10_use_so100.md

Using the SO-100 with LeRobot

A. Source the parts

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

Important: 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

On your computer:

1. Install Miniconda:

mkdir -p ~/miniconda3
# Linux:
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda3/miniconda.sh
# Mac M-series: 
# curl https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-arm64.sh -o ~/miniconda3/miniconda.sh
# Mac Intel: 
# curl https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh -o ~/miniconda3/miniconda.sh
bash ~/miniconda3/miniconda.sh -b -u -p ~/miniconda3
rm ~/miniconda3/miniconda.sh
~/miniconda3/bin/conda init bash

2. Restart shell or source ~/.bashrc (Mac: source ~/.bash_profile) or source ~/.zshrc if you're using zshell

3. Create and activate a fresh conda environment for lerobot

conda create -y -n lerobot python=3.10 && 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]"

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"

C. Configure the motors

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.

a. Run the script to find ports

Follow Step 1 of the assembly video, which illustrates the use of our scripts below.

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 YAML file

Now that you have the ports, modify the port sections in so100.yaml

2. Configure the motors

a. Set IDs for all 12 motors

Plug your first motor 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:

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

Note: These motors are currently limitated. 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

Follow step 2 of the assembly video. 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

Follow step 3 of the assembly video. 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

Follow step 4 of the assembly video. The first arm should take a bit more than 1 hour to assemble, but once you get use 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

/!\ 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 calibrate \
    --robot-path lerobot/configs/robot/so100.yaml \
    --robot-overrides '~cameras' --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 calibrate \
    --robot-path lerobot/configs/robot/so100.yaml \
    --robot-overrides '~cameras' --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 teleoperate \
    --robot-path lerobot/configs/robot/so100.yaml \
    --robot-overrides '~cameras' \
    --display-cameras 0

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 teleoperate \
    --robot-path lerobot/configs/robot/so100.yaml

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 record \
    --robot-path lerobot/configs/robot/so100.yaml \
    --fps 30 \
    --repo-id ${HF_USER}/so100_test \
    --tags so100 tutorial \
    --warmup-time-s 5 \
    --episode-time-s 40 \
    --reset-time-s 10 \
    --num-episodes 2 \
    --push-to-hub 1

H. Visualize a dataset

If you uploaded your dataset to the hub with --push-to-hub 1, 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 --push-to-hub 0, you can also visualize it locally with:

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

I. Replay an episode

Now try to replay the first episode on your robot:

python lerobot/scripts/control_robot.py replay \
    --robot-path lerobot/configs/robot/so100.yaml \
    --fps 30 \
    --repo-id ${HF_USER}/so100_test \
    --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=act_so100_real \
  env=so100_real \
  hydra.run.dir=outputs/train/act_so100_test \
  hydra.job.name=act_so100_test \
  device=cuda \
  wandb.enable=true

Let's explain it:

1. We provided the dataset as argument with dataset_repo_id=${HF_USER}/so100_test.
2. We provided the policy with policy=act_so100_real. This loads configurations from lerobot/configs/policy/act_so100_real.yaml. Importantly, this policy uses 2 cameras as input laptop, phone.
3. We provided an environment as argument with env=so100_real. This loads configurations from lerobot/configs/env/so100_real.yaml.
4. We provided device=cuda since we are training on a Nvidia GPU, but you can also use device=mps if you are using a Mac with Apple silicon, or device=cpu otherwise.
5. 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 record \
  --robot-path lerobot/configs/robot/so100.yaml \
  --fps 30 \
  --repo-id ${HF_USER}/eval_act_so100_test \
  --tags so100 tutorial eval \
  --warmup-time-s 5 \
  --episode-time-s 40 \
  --reset-time-s 10 \
  --num-episodes 10 \
  -p 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:

1. There is an additional -p argument which indicates the path to your policy checkpoint with (e.g. -p outputs/train/eval_so100_test/checkpoints/last/pretrained_model). You can also use the model repository if you uploaded a model checkpoint to the hub (e.g. -p ${HF_USER}/act_so100_test).
2. The name of dataset begins by eval to reflect that you are running inference (e.g. --repo-id ${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.

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