Yi-Ling Qiao, Junbang Liang, Vladlen Koltun, Ming C. Lin
[Project] [arXiv] [Video] [GitHub]
- Create a conda virtual environment and activate it.
conda create -n diffsim python=3.6 -y
conda activate diffsim
# install dependencies
sudo apt install gcc-4.8 gcc-5
sudo apt-get install libblas-dev liblapack-dev
sudo apt-get install libopenblas-dev
sudo apt-get install gfortran
sudo apt-install scons
sudo apt-install libpng-dev
- Download and build the project.
git clone git@github.com:YilingQiao/diffsim.git
cd diffsim
pip install -r requirements.txt
bash script_build.sh
cd pysim
- Run the examples
python exp_inverse.py
By default, the simulation output would be stored in pysim/default_out
directory.
If you want to store the results in some other places, like ./test_out
, you can specify it by python exp_inverse.py test_out
To visualize the simulation results, use
python msim.py
You can change the source folder of the visualization in msim.py
. More functionality of msim.py
can be found in arcsim/src/msim.cpp
.
The visualization is the same for all other experiments.
python exp_learn_cloth.py
python exp_learn_stick.py
Figure 3, first row.
bash script_multibody.sh
Figure 3, second row.
bash script_scale.sh
Table 1, sparse collision handling.
bash script_absparse.sh
Table 2, fast differentiation.
bash script_abqr.sh
python exp_momentum.py
python exp_trampoline.py
python exp_domino.py
python exp_bunny.py
This experiment requires MuJoCo environment. Install MuJoCo and its python interface mujoco_py before running this script.
python exp_mujoco.py
Differentiable Soft Body Dynamics Code Paper Differentiable Simulation of Soft Multi-body Systems. Yi-Ling Qiao, Junbang Liang, Vladlen Koltun, Ming C. Lin. (Neurips 2021)
Differentiable Articulated Body Dynamics Code Paper Efficient Differentiable Simulation of Articulated Bodies. Yi-Ling Qiao, Junbang Liang, Vladlen Koltun, Ming C. Lin. (ICML 2021)
Differentiable Dynamics for Rigid Body and Cloth Coupling Code Paper Scalable Differentiable Physics for Learning and Control. Yi-Ling Qiao, Junbang Liang, Vladlen Koltun, Ming C. Lin. (ICML 2020)
Differentiable Cloth Dynamics Code Paper Differentiable Cloth Simulation for Inverse Problems. Junbang Liang, Ming C. Lin, Vladlen Koltun. (NeurIPS 2019)
@inproceedings{Qiao2020Scalable,
author = {Qiao, Yi-Ling and Liang, Junbang and Koltun, Vladlen and Lin, Ming C.},
title = {Scalable Differentiable Physics for Learning and Control},
booktitle = {ICML},
year = {2020},
}