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Learning Particle Dynamics for Manipulating Rigid Bodies, Deformable Objects, and Fluids

Yunzhu Li, Jiajun Wu, Russ Tedrake, Joshua B. Tenenbaum, Antonio Torralba

ICLR 2019 [website] [paper] [video]

Demo

Simulation

Rollout from our learned model

Updates

(2020-9-8) An improved version of DPI-Net

You may also take a look at the following repo that we released for training and evaluating the particle dynamics networks, where we made some modifications on top of DPI-Net to make long-term predictions more stable.

https://github.com/YunzhuLi/VGPL-Dynamics-Prior

This section discussed the difference between DPI-Net and VGPL-Dynamics-Prior.

https://github.com/YunzhuLi/VGPL-Dynamics-Prior#difference-between-this-repo-and-dpi-net

Installation

Clone this repo:

git clone https://github.com/YunzhuLi/DPI-Net.git
cd DPI-Net
git submodule update --init --recursive

Install Dependencies if using Conda

For Conda users, we provide an installation script:

bash ./scripts/conda_deps.sh

Install PyFleX

Add environment variables

export PYFLEXROOT=${PWD}/PyFleX
export PYTHONPATH=${PYFLEXROOT}/bindings/build:$PYTHONPATH
export LD_LIBRARY_PATH=${PYFLEXROOT}/external/SDL2-2.0.4/lib/x64:$LD_LIBRARY_PATH

If you are using Ubuntu 16.04 LTS and CUDA 9.1, you can use the following command for compilation.

cd PyFleX/bindings; mkdir build; cd build; cmake ..; make -j

If you are using newer versions of Ubuntu or CUDA, we provide the pre-built Docker image and Dockerfile for compiling PyFleX. After compilation, you will be able to use PyFleX outside docker. Please refer to our Docker page. Note that you do not have to reclone PyFleX again as it has been included as a submodule of DPI-Net.

Test PyFleX examples

cd ${PYFLEXROOT}/bindings/examples
python test_FluidFall.py

Evaluation

Go to the root folder of DPI-Net. You can direct run the following command to use the pretrained checkpoint.

bash scripts/eval_FluidFall.sh
bash scripts/eval_BoxBath.sh
bash scripts/eval_FluidShake.sh
bash scripts/eval_RiceGrip.sh

It will first show the grount truth followed by the model rollout. The resulting rollouts will be stored in dump_[env]/eval_[env]/rollout_*, where the ground truth is stored in gt_*.tga and the rollout from the model is pred_*.tga.

Training

You can use the following command to train from scratch. Note that if you are running the script for the first time, it will start by generating training and validation data in parallel using num_workers threads. You will need to change --gen_data to 0 if the data has already been generated.

bash scripts/train_FluidFall.sh
bash scripts/train_BoxBath.sh
bash scripts/train_FluidShake.sh
bash scripts/train_RiceGrip.sh

Citing DPI-Net

If you find this codebase useful in your research, please consider citing:

@inproceedings{li2019learning,
    Title={Learning Particle Dynamics for Manipulating Rigid Bodies, Deformable Objects, and Fluids},
    Author={Li, Yunzhu and Wu, Jiajun and Tedrake, Russ and Tenenbaum, Joshua B and Torralba, Antonio},
    Booktitle = {ICLR},
    Year = {2019}
}