/IndusGrasp

This is the official implementation of our paper: "A Novel Robotic Grasp Detection Framework Using Low-Cost RGB-D Camera for Industrial Bin Picking."

Primary LanguagePython

IndusGrasp

This is the official PyTorch Implementation of IndusGrasp: A Novel Robotic Grasp Detection Method Using Synthetic Data for Disordered Industrial Scenarios

Video

  • The video is available in YouTube

The pipeline:


  • The Comparison studies in the real-world environment

Requirements: Software

sudo apt-get install libglfw3-dev libglfw3  

sudo apt-get install libassimp-dev   

pip install --pre --upgrade PyOpenGL PyOpenGL_accelerate 

pip install  cython cyglfw3 pyassimp==3.3 imgaug progressbar

pip install  -r requments.txt`

Generate dataset

  • Using the tools/pybullet_dataset/pybullet_dataset.py to create render poses with your own 3D mesh(xx.urdf)

  • Using the tools/creat_grasp.py to create grasp representation with your own 3D mesh(xx.ply)

  • Using the dataset_generate.py to create your dataset

Train the IndusGrasp

python train.py

Test the IndusGrasp

python test.py

Comparison Studies

  • Our Method in dense scenarios


  • GR-CNN, in sparse scenarios


  • GraspNet, which doesn't work for industrial parts


Generalization Studies

  • Our method can generate to unseen objects with different shapes and textures (Note: the network is only trained by Synthetic Data generated by object 1. )