Using the SO-100 with LeRobot

A. Source the parts

Follow this README. It contains the bill of materials, with a link to source the parts, as well as the instructions to 3D print the parts, and advice if it's your first time printing or if you don't own a 3D printer.

Before assembling, you will first need to configure your motors. To this end, we provide a nice script, so let's first install LeRobot. After configuration, we will also guide you through assembly.

B. Install LeRobot

Tip

We use the Command Prompt (cmd) quite a lot. If you are not comfortable using the cmd or want to brush up using the command line you can have a look here: Command line crash course

On your computer:

1. Install Miniconda:

2. Restart shell

Copy paste in your shell: source ~/.bashrc or for Mac: source ~/.bash_profile or source ~/.zshrc if you're using zshell

3. Create and activate a fresh conda environment for lerobot

Video install instructions

lerobot-install.mp4

conda create -y -n lerobot python=3.10

Then activate your conda environment (do this each time you open a shell to use lerobot!):

conda activate lerobot

4. Clone LeRobot:

git clone https://github.com/huggingface/lerobot.git ~/lerobot

5. Install LeRobot with dependencies for the feetech motors:

cd ~/lerobot && pip install -e ".[feetech]"

EXTRA: For Linux only (not Mac): install extra dependencies for recording datasets:

conda install -y -c conda-forge ffmpeg
pip uninstall -y opencv-python
conda install -y -c conda-forge "opencv>=4.10.0"

Great 🤗! You are now done installing LeRobot and we can begin assembling the SO100 arms 🤖. Every time you now want to use LeRobot you can go to the ~/lerobot folder where we installed LeRobot and run one of the commands.

C. Configure the motors

Note

Throughout this tutorial you will find videos on how to do the steps, the full video tutorial can be found here: assembly video.

1. Find the USB ports associated to each arm

Designate one bus servo adapter and 6 motors for your leader arm, and similarly the other bus servo adapter and 6 motors for the follower arm. It's convenient to label them and write on each motor if it's for the follower F or for the leader L and it's ID from 1 to 6 (F1...F6 and L1...L6).

a. Run the script to find port

Video finding port

lerobot-find-motorbus-video.mov

lerobot-find-motorbus.mov

To find the port for each bus servo adapter, run the utility script:

python lerobot/scripts/find_motors_bus_port.py

b. Example outputs

Example output when identifying the leader arm's port (e.g., /dev/tty.usbmodem575E0031751 on Mac, or possibly /dev/ttyACM0 on Linux):

Finding all available ports for the MotorBus.
['/dev/tty.usbmodem575E0032081', '/dev/tty.usbmodem575E0031751']
Remove the usb cable from your DynamixelMotorsBus and press Enter when done.

[...Disconnect leader arm and press Enter...]

The port of this DynamixelMotorsBus is /dev/tty.usbmodem575E0031751
Reconnect the usb cable.

Example output when identifying the follower arm's port (e.g., /dev/tty.usbmodem575E0032081, or possibly /dev/ttyACM1 on Linux):

Finding all available ports for the MotorBus.
['/dev/tty.usbmodem575E0032081', '/dev/tty.usbmodem575E0031751']
Remove the usb cable from your DynamixelMotorsBus and press Enter when done.

[...Disconnect follower arm and press Enter...]

The port of this DynamixelMotorsBus is /dev/tty.usbmodem575E0032081
Reconnect the usb cable.

c. Troubleshooting

On Linux, you might need to give access to the USB ports by running:

sudo chmod 666 /dev/ttyACM0
sudo chmod 666 /dev/ttyACM1

d. Update config file

IMPORTANTLY: Now that you have your ports, update the port default values of SO100RobotConfig. You will find something like:

@RobotConfig.register_subclass("so100")
@dataclass
class So100RobotConfig(ManipulatorRobotConfig):
    calibration_dir: str = ".cache/calibration/so100"
    # `max_relative_target` limits the magnitude of the relative positional target vector for safety purposes.
    # Set this to a positive scalar to have the same value for all motors, or a list that is the same length as
    # the number of motors in your follower arms.
    max_relative_target: int | None = None

    leader_arms: dict[str, MotorsBusConfig] = field(
        default_factory=lambda: {
            "main": FeetechMotorsBusConfig(
                port="/dev/tty.usbmodem58760431091",  <-- UPDATE HERE
                motors={
                    # name: (index, model)
                    "shoulder_pan": [1, "sts3215"],
                    "shoulder_lift": [2, "sts3215"],
                    "elbow_flex": [3, "sts3215"],
                    "wrist_flex": [4, "sts3215"],
                    "wrist_roll": [5, "sts3215"],
                    "gripper": [6, "sts3215"],
                },
            ),
        }
    )

