Merge branch 'main' into user/adil-zouitine/2025-1-7-port-hil-serl-new
This commit is contained in:
Adil Zouitine
@@ -1,624 +0,0 @@
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# Using the [SO-100](https://github.com/TheRobotStudio/SO-ARM100) with LeRobot
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|
||||
## Table of Contents
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|
||||
- [A. Source the parts](#a-source-the-parts)
|
||||
- [B. Install LeRobot](#b-install-lerobot)
|
||||
- [C. Configure the Motors](#c-configure-the-motors)
|
||||
- [D. Step-by-Step Assembly Instructions](#d-step-by-step-assembly-instructions)
|
||||
- [E. Calibrate](#e-calibrate)
|
||||
- [F. Teleoperate](#f-teleoperate)
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||||
- [G. Record a dataset](#g-record-a-dataset)
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||||
- [H. Visualize a dataset](#h-visualize-a-dataset)
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||||
- [I. Replay an episode](#i-replay-an-episode)
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||||
- [J. Train a policy](#j-train-a-policy)
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||||
- [K. Evaluate your policy](#k-evaluate-your-policy)
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||||
- [L. More Information](#l-more-information)
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|
||||
## A. Source the parts
|
||||
|
||||
Follow this [README](https://github.com/TheRobotStudio/SO-ARM100). It contains the bill of materials, with a link to source the parts, as well as the instructions to 3D print the parts,
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and advice if it's your first time printing or if you don't own a 3D printer.
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||||
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.
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||||
## B. Install LeRobot
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||||
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||||
> [!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](https://developer.mozilla.org/en-US/docs/Learn_web_development/Getting_started/Environment_setup/Command_line)
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||||
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||||
On your computer:
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||||
|
||||
#### 1. [Install Miniconda](https://docs.anaconda.com/miniconda/install/#quick-command-line-install):
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||||
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||||
#### 2. Restart shell
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||||
Copy paste in your shell: `source ~/.bashrc` or for Mac: `source ~/.bash_profile` or `source ~/.zshrc` if you're using zshell
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||||
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||||
#### 3. Create and activate a fresh conda environment for lerobot
|
||||
|
||||
<details>
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||||
<summary><strong>Video install instructions</strong></summary>
|
||||
|
||||
<video src="https://github.com/user-attachments/assets/17172d3b-3b64-4b80-9cf1-b2b7c5cbd236"></video>
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||||
|
||||
</details>
|
||||
|
||||
```bash
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conda create -y -n lerobot python=3.10
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```
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||||
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Then activate your conda environment (do this each time you open a shell to use lerobot!):
|
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```bash
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conda activate lerobot
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```
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||||
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#### 4. Clone LeRobot:
|
||||
```bash
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||||
git clone https://github.com/huggingface/lerobot.git ~/lerobot
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```
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||||
|
||||
#### 5. Install ffmpeg in your environment:
|
||||
When using `miniconda`, install `ffmpeg` in your environment:
|
||||
```bash
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||||
conda install ffmpeg -c conda-forge
|
||||
```
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||||
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||||
#### 6. Install LeRobot with dependencies for the feetech motors:
|
||||
```bash
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||||
cd ~/lerobot && pip install -e ".[feetech]"
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```
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Great :hugs:! You are now done installing LeRobot and we can begin assembling the SO100 arms :robot:.
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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.
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## C. Configure the motors
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||||
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||||
> [!NOTE]
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> Throughout this tutorial you will find videos on how to do the steps, the full video tutorial can be found here: [assembly video](https://www.youtube.com/watch?v=FioA2oeFZ5I).
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### 1. Find the USB ports associated to each arm
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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).
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#### a. Run the script to find port
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<details>
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||||
<summary><strong>Video finding port</strong></summary>
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||||
<video src="https://github.com/user-attachments/assets/4a21a14d-2046-4805-93c4-ee97a30ba33f"></video>
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<video src="https://github.com/user-attachments/assets/1cc3aecf-c16d-4ff9-aec7-8c175afbbce2"></video>
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||||
</details>
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||||
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||||
To find the port for each bus servo adapter, run the utility script:
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```bash
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python lerobot/scripts/find_motors_bus_port.py
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```
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#### b. Example outputs
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||||
Example output when identifying the leader arm's port (e.g., `/dev/tty.usbmodem575E0031751` on Mac, or possibly `/dev/ttyACM0` on Linux):
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```
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Finding all available ports for the MotorBus.
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['/dev/tty.usbmodem575E0032081', '/dev/tty.usbmodem575E0031751']
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Remove the usb cable from your MotorsBus and press Enter when done.
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[...Disconnect leader arm and press Enter...]
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The port of this MotorsBus is /dev/tty.usbmodem575E0031751
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Reconnect the usb cable.
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```
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Example output when identifying the follower arm's port (e.g., `/dev/tty.usbmodem575E0032081`, or possibly `/dev/ttyACM1` on Linux):
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||||
```
|
||||
Finding all available ports for the MotorBus.
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['/dev/tty.usbmodem575E0032081', '/dev/tty.usbmodem575E0031751']
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||||
Remove the usb cable from your MotorsBus and press Enter when done.
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||||
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[...Disconnect follower arm and press Enter...]
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The port of this MotorsBus is /dev/tty.usbmodem575E0032081
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Reconnect the usb cable.
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```
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#### c. Troubleshooting
|
||||
On Linux, you might need to give access to the USB ports by running:
|
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```bash
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sudo chmod 666 /dev/ttyACM0
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||||
sudo chmod 666 /dev/ttyACM1
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```
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#### d. Update config file
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IMPORTANTLY: Now that you have your ports, update the **port** default values of [`SO100RobotConfig`](../lerobot/common/robot_devices/robots/configs.py). You will find something like:
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```python
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@RobotConfig.register_subclass("so100")
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@dataclass
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class So100RobotConfig(ManipulatorRobotConfig):
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calibration_dir: str = ".cache/calibration/so100"
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# `max_relative_target` limits the magnitude of the relative positional target vector for safety purposes.
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# Set this to a positive scalar to have the same value for all motors, or a list that is the same length as
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# the number of motors in your follower arms.
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max_relative_target: int | None = None
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||||
leader_arms: dict[str, MotorsBusConfig] = field(
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default_factory=lambda: {
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"main": FeetechMotorsBusConfig(
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port="/dev/tty.usbmodem58760431091", <-- UPDATE HERE
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motors={
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# name: (index, model)
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"shoulder_pan": [1, "sts3215"],
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"shoulder_lift": [2, "sts3215"],
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"elbow_flex": [3, "sts3215"],
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"wrist_flex": [4, "sts3215"],
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"wrist_roll": [5, "sts3215"],
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"gripper": [6, "sts3215"],
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},
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),
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}
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)
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follower_arms: dict[str, MotorsBusConfig] = field(
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default_factory=lambda: {
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"main": FeetechMotorsBusConfig(
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port="/dev/tty.usbmodem585A0076891", <-- UPDATE HERE
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||||
motors={
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# name: (index, model)
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||||
"shoulder_pan": [1, "sts3215"],
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||||
"shoulder_lift": [2, "sts3215"],
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"elbow_flex": [3, "sts3215"],
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"wrist_flex": [4, "sts3215"],
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"wrist_roll": [5, "sts3215"],
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"gripper": [6, "sts3215"],
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},
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),
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}
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||||
)
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```
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### 2. Assembling the Base
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Let's begin with assembling the follower arm base
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#### a. Set IDs for all 12 motors
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<details>
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||||
<summary><strong>Video configuring motor</strong></summary>
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<video src="https://github.com/user-attachments/assets/ef9b3317-2e11-4858-b9d3-f0a02fb48ecf"></video>
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||||
<video src="https://github.com/user-attachments/assets/f36b5ed5-c803-4ebe-8947-b39278776a0d"></video>
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</details>
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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:
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```bash
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python lerobot/scripts/configure_motor.py \
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--port /dev/tty.usbmodem58760432961 \
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--brand feetech \
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--model sts3215 \
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--baudrate 1000000 \
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--ID 1
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```
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> [!NOTE]
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> 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).
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Then unplug your motor and plug the second motor and set its ID to 2.
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```bash
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python lerobot/scripts/configure_motor.py \
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--port /dev/tty.usbmodem58760432961 \
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||||
--brand feetech \
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||||
--model sts3215 \
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--baudrate 1000000 \
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--ID 2
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```
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Redo the process for all your motors until ID 6. Do the same for the 6 motors of the leader arm.
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#### b. Remove the gears of the 6 leader motors
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<details>
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||||
<summary><strong>Video removing gears</strong></summary>
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||||
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||||
<video src="https://github.com/user-attachments/assets/0c95b88c-5b85-413d-ba19-aee2f864f2a7"></video>
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||||
|
||||
</details>
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||||
|
||||
|
||||
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.
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||||
## D. Step-by-Step Assembly Instructions
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||||
|
||||
**Step 1: Clean Parts**
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||||
- Remove all support material from the 3D-printed parts.
|
||||
---
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||||
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||||
### Additional Guidance
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||||
|
||||
<details>
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||||
<summary><strong>Video assembling arms</strong></summary>
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||||
|
||||
<video src="https://github.com/user-attachments/assets/488a39de-0189-4461-9de3-05b015f90cca"></video>
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||||
|
||||
</details>
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||||
|
||||
**Note:**
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||||
This video provides visual guidance for assembling the arms, but it doesn't specify when or how to do the wiring. Inserting the cables beforehand is much easier than doing it afterward. The first arm may take a bit more than 1 hour to assemble, but once you get used to it, you can assemble the second arm in under 1 hour.
