lcccluck/mujoco-lerobot-train

mujoco_lerobot_train

Minimal MuJoCo + LeRobot pipeline for:

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

This directory is intentionally small. All parameters are read from one file:

• config.json

这是一个最小化的 MuJoCo + LeRobot 工程，包含 4 步：

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

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：

• config.json

Dependencies

• mujoco
• lerobot

See:

• requirements.txt

依赖

• mujoco
• lerobot

依赖文件见：

• requirements.txt

Files

• 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

文件说明

• 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

Run

Activate your environment first:

conda activate lerobot
cd mujoco_lerobot_train

运行

先激活环境并进入目录：

conda activate lerobot
cd mujoco_lerobot_train

Collect dataset:

python collect_dataset.py

Visualize dataset:

python viz_dataset.py

Train with the Python entry:

python train_policy.py

Closed-loop MuJoCo evaluation:

python eval_policy.py

闭环 MuJoCo 评估：

python eval_policy.py

Config

config.json controls:

• dataset repo id and local root
• image size and fps
• number of episodes
• ACT training hyperparameters
• evaluation episodes and playback speed

配置

config.json 统一控制：

• 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

Upload To Hugging Face

Login first:

huggingface-cli login

上传到 Hugging Face

先登录：

huggingface-cli login

Then upload this folder:

bash ./upload_to_hf.sh <user_or_org>/<repo_name>

Private repo:

HF_PRIVATE=1 bash ./upload_to_hf.sh <user_or_org>/<repo_name>

Dataset repo instead of model repo:

HF_REPO_TYPE=dataset bash ./upload_to_hf.sh <user_or_org>/<repo_name>

Ignored during upload:

• outputs/
• __pycache__/
• *.pyc

上传时会自动忽略：

• outputs/
• __pycache__/
• *.pyc