LeKiwi

In the steps below, we explain how to assemble the LeKiwi mobile robot.

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 advise if it’s your first time printing or if you don’t own a 3D printer.

Wired version

If you have the wired LeKiwi version, you can skip the installation of the Raspberry Pi and setting up SSH. You can also run all commands directly on your PC for both the LeKiwi scripts and the leader arm scripts for teleoperating.

Install software on Pi

Now we have to set up the remote PC that will run on the LeKiwi Robot. This is normally a Raspberry Pi, but can be any PC that can run on 5V and has enough usb ports (2 or more) for the cameras and motor control board.

Install OS

For setting up the Raspberry Pi and its SD-card see: Setup PI. Here is explained how to download the Imager to install Raspberry Pi OS or Ubuntu.

Setup SSH

After setting up your Pi, you should enable and set up SSH (Secure Shell Protocol) so you can log in to the Pi from your laptop without requiring a screen, keyboard, and mouse on the Pi. A great tutorial on how to do this can be found here. Logging into your Pi can be done in your Command Prompt (cmd) or, if you use VSCode you can use this extension.

Install LeRobot on Pi 🤗

On your Raspberry Pi install LeRobot using our Installation Guide

In addition to these instructions, you need to install the Feetech sdk on your Pi:

pip install -e ".[feetech]"

Install LeRobot locally

If you already have installed LeRobot on your laptop/pc you can skip this step; otherwise, please follow along as we do the same steps we did on the Pi.

Follow our Installation Guide

Great :hugs:! You are now done installing LeRobot, and we can begin assembling the SO100/SO101 arms and the mobile base :robot:. 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.

Step-by-Step Assembly Instructions

First, we will assemble the two SO100/SO101 arms. One to attach to the mobile base and one for teleoperation. Then we will assemble the mobile base. The instructions for assembling can be found on these two pages:

• Assemble SO101
• Assemble LeKiwi

Find the USB ports associated with motor board

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

python lerobot/find_port.py

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

[...Disconnect corresponding leader or follower arm and press Enter...]

The port of this MotorsBus is /dev/tty.usbmodem575E0032081
Reconnect the USB cable.

Configure motors

The instructions for configuring the motors can be found in the SO101 docs. Besides the ids for the arm motors, we also need to set the motor ids for the mobile base. These need to be in a specific order to work. Below an image of the motor ids and motor mounting positions for the mobile base. Note that we only use one Motor Control board on LeKiwi. This means the motor ids for the wheels are 7, 8 and 9.

You can run this command to setup motors for LeKiwi. It will first setup the motors for arm (id 6..1) and then setup motors for wheels (9,8,7)

python -m lerobot.setup_motors \
    --robot.type=lekiwi \
    --robot.port=/dev/tty.usbmodem58760431551 # <- paste here the port found at previous step

Troubleshoot communication

If you are having trouble connecting to the Mobile SO100, follow these steps to diagnose and resolve the issue.

1. Verify IP Address Configuration

Make sure that the correct IP for the Pi is used in the commands or in your code. To check the Raspberry Pi’s IP address, run (on the Pi command line):

hostname -I

2. Check if Pi is reachable from laptop/pc

Try pinging the Raspberry Pi from your laptop:

ping <your_pi_ip_address>

If the ping fails:

• Ensure the Pi is powered on and connected to the same network.
• Check if SSH is enabled on the Pi.

3. Try SSH connection

If you can’t SSH into the Pi, it might not be properly connected. Use:

ssh <your_pi_user_name>@<your_pi_ip_address>

If you get a connection error:

• Ensure SSH is enabled on the Pi by running:
  Copied

  sudo raspi-config

  Then navigate to: Interfacing Options -> SSH and enable it.

Calibration

Now we have to calibrate the leader arm and the follower arm. The wheel motors don’t have to be calibrated. The calibration process is very important because it allows a neural network trained on one robot to work on another.

Calibrate follower arm (on mobile base)

Make sure the arm is connected to the Raspberry Pi and run this script or API example (on the Raspberry Pi via SSH) to launch calibration of the follower arm:

python -m lerobot.calibrate \
    --robot.type=lekiwi \
    --robot.id=my_awesome_kiwi # <- Give the robot a unique name

We unified the calibration method for most robots, thus, the calibration steps for this SO100 arm are the same as the steps for the Koch and SO101. First, we have to move the robot to the position where each joint is in the middle of its range, then we press Enter. Secondly, we move all joints through their full range of motion. A video of this same process for the SO101 as reference can be found here.

Wired version

If you have the wired LeKiwi version, please run all commands on your laptop.

Calibrate leader arm

Then, to calibrate the leader arm (which is attached to the laptop/pc). Run the following command of API example on your laptop:

python -m lerobot.calibrate \
    --teleop.type=so100_leader \
    --teleop.port=/dev/tty.usbmodem58760431551 \ # <- The port of your robot
    --teleop.id=my_awesome_leader_arm # <- Give the robot a unique name

Teleoperate LeKiwi

To teleoperate, SSH into your Raspberry Pi, and run conda activate lerobot and this command:

python -m lerobot.common.robots.lekiwi.lekiwi_host --robot.id=my_awesome_kiwi

Then on your laptop, also run conda activate lerobot and run the API example, make sure you set the correct remote_ip and port in examples/lekiwi/teleoperate.py.

python examples/lekiwi/teleoperate.py

You should see on your laptop something like this: [INFO] Connected to remote robot at tcp://172.17.133.91:5555 and video stream at tcp://172.17.133.91:5556. Now you can move the leader arm and use the keyboard (w,a,s,d) to drive forward, left, backwards, right. And use (z,x) to turn left or turn right. You can use (r,f) to increase and decrease the speed of the mobile robot. There are three speed modes, see the table below:

Wired version

If you have the wired LeKiwi version, please run all commands on your laptop.

Record a dataset

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

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 episodes and upload your dataset to the hub, execute this API example tailored for LeKiwi. Make sure to first adapt the remote_ip, repo_id, port and task in the script. If you would like to run the script for longer you can increase NB_CYCLES_CLIENT_CONNECTION.

python examples/lekiwi/record.py

Dataset upload

Locally, your dataset is stored in this folder: ~/.cache/huggingface/lerobot/{repo-id}. 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.

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.

If you want to dive deeper into this important topic, you can check out the blog post we wrote on what makes a good dataset.

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.

Replay an episode

To replay an episode run the API example below, make sure to change remote_ip, port, LeRobotDatasetId and episode index.

python examples/lekiwi/replay.py

Congrats 🎉, your robot is all set to learn a task on its own. Start training it by the training part of this tutorial: Getting started with real-world robots

Evaluate your policy

To evaluate your policy run the evaluate.py API example, make sure to change remote_ip, port, model..

python examples/lekiwi/evaluate.py