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||||
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||||
---
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||||
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||||
### First Motor
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||||
|
||||
**Step 2: Insert Wires**
|
||||
- Insert two wires into the first motor.
|
||||
|
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<img src="../media/tutorial/img1.jpg" style="height:300px;">
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||||
|
||||
**Step 3: Install in Base**
|
||||
- Place the first motor into the base.
|
||||
|
||||
<img src="../media/tutorial/img2.jpg" style="height:300px;">
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||||
|
||||
**Step 4: Secure Motor**
|
||||
- Fasten the motor with 4 screws. Two from the bottom and two from top.
|
||||
|
||||
**Step 5: Attach Motor Holder**
|
||||
- Slide over the first motor holder and fasten it using two screws (one on each side).
|
||||
|
||||
<img src="../media/tutorial/img4.jpg" style="height:300px;">
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||||
|
||||
**Step 6: Attach Motor Horns**
|
||||
- Install both motor horns, securing the top horn with a screw. Try not to move the motor position when attaching the motor horn, especially for the leader arms, where we removed the gears.
|
||||
|
||||
<img src="../media/tutorial/img5.jpg" style="height:300px;">
|
||||
<details>
|
||||
<summary><strong>Video adding motor horn</strong></summary>
|
||||
<video src="https://github.com/user-attachments/assets/ef3391a4-ad05-4100-b2bd-1699bf86c969"></video>
|
||||
</details>
|
||||
|
||||
**Step 7: Attach Shoulder Part**
|
||||
- Route one wire to the back of the robot and the other to the left or in photo towards you (see photo).
|
||||
- Attach the shoulder part.
|
||||
|
||||
<img src="../media/tutorial/img6.jpg" style="height:300px;">
|
||||
|
||||
**Step 8: Secure Shoulder**
|
||||
- Tighten the shoulder part with 4 screws on top and 4 on the bottom
|
||||
*(access bottom holes by turning the shoulder).*
|
||||
|
||||
---
|
||||
|
||||
### Second Motor Assembly
|
||||
|
||||
**Step 9: Install Motor 2**
|
||||
- Slide the second motor in from the top and link the wire from motor 1 to motor 2.
|
||||
|
||||
<img src="../media/tutorial/img8.jpg" style="height:300px;">
|
||||
|
||||
**Step 10: Attach Shoulder Holder**
|
||||
- Add the shoulder motor holder.
|
||||
- Ensure the wire from motor 1 to motor 2 goes behind the holder while the other wire is routed upward (see photo).
|
||||
- This part can be tight to assemble, you can use a workbench like the image or a similar setup to push the part around the motor.
|
||||
|
||||
<div style="display: flex;">
|
||||
<img src="../media/tutorial/img9.jpg" style="height:250px;">
|
||||
<img src="../media/tutorial/img10.jpg" style="height:250px;">
|
||||
<img src="../media/tutorial/img12.jpg" style="height:250px;">
|
||||
</div>
|
||||
|
||||
**Step 11: Secure Motor 2**
|
||||
- Fasten the second motor with 4 screws.
|
||||
|
||||
**Step 12: Attach Motor Horn**
|
||||
- Attach both motor horns to motor 2, again use the horn screw.
|
||||
|
||||
**Step 13: Attach Base**
|
||||
- Install the base attachment using 2 screws.
|
||||
|
||||
<img src="../media/tutorial/img11.jpg" style="height:300px;">
|
||||
|
||||
**Step 14: Attach Upper Arm**
|
||||
- Attach the upper arm with 4 screws on each side.
|
||||
|
||||
<img src="../media/tutorial/img13.jpg" style="height:300px;">
|
||||
|
||||
---
|
||||
|
||||
### Third Motor Assembly
|
||||
|
||||
**Step 15: Install Motor 3**
|
||||
- Route the motor cable from motor 2 through the cable holder to motor 3, then secure motor 3 with 4 screws.
|
||||
|
||||
**Step 16: Attach Motor Horn**
|
||||
- Attach both motor horns to motor 3 and secure one again with a horn screw.
|
||||
|
||||
<img src="../media/tutorial/img14.jpg" style="height:300px;">
|
||||
|
||||
**Step 17: Attach Forearm**
|
||||
- Connect the forearm to motor 3 using 4 screws on each side.
|
||||
|
||||
<img src="../media/tutorial/img15.jpg" style="height:300px;">
|
||||
|
||||
---
|
||||
|
||||
### Fourth Motor Assembly
|
||||
|
||||
**Step 18: Install Motor 4**
|
||||
- Slide in motor 4, attach the cable from motor 3, and secure the cable in its holder with a screw.
|
||||
|
||||
<div style="display: flex;">
|
||||
<img src="../media/tutorial/img16.jpg" style="height:300px;">
|
||||
<img src="../media/tutorial/img19.jpg" style="height:300px;">
|
||||
</div>
|
||||
|
||||
**Step 19: Attach Motor Holder 4**
|
||||
- Install the fourth motor holder (a tight fit). Ensure one wire is routed upward and the wire from motor 3 is routed downward (see photo).
|
||||
|
||||
<img src="../media/tutorial/img17.jpg" style="height:300px;">
|
||||
|
||||
**Step 20: Secure Motor 4 & Attach Horn**
|
||||
- Fasten motor 4 with 4 screws and attach its motor horns, use for one a horn screw.
|
||||
|
||||
<img src="../media/tutorial/img18.jpg" style="height:300px;">
|
||||
|
||||
---
|
||||
|
||||
### Wrist Assembly
|
||||
|
||||
**Step 21: Install Motor 5**
|
||||
- Insert motor 5 into the wrist holder and secure it with 2 front screws.
|
||||
|
||||
<img src="../media/tutorial/img20.jpg" style="height:300px;">
|
||||
|
||||
**Step 22: Attach Wrist**
|
||||
- Connect the wire from motor 4 to motor 5. And already insert the other wire for the gripper.
|
||||
- Secure the wrist to motor 4 using 4 screws on both sides.
|
||||
|
||||
<img src="../media/tutorial/img22.jpg" style="height:300px;">
|
||||
|
||||
**Step 23: Attach Wrist Horn**
|
||||
- Install only one motor horn on the wrist motor and secure it with a horn screw.
|
||||
|
||||
<img src="../media/tutorial/img23.jpg" style="height:300px;">
|
||||
|
||||
---
|
||||
|
||||
### Follower Configuration
|
||||
|
||||
**Step 24: Attach Gripper**
|
||||
- Attach the gripper to motor 5.
|
||||
|
||||
<img src="../media/tutorial/img24.jpg" style="height:300px;">
|
||||
|
||||
**Step 25: Install Gripper Motor**
|
||||
- Insert the gripper motor, connect the motor wire from motor 5 to motor 6, and secure it with 3 screws on each side.
|
||||
|
||||
<img src="../media/tutorial/img25.jpg" style="height:300px;">
|
||||
|
||||
**Step 26: Attach Gripper Horn & Claw**
|
||||
- Attach the motor horns and again use a horn screw.
|
||||
- Install the gripper claw and secure it with 4 screws on both sides.
|
||||
|
||||
<img src="../media/tutorial/img26.jpg" style="height:300px;">
|
||||
|
||||
**Step 27: Mount Controller**
|
||||
- Attach the motor controller on the back.
|
||||
|
||||
<div style="display: flex;">
|
||||
<img src="../media/tutorial/img27.jpg" style="height:300px;">
|
||||
<img src="../media/tutorial/img28.jpg" style="height:300px;">
|
||||
</div>
|
||||
|
||||
*Assembly complete – proceed to Leader arm assembly.*
|
||||
|
||||
---
|
||||
|
||||
### Leader Configuration
|
||||
|
||||
For the leader configuration, perform **Steps 1–23**. Make sure that you removed the motor gears from the motors.
|
||||
|
||||
**Step 24: Attach Leader Holder**
|
||||
- Mount the leader holder onto the wrist and secure it with a screw.
|
||||
|
||||
<img src="../media/tutorial/img29.jpg" style="height:300px;">
|
||||
|
||||
**Step 25: Attach Handle**
|
||||
- Attach the handle to motor 5 using 4 screws.
|
||||
|
||||
<img src="../media/tutorial/img30.jpg" style="height:300px;">
|
||||
|
||||
**Step 26: Install Gripper Motor**
|
||||
- Insert the gripper motor, secure it with 3 screws on each side, attach a motor horn using a horn screw, and connect the motor wire.
|
||||
|
||||
<img src="../media/tutorial/img31.jpg" style="height:300px;">
|
||||
|
||||
**Step 27: Attach Trigger**
|
||||
- Attach the follower trigger with 4 screws.
|
||||
|
||||
<img src="../media/tutorial/img32.jpg" style="height:300px;">
|
||||
|
||||
**Step 28: Mount Controller**
|
||||
- Attach the motor controller on the back.
|
||||
|
||||
<div style="display: flex;">
|
||||
<img src="../media/tutorial/img27.jpg" style="height:300px;">
|
||||
<img src="../media/tutorial/img28.jpg" style="height:300px;">
|
||||
</div>
|
||||
|
||||
*Assembly complete – proceed to calibration.*
|
||||
|
||||
|
||||
## 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](https://youtu.be/FioA2oeFZ5I?t=724) 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 |
|
||||
| ------------------------------------------------------------------------------------------------------------------------------------------------------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------ |
|
||||
| <img src="../media/so100/follower_zero.webp?raw=true" alt="SO-100 follower arm zero position" title="SO-100 follower arm zero position" style="width:100%;"> | <img src="../media/so100/follower_rotated.webp?raw=true" alt="SO-100 follower arm rotated position" title="SO-100 follower arm rotated position" style="width:100%;"> | <img src="../media/so100/follower_rest.webp?raw=true" alt="SO-100 follower arm rest position" title="SO-100 follower arm rest position" style="width:100%;"> |
|
||||
|
||||
Make sure both arms are connected and run this script to launch manual calibration:
|
||||
```bash
|
||||
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](https://youtu.be/FioA2oeFZ5I?t=724) 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 |
|
||||
| ------------------------------------------------------------------------------------------------------------------------------------------------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------ |
|
||||
| <img src="../media/so100/leader_zero.webp?raw=true" alt="SO-100 leader arm zero position" title="SO-100 leader arm zero position" style="width:100%;"> | <img src="../media/so100/leader_rotated.webp?raw=true" alt="SO-100 leader arm rotated position" title="SO-100 leader arm rotated position" style="width:100%;"> | <img src="../media/so100/leader_rest.webp?raw=true" alt="SO-100 leader arm rest position" title="SO-100 leader arm rest position" style="width:100%;"> |
|
||||
|
||||
Run this script to launch manual calibration:
|
||||
```bash
|
||||
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):
|
||||
```bash
|
||||
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](https://github.com/huggingface/lerobot/blob/main/examples/7_get_started_with_real_robot.md#c-add-your-cameras-with-opencvcamera). 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.
|
||||
|
||||
> **NOTE:** To visualize the data, enable `--control.display_data=true`. This streams the data using `rerun`.
|
||||
|
||||
```bash
|
||||
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](https://huggingface.co/settings/tokens):
|
||||
```bash
|
||||
huggingface-cli login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential
|
||||
```
|
||||
|
||||
Store your Hugging Face repository name in a variable to run these commands:
|
||||
```bash
|
||||
HF_USER=$(huggingface-cli whoami | head -n 1)
|
||||
echo $HF_USER
|
||||
```
|
||||
|
||||
Record 2 episodes and upload your dataset to the hub:
|
||||
```bash
|
||||
python lerobot/scripts/control_robot.py \
|
||||
--robot.type=so100 \
|
||||
--control.type=record \
|
||||
--control.fps=30 \
|
||||
--control.single_task="Grasp a lego block and put it in the bin." \
|
||||
--control.repo_id=${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](https://huggingface.co/spaces/lerobot/visualize_dataset) by copy pasting your repo id given by:
|
||||
```bash
|
||||
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):
|
||||
```bash
|
||||
python lerobot/scripts/visualize_dataset_html.py \
|
||||
--repo-id ${HF_USER}/so100_test \
|
||||
--local-files-only 1
|
||||
```
|
||||
|
||||
## I. Replay an episode
|
||||
|
||||
Now try to replay the first episode on your robot:
|
||||
```bash
|
||||
python lerobot/scripts/control_robot.py \
|
||||
--robot.type=so100 \
|
||||
--control.type=replay \
|
||||
--control.fps=30 \
|
||||
--control.repo_id=${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`](../lerobot/scripts/train.py) script. A few arguments are required. Here is an example command:
|
||||
```bash
|
||||
python lerobot/scripts/train.py \
|
||||
--dataset.repo_id=${HF_USER}/so100_test \
|
||||
--policy.type=act \
|
||||
--output_dir=outputs/train/act_so100_test \
|
||||
--job_name=act_so100_test \
|
||||
--policy.device=cuda \
|
||||
--wandb.enable=true
|
||||
```
|
||||
|
||||
Let's explain it:
|
||||
1. We provided the dataset as argument with `--dataset.repo_id=${HF_USER}/so100_test`.
|
||||
2. We provided the policy with `policy.type=act`. This loads configurations from [`configuration_act.py`](../lerobot/common/policies/act/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.
