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When Transformer Meets Robotic Grasping: Exploits Context for Efficient Grasping Detection

PyTorch implementation of paper "When Transformer Meets Robotic Grasping: Exploits Context for Efficient Grasping Detection"

Visualization of the architecture

This code was developed with Python 3.6 on Ubuntu 16.04. Python requirements can installed by:

pip install -r requirements.txt

Datasets

Currently, both the Cornell Grasping Dataset, Jacquard Dataset , and GraspNet 1Billion are supported.

Cornell Grasping Dataset

1. Download the and extract Cornell Grasping Dataset.

Jacquard Dataset

1. Download and extract the Jacquard Dataset.

GraspNet 1Billion dataset

1. The dataset can be downloaded here.

2. Install graspnetAPI following here.

   pip install graspnetAPI
   

3. We use the setting in here

Training

Training is done by the main.py script.

Some basic examples:

# Train  on Cornell Dataset
python main.py   --dataset cornell

# k-fold training
python main_k_fold.py  --dataset cornell 

#  GraspNet 1
python main_grasp_1b.py 

Trained models are saved in output/models by default, with the validation score appended.

Visualize

Some basic examples:

# visulaize grasp rectangles
python visualise_grasp_rectangle.py   --network your network address

# visulaize heatmaps
python visulaize_heatmaps.py  --network your network address

Running on a Robot

Our ROS implementation for running the grasping system see https://github.com/USTC-ICR/SimGrasp/tree/main/SimGrasp.

The original implementation for running experiments on a Kinva Mico arm can be found in the repository https://github.com/dougsm/ggcnn_kinova_grasping.

Acknowledgement

Code heavily inspired and modified from https://github.com/dougsm/ggcnn

If you find this helpful, please cite

@ARTICLE{9810182,
  author={Wang, Shaochen and Zhou, Zhangli and Kan, Zhen},
  journal={IEEE Robotics and Automation Letters}, 
  title={When Transformer Meets Robotic Grasping: Exploits Context for Efficient Grasp Detection}, 
  year={2022},
  volume={},
  number={},
  pages={1-8},
  doi={10.1109/LRA.2022.3187261}}