Dataset Preparation with LeRobot CLI

Prerequisites

Access the Robot PC either directly or via SSH, and follow the steps below. (Refer to the Setup Guide for instructions on how to connect via SSH.)

1. Launch the ROS 2 teleoperation node

Open a new terminal and enter the Docker container

cd ai_worker
./docker/container.sh enter

Launch the ROS 2 teleoperation node

ffw_bg2_ai

2. Authenticate with Hugging Face

INFO

If you do not wish to use Hugging Face, you may skip this step and proceed to the next section, "Prerequisite without Hugging Face."

Open a new terminal and enter the Docker container:

cd ai_worker
./docker/container.sh enter

Navigate to the lerobot directory:

cd /root/ros2_ws/src/physical_ai_tools/lerobot

To create a Hugging Face dataset, you first need to log in using a write access token, which can be generated from your Hugging Face settings:

huggingface-cli login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential

Store your Hugging Face username in a variable:

export HF_USER=$(huggingface-cli whoami | head -n 1)
echo $HF_USER

You should see an output similar to the following:

YourUserName

Prerequisite without Hugging Face

If you do not intend to use the Hugging Face Hub, you can still define a placeholder username for local dataset handling:

export HF_USER=AnyNameYouWant
echo $HF_USER

You should see an output like:

AnyNameYouWant

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• Make sure to replace ${HF_USER} with your actual Hugging Face username.

Record your dataset

To start recording, run the following command inside the Docker container.

cd /root/ros2_ws/src/physical_ai_tools/lerobot

python lerobot/scripts/control_robot.py \
  --robot.type=ffw \
  --control.type=record \
  --control.single_task="pick and place objects" \
  --control.fps=15 \
  --control.repo_id=${HF_USER}/ffw_test \
  --control.tags='["tutorial"]' \
  --control.warmup_time_s=5 \
  --control.episode_time_s=30 \
  --control.reset_time_s=10 \
  --control.num_episodes=10 \
  --control.push_to_hub=true \
  --control.play_sounds=false

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• To save the dataset locally without uploading to the Hugging Face Hub, set --control.push_to_hub=false. This option is essential if you choose not to use Hugging Face.
• If you are controlling the robot remotely via VSCode or SSH, the arrow keys may not work during data recording due to a pynput limitation. In this case, it's recommended to set --control.episode_time_s and --control.reset_time_s appropriately.
• To use the arrow keys for teleoperation, connect a monitor and keyboard directly to the robot (Right arrow key: Save immediately, Left arrow key: Cancel).

👉 Key Parameters to Customize

Parameter	Description	Example
--control.repo_id	The Hugging Face dataset repository ID in the format <username>/<dataset_name>	username/ffw_pick_place
--control.single_task	The name of the task you are performing	"pick and place objects"
--control.fps	Frame rate for dataset recording	15 (recommended)
--control.episode_time_s	Duration (in seconds) to record each episode	30-60 for simple tasks
--control.reset_time_s	Time allocated (in seconds) for resetting between episodes	10-20 seconds
--control.num_episodes	Total number of episodes to record	10-50 depending on task complexity

Of course, you can modify additional parameters as needed to fit your specific use case.

The dataset is located at:

INFO

This path refers to your host system, not inside the Docker container.

~/ai_worker/docker/huggingface/lerobot

Dataset Visualization

Once data collection is complete, you can preview and inspect your recorded dataset using the following command:

cd /root/ros2_ws/src/physical_ai_tools/lerobot

python lerobot/scripts/visualize_dataset_html.py \
  --host 0.0.0.0 \
  --port 9091 \
  --repo-id ${HF_USER}/ffw_test

You should see an output similar to the following:

Fetching 4 files: 100%|██████████| 4/4 [00:00<00:00, 3457.79it/s]
.gitattributes: 100%|██████████| 2.46k/2.46k [00:00<00:00, 45.9MB/s]
Fetching 126 files: 100%|██████████| 126/126 [00:00<00:00, 266.66it/s]
Resolving data files: 100%|██████████| 30/30 [00:00<00:00, 662258.53it/s]
INFO 2025-05-15 16:18:07 set_html.py:364 Output directory already exists. Loading from it: '/tmp/lerobot_visualize_dataset_uo6ddbb1'
 * Serving Flask app 'visualize_dataset_html'
 * Debug mode: off
INFO 2025-05-15 16:18:07 _internal.py:97 WARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead.
 * Running on http://127.0.0.1:9091
INFO 2025-05-15 16:18:07 _internal.py:97 Press CTRL+C to quit

Access http://127.0.0.1:9091 to view the dataset. You should see a web interface similar to the one below:

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Once the server is running, open http://127.0.0.1:9091 in your browser to preview the dataset.

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If you have a another device connected to the same network as the host machine, open http://ffw-{serial number}.local:9091 in your browser to preview the dataset.

For example, http://ffw-SNPR48A0000.local:9091.