|
||||
4. We provided `policy.device=cuda` since we are training on a Nvidia GPU, but you could use `policy.device=mps` to train on Apple silicon.
|
||||
5. We provided `wandb.enable=true` to use [Weights and Biases](https://docs.wandb.ai/quickstart) 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`.
|
||||
|
||||
To resume training from a checkpoint, below is an example command to resume from `last` checkpoint of the `act_so100_test` policy:
|
||||
```bash
|
||||
python lerobot/scripts/train.py \
|
||||
--config_path=outputs/train/act_so100_test/checkpoints/last/pretrained_model/train_config.json \
|
||||
--resume=true
|
||||
```
|
||||
|
||||
## K. Evaluate your policy
|
||||
|
||||
You can use the `record` function from [`lerobot/scripts/control_robot.py`](../lerobot/scripts/control_robot.py) but with a policy checkpoint as input. For instance, run this command to record 10 evaluation episodes:
|
||||
```bash
|
||||
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=${HF_USER}/eval_act_so100_test \
|
||||
--control.tags='["tutorial"]' \
|
||||
--control.warmup_time_s=5 \
|
||||
--control.episode_time_s=30 \
|
||||
--control.reset_time_s=30 \
|
||||
--control.num_episodes=10 \
|
||||
--control.push_to_hub=true \
|
||||
--control.policy.path=outputs/train/act_so100_test/checkpoints/last/pretrained_model
|
||||
```
|
||||
|
||||
As you can see, it's almost the same command as previously used to record your training dataset. Two things changed:
|
||||
1. There is an additional `--control.policy.path` argument which indicates the path to your policy checkpoint with (e.g. `outputs/train/eval_act_so100_test/checkpoints/last/pretrained_model`). You can also use the model repository if you uploaded a model checkpoint to the hub (e.g. `${HF_USER}/act_so100_test`).
|
||||
2. 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](https://github.com/huggingface/lerobot/blob/main/examples/7_get_started_with_real_robot.md#4-train-a-policy-on-your-data) 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](https://discord.com/invite/s3KuuzsPFb) in the channel [`#so100-arm`](https://discord.com/channels/1216765309076115607/1237741463832363039).
|
||||
@@ -1,597 +0,0 @@
|
||||
# Using the [LeKiwi](https://github.com/SIGRobotics-UIUC/LeKiwi) Robot with LeRobot
|
||||
|
||||
## Table of Contents
|
||||
|
||||
- [A. Source the parts](#a-source-the-parts)
|
||||
- [B. Install software Pi](#b-install-software-on-pi)
|
||||
- [C. Setup LeRobot laptop/pc](#c-install-lerobot-on-laptop)
|
||||
- [D. Assemble the arms](#d-assembly)
|
||||
- [E. Calibrate](#e-calibration)
|
||||
- [F. Teleoperate](#f-teleoperate)
|
||||
- [G. Record a dataset](#g-record-a-dataset)
|
||||
- [H. Visualize a dataset](#h-visualize-a-dataset)
|
||||
- [I. Replay an episode](#i-replay-an-episode)
|
||||
- [J. Train a policy](#j-train-a-policy)
|
||||
- [K. Evaluate your policy](#k-evaluate-your-policy)
|
||||
|
||||
> [!TIP]
|
||||
> If you have any questions or need help, please reach out on [Discord](https://discord.com/invite/s3KuuzsPFb) in the channel [`#mobile-so-100-arm`](https://discord.com/channels/1216765309076115607/1318390825528332371).
|
||||
|
||||
## A. Source the parts
|
||||
|
||||
Follow this [README](https://github.com/SIGRobotics-UIUC/LeKiwi). 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.
|
||||
|
||||
### 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.
|
||||
|
||||
## B. Install software on Pi
|
||||
Now we have to setup 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](https://www.raspberrypi.com/documentation/computers/getting-started.html). Here is explained how to download the [Imager](https://www.raspberrypi.com/software/) to install Raspberry Pi OS or Ubuntu.
|
||||
|
||||
### Setup SSH
|
||||
After setting up your Pi, you should enable and setup [SSH](https://www.raspberrypi.com/news/coding-on-raspberry-pi-remotely-with-visual-studio-code/) (Secure Shell Protocol) so you can login into the Pi from your laptop without requiring a screen, keyboard and mouse in the Pi. A great tutorial on how to do this can be found [here](https://www.raspberrypi.com/documentation/computers/remote-access.html#ssh). Logging into your Pi can be done in your Command Prompt (cmd) or if you use VSCode you can use [this](https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.remote-ssh) extension.
|
||||
|
||||
### Install LeRobot
|
||||
|
||||
On your Raspberry Pi:
|
||||
|
||||
#### 1. [Install Miniconda](https://docs.anaconda.com/miniconda/install/#quick-command-line-install):
|
||||
|
||||
#### 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
|
||||
|
||||
<details>
|
||||
<summary><strong>Video install instructions</strong></summary>
|
||||
|
||||
<video src="https://github.com/user-attachments/assets/17172d3b-3b64-4b80-9cf1-b2b7c5cbd236"></video>
|
||||
|
||||
</details>
|
||||
|
||||
```bash
|
||||
conda create -y -n lerobot python=3.10
|
||||
```
|
||||
|
||||
Then activate your conda environment (do this each time you open a shell to use lerobot!):
|
||||
```bash
|
||||
conda activate lerobot
|
||||
```
|
||||
|
||||
#### 4. Clone LeRobot:
|
||||
```bash
|
||||
git clone https://github.com/huggingface/lerobot.git ~/lerobot
|
||||
```
|
||||
|
||||
#### 5. Install ffmpeg in your environment:
|
||||
When using `miniconda`, install `ffmpeg` in your environment:
|
||||
```bash
|
||||
conda install ffmpeg -c conda-forge
|
||||
```
|
||||
|
||||
#### 6. Install LeRobot with dependencies for the feetech motors:
|
||||
```bash
|
||||
cd ~/lerobot && pip install -e ".[feetech]"
|
||||
```
|
||||
|
||||
## C. Install LeRobot on laptop
|
||||
If you already have install LeRobot on your laptop you can skip this step, otherwise please follow along as we do the same steps we did on the Pi.
|
||||
|
||||
> [!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](https://developer.mozilla.org/en-US/docs/Learn_web_development/Getting_started/Environment_setup/Command_line)
|
||||
|
||||
On your computer:
|
||||
|
||||
#### 1. [Install Miniconda](https://docs.anaconda.com/miniconda/install/#quick-command-line-install):
|
||||
|
||||
#### 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
|
||||
|
||||
<details>
|
||||
<summary><strong>Video install instructions</strong></summary>
|
||||
|
||||
<video src="https://github.com/user-attachments/assets/17172d3b-3b64-4b80-9cf1-b2b7c5cbd236"></video>
|
||||
|
||||
</details>
|
||||
|
||||
```bash
|
||||
conda create -y -n lerobot python=3.10
|
||||
```
|
||||
|
||||
Then activate your conda environment (do this each time you open a shell to use lerobot!):
|
||||
```bash
|
||||
conda activate lerobot
|
||||
```
|
||||
|
||||
#### 4. Clone LeRobot:
|
||||
```bash
|
||||
git clone https://github.com/huggingface/lerobot.git ~/lerobot
|
||||
```
|
||||
|
||||
#### 5. Install ffmpeg in your environment:
|
||||
When using `miniconda`, install `ffmpeg` in your environment:
|
||||
```bash
|
||||
conda install ffmpeg -c conda-forge
|
||||
```
|
||||
|
||||
#### 6. Install LeRobot with dependencies for the feetech motors:
|
||||
```bash
|
||||
cd ~/lerobot && pip install -e ".[feetech]"
|
||||
```
|
||||
|
||||
Great :hugs:! You are now done installing LeRobot and we can begin assembling the SO100 arms and 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.
|
||||
|
||||
# D. Assembly
|
||||
|
||||
First we will assemble the two SO100 arms. One to attach to the mobile base and one for teleoperation. Then we will assemble the mobile base.
|
||||
|
||||
## SO100 Arms
|
||||
### Configure motors
|
||||
The instructions for configuring the motors can be found [Here](https://github.com/huggingface/lerobot/blob/main/examples/10_use_so100.md#c-configure-the-motors) in step C of the SO100 tutorial. Besides the ID's for the arm motors we also need to set the motor ID's for the mobile base. These needs to be in a specific order to work. Below an image of the motor ID's and motor mounting positions for the mobile base. Note that we only use one Motor Control board on LeKiwi. This means the motor ID's for the wheels are 7, 8 and 9.
|
||||
|
||||
<img src="../media/lekiwi/motor_ids.webp?raw=true" alt="Motor ID's for mobile robot" title="Motor ID's for mobile robot" width="60%">
|
||||
|
||||
### Assemble arms
|
||||
[Assemble arms instruction](https://github.com/huggingface/lerobot/blob/main/examples/10_use_so100.md#d-assemble-the-arms)
|
||||
|
||||
## Mobile base (LeKiwi)
|
||||
[Assemble LeKiwi](https://github.com/SIGRobotics-UIUC/LeKiwi)
|
||||
|
||||
### Update config
|
||||
Both config files on the LeKiwi LeRobot and on the laptop should be the same. First we should find the Ip address of the Raspberry Pi of the mobile manipulator. This is the same Ip address used in SSH. We also need the usb port of the control board of the leader arm on the laptop and the port of the control board on LeKiwi. We can find these ports with the following script.
