Search},
author = {Ryo Yonetani and
Tatsunori Taniai and
Mohammadamin Barekatain and
Mai Nishimura and
Asako Kanezaki},
booktitle = {Proceedings of the 38th International Conference on Machine Learning},
pages = {12029--12039},
year = {2021},
editor = {Meila, Marina and Zhang, Tong},
volume = {139},
series = {Proceedings of Machine Learning Research},
month = {18--24 Jul},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v139/yonetani21a/yonetani21a.pdf},
url = {http://proceedings.mlr.press/v139/yonetani21a.html},
}
# arXiv version
@article{DBLP:journals/corr/abs-2009-07476,
author = {Ryo Yonetani and
Tatsunori Taniai and
Mohammadamin Barekatain and
Mai Nishimura and
Asako Kanezaki},
title = {Path Planning using Neural A* Search},
journal = {CoRR},
volume = {abs/2009.07476},
year = {2020},
url = {https://arxiv.org/abs/2009.07476},
archivePrefix = {arXiv},
eprint = {2009.07476},
timestamp = {Wed, 23 Sep 2020 15:51:46 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2009-07476.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
This repository includes some code from RLAgent/gated-path-planning-networks [1] with permission of the authors and from martius-lab/blackbox-backprop [2].