How to use the SO-ARM100 robotic arm in Lerobot
Introduction
The SO-100ARM is a fully open-source robotic arm project launched by TheRobotStudio. It includes the follower arm and the leader robotic arm, and also provides detailed 3D printing files and operation guides. LeRobot is committed to providing models, datasets and tools for real-world robotics in PyTorch. Its aim is to reduce the entry barrier of robotics, enabling everyone to contribute and benefit from sharing datasets and pretrained models. LeRobot integrates cutting-edge methodologies validated for real-world application, centering on imitation learning and reinforcement learning. It has furnished a suite of pre-trained models, datasets featuring human-gathered demonstrations, and simulation environments, enabling users to commence without the necessity of robot assembly. In the forthcoming weeks, the intention is to augment support for real-world robotics on the most cost-effective and competent robots presently accessible.
Projects Introduction
The SO-ARM100 and reComputer Jetson AI intelligent robot kit seamlessly combine high-precision robotic arm control with a powerful AI computing platform, providing a comprehensive robot development solution. This kit is based on the Jetson Orin or AGX Orin platform, combined with the SO-ARM100 robotic arm and the LeRobot AI framework, offering users an intelligent robot system applicable to multiple scenarios such as education, research, and industrial automation. This wiki provides the assembly and debugging tutorial for the SO ARM100 and realizes data collection and training within the Lerobot framework.
Main Features
- High-Precision Robotic Arm: The SO-ARM100 robotic arm employs high-precision servo motors and advanced motion control algorithms, and is suitable for a variety of tasks such as grasping, assembly, and inspection.
- reComputer Jetson Platform: It uses the SeeedStudio reComputer Jetson Orin or AGX Orin Dev Kit as the AI computing platform, which supports deep learning, computer vision, and data processing tasks, providing powerful computing capabilities.
- AI-Driven: It integrates the LeRobot AI framework of Hugging Face, supports natural language processing (NLP) and computer vision, enabling the robot to intelligently understand instructions and perceive the environment.
- Open Source and Flexible Expansion: It is an open-source platform that is easy to customize and expand, suitable for developers and researchers to conduct secondary development, and supports the integration of multiple sensors and tools.
- Multi-Scene Application: It is applicable to fields such as education, scientific research, automated production, and robotics, helping users achieve efficient and precise robot operations in various complex tasks.
Specification
| Specification | Arm Kit | Arm Kit Pro |
|---|---|---|
| Type | Arm Kit | Arm Kit Pro |
| Degree of freedom | 6 | 6 |
| Max Torque | 19.5kg.cm 7.4V | 30kg.cm 12V |
| Servo | STS3215 Bus Servo | STS3215 Bus Servo |
| Power Supply | 5.5mm*2.1mm DC 5V4A | 5.5mm*2.1mm DC 12V1A |
| Angle sensor | 12-bit magnetic encoder | 12-bit magnetic encoder |
| Recommended Operating Temperature Range | 0℃～40℃ | 0℃～40℃ |
| Communication Method | UART | UART |
| Control Method | PC | PC |
Bill of Materials(BOM)
| Part | Amount | Included |
|---|---|---|
| STS3215 Servo1 | 12 | ✅ |
| Motor Control Board | 2 | ✅ |
| USB-C Cable 2 pcs | 1 | ✅ |
| Power Supply2 | 2 | ✅ |
| Table Clamp | 1 | ❌ |
| 3D printed parts of the arm | 1 | ❌ |
The 3D printed parts and table clamps are not included in the product. However, the SO-100ARM provides detailed 3D printing STL files and printing parameters. Besides, we also offer the 3D printed parts of the Table Clamp.
Table of Contents
On February 13, 2025, we fully updated the algorithms in the GitHub repository following the official guidelines of Huggingface. These algorithms are now compatible with and adapted to the Pi0 Vision-Action Large Model. Previous users are required to reinstall the Lerobot environment and re - calibrate and initialize each servo.
