| --- |
| library_name: lerobot |
| tags: |
| - mujoco |
| - lerobot |
| - robotics |
| - imitation-learning |
| - act |
| --- |
| |
| # mujoco_lerobot_train |
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| Minimal MuJoCo + LeRobot pipeline for: |
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| 1. collecting a standard LeRobot dataset |
| 2. visualizing the dataset with `lerobot-dataset-viz` |
| 3. training an ACT policy with `lerobot-train` |
| 4. running closed-loop MuJoCo evaluation with the trained policy |
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| This directory is intentionally small. All parameters are read from one file: |
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| - `config.json` |
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| 这是一个最小化的 MuJoCo + LeRobot 工程，包含 4 步： |
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| 1. 用 MuJoCo 采集标准 LeRobot 数据集 |
| 2. 用 `lerobot-dataset-viz` 可视化数据 |
| 3. 用 `lerobot-train` 训练 ACT 策略 |
| 4. 在 MuJoCo 中闭环评估训练好的策略 |
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| 整个目录尽量保持小而清晰，所有参数都只从一个文件读取： |
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| - `config.json` |
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| ## Dependencies |
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| - `mujoco` |
| - `lerobot` |
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| See: |
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| - `requirements.txt` |
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| ## 依赖 |
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| - `mujoco` |
| - `lerobot` |
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| 依赖文件见： |
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| - `requirements.txt` |
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| ## Files |
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| - `collect_dataset.py`: collect a MuJoCo pick-place dataset in LeRobot format |
| - `viz_dataset.py`: open `lerobot-dataset-viz` for the configured dataset |
| - `train_policy.py`: Python entry that reads config and launches training |
| - `eval_policy.py`: closed-loop MuJoCo evaluation using the trained policy |
| - `common.py`: shared minimal implementation |
| - `config.json`: all parameters |
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| ## 文件说明 |
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| - `collect_dataset.py`：采集 MuJoCo 抓取放置数据，并写成 LeRobot 标准格式 |
| - `viz_dataset.py`：调用 `lerobot-dataset-viz` 可视化当前数据集 |
| - `train_policy.py`：读取配置后启动训练 |
| - `eval_policy.py`：在 MuJoCo 中闭环评估训练好的策略 |
| - `common.py`：公共最小实现 |
| - `config.json`：全部参数 |
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| ## Run |
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| Activate your environment first: |
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| ```bash |
| conda activate lerobot |
| cd mujoco_lerobot_train |
| ``` |
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| ## 运行 |
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| 先激活环境并进入目录： |
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| ```bash |
| conda activate lerobot |
| cd mujoco_lerobot_train |
| ``` |
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| Collect dataset: |
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| ```bash |
| python collect_dataset.py |
| ``` |
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| Visualize dataset: |
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| ```bash |
| python viz_dataset.py |
| ``` |
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| Train with the Python entry: |
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| ```bash |
| python train_policy.py |
| ``` |
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| Closed-loop MuJoCo evaluation: |
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| ```bash |
| python eval_policy.py |
| ``` |
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| 闭环 MuJoCo 评估： |
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| ```bash |
| python eval_policy.py |
| ``` |
|
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| ## Config |
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| `config.json` controls: |
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| - dataset repo id and local root |
| - image size and fps |
| - number of episodes |
| - ACT training hyperparameters |
| - evaluation episodes and playback speed |
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| ## 配置 |
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| `config.json` 统一控制： |
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| - 数据集 repo id 和本地路径 |
| - 图像分辨率和 fps |
| - episode 数量 |
| - ACT 训练参数 |
| - 评估轮数和播放速度 |
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| ## Upload To Hugging Face |
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| Login first: |
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| ```bash |
| huggingface-cli login |
| ``` |
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| ## 上传到 Hugging Face |
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| 先登录： |
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| ```bash |
| huggingface-cli login |
| ``` |
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| Then upload this folder: |
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| ```bash |
| bash ./upload_to_hf.sh <user_or_org>/<repo_name> |
| ``` |
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| Private repo: |
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| ```bash |
| HF_PRIVATE=1 bash ./upload_to_hf.sh <user_or_org>/<repo_name> |
| ``` |
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| Dataset repo instead of model repo: |
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| ```bash |
| HF_REPO_TYPE=dataset bash ./upload_to_hf.sh <user_or_org>/<repo_name> |
| ``` |
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| Ignored during upload: |
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| - `outputs/` |
| - `__pycache__/` |
| - `*.pyc` |
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| 上传时会自动忽略： |
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| - `outputs/` |
| - `__pycache__/` |
| - `*.pyc` |
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