Getting Started with Real-World Robots

This tutorial will explain you how to train a neural network to autonomously control a real robot.

You’ll learn:

1. How to record and visualize your dataset.
2. How to train a policy using your data and prepare it for evaluation.
3. How to evaluate your policy and visualize the results.

By following these steps, you’ll be able to replicate tasks like picking up a Lego block and placing it in a bin with a high success rate, as demonstrated in this video.

This tutorial is specifically made for the affordable SO-101 robot, but it contains additional information to be easily adapted to various types of robots like Aloha bimanual robot by changing some configurations. The SO-101 consists of a leader arm and a follower arm, each with 6 motors. It can work with one or several cameras to record the scene, which serve as visual sensors for the robot.

During the data collection phase, you will control the follower arm by moving the leader arm. This process is known as “teleoperation.” This technique is used to collect robot trajectories. Afterward, you’ll train a neural network to imitate these trajectories and deploy the network to enable your robot to operate autonomously.

If you encounter any issues at any step of the tutorial, feel free to seek help on Discord or don’t hesitate to iterate with us on the tutorial by creating issues or pull requests.

Setup and Calibrate

If you haven’t yet setup and calibrate the SO-101 follow these steps:

1. Find ports and update config file
2. Calibrate

Teleoperate

Run this simple script to teleoperate your robot (it won’t connect and display the cameras):

python lerobot/scripts/control_robot.py \
  --robot.type=so101 \
  --robot.cameras='{}' \
  --control.type=teleoperate

The teleoperate command will automatically:

1. Identify any missing calibrations and initiate the calibration procedure.
2. Connect the robot and start teleoperation.

Setup Cameras

To connect a camera you have three options:

1. OpenCVCamera which allows us to use any camera: usb, realsense, laptop webcam
2. iPhone camera with MacOS
3. Phone camera on Linux

Use OpenCVCamera

The OpenCVCamera class allows you to efficiently record frames from most cameras using the opencv2 library. For more details on compatibility, see Video I/O with OpenCV Overview.

To instantiate an OpenCVCamera, you need a camera index (e.g. OpenCVCamera(camera_index=0)). When you only have one camera like a webcam of a laptop, the camera index is usually 0 but it might differ, and the camera index might change if you reboot your computer or re-plug your camera. This behavior depends on your operating system.

To find the camera indices, run the following utility script, which will save a few frames from each detected camera:

python lerobot/common/robot_devices/cameras/opencv.py \
    --images-dir outputs/images_from_opencv_cameras

The output will look something like this if you have two cameras connected:

Mac or Windows detected. Finding available camera indices through scanning all indices from 0 to 60
[...]
Camera found at index 0
Camera found at index 1
[...]
Connecting cameras
OpenCVCamera(0, fps=30.0, width=1920.0, height=1080.0, color_mode=rgb)
OpenCVCamera(1, fps=24.0, width=1920.0, height=1080.0, color_mode=rgb)
Saving images to outputs/images_from_opencv_cameras
Frame: 0000	Latency (ms): 39.52
[...]
Frame: 0046	Latency (ms): 40.07
Images have been saved to outputs/images_from_opencv_cameras

Check the saved images in outputs/images_from_opencv_cameras to identify which camera index corresponds to which physical camera (e.g. 0 for camera_00 or 1 for camera_01):

camera_00_frame_000000.png
[...]
camera_00_frame_000047.png
camera_01_frame_000000.png
[...]
camera_01_frame_000047.png

Note: Some cameras may take a few seconds to warm up, and the first frame might be black or green.

Now that you have the camera indexes, you should specify the camera’s in the config.

Use your phone

Teleoperate with cameras

We can now teleoperate again while at the same time visualizing the cameras and joint positions with rerun.

python lerobot/scripts/control_robot.py \
  --robot.type=so101 \
  --control.type=teleoperate
  --control.display_data=true

Record a dataset

Once you’re familiar with teleoperation, you can record your first dataset with SO-101.

We use the Hugging Face hub features for uploading your dataset. If you haven’t previously used the Hub, make sure you can login via the cli using a write-access token, this token can be generated from the Hugging Face settings.