|
||||
|
||||
#### a. Run the script to find port
|
||||
|
||||
<details>
|
||||
<summary><strong>Video finding port</strong></summary>
|
||||
<video src="https://github.com/user-attachments/assets/4a21a14d-2046-4805-93c4-ee97a30ba33f"></video>
|
||||
<video src="https://github.com/user-attachments/assets/1cc3aecf-c16d-4ff9-aec7-8c175afbbce2"></video>
|
||||
</details>
|
||||
|
||||
To find the port for each bus servo adapter, run the utility script:
|
||||
```bash
|
||||
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:
|
||||
```bash
|
||||
sudo chmod 666 /dev/ttyACM0
|
||||
sudo chmod 666 /dev/ttyACM1
|
||||
```
|
||||
|
||||
#### d. Update config file
|
||||
|
||||
IMPORTANTLY: Now that you have your ports of leader and follower arm and ip address of the mobile-so100, update the **ip** in Network configuration, **port** in leader_arms and **port** in lekiwi. In the [`LeKiwiRobotConfig`](../lerobot/common/robot_devices/robots/configs.py) file. Where you will find something like:
|
||||
```python
|
||||
@RobotConfig.register_subclass("lekiwi")
|
||||
@dataclass
|
||||
class LeKiwiRobotConfig(RobotConfig):
|
||||
# `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
|
||||
|
||||
# Network Configuration
|
||||
ip: str = "172.17.133.91"
|
||||
port: int = 5555
|
||||
video_port: int = 5556
|
||||
|
||||
cameras: dict[str, CameraConfig] = field(
|
||||
default_factory=lambda: {
|
||||
"mobile": OpenCVCameraConfig(camera_index="/dev/video0", fps=30, width=640, height=480),
|
||||
"mobile2": OpenCVCameraConfig(camera_index="/dev/video2", fps=30, width=640, height=480),
|
||||
}
|
||||
)
|
||||
|
||||
calibration_dir: str = ".cache/calibration/lekiwi"
|
||||
|
||||
leader_arms: dict[str, MotorsBusConfig] = field(
|
||||
default_factory=lambda: {
|
||||
"main": FeetechMotorsBusConfig(
|
||||
port="/dev/tty.usbmodem585A0077581",
|
||||
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/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"],
|
||||
"left_wheel": (7, "sts3215"),
|
||||
"back_wheel": (8, "sts3215"),
|
||||
"right_wheel": (9, "sts3215"),
|
||||
},
|
||||
),
|
||||
}
|
||||
)
|
||||
|
||||
teleop_keys: dict[str, str] = field(
|
||||
default_factory=lambda: {
|
||||
# Movement
|
||||
"forward": "w",
|
||||
"backward": "s",
|
||||
"left": "a",
|
||||
"right": "d",
|
||||
"rotate_left": "z",
|
||||
"rotate_right": "x",
|
||||
# Speed control
|
||||
"speed_up": "r",
|
||||
"speed_down": "f",
|
||||
# quit teleop
|
||||
"quit": "q",
|
||||
}
|
||||
)
|
||||
|
||||
mock: bool = False
|
||||
```
|
||||
|
||||
## Wired version
|
||||
|
||||
For the wired LeKiwi version your configured IP address should refer to your own laptop (127.0.0.1), because leader arm and LeKiwi are in this case connected to own laptop. Below and example configuration for this wired setup:
|
||||
```python
|
||||
@RobotConfig.register_subclass("lekiwi")
|
||||
@dataclass
|
||||
class LeKiwiRobotConfig(RobotConfig):
|
||||
# `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
|
||||
|
||||
# Network Configuration
|
||||
ip: str = "127.0.0.1"
|
||||
port: int = 5555
|
||||
video_port: int = 5556
|
||||
|
||||
cameras: dict[str, CameraConfig] = field(
|
||||
default_factory=lambda: {
|
||||
"front": OpenCVCameraConfig(
|
||||
camera_index=0, fps=30, width=640, height=480, rotation=90
|
||||
),
|
||||
"wrist": OpenCVCameraConfig(
|
||||
camera_index=1, fps=30, width=640, height=480, rotation=180
|
||||
),
|
||||
}
|
||||
)
|
||||
|
||||
calibration_dir: str = ".cache/calibration/lekiwi"
|
||||
|
||||
leader_arms: dict[str, MotorsBusConfig] = field(
|
||||
default_factory=lambda: {
|
||||
"main": FeetechMotorsBusConfig(
|
||||
port="/dev/tty.usbmodem585A0077581",
|
||||
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.usbmodem58760431061",
|
||||
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"],
|
||||
"left_wheel": (7, "sts3215"),
|
||||
"back_wheel": (8, "sts3215"),
|
||||
"right_wheel": (9, "sts3215"),
|
||||
},
|
||||
),
|
||||
}
|
||||
)
|
||||
|
||||
teleop_keys: dict[str, str] = field(
|
||||
default_factory=lambda: {
|
||||
# Movement
|
||||
"forward": "w",
|
||||
"backward": "s",
|
||||
"left": "a",
|
||||
"right": "d",
|
||||
"rotate_left": "z",
|
||||
"rotate_right": "x",
|
||||
# Speed control
|
||||
"speed_up": "r",
|
||||
"speed_down": "f",
|
||||
# quit teleop
|
||||
"quit": "q",
|
||||
}
|
||||
)
|
||||
|
||||
mock: bool = False
|
||||
```
|
||||
|
||||
# E. Calibration
|
||||
Now we have to calibrate the leader arm and the follower arm. The wheel motors don't have to be calibrated.
|
||||
|
||||
|
||||
### Calibrate follower arm (on mobile base)
|
||||
> [!IMPORTANT]
|
||||
> Contrarily to step 6 of the [assembly video](https://youtu.be/FioA2oeFZ5I?t=724) 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 |
|
||||
| ----------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| <img src="../media/lekiwi/mobile_calib_zero.webp?raw=true" alt="SO-100 follower arm zero position" title="SO-100 follower arm zero position" style="width:100%;"> | <img src="../media/lekiwi/mobile_calib_rotated.webp?raw=true" alt="SO-100 follower arm rotated position" title="SO-100 follower arm rotated position" style="width:100%;"> | <img src="../media/lekiwi/mobile_calib_rest.webp?raw=true" alt="SO-100 follower arm rest position" title="SO-100 follower arm rest position" style="width:100%;"> |
|
||||
|
||||
Make sure the arm is connected to the Raspberry Pi and run this script (on the Raspberry Pi) to launch manual calibration:
|
||||
```bash
|
||||
python lerobot/scripts/control_robot.py \
|
||||
--robot.type=lekiwi \
|
||||
--robot.cameras='{}' \
|
||||
--control.type=calibrate \
|
||||
--control.arms='["main_follower"]'
|
||||
```
|
||||
|
||||
### Wired version
|
||||
If you have the **wired** LeKiwi version please run all commands including this calibration command on your laptop.
|
||||
|
||||
### Calibrate leader arm
|
||||
Then to calibrate the leader arm (which is attached to the laptop/pc). You will need to move the leader arm to these positions sequentially:
|
||||
|
||||
| 1. Zero position | 2. Rotated position | 3. Rest position |
|
||||
| ------------------------------------------------------------------------------------------------------------------------------------------------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------ |
|
||||
| <img src="../media/so100/leader_zero.webp?raw=true" alt="SO-100 leader arm zero position" title="SO-100 leader arm zero position" style="width:100%;"> | <img src="../media/so100/leader_rotated.webp?raw=true" alt="SO-100 leader arm rotated position" title="SO-100 leader arm rotated position" style="width:100%;"> | <img src="../media/so100/leader_rest.webp?raw=true" alt="SO-100 leader arm rest position" title="SO-100 leader arm rest position" style="width:100%;"> |
|
||||
|
||||
Run this script (on your laptop/pc) to launch manual calibration:
|
||||
```bash
|
||||
python lerobot/scripts/control_robot.py \
|
||||
--robot.type=lekiwi \
|
||||
--robot.cameras='{}' \
|
||||
--control.type=calibrate \
|
||||
--control.arms='["main_leader"]'
|
||||
```
|
||||
|
||||
# F. Teleoperate
|
||||
|
||||
> [!TIP]
|
||||
> If you're using a Mac, you might need to give Terminal permission to access your keyboard. Go to System Preferences > Security & Privacy > Input Monitoring and check the box for Terminal.
|
||||
|
||||
To teleoperate SSH into your Raspberry Pi, and run `conda activate lerobot` and this script:
|
||||
```bash
|
||||
python lerobot/scripts/control_robot.py \
|
||||
--robot.type=lekiwi \
|
||||
--control.type=remote_robot
|
||||
```
|
||||
|
||||
Then on your laptop, also run `conda activate lerobot` and this script:
|
||||
```bash
|
||||
python lerobot/scripts/control_robot.py \
|
||||
--robot.type=lekiwi \
|
||||
--control.type=teleoperate \
|
||||
--control.fps=30
|
||||
```
|
||||
|
||||
> **NOTE:** To visualize the data, enable `--control.display_data=true`. This streams the data using `rerun`. For the `--control.type=remote_robot` you will also need to set `--control.viewer_ip` and `--control.viewer_port`
|
||||
|
||||
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:
|
||||
| Speed Mode | Linear Speed (m/s) | Rotation Speed (deg/s) |
|
||||
| ---------- | ------------------ | ---------------------- |
|
||||
| Fast | 0.4 | 90 |
|
||||
| Medium | 0.25 | 60 |
|
||||
| Slow | 0.1 | 30 |
|
||||
|
||||
|
||||
| Key | Action |
|
||||
| --- | -------------- |
|
||||
| W | Move forward |
|
||||
| A | Move left |
|
||||
| S | Move backward |
|
||||
| D | Move right |
|
||||
| Z | Turn left |
|
||||
| X | Turn right |
|
||||
| R | Increase speed |
|
||||
| F | Decrease speed |
|
||||
|
||||
> [!TIP]
|
||||
> If you use a different keyboard you can change the keys for each command in the [`LeKiwiRobotConfig`](../lerobot/common/robot_devices/robots/configs.py).
|
||||
|
||||
### Wired version
|
||||
If you have the **wired** LeKiwi version please run all commands including both these teleoperation commands on your laptop.
|
||||
|
||||
## 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 set in the configuration file. To check the Raspberry Pi's IP address, run (on the Pi command line):
|
||||
```bash
|
||||
hostname -I
|
||||
```
|
||||
|
||||
### 2. Check if Pi is reachable from laptop/pc
|
||||
Try pinging the Raspberry Pi from your laptop:
|
||||
```bach
|
||||
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:
|
||||
```bash
|
||||
ssh <your_pi_user_name>@<your_pi_ip_address>
|
||||
```
|
||||
If you get a connection error:
|
||||
- Ensure SSH is enabled on the Pi by running:
|
||||
```bash
|
||||
sudo raspi-config
|
||||
```
|
||||
Then navigate to: **Interfacing Options -> SSH** and enable it.
|
||||
|
||||
### 4. Same config file
|
||||
Make sure the configuration file on both your laptop/pc and the Raspberry Pi is the same.
|
||||
|
||||
# G. Record a dataset
|
||||
Once you're familiar with teleoperation, you can record your first dataset with LeKiwi.
|
||||
|
||||
To start the program on LeKiwi, SSH into your Raspberry Pi, and run `conda activate lerobot` and this script:
|
||||
```bash
|
||||
python lerobot/scripts/control_robot.py \
|
||||
--robot.type=lekiwi \
|
||||
--control.type=remote_robot
|
||||
```
|
||||
|
||||
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](https://huggingface.co/settings/tokens):
|
||||
```bash
|
||||
huggingface-cli login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential
|
||||
```
|
||||
|
||||
Store your Hugging Face repository name in a variable to run these commands:
|
||||
```bash
|
||||
HF_USER=$(huggingface-cli whoami | head -n 1)
|
||||
echo $HF_USER
|
||||
```
|
||||
On your laptop then run this command to record 2 episodes and upload your dataset to the hub:
|
||||
```bash
|
||||
python lerobot/scripts/control_robot.py \
|
||||
--robot.type=lekiwi \
|
||||
--control.type=record \
|
||||
--control.fps=30 \
|
||||
--control.single_task="Grasp a lego block and put it in the bin." \
|
||||
--control.repo_id=${HF_USER}/lekiwi_test \
|
||||
--control.tags='["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`.
|
||||
|
||||
### Wired version
|
||||
If you have the **wired** LeKiwi version please run all commands including both these record dataset commands on your laptop.
