GAMMA: Generalizable Articulation Modeling and Manipulation for Articulated Objects

Overview

This repository contains the implementation of the paper GAMMA: Generalizable Articulation Modeling and Manipulation for Articulated Objects

About this repository

data_generation            # contains  data_generation code for the project
datasets/                  # contains  dataloader code for the project
example_data/              # contains  example data for the project
visual_model/              # contains  code for the model

Articulation data and affordance data

The code for generating articulation and affordance data will be released later.

1. Install dependencies

This code has been tested on Ubuntu 20.04 with Cuda 11.1, Python 3.6, and PyTorch 1.8.1.

conda create -n GAMMA python=3.8
conda activate GAMMA
pip install sapien==2.2.2
pip install torch==1.8.1+cu111 torchvision==0.9.1+cu111 torchaudio==0.8.1 -f https://download.pytorch.org/whl/torch_stable.html
pip install -r requirements.txt

The backbone depends on PointNet++.

git clone --recursive https://github.com/erikwijmans/Pointnet2_PyTorch
cd Pointnet2_PyTorch
# [IMPORTANT] comment these two lines of code:
#   https://github.com/erikwijmans/Pointnet2_PyTorch/blob/master/pointnet2_ops_lib/pointnet2_ops/_ext-src/src/sampling_gpu.cu#L100-L101
pip install -r requirements.txt
pip install -e .

2. Train the model

 python train_model.py --batch_size 16 --train_data_path=<PATH_CONFIG_OPTION>  --test_data_path=<PATH_CONFIG_OPTION> 

3. Evaluation the model

  python train_model.py  --train 0  --batch_size 1 --test_data_path=<PATH_CONFIG_OPTION> 

4. Inference and Visualization

 python inference_demo.py

Citing

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

@article{yu2024gamma,
  title={GAMMA: Generalizable Articulation Modeling and Manipulation for Articulated Objects},
  author={Yu, Qiaojun and Wang, Junbo and Liu, Wenhai and Hao, Ce and Liu, Liu and Shao, Lin and Wang, Weiming and Lu, Cewu},
  booktitle={2024 International Conference on Robotics and Automation (ICRA)},
  year={2024},
  organization={IEEE},
}