Add your token to the cli by running this command:

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

Then store your Hugging Face repository name in a variable:

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

Now you can record a dataset, to record 2 episodes and upload your dataset to the hub execute this command:

python lerobot/scripts/control_robot.py \
  --robot.type=so101 \
  --control.type=record \
  --control.fps=30 \
  --control.single_task="Grasp a lego block and put it in the bin." \
  --control.repo_id=${HF_USER}/so101_test \
  --control.tags='["so101","tutorial"]' \
  --control.warmup_time_s=5 \
  --control.episode_time_s=30 \
  --control.reset_time_s=30 \
  --control.num_episodes=2 \
  --control.push_to_hub=true

You will see a lot of lines appearing like this one:

INFO 2024-08-10 15:02:58 ol_robot.py:219 dt:33.34 (30.0hz) dtRlead: 5.06 (197.5hz) dtWfoll: 0.25 (3963.7hz) dtRfoll: 6.22 (160.7hz) dtRlaptop: 32.57 (30.7hz) dtRphone: 33.84 (29.5hz)

Dataset upload

Locally your dataset is stored in this folder: ~/.cache/huggingface/lerobot/{repo-id} (e.g. data/cadene/so101_test). At the end of data recording, your dataset will be uploaded on your Hugging Face page (e.g. https://huggingface.co/datasets/cadene/so101_test) that you can obtain by running:

echo https://huggingface.co/datasets/${HF_USER}/so101_test

Your dataset will be automatically tagged with LeRobot for the community to find it easily, and you can also add custom tags (in this case tutorial for example).

You can look for other LeRobot datasets on the hub by searching for LeRobot tags.

Record function

The record function provides a suite of tools for capturing and managing data during robot operation:

1. Frame Capture and Video Encoding

• Frames from cameras are saved to disk during recording.
• At the end of each episode, frames are encoded into video files.

2. Data Storage

• Data is stored using the LeRobotDataset format.
• By default, the dataset is pushed to your Hugging Face page.
  • To disable uploading, use --control.push_to_hub=false.

3. Checkpointing and Resuming

• Checkpoints are automatically created during recording.
• If an issue occurs, you can resume by re-running the same command with --control.resume=true.
• To start recording from scratch, manually delete the dataset directory.

4. Recording Parameters

Set the flow of data recording using command-line arguments:

• --control.warmup_time_s=10 Number of seconds before starting data collection (default: 10 seconds). Allows devices to warm up and synchronize.
• --control.episode_time_s=60 Duration of each data recording episode (default: 60 seconds).
• --control.reset_time_s=60 Duration for resetting the environment after each episode (default: 60 seconds).
• --control.num_episodes=50 Total number of episodes to record (default: 50).

5. Keyboard Controls During Recording

Control the data recording flow using keyboard shortcuts:

• Press Right Arrow (→): Early stop the current episode or reset time and move to the next.
• Press Left Arrow (←): Cancel the current episode and re-record it.
• Press Escape (ESC): Immediately stop the session, encode videos, and upload the dataset.

Tips for gathering data

Once you’re comfortable with data recording, you can create a larger dataset for training. A good starting task is grasping an object at different locations and placing it in a bin. We suggest recording at least 50 episodes, with 10 episodes per location. Keep the cameras fixed and maintain consistent grasping behavior throughout the recordings. Also make sure the object you are manipulating is visible on the camera’s. A good rule of thumb is you should be able to do the task yourself by only looking at the camera images.

In the following sections, you’ll train your neural network. After achieving reliable grasping performance, you can start introducing more variations during data collection, such as additional grasp locations, different grasping techniques, and altering camera positions.

Avoid adding too much variation too quickly, as it may hinder your results.

Troubleshooting:

• On Linux, if the left and right arrow keys and escape key don’t have any effect during data recording, make sure you’ve set the $DISPLAY environment variable. See pynput limitations.