|
||||
|
||||
# H. Visualize a dataset
|
||||
|
||||
If you uploaded your dataset to the hub with `--control.push_to_hub=true`, you can [visualize your dataset online](https://huggingface.co/spaces/lerobot/visualize_dataset) by copy pasting your repo id given by:
|
||||
```bash
|
||||
echo ${HF_USER}/lekiwi_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):
|
||||
```bash
|
||||
python lerobot/scripts/visualize_dataset_html.py \
|
||||
--repo-id ${HF_USER}/lekiwi_test \
|
||||
--local-files-only 1
|
||||
```
|
||||
|
||||
# I. Replay an episode
|
||||
Now try to replay the first episode on your robot:
|
||||
```bash
|
||||
python lerobot/scripts/control_robot.py \
|
||||
--robot.type=lekiwi \
|
||||
--control.type=replay \
|
||||
--control.fps=30 \
|
||||
--control.repo_id=${HF_USER}/lekiwi_test \
|
||||
--control.episode=0
|
||||
```
|
||||
|
||||
## J. Train a policy
|
||||
|
||||
To train a policy to control your robot, use the [`python lerobot/scripts/train.py`](../lerobot/scripts/train.py) script. A few arguments are required. Here is an example command:
|
||||
```bash
|
||||
python lerobot/scripts/train.py \
|
||||
--dataset.repo_id=${HF_USER}/lekiwi_test \
|
||||
--policy.type=act \
|
||||
--output_dir=outputs/train/act_lekiwi_test \
|
||||
--job_name=act_lekiwi_test \
|
||||
--policy.device=cuda \
|
||||
--wandb.enable=true
|
||||
```
|
||||
|
||||
Let's explain it:
|
||||
1. We provided the dataset as argument with `--dataset.repo_id=${HF_USER}/lekiwi_test`.
|
||||
2. We provided the policy with `policy.type=act`. This loads configurations from [`configuration_act.py`](../lerobot/common/policies/act/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.
|
||||
4. We provided `policy.device=cuda` since we are training on a Nvidia GPU, but you could use `policy.device=mps` to train on Apple silicon.
|
||||
5. We provided `wandb.enable=true` to use [Weights and Biases](https://docs.wandb.ai/quickstart) 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_lekiwi_test/checkpoints`.
|
||||
|
||||
## K. Evaluate your policy
|
||||
|
||||
You can use the `record` function from [`lerobot/scripts/control_robot.py`](../lerobot/scripts/control_robot.py) but with a policy checkpoint as input. For instance, run this command to record 10 evaluation episodes:
|
||||
```bash
|
||||
python lerobot/scripts/control_robot.py \
|
||||
--robot.type=lekiwi \
|
||||
--control.type=record \
|
||||
--control.fps=30 \
|
||||
--control.single_task="Drive to the red block and pick it up" \
|
||||
--control.repo_id=${HF_USER}/eval_act_lekiwi_test \
|
||||
--control.tags='["tutorial"]' \
|
||||
--control.warmup_time_s=5 \
|
||||
--control.episode_time_s=30 \
|
||||
--control.reset_time_s=30 \
|
||||
--control.num_episodes=10 \
|
||||
--control.push_to_hub=true \
|
||||
--control.policy.path=outputs/train/act_lekiwi_test/checkpoints/last/pretrained_model
|
||||
```
|
||||
|
||||
As you can see, it's almost the same command as previously used to record your training dataset. Two things changed:
|
||||
1. There is an additional `--control.policy.path` argument which indicates the path to your policy checkpoint with (e.g. `outputs/train/eval_act_lekiwi_test/checkpoints/last/pretrained_model`). You can also use the model repository if you uploaded a model checkpoint to the hub (e.g. `${HF_USER}/act_lekiwi_test`).
|
||||
2. The name of dataset begins by `eval` to reflect that you are running inference (e.g. `${HF_USER}/eval_act_lekiwi_test`).
|
||||
@@ -1,337 +0,0 @@
|
||||
This tutorial explains how to use [Moss v1](https://github.com/jess-moss/moss-robot-arms) with LeRobot.
|
||||
|
||||
## Source the parts
|
||||
|
||||
Follow this [README](https://github.com/jess-moss/moss-robot-arms). It contains the bill of materials with 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 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.
|
||||
|
||||
## Install LeRobot
|
||||
|
||||
On your computer:
|
||||
|
||||
1. [Install Miniconda](https://docs.anaconda.com/miniconda/#quick-command-line-install):
|
||||
```bash
|
||||
mkdir -p ~/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
|
||||
~/miniconda3/bin/conda init bash
|
||||
```
|
||||
|
||||
2. Restart shell or `source ~/.bashrc`
|
||||
|
||||
3. Create and activate a fresh conda environment for lerobot
|
||||
```bash
|
||||
conda create -y -n lerobot python=3.10 && conda activate lerobot
|
||||
```
|
||||
|
||||
4. Clone LeRobot:
|
||||
```bash
|
||||
git clone https://github.com/huggingface/lerobot.git ~/lerobot
|
||||
```
|
||||
|
||||
5. Install ffmpeg in your environment:
|
||||
When using `miniconda`, install `ffmpeg` in your environment:
|
||||
```bash
|
||||
conda install ffmpeg -c conda-forge
|
||||
```
|
||||
|
||||
6. Install LeRobot with dependencies for the feetech motors:
|
||||
```bash
|
||||
cd ~/lerobot && pip install -e ".[feetech]"
|
||||
```
|
||||
|
||||
## Configure the motors
|
||||
|
||||
Follow steps 1 of the [assembly video](https://www.youtube.com/watch?v=DA91NJOtMic) 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:
|
||||
```bash
|
||||
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):
|
||||
```
|
||||
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.
|
||||
```
|
||||
|
||||
Troubleshooting: On Linux, you might need to give access to the USB ports by running:
|
||||
```bash
|
||||
sudo chmod 666 /dev/ttyACM0
|
||||
sudo chmod 666 /dev/ttyACM1
|
||||
```
|
||||
|
||||
#### Update config file
|
||||
|
||||
IMPORTANTLY: Now that you have your ports, update the **port** default values of [`MossRobotConfig`](../lerobot/common/robot_devices/robots/configs.py). You will find something like:
|
||||
```python
|
||||
@RobotConfig.register_subclass("moss")
|
||||
@dataclass
|
||||
class MossRobotConfig(ManipulatorRobotConfig):
|
||||
calibration_dir: str = ".cache/calibration/moss"
|
||||
# `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"],
|
||||
},
|
||||
),
|
||||
}
|
||||
)
|
||||
```
|
||||
|
||||
**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:
|
||||
```bash
|
||||
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.
|
||||
```bash
|
||||
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.
|
||||
|
||||
**Remove the gears of the 6 leader motors**
|
||||
Follow step 2 of the [assembly video](https://www.youtube.com/watch?v=DA91NJOtMic). 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.
|
||||
|
||||
**Add motor horn to the motors**
|
||||
Follow step 3 of the [assembly video](https://www.youtube.com/watch?v=DA91NJOtMic). For Moss v1, you need to align the holes on the motor horn to the motor spline to be approximately 3, 6, 9 and 12 o'clock.
|
||||
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.
|
||||
|
||||
## Assemble the arms
|
||||
|
||||
Follow step 4 of the [assembly video](https://www.youtube.com/watch?v=DA91NJOtMic). 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.
|
||||
|
||||
## Calibrate
|
||||
|
||||
Next, you'll need to calibrate your Moss v1 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 Moss v1 robot to work on another.
|
||||
|
||||
**Manual calibration of follower arm**
|
||||
/!\ Contrarily to step 6 of the [assembly video](https://www.youtube.com/watch?v=DA91NJOtMic) 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 |
|
||||
| ------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| <img src="../media/moss/follower_zero.webp?raw=true" alt="Moss v1 follower arm zero position" title="Moss v1 follower arm zero position" style="width:100%;"> | <img src="../media/moss/follower_rotated.webp?raw=true" alt="Moss v1 follower arm rotated position" title="Moss v1 follower arm rotated position" style="width:100%;"> | <img src="../media/moss/follower_rest.webp?raw=true" alt="Moss v1 follower arm rest position" title="Moss v1 follower arm rest position" style="width:100%;"> |
|
||||
|
||||
Make sure both arms are connected and run this script to launch manual calibration:
|
||||
```bash
|
||||
python lerobot/scripts/control_robot.py \
|
||||
--robot.type=moss \
|
||||
--robot.cameras='{}' \
|
||||
--control.type=calibrate \
|
||||
--control.arms='["main_follower"]'
|
||||
```
|
||||
|
||||
**Manual calibration of leader arm**
|
||||
Follow step 6 of the [assembly video](https://www.youtube.com/watch?v=DA91NJOtMic) 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 |
|
||||
| ------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| <img src="../media/moss/leader_zero.webp?raw=true" alt="Moss v1 leader arm zero position" title="Moss v1 leader arm zero position" style="width:100%;"> | <img src="../media/moss/leader_rotated.webp?raw=true" alt="Moss v1 leader arm rotated position" title="Moss v1 leader arm rotated position" style="width:100%;"> | <img src="../media/moss/leader_rest.webp?raw=true" alt="Moss v1 leader arm rest position" title="Moss v1 leader arm rest position" style="width:100%;"> |
|
||||
|
||||
Run this script to launch manual calibration:
|
||||
```bash
|
||||
python lerobot/scripts/control_robot.py \
|
||||
--robot.type=moss \
|
||||
--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):
|
||||
```bash
|
||||
python lerobot/scripts/control_robot.py \
|
||||
--robot.type=moss \
|
||||
--robot.cameras='{}' \
|
||||
--control.type=teleoperate
|
||||
```
|
||||
|
||||
|
||||
**Teleop with displaying cameras**
|
||||
Follow [this guide to setup your cameras](https://github.com/huggingface/lerobot/blob/main/examples/7_get_started_with_real_robot.md#c-add-your-cameras-with-opencvcamera). 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.
|
||||
|
||||
> **NOTE:** To visualize the data, enable `--control.display_data=true`. This streams the data using `rerun`.
|
||||
|
||||
```bash
|
||||
python lerobot/scripts/control_robot.py \
|
||||
--robot.type=moss \
|
||||
--control.type=teleoperate
|
||||
```
|
||||
|
||||
## Record a dataset
|
||||
|
||||
Once you're familiar with teleoperation, you can record your first dataset with Moss v1.
|
||||
|
||||
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](https://huggingface.co/settings/tokens):
|
||||
```bash
|
||||
huggingface-cli login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential
|
||||
```
|
||||
|
||||
Store your Hugging Face repository name in a variable to run these commands:
|
||||
```bash
|
||||
HF_USER=$(huggingface-cli whoami | head -n 1)
|
||||
echo $HF_USER
|
||||
```
|
||||
|
||||
Record 2 episodes and upload your dataset to the hub:
|
||||
```bash
|
||||
python lerobot/scripts/control_robot.py \
|
||||
--robot.type=moss \
|
||||
--control.type=record \
|
||||
--control.fps=30 \
|
||||
--control.single_task="Grasp a lego block and put it in the bin." \
|
||||
--control.repo_id=${HF_USER}/moss_test \
|
||||
--control.tags='["moss","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`.
