This code implements a basic simulation and control for full-body Musculoskeletal system. Skeletal movements are driven by the actuation of the muscles, coordinated by activation levels. Interfacing with python and pytorch, it is available to use Deep Reinforcement Learning(DRL) algorithm such as Proximal Policy Optimization(PPO).

Seunghwan Lee, Kyoungmin Lee, Moonseok Park, and Jehee Lee Scalable Muscle-actuated Human Simulation and Control, ACM Transactions on Graphics (SIGGRAPH 2019), Volume 37, Article 73.

Project Page : http://mrl.snu.ac.kr/research/ProjectScalable/Page.htm

Youtube : https://youtu.be/a3jfyJ9JVeM

Paper : http://mrl.snu.ac.kr/research/ProjectScalable/Paper.pdf

sudo apt-get install libtinyxml-dev libeigen3-dev libxi-dev libxmu-dev freeglut3-dev libassimp-dev libpython3-dev python3-tk python3-numpy virtualenv ipython3 cmake-curses-gui

We strongly recommand that you install boost libraries from the source code (not apt-get, etc...).

• Download boost sources with the version over 1.66.(https://www.boost.org/users/history/version_1_66_0.html)

• Compile and Install the sources

cd /path/to/boost_1_xx/
./bootstrap.sh --with-python=python3
sudo ./b2 --with-python --with-filesystem --with-system --with-regex install

• Check yourself that the libraries are installed well in your directory /usr/local/. (or /usr/)

If installed successfully, you should have something like

Include

• /usr/local/include/boost/
• /usr/local/include/boost/python/
• /usr/local/include/boost/python/numpy

Lib

• /usr/local/lib/libboost_filesystem.so
• /usr/local/lib/libboost_python3.so
• /usr/local/lib/libboost_numpy3.so

Please refer to http://dartsim.github.io/ (Install version 6.3)

You should first activate virtualenv.

virtualenv /path/to/venv --python=python3
source /path/to/venv/bin/activate

• pytorch(https://pytorch.org/)

pip3 install http://download.pytorch.org/whl/cpu/torch-0.4.1-cp35-cp35m-linux_x86_64.whl 
pip3 install torchvision

• numpy, matplotlib

pip3 install numpy matplotlib ipython

Our system require a reference motion to imitate. We provide sample references such as walking, running, and etc...

To learn and simulate, we should provide such a meta data. We provide default meta data in /data/metadata.txt. We parse the text and set the environment. Please note that the learning settings and the test settings should be equal.(metadata.txt should not be changed.)

mkdir build
cd build
cmake .. 
make -j8

• Run Training

cd python
source /path/to/virtualenv/
python3 main.py -d ../data/metadata.txt

All the training networks are saved in /nn folder.

• Run UI

source /path/to/virtualenv/
./render/render ../data/metadata.txt

• Run Trained data

source /path/to/virtualenv/
./render/render ../data/metadata.txt ../nn/xxx.pt ../nn/xxx_muscle.pt

If you are simulating with the torque-actuated model,

source /path/to/virtualenv/
./render/render ../data/metadata.txt ../nn/xxx.pt