1. Please check out our new paper if interested: RoboCook: Long-Horizon Elasto-Plastic Object Manipulation with Diverse Tools
   • Website
   • Code
   • Paper
2. Please see the dev branch for an improved version of the RoboCraft codebase, especially if you're interested in real-world experiments!

If you use the codebase in your research, please cite:

@article{shi2022robocraft,
  title={RoboCraft: Learning to See, Simulate, and Shape Elasto-Plastic Objects with Graph Networks},
  author={Shi, Haochen and Xu, Huazhe and Huang, Zhiao and Li, Yunzhu and Wu, Jiajun},
  journal={arXiv preprint arXiv:2205.02909},
  year={2022}
}

This is the codebase of RoboCraft in the simulator.

Project Page | Paper

• Linux or macOS (Tested on Ubuntu 20.04)
• Python 3
• CPU or NVIDIA GPU + CUDA CuDNN
• Conda

# clone the repo
git clone https://github.com/hshi74/RoboCraft.git
cd RoboCraft

# create the conda environment
conda env create -f robocraft.yml
conda activate robocraft

# install requirements for the simulator
cd simulator
pip install -e .

• Run all the blocks in simulator/plb/algorithms/test_tasks.ipynb. Note that it is easier to use ipython notebook when dealing with Taichi env for fast materialization.
• You can control the number of videos to generate by changing the variable n_vid. The default is 5 for the purpose of debugging. We used 50 in the paper.

• cd simulator/plb/algorithms
• Go to line 598 in sample_data.py and replace the string with the output folder name you just generated in simulator/dataset
• Run python sample_data.py. Note that this step may take a while.

• You will need to remove the old dataset if you want to update the dataset

cd ../../../robocraft
bash scripts/utils/move_data.sh ngrip_fixed sample_ngrip_fixed_[timestamp of the folder you just generated]

bash scripts/dynamics/train.sh

• Go to line 6 in robocraft/scripts/control/control.sh and change the model path to the path to the checkpoint you just trained.
• bash scripts/control/control.sh

• The simulator folder contains the simulation environment we used for data collection and particle sampling.
• The robocraft folder contains the code for learning the GNN and planning.