|
||||
|
||||
## Visualize a dataset
|
||||
|
||||
If you uploaded your dataset to the hub with `--control.push_to_hub=true`, you can [visualize your dataset online](https://huggingface.co/spaces/lerobot/visualize_dataset) by copy pasting your repo id given by:
|
||||
```bash
|
||||
echo ${HF_USER}/moss_test
|
||||
```
|
||||
|
||||
If you didn't upload with `--control.push_to_hub=false`, you can also visualize it locally with:
|
||||
```bash
|
||||
python lerobot/scripts/visualize_dataset_html.py \
|
||||
--repo-id ${HF_USER}/moss_test \
|
||||
--local-files-only 1
|
||||
```
|
||||
|
||||
## Replay an episode
|
||||
|
||||
Now try to replay the first episode on your robot:
|
||||
```bash
|
||||
python lerobot/scripts/control_robot.py \
|
||||
--robot.type=moss \
|
||||
--control.type=replay \
|
||||
--control.fps=30 \
|
||||
--control.repo_id=${HF_USER}/moss_test \
|
||||
--control.episode=0
|
||||
```
|
||||
|
||||
## Train a policy
|
||||
|
||||
To train a policy to control your robot, use the [`python lerobot/scripts/train.py`](../lerobot/scripts/train.py) script. A few arguments are required. Here is an example command:
|
||||
```bash
|
||||
python lerobot/scripts/train.py \
|
||||
--dataset.repo_id=${HF_USER}/moss_test \
|
||||
--policy.type=act \
|
||||
--output_dir=outputs/train/act_moss_test \
|
||||
--job_name=act_moss_test \
|
||||
--policy.device=cuda \
|
||||
--wandb.enable=true
|
||||
```
|
||||
|
||||
Let's explain it:
|
||||
1. We provided the dataset as argument with `--dataset.repo_id=${HF_USER}/moss_test`.
|
||||
2. We provided the policy with `policy.type=act`. This loads configurations from [`configuration_act.py`](../lerobot/common/policies/act/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.
|
||||
4. We provided `policy.device=cuda` since we are training on a Nvidia GPU, but you could use `policy.device=mps` to train on Apple silicon.
|
||||
5. We provided `wandb.enable=true` to use [Weights and Biases](https://docs.wandb.ai/quickstart) 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_moss_test/checkpoints`.
|
||||
|
||||
## Evaluate your policy
|
||||
|
||||
You can use the `record` function from [`lerobot/scripts/control_robot.py`](../lerobot/scripts/control_robot.py) but with a policy checkpoint as input. For instance, run this command to record 10 evaluation episodes:
|
||||
```bash
|
||||
python lerobot/scripts/control_robot.py \
|
||||
--robot.type=moss \
|
||||
--control.type=record \
|
||||
--control.fps=30 \
|
||||
--control.single_task="Grasp a lego block and put it in the bin." \
|
||||
--control.repo_id=${HF_USER}/eval_act_moss_test \
|
||||
--control.tags='["tutorial"]' \
|
||||
--control.warmup_time_s=5 \
|
||||
--control.episode_time_s=30 \
|
||||
--control.reset_time_s=30 \
|
||||
--control.num_episodes=10 \
|
||||
--control.push_to_hub=true \
|
||||
--control.policy.path=outputs/train/act_moss_test/checkpoints/last/pretrained_model
|
||||
```
|
||||
|
||||
As you can see, it's almost the same command as previously used to record your training dataset. Two things changed:
|
||||
1. There is an additional `--control.policy.path` argument which indicates the path to your policy checkpoint with (e.g. `outputs/train/eval_act_moss_test/checkpoints/last/pretrained_model`). You can also use the model repository if you uploaded a model checkpoint to the hub (e.g. `${HF_USER}/act_moss_test`).
|
||||
2. The name of dataset begins by `eval` to reflect that you are running inference (e.g. `${HF_USER}/eval_act_moss_test`).
|
||||
|
||||
## More
|
||||
|
||||
Follow this [previous tutorial](https://github.com/huggingface/lerobot/blob/main/examples/7_get_started_with_real_robot.md#4-train-a-policy-on-your-data) 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 [`#moss-arm`](https://discord.com/channels/1216765309076115607/1275374638985252925).
|
||||
@@ -13,7 +13,7 @@
|
||||
# limitations under the License.
|
||||
|
||||
"""
|
||||
This scripts demonstrates how to evaluate a pretrained policy from the HuggingFace Hub or from your local
|
||||
This script demonstrates how to evaluate a pretrained policy from the HuggingFace Hub or from your local
|
||||
training outputs directory. In the latter case, you might want to run examples/3_train_policy.py first.
|
||||
|
||||
It requires the installation of the 'gym_pusht' simulation environment. Install it by running:
|
||||
@@ -119,7 +119,7 @@ while not done:
|
||||
rewards.append(reward)
|
||||
frames.append(env.render())
|
||||
|
||||
# The rollout is considered done when the success state is reach (i.e. terminated is True),
|
||||
# The rollout is considered done when the success state is reached (i.e. terminated is True),
|
||||
# or the maximum number of iterations is reached (i.e. truncated is True)
|
||||
done = terminated | truncated | done
|
||||
step += 1
|
||||
|
||||
@@ -12,7 +12,7 @@
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
"""This scripts demonstrates how to train Diffusion Policy on the PushT environment.
|
||||
"""This script demonstrates how to train Diffusion Policy on the PushT environment.
|
||||
|
||||
Once you have trained a model with this script, you can try to evaluate it on
|
||||
examples/2_evaluate_pretrained_policy.py
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
This tutorial will explain the training script, how to use it, and particularly how to configure everything needed for the training run.
|
||||
> **Note:** The following assume you're running these commands on a machine equipped with a cuda GPU. If you don't have one (or if you're using a Mac), you can add `--policy.device=cpu` (`--policy.device=mps` respectively). However, be advised that the code executes much slower on cpu.
|
||||
> **Note:** The following assumes you're running these commands on a machine equipped with a cuda GPU. If you don't have one (or if you're using a Mac), you can add `--policy.device=cpu` (`--policy.device=mps` respectively). However, be advised that the code executes much slower on cpu.
|
||||
|
||||
|
||||
## The training script
|
||||
@@ -23,7 +23,7 @@ def train(cfg: TrainPipelineConfig):
|
||||
|
||||
You can inspect the `TrainPipelineConfig` defined in [`lerobot/configs/train.py`](../lerobot/configs/train.py) (which is heavily commented and meant to be a reference to understand any option)
|
||||
|
||||
When running the script, inputs for the command line are parsed thanks to the `@parser.wrap()` decorator and an instance of this class is automatically generated. Under the hood, this is done with [Draccus](https://github.com/dlwh/draccus) which is a tool dedicated for this purpose. If you're familiar with Hydra, Draccus can similarly load configurations from config files (.json, .yaml) and also override their values through command line inputs. Unlike Hydra, these configurations are pre-defined in the code through dataclasses rather than being defined entirely in config files. This allows for more rigorous serialization/deserialization, typing, and to manipulate configuration as objects directly in the code and not as dictionaries or namespaces (which enables nice features in an IDE such as autocomplete, jump-to-def, etc.)
|
||||
When running the script, inputs for the command line are parsed thanks to the `@parser.wrap()` decorator and an instance of this class is automatically generated. Under the hood, this is done with [Draccus](https://github.com/dlwh/draccus) which is a tool dedicated to this purpose. If you're familiar with Hydra, Draccus can similarly load configurations from config files (.json, .yaml) and also override their values through command line inputs. Unlike Hydra, these configurations are pre-defined in the code through dataclasses rather than being defined entirely in config files. This allows for more rigorous serialization/deserialization, typing, and to manipulate configuration as objects directly in the code and not as dictionaries or namespaces (which enables nice features in an IDE such as autocomplete, jump-to-def, etc.)
|
||||
|
||||
Let's have a look at a simplified example. Amongst other attributes, the training config has the following attributes:
|
||||
```python
|
||||
@@ -43,7 +43,7 @@ class DatasetConfig:
|
||||
```
|
||||
|
||||
This creates a hierarchical relationship where, for example assuming we have a `cfg` instance of `TrainPipelineConfig`, we can access the `repo_id` value with `cfg.dataset.repo_id`.
|
||||
From the command line, we can specify this value with using a very similar syntax `--dataset.repo_id=repo/id`.
|
||||
From the command line, we can specify this value by using a very similar syntax `--dataset.repo_id=repo/id`.
|
||||
|
||||
By default, every field takes its default value specified in the dataclass. If a field doesn't have a default value, it needs to be specified either from the command line or from a config file – which path is also given in the command line (more in this below). In the example above, the `dataset` field doesn't have a default value which means it must be specified.
|
||||
|
||||
@@ -135,7 +135,7 @@ will start a training run with the same configuration used for training [lerobot
|
||||
|
||||
## Resume training
|
||||
|
||||
Being able to resume a training run is important in case it crashed or aborted for any reason. We'll demonstrate how to that here.
|
||||
Being able to resume a training run is important in case it crashed or aborted for any reason. We'll demonstrate how to do that here.
|
||||
|
||||
Let's reuse the command from the previous run and add a few more options:
|
||||
```bash
|
||||
|
||||
@@ -83,7 +83,7 @@ python lerobot/scripts/configure_motor.py \
|
||||
--brand dynamixel \
|
||||
--model xl330-m288 \
|
||||
--baudrate 1000000 \
|
||||
--ID 1
|
||||
--id 1
|
||||
```
|
||||
|
||||
Then unplug your first motor and plug the second motor and set its ID to 2.
|
||||
@@ -93,7 +93,7 @@ python lerobot/scripts/configure_motor.py \
|
||||
--brand dynamixel \
|
||||
--model xl330-m288 \
|
||||
--baudrate 1000000 \
|
||||
--ID 2
|
||||
--id 2
|
||||
```
|
||||
|
||||
Redo the process for all your motors until ID 6.
|
||||
@@ -377,7 +377,7 @@ robot = ManipulatorRobot(robot_config)
|
||||
|
||||
The `KochRobotConfig` is used to set the associated settings and calibration process. For instance, we activate the torque of the gripper of the leader Koch v1.1 arm and position it at a 40 degree angle to use it as a trigger.
|
||||
|
||||
For the [Aloha bimanual robot](https://aloha-2.github.io), we would use `AlohaRobotConfig` to set different settings such as a secondary ID for shadow joints (shoulder, elbow). Specific to Aloha, LeRobot comes with default calibration files stored in in `.cache/calibration/aloha_default`. Assuming the motors have been properly assembled, no manual calibration step is expected for Aloha.
|
||||
For the [Aloha bimanual robot](https://aloha-2.github.io), we would use `AlohaRobotConfig` to set different settings such as a secondary ID for shadow joints (shoulder, elbow). Specific to Aloha, LeRobot comes with default calibration files stored in `.cache/calibration/aloha_default`. Assuming the motors have been properly assembled, no manual calibration step is expected for Aloha.
|
||||
|
||||
**Calibrate and Connect the ManipulatorRobot**
|
||||
|
||||
@@ -399,7 +399,7 @@ And here are the corresponding positions for the leader arm:
|
||||
|
||||
You can watch a [video tutorial of the calibration procedure](https://youtu.be/8drnU9uRY24) for more details.
