You can test whether isaacgym can be used.

June 17, 2025 · View on GitHub

AffordDP: Generalizable Diffusion Policy with Transferable Affordance

Project Page  |  arXiv  |  Paper

Table of Contents

⚙️ Setup

# Create your python env
conda create -n afforddp python=3.8
conda activate afforddp
# Install torch
pip install torch==2.4.1 torchvision==0.19.1 torchaudio==2.4.1 --index-url https://download.pytorch.org/whl/cu118 
# Install Xformers, note the version compatibility with torch.
pip install -U xformers==0.0.28.post1 --index-url https://download.pytorch.org/whl/cu118
# Other package
pip install -r requirements.txt

Install Pytorch3D

pip install "git+https://github.com/facebookresearch/pytorch3d.git"

Install cuRobo

cd third_party
cd curobo
pip install -e . --no-build-isolation

Install GroundedSAM

cd third_party
cd GroundedSAM
pip install -e GroundingDINO
pip install -e segment_anything
# Pretrained model weight
cd ../..
wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth -P assets/ckpts/
wget https://github.com/IDEA-Research/GroundingDINO/releases/download/v0.1.0-alpha/groundingdino_swint_ogc.pth -P assets/ckpts/

Install Point_SAM

cd third_party
cd Point_SAM
# Install torkit3d
pip install third_party/torkit3d
# Install apex
pip install -v --disable-pip-version-check --no-cache-dir --no-build-isolation --config-settings "--build-option=--cpp_ext" --config-settings "--build-option=--cuda_ext" third_party/apex

Install IsaacGym

tar -zxvf IsaacGym_Preview_4_Package.tar.gz
cd isaacgym/python
pip install -e .
# You can test whether isaacgym can be used.
cd examples
python joint_monkey.py

📦 Asset Prepartion

You need to prepare the gapartnet assets. For download instructions, please follow this link. Put them to asset/partnet_mobility_part.

assets/
├── partnet_mobility_part/
│ ├── 4108/
│ ├── 7119/
│ ├── 7120/
│ ├── ...

🛠️ Quick Start

Expert Demonstration Collection

You could generate demonstrations by yourself using our provided expert policies. Generated demonstrations are under $YOUR_DATA_SAVE_PATH. Default save path is record.

python collect_demonstrations.py --save_dir $YOUR_DATA_SAVE_PATH --object_id $GAPartNet_obj_id --part_id $Manip_Part_id

By this way, you will be able to collect expert trajectories for specific parts of an object. After collection, you need to process these datasets.

python process_data.py --data_dir $YOUR_DATA_SAVE_PATH --save_dir $PROCESS_DATA_SAVE_PATH

The data processing script will convert all collected data into zarr format and save it to your specified directory. Default save path is data.

Policy Training

You need to modify the configuration parameters in afforddp/config/task/PullDrawer.yaml. Set zarr_path to your custom data path

dataset:
  _target_: afforddp.dataset.Cabinet_afford_dataset.CabinetManipAffordDataset
  zarr_path: your/custom/path/to/data.zarr
  horizon: ${horizon}
  pad_before: ${eval:'${n_obs_steps}-1'}
  pad_after: ${eval:'${n_action_steps}-1'}
  seed: 42
  val_ratio: 0.00
  max_train_episodes: 90

sh train.sh ${seed} ${cuda_id}

Policy Evaluation

sh eval.sh ${ckpt_path} ${object_id}

Affordance Transfer Demo

Before running this demo, you must collect and process the required data. Please follow this.

python demo.py

Acknowledgement

Our code is generally built upon: Diffusion Policy, DP3, RAM, GAPartNet. We thank all these authors for their nicely open sourced code and their great contributions to the community.

📚 Citation

If you find our work useful, please consider citing:

@inproceedings{wu2025afforddp,
  title={Afforddp: Generalizable diffusion policy with transferable affordance},
  author={Wu, Shijie and Zhu, Yihang and Huang, Yunao and Zhu, Kaizhen and Gu, Jiayuan and Yu, Jingyi and Shi, Ye and Wang, Jingya},
  booktitle={Proceedings of the Computer Vision and Pattern Recognition Conference},
  pages={6971--6980},
  year={2025}
}