This is my rotation project with Prof. Schweighofer trying to replicate the model proposed by Nutan Chen at. al. in their Efficient movement representation by embedding dynamic movement premitives in deep autoencoders (2015 IEEE-RAS). https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7363570
The model is able to reconstruct human movements by embedding dynamic movement primitives in latent space of the variational autoencoder with variational bayes fileters (Maximilian Karl et al. ICLR 2017)https://arxiv.org/abs/1605.06432.
The movement dataset comes from Master Motor Map - Framework and Toolkit for Capturing. Representing, and Reproducing Human Motion on Humanoid Robots (Oemer Terlemez et al.) https://ieeexplore.ieee.org/document/7041470/.
The project aims to use the powerful generative model to help rehabilitate patients.
These folder contains all scripts written during my rotation
The files include dynamic motor primitive example based on Matlab code from Prof. Stefan Schaal's websit
The files include stacked auto-encoders and variational auto-encoders for NMIST dataset and CIFAR10 dataset
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Writing the data pipeline interface from Master Motor Map for the auto-encoder, and test able to generate stationary movements
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Integrating Dynamic Movement Primitive to the variational autoencoder, and test able to generate dynamice movments
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Integrate deep Bayesian filters to the variational autoencoder, and test able to generate movement transitions
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Finally, design better regularizers to learn more efficient ebeddings in latent space
Script Dependencies
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Linxu operation system (Ubuntu)
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Python3
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Matplotlib
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Numpy
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Tensorflow-gpu
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Spyder
Detailed instruction : https://medium.com/@taylordenouden/installing-tensorflow-gpu-on-ubuntu-18-04-89a142325138
Depending on the GPU card you have, update your GPU driver and reboot your computer.
For example, install nvidia-390 for Nvidia 1080Ti GPU.https://tecadmin.net/install-latest-nvidia-drivers-ubuntu/
You must have Nvidia GPU in order to run Tensorflow on GPU. Otherwise you can run Tensorflow on CPU.
Download and install CUDA Toolkit https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html
Download and install CuDNN
Then :
sudo apt-get install pip
pip install python
pip install matplotlib
pip install numpy
pip install tensorflow-gpu
pip install spyder
Virtual environemtn prevents dependencies conflicts of different softwares. For more information ask Google.
Download Anaconda and install it folloing the installation guide on https://conda.io/docs/user-guide/install/index.html
after install Anaconda, create an virtual environment 'tensorflow' in Python3 :
conda create -n tensorflow python = python3
source activate tensorflow
conda install matplotlib, spyder
conda install tensorflow-gpu
sudo apt-get install pip
pip install python
pip install matplotlib
pip install numpy
pip install tensorflow
pip install spyder
download Anaconda and install it folloing the installation guide on https://conda.io/docs/user-guide/install/index.html
after install Anaconda, create an virtual environment 'tensorflow' in Python3 :
conda create -n tensorflow python = python3
source activate tensorflow
conda install matplotlib, spyder
conda install tensorflow
Each script is independent from each other. So each of them can be executed independently by typing in your terminal :
$ python the/path/to/the/script.py
Or run script in Spyder editor by typing in your terminal :
$ spyder
In Spyder3, open the respective file by clicking the open file icon on the toolbar in the API
However, it is recommended to run the code in Python edictor such as Spyder3 https://pythonhosted.org/spyder/ to produce images correctly.