115 lines
3.6 KiB
Markdown
115 lines
3.6 KiB
Markdown
# go2_rl_robotlab
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## Overview
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Train Unitree Go2 with MoE-CTS on IsaacLab and deploy it to MuJoCo.
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This is a reproduction version of [go2_rl_gym](https://github.com/wty-yy/go2_rl_gym) on RobotLab/IsaacLab.
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## Installation Guide
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### 1. Install IsaacLab
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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#).
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After installation, your environment should satisfy the following requirements:
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```
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isaacsim <= 5.1.0.0 # tested on 5.1.0.0
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isaaclab <= 0.53.1 # tested on 0.53.1
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isaaclab-rl <= 0.4.7 # tested on 0.4.7
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```
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Higher version may cause conflicts with our customized `rsl_rl==3.3.0` and `robot_lab==2.3.0`.
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### 2. Install customized RSL-RL and RobotLab
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We uses a customized version of `rsl_rl` and `robot_lab`. To install it, run the following commands:
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```bash
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python -m pip install -e source/robot_lab
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python -m pip install -e source/rsl_rl
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```
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### 3. Install MuJoCo for Sim2Sim (optional)
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If you want to use `mujoco` for Sim2Sim, install it by running the following command:
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```bash
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pip install mujoco # tested on mujoco 3.4.0 & 3.6.0
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```
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## Train and Play
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Use the following commands to train and play:
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```bash
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# Train
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python scripts/reinforcement_learning/rsl_rl/train.py --task=RobotLab-Go2-v0 --headless
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# Play
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python scripts/reinforcement_learning/rsl_rl/play.py --task=RobotLab-Go2-v0
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```
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## Configuration
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1. Modify `source/robot_lab/robot_lab/tasks/go2/env_cfg.py` for environment config.
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2. Modify `source/robot_lab/robot_lab/tasks/go2/rsl_rl_cfg.py` for algorithm config.
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3. Modify `source/robot_lab/robot_lab/tasks/go2/__init__.py` to add your own task with new config.
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4. Add args in commands to override above configs, for example:
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```
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--experiment_name=moe_cts
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--run_name=v1
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--num_envs=16384
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--resume
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--checkpoint=path/to/your/checkpoint
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```
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for more usage, see [robot_lab](https://github.com/fan-ziqi/robot_lab.git).
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## MuJoCo Sim2Sim
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Use the following command to run the Sim2Sim with MuJoCo:
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```bash
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python deploy/deploy_mujoco/deploy_go2.py
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```
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- **Automatic detection**: When connecting the handle, the script automatically activates the handle control mode.
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- **Controller not available**: The script will use the default commands in the configuration file.
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Handle axis mapping:
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- `LX/LY`: Forward/Lateral Speed Command
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- `RX`: Angular velocity (steering) command
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Modify the `xml_path` parameter in `deploy/deploy_mujoco/config/go2.yaml` to switch simulation scenarios:
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```yaml
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# Flat
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xml_path: "{ROOT_DIR}/resources/go2/flat.xml"
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# Stairs
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xml_path: "{ROOT_DIR}/resources/go2/stairs.xml"
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# Boxes
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xml_path: "{ROOT_DIR}/resources/go2/boxes.xml"
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# Custom
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xml_path: "{ROOT_DIR}/resource/go2/your-custom-scene.xml"
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```
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## Differences with `go2_rl_gym`
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- Terrain's composition are different(see code).
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- tracking reward are different (fixed sigma vs. dynamic sigma).
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## Acknowledgements
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This repository would not exist without the following open-source projects:
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- [isaac_lab](https://github.com/isaac-sim/IsaacLab): Unified framework for robot learning built on NVIDIA Isaac Sim.
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- [rsl_rl](https://github.com/leggedrobotics/rsl_rl.git): Reinforcement learning algorithms.
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- [robot_lab](https://github.com/fan-ziqi/robot_lab.git): RL Extension Library for Robots, Based on IsaacLab.
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- [mujoco](https://github.com/google-deepmind/mujoco.git): High-performance CPU physics simulator.
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Related publications implemented in this repo:
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- [CTS: Concurrent Teacher-Student Reinforcement Learning for Legged Locomotion](https://arxiv.org/pdf/2405.10830)
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