|
||||
|
||||
During calibration, we count the number of full 360-degree rotations your motors have made since they were first used. That's why we ask yo to move to this arbitrary "zero" position. We don't actually "set" the zero position, so you don't need to be accurate. After calculating these "offsets" to shift the motor values around 0, we need to assess the rotation direction of each motor, which might differ. That's why we ask you to rotate all motors to roughly 90 degrees, to measure if the values changed negatively or positively.
|
||||
During calibration, we count the number of full 360-degree rotations your motors have made since they were first used. That's why we ask you to move to this arbitrary "zero" position. We don't actually "set" the zero position, so you don't need to be accurate. After calculating these "offsets" to shift the motor values around 0, we need to assess the rotation direction of each motor, which might differ. That's why we ask you to rotate all motors to roughly 90 degrees, to measure if the values changed negatively or positively.
|
||||
|
||||
Finally, the rest position ensures that the follower and leader arms are roughly aligned after calibration, preventing sudden movements that could damage the motors when starting teleoperation.
|
||||
|
||||
@@ -622,7 +622,7 @@ camera_01_frame_000047.png
|
||||
|
||||
Note: Some cameras may take a few seconds to warm up, and the first frame might be black or green.
|
||||
|
||||
Finally, run this code to instantiate and connectyour camera:
|
||||
Finally, run this code to instantiate and connect your camera:
|
||||
```python
|
||||
from lerobot.common.robot_devices.cameras.configs import OpenCVCameraConfig
|
||||
from lerobot.common.robot_devices.cameras.opencv import OpenCVCamera
|
||||
@@ -1,161 +0,0 @@
|
||||
This tutorial explains how to use [Stretch 3](https://hello-robot.com/stretch-3-product) with LeRobot.
|
||||
|
||||
## Setup
|
||||
|
||||
Familiarize yourself with Stretch by following its [tutorials](https://docs.hello-robot.com/0.3/getting_started/hello_robot/) (recommended).
|
||||
|
||||
To use LeRobot on Stretch, 3 options are available:
|
||||
- [tethered setup](https://docs.hello-robot.com/0.3/getting_started/connecting_to_stretch/#tethered-setup)
|
||||
- [untethered setup](https://docs.hello-robot.com/0.3/getting_started/connecting_to_stretch/#untethered-setup)
|
||||
- ssh directly into Stretch (you will first need to install and configure openssh-server on stretch using one of the two above setups)
|
||||
|
||||
|
||||
## Install LeRobot
|
||||
|
||||
On Stretch's CLI, follow these steps:
|
||||
|
||||
1. [Install Miniconda](https://docs.anaconda.com/miniconda/#quick-command-line-install):
|
||||
```bash
|
||||
mkdir -p ~/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
|
||||
~/miniconda3/bin/conda init bash
|
||||
```
|
||||
|
||||
2. Comment out these lines in `~/.profile` (this can mess up paths used by conda and ~/.local/bin should already be in your PATH)
|
||||
```
|
||||
# set PATH so it includes user's private bin if it exists
|
||||
if [ -d "$HOME/.local/bin" ] ; then
|
||||
PATH="$HOME/.local/bin:$PATH"
|
||||
fi
|
||||
```
|
||||
|
||||
3. Restart shell or `source ~/.bashrc`
|
||||
|
||||
4. Create and activate a fresh conda environment for lerobot
|
||||
```bash
|
||||
conda create -y -n lerobot python=3.10 && conda activate lerobot
|
||||
```
|
||||
|
||||
5. Clone LeRobot:
|
||||
```bash
|
||||
git clone https://github.com/huggingface/lerobot.git ~/lerobot
|
||||
```
|
||||
|
||||
6. When using `miniconda`, install `ffmpeg` in your environment:
|
||||
```bash
|
||||
conda install ffmpeg -c conda-forge
|
||||
```
|
||||
|
||||
7. Install LeRobot with stretch dependencies:
|
||||
```bash
|
||||
cd ~/lerobot && pip install -e ".[stretch]"
|
||||
```
|
||||
|
||||
> **Note:** If you get this message, you can ignore it: `ERROR: pip's dependency resolver does not currently take into account all the packages that are installed.`
|
||||
|
||||
8. Run a [system check](https://docs.hello-robot.com/0.3/getting_started/stretch_hardware_overview/#system-check) to make sure your robot is ready:
|
||||
```bash
|
||||
stretch_system_check.py
|
||||
```
|
||||
|
||||
> **Note:** You may need to free the "robot process" after booting Stretch by running `stretch_free_robot_process.py`. For more info this Stretch's [doc](https://docs.hello-robot.com/0.3/getting_started/stretch_hardware_overview/#turning-off-gamepad-teleoperation).
|
||||
|
||||
You should get something like this:
|
||||
```bash
|
||||
For use with S T R E T C H (R) from Hello Robot Inc.
|
||||
---------------------------------------------------------------------
|
||||
|
||||
Model = Stretch 3
|
||||
Tool = DexWrist 3 w/ Gripper
|
||||
Serial Number = stretch-se3-3054
|
||||
|
||||
---- Checking Hardware ----
|
||||
[Pass] Comms are ready
|
||||
[Pass] Actuators are ready
|
||||
[Warn] Sensors not ready (IMU AZ = -10.19 out of range -10.1 to -9.5)
|
||||
[Pass] Battery voltage is 13.6 V
|
||||
|
||||
---- Checking Software ----
|
||||
[Pass] Ubuntu 22.04 is ready
|
||||
[Pass] All APT pkgs are setup correctly
|
||||
[Pass] Firmware is up-to-date
|
||||
[Pass] Python pkgs are up-to-date
|
||||
[Pass] ROS2 Humble is ready
|
||||
```
|
||||
|
||||
## Teleoperate, record a dataset and run a policy
|
||||
|
||||
**Calibrate (Optional)**
|
||||
Before operating Stretch, you need to [home](https://docs.hello-robot.com/0.3/getting_started/stretch_hardware_overview/#homing) it first. Be mindful about giving Stretch some space as this procedure will move the robot's arm and gripper. Now run this command:
|
||||
```bash
|
||||
python lerobot/scripts/control_robot.py \
|
||||
--robot.type=stretch \
|
||||
--control.type=calibrate
|
||||
```
|
||||
This is equivalent to running `stretch_robot_home.py`
|
||||
|
||||
> **Note:** If you run any of the LeRobot scripts below and Stretch is not properly homed, it will automatically home/calibrate first.
|
||||
|
||||
**Teleoperate**
|
||||
Before trying teleoperation, you need activate the gamepad controller by pressing the middle button. For more info, see Stretch's [doc](https://docs.hello-robot.com/0.3/getting_started/hello_robot/#gamepad-teleoperation).
|
||||
|
||||
Now try out teleoperation (see above documentation to learn about the gamepad controls):
|
||||
|
||||
> **NOTE:** To visualize the data, enable `--control.display_data=true`. This streams the data using `rerun`.
|
||||
```bash
|
||||
python lerobot/scripts/control_robot.py \
|
||||
--robot.type=stretch \
|
||||
--control.type=teleoperate
|
||||
```
|
||||
This is essentially the same as running `stretch_gamepad_teleop.py`
|
||||
|
||||
**Record a dataset**
|
||||
Once you're familiar with the gamepad controls and after a bit of practice, you can try to record your first dataset with Stretch.
|
||||
|
||||
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](https://huggingface.co/settings/tokens):
|
||||
```bash
|
||||
huggingface-cli login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential
|
||||
```
|
||||
|
||||
Store your Hugging Face repository name in a variable to run these commands:
|
||||
```bash
|
||||
HF_USER=$(huggingface-cli whoami | head -n 1)
|
||||
echo $HF_USER
|
||||
```
|
||||
|
||||
Record one episode:
|
||||
```bash
|
||||
python lerobot/scripts/control_robot.py \
|
||||
--robot.type=stretch \
|
||||
--control.type=record \
|
||||
--control.fps=30 \
|
||||
--control.single_task="Grasp a lego block and put it in the bin." \
|
||||
--control.repo_id=${HF_USER}/stretch_test \
|
||||
--control.tags='["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:** If you're using ssh to connect to Stretch and run this script, you won't be able to visualize its cameras feed (though they will still be recording). To see the cameras stream, use [tethered](https://docs.hello-robot.com/0.3/getting_started/connecting_to_stretch/#tethered-setup) or [untethered setup](https://docs.hello-robot.com/0.3/getting_started/connecting_to_stretch/#untethered-setup).
|
||||
|
||||
**Replay an episode**
|
||||
Now try to replay this episode (make sure the robot's initial position is the same):
|
||||
```bash
|
||||
python lerobot/scripts/control_robot.py \
|
||||
--robot.type=stretch \
|
||||
--control.type=replay \
|
||||
--control.fps=30 \
|
||||
--control.repo_id=${HF_USER}/stretch_test \
|
||||
--control.episode=0
|
||||
```
|
||||
|
||||
Follow [previous tutorial](https://github.com/huggingface/lerobot/blob/main/examples/7_get_started_with_real_robot.md#4-train-a-policy-on-your-data) to train a policy on your data and run inference on your robot. You will need to adapt the code for Stretch.
|
||||
|
||||
> TODO(rcadene, aliberts): Add already setup environment and policy yaml configuration files
|
||||
|
||||
If you need help, please reach out on Discord in the channel `#stretch3-mobile-arm`.
|
||||
@@ -1,182 +0,0 @@
|
||||
This tutorial explains how to use [Aloha and Aloha 2 stationary](https://www.trossenrobotics.com/aloha-stationary) with LeRobot.
|
||||
|
||||
## Setup
|
||||
|
||||
Follow the [documentation from Trossen Robotics](https://docs.trossenrobotics.com/aloha_docs/2.0/getting_started/stationary/hardware_setup.html) for setting up the hardware and plugging the 4 arms and 4 cameras to your computer.
|
||||
|
||||
|
||||
## Install LeRobot
|
||||
|
||||
On your computer:
|
||||
|
||||
1. [Install Miniconda](https://docs.anaconda.com/miniconda/#quick-command-line-install):
|
||||
```bash
|
||||
mkdir -p ~/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
|
||||
~/miniconda3/bin/conda init bash
|
||||
```
|
||||
|
||||
2. Restart shell or `source ~/.bashrc`
|
||||
|
||||
3. Create and activate a fresh conda environment for lerobot
|
||||
```bash
|
||||
conda create -y -n lerobot python=3.10 && conda activate lerobot
|
||||
```
|
||||
|
||||
4. Clone LeRobot:
|
||||
```bash
|
||||
git clone https://github.com/huggingface/lerobot.git ~/lerobot
|
||||
```
|
||||
|
||||
5. When using `miniconda`, install `ffmpeg` in your environment:
|
||||
```bash
|
||||
conda install ffmpeg -c conda-forge
|
||||
```
|
||||
|
||||
6. Install LeRobot with dependencies for the Aloha motors (dynamixel) and cameras (intelrealsense):
|
||||
```bash
|
||||
cd ~/lerobot && pip install -e ".[dynamixel, intelrealsense]"
|
||||
```
|
||||
|
||||
## Teleoperate
|
||||
|
||||
**/!\ FOR SAFETY, READ THIS /!\**
|
||||
Teleoperation consists in manually operating the leader arms to move the follower arms. Importantly:
|
||||
1. Make sure your leader arms are in the same position as the follower arms, so that the follower arms don't move too fast to match the leader arms,
|
||||
2. Our code assumes that your robot has been assembled following Trossen Robotics instructions. This allows us to skip calibration, as we use the pre-defined calibration files in `.cache/calibration/aloha_default`. If you replace a motor, make sure you follow the exact instructions from Trossen Robotics.