3D Printing Guide
A variety of 3D printers are acceptable to print the parts necessary of the follower and leader arm. Follow the steps below to ensure a good print.
Choose a printer: The STL files provided ready to print on many FDM printers. Below are the tested and suggested settings though others may work.
- Material: PLA
- Nozzle Diameter and Precision: 0.4mm nozzle diameter at 0.2mm layer height or 0.6mm nozzle at 0.4mm layer height.
- Infill Density: 13%
Acquisition of 3D printing files: All the parts for the leader or follower are contained in a single file, correctly orientated for z upwards to minimize supports.
For printer bed sizes of 220mmx220mm (such as the Ender), print:
For printer bed sizes of 205mm x 250mm (such as the Prusa/Up), print:
There will be certain precision errors in the printed parts, which may make disassembly and assembly rather difficult. You can use a grinding machine to polish the interior.
For easier download, we provide the optimized 3D model of Lerobot to Makerworld platform, including the Table Clamps.
Install LeRobot
Environments such as pytorch and torchvision need to be installed based on your CUDA. Then, on your reComputer Nvidia Jetson:
- Install Miniconda: For Jetson:
mkdir -p ~/miniconda3
cd ~/miniconda3
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-aarch64.sh
chmod +x Miniconda3-latest-Linux-aarch64.sh
./Miniconda3-latest-Linux-aarch64.sh
source ~/.bashrc
Or, For Windows Linux:
mkdir -p ~/miniconda3
cd miniconda3
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda3/miniconda.sh
bash ~/miniconda3/miniconda.sh -b -u -p ~/miniconda3
rm ~/miniconda3/miniconda.sh
source ~/miniconda3/bin/activate
conda init --all
- Create and activate a fresh conda environment for lerobot
conda create -y -n lerobot python=3.10 && conda activate lerobot
- Clone Lerobot:
git clone https://github.com/ZhuYaoHui1998/lerobot.git ~/lerobot
- 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"
- Check Pytorch and Torchvision
Since installing the lerobot environment via pip will uninstall the original Pytorch and Torchvision and install the CPU versions of Pytorch and Torchvision, you need to perform a check in Python.
import torch
print(torch.cuda.is_available())
If the printed result is False, you need to reinstall Pytorch and Torchvision according to the official website tutorial.
If you are using a Jetson device, install Pytorch and Torchvision according to this tutorial.
Configure the motors
Follow steps 1 of the assembly video which illustrates the use of our scripts below.
Find USB ports associated to your arms To find the correct ports for each arm, run the utility script twice:
python lerobot/scripts/find_motors_bus_port.py
Example output when identifying the leader arm's port (e.g., /dev/tty.usbmodem575E0031751 on Mac, or possibly /dev/ttyACM0 on Linux):
Example output when identifying the follower arm's port (e.g., /dev/tty.usbmodem575E0032081, or possibly /dev/ttyACM1 on Linux):
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
Configure your 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/ttyACM0 \
--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/ttyACM0 \
--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.
Assembly
Detailed video instructions are on the HuggingFace Youtube
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.
The calibration of the robotic arm should be carried out strictly in accordance with the "Calibrate" steps in the official tutorial of Lerobot.
Manual calibration of follower arm
IMPORTANTLY: Now that you have your ports, update the port default values of SO100RobotConfig(lerobot/lerobot/common/robot_devices/robots
/configs.py). 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"],
},
),
}
)
You will need to move the follower arm to these positions sequentially:
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"]'
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:
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"]'
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
Add cameras
After inserting your two USB cameras, run the following script to check the port numbers of the cameras.
python lerobot/common/robot_devices/cameras/opencv.py \
--images-dir outputs/images_from_opencv_cameras
The terminal will print out the following information.
Mac or Windows detected. Finding available camera indices through scanning all indices from 0 to 60
[...]
Camera found at index 2
Camera found at index 4
[...]
