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## Running locally
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### Installing Dependencies
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This code depends on [Drake](https://drake.mit.edu), specifically its Python bindings. To install Drake bindings follow the instruction on [Drake's Installation Page](https://drake.mit.edu/installation.html). Any of the installation methods listed should work. You can check that the installation was sucessful by following the instruction [here](https://drake.mit.edu/python_bindings.html#using-the-python-bindings).
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### Option 1: Docker
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We provide a [dockerfile](docker/deepnote/Dockerfile) with a custom build of [Drake](https://drake.mit.edu) that entails sampling based planners for the comparison. Note that the docker does not include a build of Gurobi, which has been only used in the Iris region generation.
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We have used Mosek to solve most of the examples. To solve using Mosek, you'll need to give Drake access to a Mosek license file as described [here](https://drake.mit.edu/bazel.html#mosek). Mosek provides a personal academic license for free.
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You will also need to install `gcs` and its dependencies. You can do this by running
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.
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On your machine go to http://localhost:8888/ You will find the reproduction notebooks in the reproduction folder.
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### Option 2: Local Installation
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If you want to compare GCS to sampling based planners (such as PRM), you'll need to install a custom fork of [Drake](https://drake.mit.edu) that includes bindings for sampling based planners. To do this run the following, including any of the proprietary solvers you have access to. You may build it with Gurobi.
We have used Mosek to solve most of the examples. To solve using Mosek, you'll need to give Drake access to a Mosek license file as described [here](https://drake.mit.edu/bazel.html#mosek). Mosek provides a personal academic license for free.
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You will also need to install `gcs-science-robotics` and its dependencies. From inside this repository, run the following:
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```
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pip install -e .
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```
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Once all the dependencies have been installed, you can run the examples with jupyter notebooks which can be launched by calling
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