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[CoRL'18] Conditional Affordance Learning

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최종 버전 다운로드 (.zip)

- - __init__.py
- - __init__.py
  - carla_server_pb2.py
  - client.py
  - image_converter.py
  - sensor.py
  - settings.py
  - tcp.py
  - transform.py
  - util.py
- driving_benchmark.py
- - 2019_06_13_19_55.json
  - gru.json
  - lstm.json
  - tempconv.json
  - test.json
- dataloader.py
- dataset
- make_validationset.py
- metrics.py
- net.py
- train.py
- training_CAL.ipynb
.gitignore
CAL.gif
download_binaries_and_models.sh
environment.yml
LICENSE
README.md
windows.yml

// repository documentation

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:star2: We updated the repo with training code! :star2:

## Conditional Affordance Learning  ##
#### In CoRL 2018 [[Paper]](https://arxiv.org/abs/1806.06498) [[Video]](https://www.youtube.com/watch?v=UtUbpigMgr0) [[Talk]](https://www.youtube.com/watch?v=SceH3Al9w_M)

[Axel Sauer 1, 2](https://axelsauer.com/),
[Nikolay Savinov 1](https://www.nsavinov.com/),
[Andreas Geiger 1, 3](https://ps.is.tuebingen.mpg.de/person/ageiger)
 
1 ETH Zurich, 2 TU Munich, 3 MPI for Intelligent Systems and University of Tubingen

If you use this implementation, please cite our CoRL 2018 paper.
```
@inproceedings{Sauer2018CORL,
  author={Sauer, Axel and Savinov, Nikolay and Geiger, Andreas},
  title={Conditional Affordance Learning for Driving in Urban Environments},
  booktitle={Conference on Robot Learning (CoRL)},
  year={2018}
}
```
### Installation

```Shell
# install anaconda if you don't have it yet
wget https://repo.continuum.io/archive/Anaconda3-5.3.0-Linux-x86_64.sh
bash Anaconda3-5.3.0-Linux-x86_64.sh
source ~/.profile
# or use source ~/.bashrc - depending on where anaconda was added to PATH as the result of the installation
```

Now we will:
1. create a conda environment named CAL and install all dependencies
2. download the binaries for CARLA version 0.8.2 [[CARLA releases](https://github.com/carla-simulator/carla/releases)]
3. download the model weights

```Shell
git clone https://github.com/xl-sr/CAL.git
cd CAL

# create the training environment
conda env create -f requirements.yml
source activate CAL

# run download script
./download_binaries_and_models.sh

```

### Run the Agent

In CARLA_0.8.2/ start the server with for example: (see the [CARLA documentation](https://carla.readthedocs.io/en/stable/) for details)

```Shell
./CarlaUE4.sh Town01 -carla-server -windowed -benchmark -fps=20 -ResX=800 - ResY=600
```

Open a second terminal, and run:

```Shell
python python_client/driving_benchmark.py -c Town01 -v -n test --corl-2017

```

If you want to run the CORL 2017 benchmark add '--corl-2017'

```Shell
python python_client/driving_benchmark.py -c Town01 -v -n test --corl-2017

```

If you want to continue an experiment, you can add the 'continue-experiment' flag.

### Training
```Shell
cd training/

# download and untar the dataset
wget https://s3.eu-central-1.amazonaws.com/avg-projects/conditional_affordance_learning/dataset.tar.gz
tar -xzvf dataset.tar.gz
```

Now, open training_CAL.ipynb. The notebook walks you through the steps to train a network on the dataset.

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