Most of the examples and reproductions can be run on Deepnote.

After duplicating the project into your own account, be sure to run the MosekLicenseUpload.ipynb notebook to make your Mosek License available for solving the optimization problems.

Note: The PRM and Bimanual reproductions do not yet work on Deepnote and the UAV and Maze reproductions have been shrunk in size to avoid hitting memory limits on Deepnote.

This code depends on Drake, specifically its Python bindings. To install the bindings and other dependencies, run

pip install -r requirements.txt

Drake also requires a couple additional libraries. To install them follow the below instuctions.

For Ubuntu 20.04:

sudo apt-get update
sudo apt-get install --no-install-recommends \
  libpython3.8 libx11-6 libsm6 libxt6 libglib2.0-0

To confirm that the Drake bindings are accessible, and that you are using the right set of bindings run

python3 -c 'import pydrake.all; print(pydrake.__file__)'

and confirm that the printed path matches the expected location of the Python bindings.

Once all the dependencies have been installed, you can run the examples with jupyter notebooks which can be launched by calling

jupyter-notebook

from inside this repository.

If you want to compare GCS to sampling based planners (such as PRM), you'll need to install a custom fork of drake that includes bindings for sampling based planners. To do this run the following, including any of the proprietary solvers you have access to.

git clone -b gcs [email protected]:mpetersen94/drake.git
mkdir drake-build
cd drake-build
cmake -DWITH_MOSEK=ON [-DWITH_GUROBI=ON -DWITH_ROBOTLOCOMOTION_SNOPT=ON] ../drake
make -j

Then add the built bindings to your Python Path using

For Ubuntu 20.04:

cd drake-build
export PYTHONPATH=${PWD}/install/lib/python3.8/site-packages:$PYTHONPATH

For macOS:

cd drake-build
export PYTHONPATH=${PWD}/install/lib/python3.9/site-packages:$PYTHONPATH