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Geodesic Motion Skills

This repository constains the code for the paper Learning Riemannian Manifolds for Geodesic Motion Skills published at Robotics: Science and Systems 2021 conference.

Warning: The development of this framework is ongoing, and thus some substantial changes might occur. Sorry for the inconvenience.

Requirements

The code has been tested with Python 3.8 on Ubuntu 20.04. The installation on other OS is experimental.

Installation

  1. First download the pip Python package manager and create a virtual environment for Python as described in the following link: https://packaging.python.org/guides/installing-using-pip-and-virtualenv/ On Ubuntu, you can install pip and virtualenv by typing in the terminal:
  • In Python 3.8:
	sudo apt install python3-pip
	sudo pip install virtualenv

If you have them installed then skip this step

  1. Clone the repository and install the requirements as follows
mkdir gmg_repo
cd gmg_repo

git clone https://github.com/boschresearch/GeodesicMotionSkills.git

# Clone Stochman from here (in the same directory)
git clone https://github.com/MachineLearningLifeScience/stochman
cd stochman/
git checkout 1d092e0dffef179b706542f0693b813a8370fcf8
cd ..

# Clone hyperspherical vae from here (in the same directory)
git clone https://github.com/hadibeikm/s-vae-pytorch.git


cd GeodesicMotionSkills

# Create a virtual environment and activate it
virtualenv -p /usr/bin/python3.8 gmg
source gmg/bin/activate

# Install the requirements
pip install -r requirements.txt

# Navigate to s-vae-pytorch directory
cd ../s-vae-pytorch
# Install the package
pip install -e .

# Navigate stochman directory
cd ../stochman
# Install the package
pip install -e .

# Add the repo directory to your python path (/your/repo/directory/ is where you clone the repos)
export PYTHONPATH="${PYTHONPATH}:/your/repo/directory/gmg_repo"
  1. Execute the example code
# Navigate to the Experiments folder in GeodesicMotionSkills directory
cd ../GeodesicMotionSkills/Experiments/
python toy_example.py 
# This command will generate metric plot for our toy example mentioned in the paper
  1. You can use Pycharm to open the project and change the hyperparamters for training and test.

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