Visualize a dataset

If you uploaded your dataset to the hub with --control.push_to_hub=true, you can visualize your dataset online by copy pasting your repo id given by:

echo ${HF_USER}/so101_test

If you didn’t upload with --control.push_to_hub=false, you can visualize it locally with (via a window in the browser http://127.0.0.1:9090 with the visualization tool):

python lerobot/scripts/visualize_dataset_html.py \
  --repo-id ${HF_USER}/so101_test \
  --local-files-only 1

This will launch a local web server that looks like this:

Replay an episode

A useful feature is the replay function, which allows to replay on your robot any episode that you’ve recorded or episodes from any dataset out there. This function helps you test the repeatability of your robot’s actions and assess transferability across robots of the same model.

You can replay the first episode on your robot with:

python lerobot/scripts/control_robot.py \
  --robot.type=so101 \
  --control.type=replay \
  --control.fps=30 \
  --control.repo_id=${HF_USER}/so101_test \
  --control.episode=0

Your robot should replicate movements similar to those you recorded. For example, check out this video where we use replay on a Aloha robot from Trossen Robotics.

Train a policy

To train a policy to control your robot, use the python lerobot/scripts/train.py script. A few arguments are required. Here is an example command:

python lerobot/scripts/train.py \
  --dataset.repo_id=${HF_USER}/so101_test \
  --policy.type=act \
  --output_dir=outputs/train/act_so101_test \
  --job_name=act_so101_test \
  --policy.device=cuda \
  --wandb.enable=true

Let’s explain the command:

1. We provided the dataset as argument with --dataset.repo_id=${HF_USER}/so101_test.
2. We provided the policy with policy.type=act. This loads configurations from configuration_act.py. Importantly, this policy will automatically adapt to the number of motor states, motor actions and cameras of your robot (e.g. laptop and phone) which have been saved in your dataset.
3. We provided policy.device=cuda since we are training on a Nvidia GPU, but you could use policy.device=mps to train on Apple silicon.
4. We provided wandb.enable=true to use Weights and Biases for visualizing training plots. This is optional but if you use it, make sure you are logged in by running wandb login.

Training should take several hours. You will find checkpoints in outputs/train/act_so101_test/checkpoints.

To resume training from a checkpoint, below is an example command to resume from last checkpoint of the act_so101_test policy:

python lerobot/scripts/train.py \
  --config_path=outputs/train/act_so101_test/checkpoints/last/pretrained_model/train_config.json \
  --resume=true

Upload policy checkpoints

Once training is done, upload the latest checkpoint with:

huggingface-cli upload ${HF_USER}/act_so101_test \
  outputs/train/act_so101_test/checkpoints/last/pretrained_model

You can also upload intermediate checkpoints with:

CKPT=010000
huggingface-cli upload ${HF_USER}/act_so101_test${CKPT} \
  outputs/train/act_so101_test/checkpoints/${CKPT}/pretrained_model

Evaluate your policy

You can use the record function from lerobot/scripts/control_robot.py but with a policy checkpoint as input. For instance, run this command to record 10 evaluation episodes:

python lerobot/scripts/control_robot.py \
  --robot.type=so101 \
  --control.type=record \
  --control.fps=30 \
  --control.single_task="Grasp a lego block and put it in the bin." \
  --control.repo_id=${HF_USER}/eval_act_so101_test \
  --control.tags='["tutorial"]' \
  --control.warmup_time_s=5 \
  --control.episode_time_s=30 \
  --control.reset_time_s=30 \
  --control.num_episodes=10 \
  --control.push_to_hub=true \
  --control.policy.path=outputs/train/act_so101_test/checkpoints/last/pretrained_model

As you can see, it’s almost the same command as previously used to record your training dataset. Two things changed:

1. There is an additional --control.policy.path argument which indicates the path to your policy checkpoint with (e.g. outputs/train/eval_act_so101_test/checkpoints/last/pretrained_model). You can also use the model repository if you uploaded a model checkpoint to the hub (e.g. ${HF_USER}/act_so101_test).
2. The name of dataset begins by eval to reflect that you are running inference (e.g. ${HF_USER}/eval_act_so101_test).