irosa-lab / neugraspnet

NeuGraspNet: Learning Any-View 6DoF Robotic Grasping in Cluttered Scenes via Neural Surface Rendering

NeuGraspNet: Learning Any-View 6DoF Robotic Grasping in Cluttered Scenes via Neural Surface Rendering

Authors: Snehal Jauhri, Ishikaa Lunawat, and Georgia Chalvatzaki
Institution: PEARL Lab, TU Darmstadt, Germany
Published at: Robotics: Science and Systems, 2024

Project Site: https://sites.google.com/view/neugraspnet
Paper: https://arxiv.org/pdf/2306.07392

Release timeline:

• 11th July 2024: Initial release with pre-trained weights and simulated grasping demos
• August 2024: Dataset generation and training
• September 2024: ROS package and improvements for efficiency

Installation

Tested on Ubuntu 20.04 with an NVIDIA GPU (Recommended 8GB GPU VRAM or higher)

With environment.yml:

• Create a conda environment using the provided environment.yml file:

  cd <this repo>
  conda env create -f environment.yml
  

Or with manual conda installation:

• Create a new conda environment with Python 3.8 or higher:

  conda create --name neugraspnet python=3.8 
  

• Install requirements:
  (Due to compatibility issues with newer versions of open3d, sklearn installation needs to be enabled:)

  conda activate neugraspnet
  export SKLEARN_ALLOW_DEPRECATED_SKLEARN_PACKAGE_INSTALL=True
  pip install -r requirements.txt
  

• Install torch-scatter based on pytorch version and cuda version (https://github.com/rusty1s/pytorch_scatter). For example:

  pip install torch==1.13.0 torch-scatter==2.1.0 torchvision==0.14.0 -f https://data.pyg.org/whl/torch-1.13.0+cu117.html
  

• Install the neugraspnet package:

  cd <this repo>
  pip install -e .
  

• Build the conv_occupancy_network dependency:

  python neugraspnet/scripts/convonet_setup.py build_ext --inplace
  

Run:

• To run evaluations on the pile object dataset from VGN, run:

  cd neugraspnet/neugraspnet
  python -u scripts/test/sim_grasp_multiple.py --num-view 1 --object_set pile/test --scene pile --num-rounds 100 --model ./data/networks/neugraspnet_pile_efficient.pt --resolution=64 --type neu_grasp_pn_deeper_efficient --qual-th 0.5 --max_grasp_queries_at_once 40 --result-path ./data/results/neu_grasp_pile_efficient --sim-gui
  

  Modify the max_grasp_queries_at_once command line arguement based on your available GPU memory. (For eg. If using an RTX 3090, use max_grasp_queries_at_once= 40 or 60)
• To run evaluations on the egad object dataset (https://dougsm.github.io/egad/), run:

  python -u scripts/test/sim_grasp_multiple.py --num-view 1 --object_set egad --scene egad --num-rounds 100 --model ./data/networks/neugraspnet_pile_efficient.pt --resolution=64 --type neu_grasp_pn_deeper_efficient --qual-th 0.5 --max_grasp_queries_at_once 40 --result-path ./data/results/neu_grasp_egad_efficient --sim-gui
  

Acknowledgements:

• GPG (https://github.com/atenpas/gpg)
• Convolutional Occupancy Networks (https://github.com/autonomousvision/convolutional_occupancy_networks)
• UNISURF (https://github.com/autonomousvision/unisurf)
• GIGA (https://github.com/UT-Austin-RPL/GIGA)
• Edge-Grasp-Network (https://github.com/HaojHuang/Edge-Grasp-Network)
• VGN (https://github.com/ethz-asl/vgn)

neugraspnet's Issues

Avalibilty of Dataset.\

Thanks for the interesting work. Could you also share the dataset described in Sec IV. B, which supports Grasp Affordance Prediction on 3D AffordanceNet.