    follower_arms: dict[str, MotorsBusConfig] = field(
        default_factory=lambda: {
            "main": FeetechMotorsBusConfig(
                port="/dev/tty.usbmodem585A0076891",  <-- UPDATE HERE
                motors={
                    # name: (index, model)
                    "shoulder_pan": [1, "sts3215"],
                    "shoulder_lift": [2, "sts3215"],
                    "elbow_flex": [3, "sts3215"],
                    "wrist_flex": [4, "sts3215"],
                    "wrist_roll": [5, "sts3215"],
                    "gripper": [6, "sts3215"],
                },
            ),
        }
    )

2. Assembling the Base

Let's begin with assembling the follower arm base

a. Set IDs for all 12 motors

Video configuring motor

lerobot-setid.mov

lerobot-configure-motor.mov

Plug your first motor F1 and run this script to set its ID to 1. It will also set its present position to 2048, so expect your motor to rotate. Replace the text after --port to the corresponding follower control board port and run this command in cmd:

python lerobot/scripts/configure_motor.py \
  --port /dev/tty.usbmodem58760432961 \
  --brand feetech \
  --model sts3215 \
  --baudrate 1000000 \
  --ID 1

Note

These motors are currently limited. They can take values between 0 and 4096 only, which corresponds to a full turn. They can't turn more than that. 2048 is at the middle of this range, so we can take -2048 steps (180 degrees anticlockwise) and reach the maximum range, or take +2048 steps (180 degrees clockwise) and reach the maximum range. The configuration step also sets the homing offset to 0, so that if you misassembled the arm, you can always update the homing offset to account for a shift up to ± 2048 steps (± 180 degrees).

Then unplug your motor and plug the second motor and set its ID to 2.

python lerobot/scripts/configure_motor.py \
  --port /dev/tty.usbmodem58760432961 \
  --brand feetech \
  --model sts3215 \
  --baudrate 1000000 \
  --ID 2

Redo the process for all your motors until ID 6. Do the same for the 6 motors of the leader arm.

b. Remove the gears of the 6 leader motors

Video removing gears

lerobot-remove-gears-small.mp4

Follow the video for removing gears. You need to remove the gear for the motors of the leader arm. As a result, you will only use the position encoding of the motor and reduce friction to more easily operate the leader arm.

c. Add motor horn to all 12 motors

Video adding motor horn

lerobot-adding-motor-horn.mov

Follow the video for adding the motor horn. For SO-100, you need to align the holes on the motor horn to the motor spline to be approximately 1:30, 4:30, 7:30 and 10:30. Try to avoid rotating the motor while doing so to keep position 2048 set during configuration. It is especially tricky for the leader motors as it is more sensible without the gears, but it's ok if it's a bit rotated.

D. Assemble the arms

Video assembling arms

lerobot-assemble-arm.mov

Follow the video for assembling the arms. It is important to insert the cables into the motor that is being assembled before you assemble the motor into the arm! Inserting the cables beforehand is much easier than doing this afterward. The first arm should take a bit more than 1 hour to assemble, but once you get used to it, you can do it under 1 hour for the second arm.

E. Calibrate

Next, you'll need to calibrate your SO-100 robot to ensure that the leader and follower arms have the same position values when they are in the same physical position. This calibration is essential because it allows a neural network trained on one SO-100 robot to work on another.

a. Manual calibration of follower arm

Important

Contrarily to step 6 of the assembly video which illustrates the auto calibration, we will actually do manual calibration of follower for now.

You will need to move the follower arm to these positions sequentially:

1. Zero position	2. Rotated position	3. Rest position

Make sure both arms are connected and run this script to launch manual calibration:

python lerobot/scripts/control_robot.py \
  --robot.type=so100 \
  --robot.cameras='{}' \
  --control.type=calibrate \
  --control.arms='["main_follower"]'

b. Manual calibration of leader arm

Follow step 6 of the assembly video which illustrates the manual calibration. You will need to move the leader arm to these positions sequentially:

1. Zero position	2. Rotated position	3. Rest position

Run this script to launch manual calibration:

python lerobot/scripts/control_robot.py \
  --robot.type=so100 \
  --robot.cameras='{}' \
  --control.type=calibrate \
  --control.arms='["main_leader"]'

F. Teleoperate

Simple teleop Then you are ready to teleoperate your robot! Run this simple script (it won't connect and display the cameras):

python lerobot/scripts/control_robot.py \
  --robot.type=so100 \
  --robot.cameras='{}' \
  --control.type=teleoperate

a. Teleop with displaying cameras

Follow this guide to setup your cameras. Then you will be able to display the cameras on your computer while you are teleoperating by running the following code. This is useful to prepare your setup before recording your first dataset.

python lerobot/scripts/control_robot.py \
  --robot.type=so100 \
  --control.type=teleoperate

G. Record a dataset

Once you're familiar with teleoperation, you can record your first dataset with SO-100.

