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https://github.com/yxKryptonite/RAM_code
Official implementation of RAM: Retrieval-Based Affordance Transfer for Generalizable Zero-Shot Robotic Manipulation
https://github.com/yxKryptonite/RAM_code
Last synced: about 2 months ago
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Official implementation of RAM: Retrieval-Based Affordance Transfer for Generalizable Zero-Shot Robotic Manipulation
- Host: GitHub
- URL: https://github.com/yxKryptonite/RAM_code
- Owner: yxKryptonite
- License: other
- Created: 2024-07-06T17:02:23.000Z (6 months ago)
- Default Branch: release
- Last Pushed: 2024-09-17T14:13:20.000Z (3 months ago)
- Last Synced: 2024-09-17T17:45:11.398Z (3 months ago)
- Language: Python
- Homepage: https://yxkryptonite.github.io/RAM/
- Size: 16.2 MB
- Stars: 29
- Watchers: 3
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- Awesome-Video-Robotic-Papers - Code
README
RAM: Retrieval-Based Affordance Transfer for Generalizable Zero-Shot Robotic Manipulation
CoRL 2024 (Oral Presentation)
This is the official code release of [RAM: Retrieval-Based Affordance Transfer for Generalizable Zero-Shot Robotic Manipulation](https://arxiv.org/abs/2407.04689).
**[[paper]](https://arxiv.org/abs/2407.04689) [[project]](https://yxkryptonite.github.io/RAM/) [[code]](https://github.com/yxKryptonite/RAM_code)**
## Installation
1. Create conda environment and install pytorch
This code is tested on Python 3.8.19 on Ubuntu 20.04, with PyTorch 2.0.1+cu118:
```
conda create -n ram python=3.8
conda activate ram
# pytorch 2.0.1 with cuda 11.8
pip install torch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2 --index-url https://download.pytorch.org/whl/cu118
```
2. Grounded-SAM
Install dependencies and download the checkpoints:
```
pip install -e vision/GroundedSAM/GroundingDINO
pip install -e vision/GroundedSAM/segment_anything
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/
```
3. GSNet
First, download the pretrained [checkpoints](https://drive.google.com/drive/folders/1iYTIxsLIvXOaYYj4SVxWYT47fpPGhgpm?usp=sharing) and put the `.tar` file into `assets/ckpts/`. We use `minkuresunet_kinect.tar` by default.
```
# MinkowskiEngine, this may take a while
git clone [email protected]:NVIDIA/MinkowskiEngine.git
cd MinkowskiEngine
conda install openblas-devel -c anaconda
python setup.py install --blas_include_dirs=${CONDA_PREFIX}/include --blas=openblas
## pointnet2 & graspnetAPI
cd graspness_implementation
pip install -r requirements.txt
cd pointnet2
python setup.py install
cd ..
cd graspnetAPI
pip install .
pip install "numpy<1.24"
pip install pytorch-utils
```
If you want to use close-sourced [AnyGrasp](https://github.com/graspnet/anygrasp_sdk) as an alternative, please follow [anygrasp_sdk](https://github.com/graspnet/anygrasp_sdk) to setup the SDK and put the `checkpoint_detection.tar` checkpoint to `assets/ckpts/`. And `gsnet.so`, `lib_cxx.so`, and `license/` should be in the project root directory.
4. pointnet2_ops
```
# this may take a while
git clone [email protected]:erikwijmans/Pointnet2_PyTorch.git
cd Pointnet2_PyTorch/pointnet2_ops_lib
pip install -e .
```
5. Other requirements
```
pip install -r requirements.txt
```
6. (Optional) Retrieval data
If you want to use the retrieval pipeline, please download the retrieval data from [Google Drive](https://drive.google.com/file/d/1Ta4MJioAlvrQiczQo-8A-PELRynhsHs1/view?usp=sharing) and unzip the data to `assets/data/`.
## Inference and Visualization
Run commands below to run the demo:
```bash
export PYTHONPATH=$PWD
python run_realworld/run.py --config configs/drawer_open.yaml # add --retrieve to enable retrieval
```
After finished, you shall see printed 3D affordance results w/ grasp and visualization at `run_realworld/gym_outputs/drawer_open/` like below:
## TODO
- [x] Release the method code and demo.
- [x] Release the retrieval pipeline and data.
- [ ] More to come... (Feel free to open issues and PRs!)
**Please stay tuned for any updates of the dataset and code!**
## Acknowledgments
We thank authors of [dift](https://github.com/Tsingularity/dift), [GeoAware-SC](https://github.com/Junyi42/geoaware-sc), [graspness_implementation](https://github.com/rhett-chen/graspness_implementation) and [Grounded-Segment-Anything](https://github.com/IDEA-Research/Grounded-Segment-Anything) for their great work and open-source spirit!
## Citation
If you find this work helpful, please consider citing:
```
@article{kuang2024ram,
title={RAM: Retrieval-Based Affordance Transfer for Generalizable Zero-Shot Robotic Manipulation},
author={Kuang, Yuxuan and Ye, Junjie and Geng, Haoran and Mao, Jiageng and Deng, Congyue and Guibas, Leonidas and Wang, He and Wang, Yue},
journal={arXiv preprint arXiv:2407.04689},
year={2024}
}
```