edbeeching / learning_to_plan

Code for paper "Learning to Plan with Uncertain Topological Maps"

Learning to Plan with Uncertain Topological Maps

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

Contents

• 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}}