If you want to use the Hugging Face hub features for uploading your dataset and you haven't previously done it, make sure you've logged in using a write-access token, which can be generated from the Hugging Face settings:

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

Store your Hugging Face repository name in a variable to run these commands:

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

Record 2 episodes and upload your dataset to the hub:

python lerobot/scripts/control_robot.py \
  --robot.type=so100 \
  --control.type=record \
  --control.fps=30 \
  --control.single_task="Grasp a lego block and put it in the bin." \
  --control.repo_id=${HF_USER}/so100_test \
  --control.tags='["so100","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

Note: You can resume recording by adding --control.resume=true.

H. 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}/so100_test

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

python lerobot/scripts/visualize_dataset_html.py \
  --repo-id ${HF_USER}/so100_test \
  --local-files-only 1

I. Replay an episode

Now try to replay the first episode on your robot:

python lerobot/scripts/control_robot.py \
  --robot.type=so100 \
  --control.type=replay \
  --control.fps=30 \
  --control.repo_id=${HF_USER}/so100_test \
  --control.episode=0

J. 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}/so100_test \
  --policy.type=act \
  --output_dir=outputs/train/act_so100_test \
  --job_name=act_so100_test \
  --device=cuda \
  --wandb.enable=true

Let's explain it:

We provided the dataset as argument with --dataset.repo_id=${HF_USER}/so100_test.
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 sates, motor actions and cameras of your robot (e.g. laptop and phone) which have been saved in your dataset.
We provided device=cuda since we are training on a Nvidia GPU, but you could use device=mps to train on Apple silicon.
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_so100_test/checkpoints.

K. 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=so100 \
  --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_so100_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_so100_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:

There is an additional --control.policy.path argument which indicates the path to your policy checkpoint with (e.g. outputs/train/eval_act_so100_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_so100_test).
The name of dataset begins by eval to reflect that you are running inference (e.g. ${HF_USER}/eval_act_so100_test).

L. More Information

Follow this previous tutorial for a more in-depth tutorial on controlling real robots with LeRobot.

Tip

If you have any questions or need help, please reach out on Discord in the channel #so100-arm.

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10_use_so100.md

10_use_so100.md

Using the SO-100 with LeRobot

Table of Contents

A. Source the parts

B. Install LeRobot

1. Install Miniconda:

2. Restart shell

3. Create and activate a fresh conda environment for lerobot

4. Clone LeRobot:

5. Install LeRobot with dependencies for the feetech motors:

C. Configure the motors

1. Find the USB ports associated to each arm

a. Run the script to find port

b. Example outputs

c. Troubleshooting

d. Update config file

2. Assembling the Base

a. Set IDs for all 12 motors

b. Remove the gears of the 6 leader motors

c. Add motor horn to all 12 motors

D. Assemble the arms

E. Calibrate

a. Manual calibration of follower arm

b. Manual calibration of leader arm

F. Teleoperate

a. Teleop with displaying cameras

G. Record a dataset

H. Visualize a dataset

I. Replay an episode

J. Train a policy

K. Evaluate your policy

L. More Information

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10_use_so100.md

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10_use_so100.md

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Using the SO-100 with LeRobot

Table of Contents

A. Source the parts

B. Install LeRobot

1. Install Miniconda:

2. Restart shell

3. Create and activate a fresh conda environment for lerobot

4. Clone LeRobot:

5. Install LeRobot with dependencies for the feetech motors:

C. Configure the motors

1. Find the USB ports associated to each arm

a. Run the script to find port

b. Example outputs

c. Troubleshooting

d. Update config file

2. Assembling the Base

a. Set IDs for all 12 motors

b. Remove the gears of the 6 leader motors

c. Add motor horn to all 12 motors

D. Assemble the arms

E. Calibrate

a. Manual calibration of follower arm

b. Manual calibration of leader arm

F. Teleoperate

a. Teleop with displaying cameras

G. Record a dataset

H. Visualize a dataset

I. Replay an episode

J. Train a policy

K. Evaluate your policy

L. More Information