Connecting cameras
OpenCVCamera(2, fps=30.0, width=640, height=480, color_mode=rgb)
OpenCVCamera(4, fps=30.0, width=640, height=480, 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
You can find the pictures taken by each camera in the outputs/images_from_opencv_cameras directory, and confirm the port index information corresponding to the cameras at different positions. Then complete the alignment of the camera parameters in the lerobot/lerobot/common/robot_devices/robots
/configs.py file.
@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/ttyACM0",
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/ttyttyACM1",
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"],
},
),
}
)
cameras: dict[str, CameraConfig] = field(
default_factory=lambda: {
"laptop": OpenCVCameraConfig(
camera_index=0, ##### UPDATE HEARE
fps=30,
width=640,
height=480,
),
"phone": OpenCVCameraConfig(
camera_index=1, ##### UPDATE HEARE
fps=30,
width=640,
height=480,
),
}
)
mock: bool = False
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
Record the 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
Parameter Explanations
- wormup-time-s: It refers to the initialization time.
- episode-time-s: It represents the time for collecting data each time.
- reset-time-s: It is the preparation time between each data collection.
- num-episodes: It indicates how many groups of data are expected to be collected.
- push-to-hub: It determines whether to upload the data to the HuggingFace Hub.
Note: You can resume recording by adding --control.resume=true. Also if you didn't push your dataset yet, add --control.local_files_only=true.
Visualize the 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:
python lerobot/scripts/visualize_dataset_html.py \
--repo-id ${HF_USER}/so100_test \
--local-files-only 1
Replay an episode
Now try to replay the first episode on your robot:
python lerobot/scripts/control_robot.py \
--robot.type=so100 \
--control.type=replay \
--control.fps=30 \
--control.repo_id=${HF_USER}/so100_test \
--control.episode=0
Note: If you didn't push your dataset yet, add --control.local_files_only=true .
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=${HF_USER}/so100_test \
--policy.type=act \ #[act,diffusion,pi0,tdmpc,vqbet]
--output_dir=outputs/train/act_so100_test \
--job_name=act_so100_test \
--device=cuda \
--wandb.enable=true
Note: If you didn't push your dataset yet, add --control.local_files_only=true.
Let's explain it:
- We provided the dataset as argument with
--dataset.repo_id=${HF_USER}/so100_test. - We provided the policy with
policy.type=act. This loads configurations fromlerobot/lerobot/common/policies/act /configuration_act.py. Importantly, this policy uses 2 cameras as inputlaptop,phone. - We provided
device=cudasince we are training on a Nvidia GPU, but you can also usedevice=mpsif you are using a Mac with Apple silicon, ordevice=cpuotherwise. - We provided
wandb.enable=trueto use Weights and Biases for visualizing training plots. This is optional but if you use it, make sure you are logged in by runningwandb login.
Training should take several hours. You will find checkpoints in outputs/train/act_so100_test/checkpoints.
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=${HF_USER}/eval_act_so100_test \
--control.tags='["tutorial"]' \
--control.warmup_time_s=5 \
--control.episode_time_s=30 \
--control.reset_time_s=30 \
--control.num_episodes=10 \
--control.push_to_hub=true \
--control.policy.path=outputs/train/act_so100_test/checkpoints/last/pretrained_model
As you can see, it's almost the same command as previously used to record your training dataset. Two things changed:
- There is an additional
--control.policy.pathargument which indicates the path to your policy checkpoint with (e.g.outputs/train/eval_act_so100_test/checkpoints/last/pretrained_model). You can also use the model repository if you uploaded a model checkpoint to the hub (e.g.${HF_USER}/act_so100_test). - The name of dataset begins by
evalto reflect that you are running inference (e.g.${HF_USER}/eval_act_so100_test).
Citation
TheRobotStudio Project: SO-ARM100
Huggingface Project: Lerobot
Dnsty: Jetson Containers
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