Yunzhu Li, Jiajun Wu, Russ Tedrake, Joshua B. Tenenbaum, Antonio Torralba
ICLR 2019 [website] [paper] [video]
Rollout from our learned model
(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
Clone this repo:
git clone https://github.com/YunzhuLi/DPI-Net.git
cd DPI-Net
git submodule update --init --recursive
For Conda users, we provide an installation script:
bash ./scripts/conda_deps.sh
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.
cd ${PYFLEXROOT}/bindings/examples
python test_FluidFall.py
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.
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
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}
}