cgn-pytorch 0.4.3

Pytorch implementation of Contact Graspnet

Contact-GraspNet Pytorch

Pytorch implementation of Contact-GraspNet. This repo is based heavily on https://github.com/alinasarmiento/pytorch_contactnet. Original Tensorflow implementation can be found at: https://github.com/NVlabs/contact_graspnet

Installation Instructions

pip install cgn-pytorch

Usage:

import cgn_pytorch

cgn_model, optimizer, config_dict = cgn_pytorch.from_pretrained(cpu=False)

Run The Demo

Clone this Repo

git clone https://github.com/sebbyjp/cgn_pytorch.git

Install Dependencies

pip3 install -r requirements.txt

Vizualization

We're doing our visualizations in MeshCat. In a separate tab, start a meshcat server with meshcat-server

From here you can run python3 eval.py

To visualize different confidence threshold masks on the grasps, use the --threshold argument such as --threshold=0.8

To visualize different cluttered scenes (8-12 tabletop objects rendered in pyrender from ACRONYM), use the argument --scene and manually feed in a file name such as --scene=002330.npz.Your possible files are:

• 002330.npz
• 004086.npz
• 005274.npz

Predicting grasps on your own pointclouds

The model should work on any pointcloud of shape (Nx3). For most consistent results, please make sure to put the pointcloud in the world frame and center it by subtracting the mean. Do not normalize the pointcloud to a unit sphere or unit box, as "graspability" naturally changes depending on the size of the objects (so we don't want to lose that information about the scene by scaling it).