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.

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Main Features

1. 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.
2. 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.
3. 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.
4. 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.
5. 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	❌

caution

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.

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.

1. 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%

2. 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:

   • Print_Follower_SO_ARM100_08_Ender.STL
   • Print_Leader_SO_ARM100_08_Ender.STL

   For printer bed sizes of 205mm x 250mm (such as the Prusa/Up), print:

   • Print_Follower_SO_ARM100_08_UP_Prusa.STL
   • Print_Leader_SO_ARM100_08_UP_Prusa.STL

For the convenience of downloading, we have already packaged all the files on the Makerworld platform, including the Table Clamps.

Install LeRobot

On your reComputer Nvidia Jetson:

1. Install Miniconda:

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

2. Restart shell or source ~/.bashrc

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"

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.

info

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

Firstly, you need to ensure that the serial port numbers of the robotic arm in the \lerobot\lerobot\configs\robot\so100.yaml file are consistent with yours, as shown in the following figure. You can view all serial port names according to ls /dev/ttyACM*.

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

Teleop with displaying 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\configs\robot\so100.yaml file.

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

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 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
    --single-task seeedstudio

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. 

Visualize the 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

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

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=false

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.

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).

Citation

TheRobotStudio Project: SO-ARM100

Huggingface Project: Lerobot

Dnsty: Jetson Containers

Jetson AI Lab

Diffusion Policy

ACT or ALOHA

TDMPC

VQ-BeT

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