This repo contains the training & evaluation data used in the paper
Memory-Augmented Reinforcement Learning for Image-Goal Navigation [Paper]
The train (resp. evaluation) set is split in 72 (resp. 14) files corresponding to the scenes in Habitat. Each scene contains episodes of various difficulty (Easy, Medium Hard & Extra) depending on the start/goal distance. The histogram of number of trajectories with respect to start-goal distance per split is shown below.
If you use this data in your research, please consider citing the paper as follows
@article{mezghani2021memory,
title={Memory-augmented reinforcement learning for image-goal navigation},
author={Mezghani, Lina and Sukhbaatar, Sainbayar and Lavril, Thibaut and Maksymets, Oleksandr and Batra, Dhruv and Bojanowski, Piotr and Alahari, Karteek},
journal={arXiv preprint arXiv:2101.05181},
year={2021}
}
image-goal-nav-dataset is CC-BY-NC 4.0 licensed, as found in the LICENSE file.
Some information in the data may be derived from the Gibson dataset, which is available under the following license.