README.md · iedxtu/agibot_x1_train

English | [中文](README.zh_CN.md)
## Introduction
[AgiBot X1](https://www.zhiyuan-robot.com/qzproduct/169.html) is a modular humanoid robot with high dof developed and open-sourced by AgiBot. It is built upon AgiBot's open-source framework `AimRT` as middleware and using reinforcement learning for locomotion control.

This project is about the reinforcement learning training code used by AgiBot X1. It can be used in conjunction with the [inference software](https://aimrt.org/) provided with AgiBot X1 for real-robot and simulated walking debugging, or be imported to other robot models for training.
![](doc/id.jpg)

## Start

### Install Dependencies
1. Create a new Python 3.8 virtual environment:
   - `conda create -n myenv python=3.8`.
2. Install pytorch 1.13 and cuda-11.7:
   - `conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.7 -c pytorch -c nvidia`
3. Install numpy-1.23:
   - `conda install numpy=1.23`.
4. Install Isaac Gym:
   - Download and install Isaac Gym Preview 4 from https://developer.nvidia.com/isaac-gym.
   - `cd isaacgym/python && pip install -e .`
   - Run an example with `cd examples && python 1080_balls_of_solitude.py`.
   - Consult `isaacgym/docs/index.html` for troubleshooting.
6. Install the training code dependencies:
   - Clone this repository.
   - `pip install -e .`
### Usage
#### Train:
```python scripts/train.py --task=x1_dh_stand --run_name=<run_name> --headless```
- The trained model will be saved in `/log/<experiment_name>/exported_data/<date_time><run_name>/model_<iteration>.pt`, where `<experiment_name>` is defined in the config file.
![](doc/train.gif)

#### Play:
```python /scripts/play.py --task=x1_dh_stand --load_run=<date_time><run_name>```
![](doc/play.gif)
#### Generate the JIT Model:
``` python scripts/export_policy_dh.py --task=x1_dh_stand --load_run=<date_time><run_name>  ```
- The JIT model will be saved in ``` log/exported_policies/<date_time>```

#### Generate the ONNX Model:
``` python scripts/export_onnx_dh.py --task=x1_dh_stand --load_run=<date_time>  ```
- The ONNX model will be saved at ```log/exported_policies/<date_time>```

#### Parameter Descriptions:
- task: Task name
- resume: Resume training from a checkpoint
- experiment_name:  Name of the experiment to run or load.
- run_name: Name of the run.
- load_run: Name of the run to load when resume=True. If -1: will load the last run.
- checkpoint: Saved model checkpoint number. If -1: will load the last checkpoint.
- num_envs: Number of environments to create.
- seed: Random seed.
- max_iterations: Maximum number of training iterations.

### Add New Environments
1. Create a new folder under the `envs/` directory, and then create a configuration file `<your_env>_config.py` and an environment file `<your_env>_env.py` in the folder. The two files should inherit `LeggedRobotCfg` and `LeggedRobot` respectively.

2. Place the URDF, mesh, and MJCF files of the new robot in the `resources/` folder.
- Configure the URDF path, PD gain, body name, default_joint_angles, experiment_name, etc., for the new robot in `<your_env>_config.py`.

3. Register the new robot in `humanoid/envs/__init__.py`.
### sim2sim
Use Mujoco for sim2sim validation:
  ```
  python scripts/sim2sim.py --task=x1_dh_stand --load_model /path/to/exported_policies/
  ```
![](doc/mujoco.gif)
### Usage of Joystick
We use the Logitech F710 Joystick. When starting play.py and sim2sim.py, press and hold button 4 while rotating the joystick to control the robot to move forward/backward, strafe left/right or rotate.
![](doc/joy_map.jpg)
|         Button           |         Command         |
| -------------------- |:--------------------:|
|         4 + 1-        |         Move forward          |
|         4 + 1+        |         Move backward          |
|         4 + 0-        |        Strafe left         |
|         4 + 0+        |        Strafe right         |
|         4 + 3-        |       Rotate counterclockwise       |
|         4 + 3+        |       Rotate clockwise       |

> References
> * [GitHub - leggedrobotics/legged_gym: Isaac Gym Environments for Legged Robots](https://github.com/leggedrobotics/legged_gym)
> * [GitHub - leggedrobotics/rsl_rl: Fast and simple implementation of RL algorithms, designed to run fully on GPU.](https://github.com/leggedrobotics/rsl_rl)
> * [GitHub - roboterax/humanoid-gym: Humanoid-Gym: Reinforcement Learning for Humanoid Robot with Zero-Shot Sim2Real Transfer https://arxiv.org/abs/2404.05695](https://github.com/roboterax/humanoid-gym)

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