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https://github.com/lsw9021/MASS
https://github.com/lsw9021/MASS
Last synced: 24 days ago
JSON representation
- Host: GitHub
- URL: https://github.com/lsw9021/MASS
- Owner: lsw9021
- License: apache-2.0
- Created: 2019-04-29T09:31:19.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2023-09-19T22:42:33.000Z (over 1 year ago)
- Last Synced: 2024-08-09T13:19:31.159Z (4 months ago)
- Language: Mathematica
- Size: 5.7 MB
- Stars: 572
- Watchers: 29
- Forks: 105
- Open Issues: 25
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# MASS(Muscle-Actuated Skeletal System)
## Abstract
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).
## Publications
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
## How to install
### Install TinyXML, Eigen, OpenGL, assimp, Python3, etc...
```bash
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 libbullet-dev pybind11-dev
```
### Install DART 6.8
Please refer to http://dartsim.github.io/ (Install version 6.8)
Manual from DART(http://dartsim.github.io/install_dart_on_ubuntu.html)
### Install PIP things
You should first activate virtualenv.
```bash
virtualenv /path/to/venv --python=python3
source /path/to/venv/bin/activate
```
- pytorch(https://pytorch.org/)
- numpy, matplotlib
```bash
pip3 install numpy matplotlib ipython
```
## How to compile and run
### Resource
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.)
### Compile and Run
```bash
mkdir build
cd build
cmake ..
make -j8
```
- Run Training
```bash
cd python
source /path/to/virtualenv/
python3 main.py -d ../data/metadata.txt
```
All the training networks are saved in /nn folder.
- Run UI
```bash
source /path/to/virtualenv/
./render/render ../data/metadata.txt
```
- Run Trained data
```bash
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,
```bash
source /path/to/virtualenv/
./render/render ../data/metadata.txt ../nn/xxx.pt
```
## Model Creation & Retargeting (This module is ongoing project.)
### This requires Maya and MotionBuilder.
There is a sample model in data/maya folder that I generally use. Currently if you are trying to edit the model, you have to make your own export maya-python code and xml writer so that the simulation code correctly read the musculoskeletal structure.
There is also a rig model that is useful to retarget a new motion.