Learning to Plan with Uncertain Topological Maps

This repository contains code for paper Learning to Plan with Uncertain Topological Maps ECCV 2020 (Spotlight)

Requirements
Dataset
Training
Pretrained model
Citation

Requirements:

pip install -R requirements.txt

Dataset

The dataset is make available on Zenodo https://zenodo.org/record/4003445 The dataset is 4.8 GB in size

mkdir data && cd data
wget -O graph_data3_distance_weights.gz  https://zenodo.org/record/4003445/files/file.data?download=1 
tar -xvzf graph_data3_distance_weights.gz

Training

Please use the following hyperparameters for training:

python train.py --data_path data/graph_data3_distance_weights/train --hidden_size 256 --batch_size 32 --max_grad_norm 2 --weight_decay 0.0001 --lr 0.001 --schedule 120 --n_steps 6 --data_limit 74000 --use_weights --normalize --gru_depth 2 --save_model --bound_update --new_bound_net --store_hidden --use_probs --use_features

Pretrained model

The pretrained model can be found in models/best_model.pth

Citation

If you find this useful, consider citing the following:

@inproceedings{beeching2020learntoplan,
  title={Learning to plan with uncertain topological maps.
  },
  author={Beeching, Edward and Dibangoye, Jilles and 
          Simonin, Olivier and Wolf, Christian}
  booktitle={European Conference on Computer Vision},
  year={2020}}

learning_to_plan

Commits

Update README.md

Update README.md

created readme and code files