Yi-Ling Qiao, Junbang Liang, Vladlen Koltun, Ming C. Lin
(in progress)
- Create a conda virtual environment and activate it.
conda create -n diffsim python=3.6 -y
conda activate diffsim
- 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
@aritical{Qiao2020Scalable,
author = {Qiao, Yiling and Liang, Junbang and Koltun, Vladlen and Lin, Ming C.},
title = {Scalable Differentiable Physics for Learning and Control},
booktitle = {ICML},
year = {2020},
}