cyy/go2_rl_robotlab
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

update readme.

Browse Source
This commit is contained in:
wertyuilife
2026-03-26 21:12:27 +08:00
parent 6722a01ee3
commit 257e69f793
2 changed files with 105 additions and 43 deletions
Download Patch File Download Diff File Expand all files Collapse all files

148
README.md
View File

@@ -2,89 +2,145 @@
## Overview
Train Unitree Go2 with MoE-CTS on IsaacLab and deploy it to MuJoCo.
This is a reproduction version of [go2_rl_gym](https://github.com/wty-yy/go2_rl_gym) on RobotLab/IsaacLab.
Trains the Unitree Go2 robot using MoE-CTS in IsaacLab and deploys the trained policy to MuJoCo for simulation transfer (Sim2Sim).
It is a reproduction of [go2_rl_gym](https://github.com/wty-yy/go2_rl_gym), adapted to the RobotLab / IsaacLab ecosystem.
---
<p align="center">
<img src="resources/go2/isaaclab_scene.png" width="70%"/>
</p>
---
## Installation Guide
### 1. Install IsaacLab
Install IsaacLab 2.3.0 release by following the [installation guide](https://isaac-sim.github.io/IsaacLab/release/2.3.0/source/setup/installation/pip_installation.html#).
After installation, your environment should satisfy the following requirements:
```
isaacsim <= 5.1.0.0 # tested on 5.1.0.0
isaaclab <= 0.53.1 # tested on 0.53.1
isaaclab-rl <= 0.4.7 # tested on 0.4.7
After installation, your environment should satisfies:
```bash
isaacsim <= 5.1.0.0 # tested on 5.1.0.0
isaaclab <= 0.53.1 # tested on 0.53.1
isaaclab-rl <= 0.4.7 # tested on 0.4.7
```
Higher version may cause conflicts with our customized `rsl_rl==3.3.0` and `robot_lab==2.3.0`.
### 2. Install customized RSL-RL and RobotLab
We uses a customized version of `rsl_rl` and `robot_lab`. To install it, run the following commands:
---
### 2. Install Customized RSL-RL and RobotLab
We uses a customized version of `rsl_rl` and `robot_lab`. Install them in editable mode:
```bash
python -m pip install -e source/robot_lab
python -m pip install -e source/rsl_rl
```
### 3. Install MuJoCo for Sim2Sim (optional)
If you want to use `mujoco` for Sim2Sim, install it by running the following command:
---
### 3. Install MuJoCo (Optional, for Sim2Sim)
To enable MuJoCo-based simulation:
```bash
pip install mujoco # tested on mujoco 3.4.0 & 3.6.0
pip install mujoco # tested on 3.4.0 and 3.6.0
```
## Train and Play
---
Use the following commands to train and play:
## Training and Evaluation
Run the following commands:
```bash
# Train
python scripts/reinforcement_learning/rsl_rl/train.py --task=RobotLab-Go2-v0 --headless
python scripts/rsl_rl/train.py \
--task=RobotLab-Go2-v0 \
--headless
# Play
python scripts/reinforcement_learning/rsl_rl/play.py --task=RobotLab-Go2-v0
# Play / Evaluate
python scripts/rsl_rl/play.py \
--task=RobotLab-Go2-v0
```
---
## Configuration
1. Modify `source/robot_lab/robot_lab/tasks/go2/env_cfg.py` for environment config.
The training pipeline can be configured at two levels: task-level Python configuration files and runtime arguments passed to the training script.
2. Modify `source/robot_lab/robot_lab/tasks/go2/rsl_rl_cfg.py` for algorithm config.
### Task-Level Configuration
3. Modify `source/robot_lab/robot_lab/tasks/go2/__init__.py` to add your own task with new config.
The default task settings are defined in the following files:
4. Add args in commands to override above configs, for example:
1. **Environment configuration**
```
source/robot_lab/robot_lab/tasks/go2/env_cfg.py
```
```
--experiment_name=moe_cts
--run_name=v1
--num_envs=16384
--resume
--checkpoint=path/to/your/checkpoint
```
for more usage, see [robot_lab](https://github.com/fan-ziqi/robot_lab.git).
2. **RL algorithm configuration**
```
source/robot_lab/robot_lab/tasks/go2/rsl_rl_cfg.py
```
3. **Task registration**
```
source/robot_lab/robot_lab/tasks/go2/__init__.py
```
### Runtime Overrides
In addition to the default configuration files, `train.py` and `play.py` supports several command-line arguments for runtime overrides:
```bash
--experiment_name <YOUR_EXP_NAME>
--run_name <YOUR_RUN_NAME>
--num_envs <NUM_ENVS>
--checkpoint <PATH_TO_CHECKPOINT>
```
For more details, refer to the [robot_lab repo](https://github.com/fan-ziqi/robot_lab.git).
---
## MuJoCo Sim2Sim
Use the following command to run the Sim2Sim with MuJoCo:
Run the deployment script:
```bash
python deploy/deploy_mujoco/deploy_go2.py
```
- **Automatic detection**: When connecting the handle, the script automatically activates the handle control mode.
- **Controller not available**: The script will use the default commands in the configuration file.
### Controller Behavior
Handle axis mapping:
- **Automatic detection**: If a controller is connected, control mode is enabled automatically.
- **Fallback mode**: If no controller is detected, default commands from the config file are used.
- `LX/LY`: Forward/Lateral Speed Command
- `RX`: Angular velocity (steering) command
### Controller Mapping
Modify the `xml_path` parameter in `deploy/deploy_mujoco/config/go2.yaml` to switch simulation scenarios:
| Input | Function |
|------|--------|
| `LX / LY` | Forward / lateral velocity |
| `RX` | Angular velocity (steering) |
---
### Switching Simulation Scenarios
Modify `xml_path` in:
```
deploy/deploy_mujoco/config/go2.yaml
```
```yaml
# Flat
# Flat terrain
xml_path: "{ROOT_DIR}/resources/go2/flat.xml"
# Stairs
@@ -94,17 +150,23 @@ xml_path: "{ROOT_DIR}/resources/go2/stairs.xml"
xml_path: "{ROOT_DIR}/resources/go2/boxes.xml"
# Custom
xml_path: "{ROOT_DIR}/resource/go2/your-custom-scene.xml"
xml_path: "{ROOT_DIR}/resources/go2/your-custom-scene.xml"
```
## Differences with `go2_rl_gym`
---
- Terrain's composition are different(see code).
- tracking reward are different (fixed sigma vs. dynamic sigma).
## Differences from `go2_rl_gym`
## ToDo
- Different terrain composition
- Different tracking reward formulation (fixed sigma vs. dynamic sigma)
- Try using DelayedPDActuatorCfg to replace ActionManager-Level action delay implementation.
---
## TODO
- Try replacing ActionManager-level delay with `DelayedPDActuatorCfg`
---
## Acknowledgements
This repository would not exist without the following open-source projects: