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The reinforcement learning training code for AgiBot X1.
| Date | Stars |
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| 2026-07-24 | 1689 |
| 2026-07-25 | 1690 |
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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.  ## 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.  #### Play: ```python /scripts/play.py --task=x1_dh_stand --load_run=<date_time><run_name>```  #### 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/ ```  ### 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.  | Button | Command | | -------------------- |:--------------------:| | 4 + 1- | Move forward | | 4 + 1+ | Move backward | | 4 + 0- | Strafe left | | 4 + 0+ | Strafe right | | 4 + 3- | Rotate counterclockwise | | 4 + 3+
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:e3d954870262a1ba, topic:reinforcement-learning, desc:reinforcement learning, readme:reinforcement learning
matched fp:e3d954870262a1ba, topic:robotics, readme:humanoid