Foldsformer: Learning Sequential Multi-Step Cloth Manipulation with Space-Time Attention

Kai Mo, Chongkun Xia, Xueqian Wang, Yuhong Deng, Xuehai Gao, Bin Liang

Tsinghua University

This repository is a PyTorch implementation of the paper "Foldsformer: Learning Sequential Multi-Step Cloth Manipulation with Space-Time Attention", published in IEEE RA-L.

Website | IEEE Manuscript | ArXiv

If you find this code useful in your research, please consider citing:

@ARTICLE{mo2022foldsformer,  
    author={Mo, Kai and Xia, Chongkun and Wang, Xueqian and Deng, Yuhong and Gao, Xuehai and Liang, Bin},  
    journal={IEEE Robotics and Automation Letters},   
    title={Foldsformer: Learning Sequential Multi-Step Cloth Manipulation With Space-Time Attention},   
    year={2023},  
    volume={8},  
    number={2},  
    pages={760-767},  
    doi={10.1109/LRA.2022.3229573}
}

Table of Contents

• Installation
• Generate Data
• Train Foldsformer
• Evaluate Foldsformer

Installation

This simulation environment is based on SoftGym. You can follow the instructions in SoftGym to setup the simulator.

1. Clone this repository.

2. Follow the SoftGym to create a conda environment and install PyFlex. A nice blog written by Daniel Seita may help you get started on SoftGym.

3. Install the following packages in the created conda environment:

   • pytorch and torchvision: pip install torchvision or conda install torchvision -c pytorch
   • einops: pip install einops
   • tqdm: pip install tqdm
   • yaml: pip install PyYaml

4. Before you use the code, you should make sure the conda environment activated(conda activate softgym) and set up the paths appropriately:

   export PYFLEXROOT=${PWD}/PyFlex
   export PYTHONPATH=${PYFLEXROOT}/bindings/build:$PYTHONPATH
   export LD_LIBRARY_PATH=${PYFLEXROOT}/external/SDL2-2.0.4/lib/x64:$LD_LIBRARY_PATH
   

   The provided script prepare_1.0.sh includes these commands above.

Generate Data

• Generate initial configurations:

  python generate_configs.py --num_cached 1000 --cloth_type random
  python generate_configs.py --num_cached 100 --cloth_type square
  python generate_configs.py --num_cached 100 --cloth_type rectangle
  

  where --num_cached specifies the number of configurations to be generated, and --cloth_type specifies the cloth type (square | rectangle | random). These generated initial configurations will be saved in cached configs/

• Generate trajectories by random actions:

  python generate_random.py --gui --corner_bias --img_size 224 --cached random1000 --horizon 8
  

  where --img_size specifies the image size captured by the camera in the simulator, --cached specifies the filename of the cached configurations, and --horizon specifies the number of actions in a trajectory. You can remove --gui to run headless and remove --corner_bias to pick the cloth uniformly instead of picking the corners. These generated trajectories will be saved in data/random/corner bias and data/random/random.

• Generate expert demonstrations:

  python generate_demonstrations.py --gui --task DoubleTriangle --img_size 224 --cached square100
  python generate_demonstrations.py --gui --task DoubleStraight --img_size 224 --cached rectangle100
  python generate_demonstrations.py --gui --task AllCornersInward --img_size 224 --cached square100
  python generate_demonstrations.py --gui --task CornersEdgesInward --img_size 224 --cached square100
  

  where --task specifies the task name, --img_size specifies the image size captured by the camera in the simulator, and --cached specifies the filename of the cached configurations, and . You can remove --gui to run headless. These generated demonstrations will be saved in data/demonstrations.

  Demonstrator/demonstrator.py includes the scripted demonstrator by accessing the ground truth position of each particle.

Train Foldsformer

• Preprocess the data (split each long trajectory into sub-trajectories):

  python utils/prepare_data_list.py 
  

• Set up the model, optimizer and other details in train/train configs/train.yaml.

• Train Foldsformer:

  python train.py --config_path train
  

  where --config_path specifies the yaml configuration filename in train/train configs/.

Evaluate Foldsformer

• Download the evaluation set and model weights:

  • Download the evaluation initial configurations, and then put them in cached configs/.
  • Download the Foldsformer weights, and then put it in train/trained model/Foldsformer/
  • Download the demonstration sub-goals, and then extract them in data/demo/

• Evaluate Foldsformer by running:

  python eval.py --gui --task DoubleTriangle --model_config train --model_file foldsformer_eval --cached square
  python eval.py --gui --task DoubleStraight --model_config train --model_file foldsformer_eval --cached rectangle
  python eval.py --gui --task AllCornersInward --model_config train --model_file foldsformer_eval --cached square
  python eval.py --gui --task CornersEdgesInward --model_confilg train --model_file foldsformer_eval --cached square
  

  The evaluation results are saved in eval result/.

If you have any questions, please feel free to contact me via mok21@tsinghua.org.cn. (~~mok21@mails.tsinghua.edu.cn~~)