learning-nav-irl

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learning-nav-irl

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Learning human-aware robot navigation behavior from demonstrations via Maximum Entropy Inverse Reinforcement Learning.

learning-nav-irl 최신버젼 다운로드

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// repository documentation

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# Learning Human-Aware Robot Navigation from Physical Interaction via Inverse Reinforcement Learning

We learn the parameters of our navigation reward function via inverse reinforcement learning to eliminate the need for manual tuning of the parameters. Our navigation reward function balances social constraints and the desire to reach a goal state. We conducted a user study where 13 test subjects interacted with our mobile robot in our office hallway. The test subjects demonstrated how they wanted the robot to pass them by pushing on its force-sensitive shell.

This repository includes our experiment data and IRL code and provides a ros node to retrain on the experiment demonstrations.

If you are using our code or experiment data in your research, please consider citing our paper:

```
@INPROCEEDINGS{kollmitz20iros,
  author = {Marina Kollmitz and Torsten Koller and Joschka Boedecker and Wolfram Burgard},
  title = {Learning Human-Aware Robot Navigation from Physical Interaction via Inverse Reinforcement Learning},
  booktitle = {Proc.~of the IEEE/RSJ Int.~Conf.~on Intelligent Robots and Systems (IROS)},
  year = {2020},
  url = {http://ais.informatik.uni-freiburg.de/publications/papers/kollmitz20iros.pdf}
}
```

## Get the code and experiment data

1. Clone repository, which includes the code and experiment data:

```
cd ~/catkin_ws/src
git clone https://github.com/marinaKollmitz/learning-nav-irl/
```

2. Unpack experiment data:

```
cd learning-nav-irl/experiments/
tar -xzf runs.tar.gz
```

3. Install pytorch

Our code uses pytorch for gradient-based optimization. Follow the installation instructions from https://pytorch.org/ to install pytorch for your system and architecture. GPU support is not necessary. The standard will be:
```
pip install torch torchvision
```

## Run IRL on experiment demos

Use the ```launch/experiment_demo_irl.launch``` file to run inverse reinforcement learning on the experiment demonstrations. To learn on the demonstrations of all user study participants, run the launch file without arguments:
```
roslaunch learning-nav-irl experiment_demo_irl.launch 
```
An rviz window should pop up where you can see the demonstration trajectories, the state visitations and the learned reward.

![visualization](https://github.com/marinaKollmitz/learning-nav-irl/blob/master/img/viz.png?raw=true)

The reward parameters and the gradient are logged in the terminal window and you should find a plot of the optimization objective (maximizing the log-likelihood of the demonstrations) as a .png file in the ```learning-nav-irl``` folder.

You can also train on the passing trajectories of one participant by giving the run number (in the experiments/runs folder) as an argument:
```
roslaunch learning-nav-irl experiment_demo_irl.launch experiment_run:=2346
```

## Integration as a ROS global planner

The ```social_planner_sim.launch``` file integrates our approach as a Global planner plugin in the ros navigation stack.

The launch file starts a simple gazebo simulation with a simple mockup of our robot and the ros navigation stack. The navigation stack uses a fake localization node and the [omni_path_follower](https://github.com/marinaKollmitz/omni_path_follower) package as a simple local planner.

### Download necessary code:
```
cd ~/catkin_ws/src
git clone https://github.com/marinaKollmitz/omni_path_follower.git
cd ..
catkin_make
sudo apt-get install ros-<distro>-fake-localization
```
Also, you need to have [gazebo](http://gazebosim.org/) with ROS support installed.

### Run the Navigation

Start the launch file:
```
roslaunch learning-nav-irl social_planner_sim.launch
```
You should see an rviz window pop up, where you can set navigation goals.

![navigation](https://github.com/marinaKollmitz/learning-nav-irl/blob/master/img/nav_2ppl.png?raw=true)

## License

For academic usage, the code is released under the [GPLv3](https://www.gnu.org/licenses/gpl-3.0.en.html) license. For any commercial purpose, please contact the authors.

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