Meng Li
·
Qi Zhao
·
Shuchang Lyu
·
Chunlei Wang
·
Yujing Ma
·
Guangliang Cheng
·
Chenguang Yang
This repo is the implementation of "OVGNet: An Unified Visual-Linguistic Framework for Open-Vocabulary Robotic Grasping". we refer to Vision-Language-Grasping, GroundingDINO, VL-Grasp. Many thanks to these excellent repos.
- Novel indicates the unseen objects in training.
- Base denotes the seen objects in training.
- Battery and power drill are novel classes, which belong to hard task.
- Apple and pear are base classes, which belong to simple task.
Grasping_demo.mp4
- OVGrasping follows GroundingDINO data format.
- The OVGrapsing dataset comprises 117 categories and 63,385 instances.
- Instances are sourced from three distinct origins: RoboRefIt, GraspNet, simulated environment.
- The dataset is divided into two categories: the base category consists 51,857 instances, and the novel category comprises 11,528 instances.
- Ubantu==18.04
- Python==3.9
- Torch==1.11, Torchvision==0.12.0
- CUDA==11.3
- checkpoint==OVGANet
- assets==assets
please add the assets into OVGNet folder
please ensure the CUDA version is 11.3
conda create -n OVGNet python=3.9
conda activate OVGNet
pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 torchaudio==0.11.0 --extra-index-url https://download.pytorch.org/whl/cu113
cd /OVGNet/
pip install -r requirments.txt
cd graspnet/graspnet/pointnet2
python setup.py install
cd graspnet/graspnet/knn
python setup.py install
cd groundingdino
pip install -e .
cd /OVGNet/
python test.py --testing_case_dir ./test_cases/simple/apple --pretrain ./checkpoint/OVGANet
cd /OVGNet/test_vg/
python test_vg.py --c ./config/cfg_odvg.py --datasets ./config/datasets_vg_example.json --pretrain_model_path OVGNet/checkpoint/OVGANet
@InProceedings{Li_2024_IROS,
author = {Li Meng and Zhao Qi and Lyu Shuchang and Wang Chunlei and Ma Yujing and Cheng Guangliang and Yang Chenguang},
title = {OVGNet: A Unified Visual-Linguistic Framework for Open-Vocabulary Robotic Grasping},
year = {2024},
eprint = {2407.13175},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2407.13175},
}