Awesome
GraspTransfer
This repository contains offical implementation of "Grasp Transfer based on Self-Aligning Implicit Representations of Local Surfaces."
Data Creation:
First, generate watertight meshes using following codebase : https://github.com/hjwdzh/Manifold. (You can check manifold_scripts under scripts folder.)
To generate SDF values for training data, we have used modified mesh_to_sdf library (https://github.com/fzaero/mesh_to_sdf). (You can check pre_process_meshes.py under scripts folder.)
Simulation of objects require meshes to be processed with v-hacd. (You can check vhacd.py under scripts folder.)
Installation
Setting up the environment
Using Conda or virtual environment is recommended. Virtual environments allows easier integration with ROS.
First setup pytorch. Then use the following command to install other packages.
pip install -r /path/to/requirements.txt
Using the code
Easy way to use the code is to source setup_env.bash file from within the environment, then put the following code into begining of your script/notebook.
import sys
import os
grasp_transfer_path = os.getenv("GRASP_TRANSFER_SOURCE_DIR")
sys.path.insert(0,grasp_transfer_path)
Please check PCD_Grasp_Transfer_Examples_Real notebook for quick start.