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Mobile Robot Control via Goal-Conditioned Reinforcement Learning

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// repository documentation

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# Mobile Robot Control via Goal-Conditioned Reinforcement Learning

[![Python 3.6](https://img.shields.io/badge/python-3.11-blue.svg)](https://www.python.org/downloads/release/python-3110/)
<a href="https://github.com/psf/black"><img alt="Code style: black" src="https://img.shields.io/badge/code%20style-black-000000.svg"></a>

A collection of mobile robot environments and their goal-conditioned reinforcement learning controllers.

## Setup

Install the package via pip:

```shell
git clone https://github.com/ZikangXiong/mobrob
cd mobrob
pip install -e .
```

> This project partially relies on mujoco-py. Follow the [official guide](https://github.com/openai/mujoco-py#synopsis) to setup. mujoco-py depends on glew, mesalib, and glfw3. If you do not have permission to install these dependencies, you may use conda to circumvent this issue:
> ```shell
> conda install -c conda-forge glew 
> conda install -c conda-forge mesalib 
> conda install -c menpo glfw3
> ```

## Features

### Environments:

This repository provides five mobile robot environments:

| Body Type  | Description                  | Simulator | State dim | Action dim | Control type        | Video                                                                                                               |
| ---------- | ---------------------------- | --------- | --------- | ---------- | ------------------- | ------------------------------------------------------------------------------------------------------------------- |
| point      | point mass                   | mujoco-py | 14        | 2          | Continuous Commands | <video src='https://github.com/ZikangXiong/mobrob/assets/73256697/4a4e5280-6b2c-46be-b44d-94d5a6c96d34' width=100/> |
| car        | car-like kinematics          | mujoco-py | 26        | 2          | Continuous Commands | <video src='https://github.com/ZikangXiong/mobrob/assets/73256697/6ac6a44e-7f20-4a1a-91a9-5829989896af' width=100/> |
| doggo      | quadruped dog kinematics     | mujoco-py | 58        | 12         | Continuous Commands | <video src='https://github.com/ZikangXiong/mobrob/assets/73256697/a9be5df1-3df4-4b81-a81f-2e826588c186' width=100/> |
| drone      | drone kinematics             | pybullet  | 12        | 18         | Neural PID          | <video src='https://github.com/ZikangXiong/mobrob/assets/73256697/456281ea-03f7-4368-8a60-1a36d67f009f' width=100/> |
| turtlebot3 | turtlebot3-waffle kinematics | pybullet  | 43        | 2          | Neural Prop         | <video src='https://github.com/ZikangXiong/mobrob/assets/73256697/d14d6713-557e-4980-a82b-998ad29c104e' width=100/> |

**Continuous Commands**: continuous control commands are generated by the control policy directly.  
**Neural PID**: a neural network maps the current state to the desired PID coefficients.  
**Neural Prop**: a neural network maps the current state to the desired proportional control coefficients.

### Reinforcement Learning Controllers:

Controllers are trained using Proximal Policy Optimization (PPO).

- **Pretrained policies**: Available at [data/policies](/data/policies/). 
- **Training parameters**: Available at [data/configs](/data/configs/). Refer to [stable-baselines3 PPO](https://stable-baselines3.readthedocs.io/en/master/modules/ppo.html) for all supported parameters.
- **Training**: Use scripts in [examples/train.py](/examples/train.py). For instance, to train the point robot:

```shell
python examples/train.py --env-name point 
```

To finetune a trained policy:

```shell
python examples/train.py --env-name point --finetune
```

Training logs and intermediate policies are saved in `data/tmp`.

- **Evaluation**: Use scripts in [examples/control.py](/examples/control.py). For instance, to evaluate the point robot:

```shell
python examples/control.py --env-name point 
```

To disable the GUI in case you are running the code on a remote server:

```shell
python examples/control.py --env-name point --no-gui
```

or you can consider use [pyvirtualdisplay](https://github.com/ponty/PyVirtualDisplay), and store the video.

## Customization

For users intending to build their goal-conditioned environments, the following abstract functions in the abstract `EnvWrapper` (in [wrapper.py](/src/mobrob/envs/wrapper.py)) should be rewritten according to the specific needs of the new robot environment. The functions, along with their brief explanations, are given in the table below:

| Function Name                 | Description                                                               |
| ----------------------------- | ------------------------------------------------------------------------- |
| `_set_goal(self, goal)`       | Sets the goal position of the robot. Example: [x, y, z]                   |
| `build_env(self)`             | Constructs the environment, i.e., loads the robot and the world.          |
| `get_pos(self)`               | Retrieves the current position of the robot. Example: [x, y, z]           |
| `set_pos(self, pos)`          | Sets the position of the robot. Example: [x, y, z]                        |
| `get_obs(self)`               | Obtains the current observation of the robot. Example: [x, y, z, r, p, y] |
| `get_observation_space(self)` | Gets the observation space of the robot. Example: Box(58,)                |
| `get_action_space(self)`      | Retrieves the action space of the robot. Example: Box(12,)                |
| `get_init_space(self)`        | Fetches the initial space of the robot. Example: Box(3,)                  |
| `get_goal_space(self)`        | Acquires the goal space of the robot. Example: Box(3,)                    |

One may refer to the other robot environment wrappers in [wrapper.py](/src/mobrob/envs/wrapper.py) for more details.

## Publications

This repository is used in the following papers as the benchmark environment:

```bibtex
@inproceedings{mfnlc,
  author={Xiong, Zikang and Eappen, Joe and Qureshi, Ahmed H. and Jagannathan, Suresh},
  booktitle={2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, 
  title={Model-free Neural Lyapunov Control for Safe Robot Navigation}, 
  year={2022},
  pages={5572-5579},
  doi={10.1109/IROS47612.2022.9981632}}

@article{dscrl,
  title={Co-learning Planning and Control Policies Using Differentiable Formal Task Constraints},
  author={Xiong, Zikang and Eappen, Joe and Lawson, Daniel and Qureshi, Ahmed H and Jagannathan, Suresh},
  journal={arXiv preprint arXiv:2303.01346},
  year={2023}
}
```

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