|
||||
|
||||
By running the following code, you can start your first **SAFE** teleoperation:
|
||||
|
||||
> **NOTE:** To visualize the data, enable `--control.display_data=true`. This streams the data using `rerun`.
|
||||
|
||||
```bash
|
||||
python lerobot/scripts/control_robot.py \
|
||||
--robot.type=aloha \
|
||||
--robot.max_relative_target=5 \
|
||||
--control.type=teleoperate
|
||||
```
|
||||
|
||||
By adding `--robot.max_relative_target=5`, we override the default value for `max_relative_target` defined in [`AlohaRobotConfig`](lerobot/common/robot_devices/robots/configs.py). It is expected to be `5` to limit the magnitude of the movement for more safety, but the teleoperation won't be smooth. When you feel confident, you can disable this limit by adding `--robot.max_relative_target=null` to the command line:
|
||||
```bash
|
||||
python lerobot/scripts/control_robot.py \
|
||||
--robot.type=aloha \
|
||||
--robot.max_relative_target=null \
|
||||
--control.type=teleoperate
|
||||
```
|
||||
|
||||
## Record a dataset
|
||||
|
||||
Once you're familiar with teleoperation, you can record your first dataset with Aloha.
|
||||
|
||||
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](https://huggingface.co/settings/tokens):
|
||||
```bash
|
||||
huggingface-cli login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential
|
||||
```
|
||||
|
||||
Store your Hugging Face repository name in a variable to run these commands:
|
||||
```bash
|
||||
HF_USER=$(huggingface-cli whoami | head -n 1)
|
||||
echo $HF_USER
|
||||
```
|
||||
|
||||
Record 2 episodes and upload your dataset to the hub:
|
||||
```bash
|
||||
python lerobot/scripts/control_robot.py \
|
||||
--robot.type=aloha \
|
||||
--robot.max_relative_target=null \
|
||||
--control.type=record \
|
||||
--control.fps=30 \
|
||||
--control.single_task="Grasp a lego block and put it in the bin." \
|
||||
--control.repo_id=${HF_USER}/aloha_test \
|
||||
--control.tags='["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
|
||||
```
|
||||
|
||||
## Visualize a dataset
|
||||
|
||||
If you uploaded your dataset to the hub with `--control.push_to_hub=true`, you can [visualize your dataset online](https://huggingface.co/spaces/lerobot/visualize_dataset) by copy pasting your repo id given by:
|
||||
```bash
|
||||
echo ${HF_USER}/aloha_test
|
||||
```
|
||||
|
||||
If you didn't upload with `--control.push_to_hub=false`, you can also visualize it locally with:
|
||||
```bash
|
||||
python lerobot/scripts/visualize_dataset_html.py \
|
||||
--repo-id ${HF_USER}/aloha_test
|
||||
```
|
||||
|
||||
## Replay an episode
|
||||
|
||||
**/!\ FOR SAFETY, READ THIS /!\**
|
||||
Replay consists in automatically replaying the sequence of actions (i.e. goal positions for your motors) recorded in a given dataset episode. Make sure the current initial position of your robot is similar to the one in your episode, so that your follower arms don't move too fast to go to the first goal positions. For safety, you might want to add `--robot.max_relative_target=5` to your command line as explained above.
|
||||
|
||||
Now try to replay the first episode on your robot:
|
||||
```bash
|
||||
python lerobot/scripts/control_robot.py \
|
||||
--robot.type=aloha \
|
||||
--robot.max_relative_target=null \
|
||||
--control.type=replay \
|
||||
--control.fps=30 \
|
||||
--control.repo_id=${HF_USER}/aloha_test \
|
||||
--control.episode=0
|
||||
```
|
||||
|
||||
## Train a policy
|
||||
|
||||
To train a policy to control your robot, use the [`python lerobot/scripts/train.py`](../lerobot/scripts/train.py) script. A few arguments are required. Here is an example command:
|
||||
```bash
|
||||
python lerobot/scripts/train.py \
|
||||
--dataset.repo_id=${HF_USER}/aloha_test \
|
||||
--policy.type=act \
|
||||
--output_dir=outputs/train/act_aloha_test \
|
||||
--job_name=act_aloha_test \
|
||||
--policy.device=cuda \
|
||||
--wandb.enable=true
|
||||
```
|
||||
|
||||
Let's explain it:
|
||||
1. We provided the dataset as argument with `--dataset.repo_id=${HF_USER}/aloha_test`.
|
||||
2. We provided the policy with `policy.type=act`. This loads configurations from [`configuration_act.py`](../lerobot/common/policies/act/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.
|
||||
4. We provided `policy.device=cuda` since we are training on a Nvidia GPU, but you could use `policy.device=mps` to train on Apple silicon.
|
||||
5. We provided `wandb.enable=true` to use [Weights and Biases](https://docs.wandb.ai/quickstart) for visualizing training plots. This is optional but if you use it, make sure you are logged in by running `wandb login`.
|
||||
|
||||
For more information on the `train` script see the previous tutorial: [`examples/4_train_policy_with_script.md`](../examples/4_train_policy_with_script.md)
|
||||
|
||||
Training should take several hours. You will find checkpoints in `outputs/train/act_aloha_test/checkpoints`.
|
||||
|
||||
## Evaluate your policy
|
||||
|
||||
You can use the `record` function from [`lerobot/scripts/control_robot.py`](../lerobot/scripts/control_robot.py) but with a policy checkpoint as input. For instance, run this command to record 10 evaluation episodes:
|
||||
```bash
|
||||
python lerobot/scripts/control_robot.py \
|
||||
--robot.type=aloha \
|
||||
--control.type=record \
|
||||
--control.fps=30 \
|
||||
--control.single_task="Grasp a lego block and put it in the bin." \
|
||||
--control.repo_id=${HF_USER}/eval_act_aloha_test \
|
||||
--control.tags='["tutorial"]' \
|
||||
--control.warmup_time_s=5 \
|
||||
--control.episode_time_s=30 \
|
||||
--control.reset_time_s=30 \
|
||||
--control.num_episodes=10 \
|
||||
--control.push_to_hub=true \
|
||||
--control.policy.path=outputs/train/act_aloha_test/checkpoints/last/pretrained_model \
|
||||
--control.num_image_writer_processes=1
|
||||
```
|
||||
|
||||
As you can see, it's almost the same command as previously used to record your training dataset. Two things changed:
|
||||
1. There is an additional `--control.policy.path` argument which indicates the path to your policy checkpoint with (e.g. `outputs/train/eval_act_aloha_test/checkpoints/last/pretrained_model`). You can also use the model repository if you uploaded a model checkpoint to the hub (e.g. `${HF_USER}/act_aloha_test`).
|
||||
2. The name of dataset begins by `eval` to reflect that you are running inference (e.g. `${HF_USER}/eval_act_aloha_test`).
|
||||
3. We use `--control.num_image_writer_processes=1` instead of the default value (`0`). On our computer, using a dedicated process to write images from the 4 cameras on disk allows to reach constant 30 fps during inference. Feel free to explore different values for `--control.num_image_writer_processes`.
|
||||
|
||||
## More
|
||||
|
||||
Follow this [previous tutorial](https://github.com/huggingface/lerobot/blob/main/examples/7_get_started_with_real_robot.md#4-train-a-policy-on-your-data) for a more in-depth explanation.
|
||||
|
||||
If you have any question or need help, please reach out on Discord in the channel `#aloha-arm`.
|
||||
@@ -66,7 +66,7 @@ def main():
|
||||
print(f"Number of episodes in full dataset: {total_episodes}")
|
||||
print(f"Number of episodes in training dataset (90% subset): {len(train_episodes)}")
|
||||
print(f"Number of episodes in validation dataset (10% subset): {len(val_episodes)}")
|
||||
# - Load train an val datasets
|
||||
# - Load train and val datasets
|
||||
train_dataset = LeRobotDataset(
|
||||
"lerobot/pusht", episodes=train_episodes, delta_timestamps=delta_timestamps
|
||||
)
|
||||
|
||||
94
examples/robots/lekiwi_client_app.py
Executable file
94
examples/robots/lekiwi_client_app.py
Executable file
@@ -0,0 +1,94 @@
|
||||
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import logging
|
||||
import time
|
||||
|
||||
from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
|
||||
from lerobot.common.datasets.utils import hw_to_dataset_features
|
||||
from lerobot.common.robots.lekiwi.config_lekiwi import LeKiwiClientConfig
|
||||
from lerobot.common.robots.lekiwi.lekiwi_client import LeKiwiClient
|
||||
from lerobot.common.teleoperators.keyboard import KeyboardTeleop, KeyboardTeleopConfig
|
||||
from lerobot.common.teleoperators.so100_leader import SO100Leader, SO100LeaderConfig
|
||||
|
||||
NB_CYCLES_CLIENT_CONNECTION = 250
|
||||
|
||||
|
||||
def main():
|
||||
logging.info("Configuring Teleop Devices")
|
||||
leader_arm_config = SO100LeaderConfig(port="/dev/tty.usbmodem58760433331")
|
||||
leader_arm = SO100Leader(leader_arm_config)
|
||||
|
||||
keyboard_config = KeyboardTeleopConfig()
|
||||
keyboard = KeyboardTeleop(keyboard_config)
|
||||
|
||||
logging.info("Configuring LeKiwi Client")
|
||||
robot_config = LeKiwiClientConfig(remote_ip="172.18.134.136", id="lekiwi")
|
||||
robot = LeKiwiClient(robot_config)
|
||||
|
||||
logging.info("Creating LeRobot Dataset")
|
||||
|
||||
action_features = hw_to_dataset_features(robot.action_features, "action")
|
||||
obs_features = hw_to_dataset_features(robot.observation_features, "observation")
|
||||
dataset_features = {**action_features, **obs_features}
|
||||
|
||||
dataset = LeRobotDataset.create(
|
||||
repo_id="user/lekiwi" + str(int(time.time())),
|
||||
fps=10,
|
||||
features=dataset_features,
|
||||
robot_type=robot.name,
|
||||
)
|
||||
|
||||
logging.info("Connecting Teleop Devices")
|
||||
leader_arm.connect()
|
||||
keyboard.connect()
|
||||
|
||||
logging.info("Connecting remote LeKiwi")
|
||||
robot.connect()
|
||||
|
||||
if not robot.is_connected or not leader_arm.is_connected or not keyboard.is_connected:
|
||||
logging.error("Failed to connect to all devices")
|
||||
return
|
||||
|
||||
logging.info("Starting LeKiwi teleoperation")
|
||||
i = 0
|
||||
while i < NB_CYCLES_CLIENT_CONNECTION:
|
||||
arm_action = leader_arm.get_action()
|
||||
base_action = keyboard.get_action()
|
||||
action = {**arm_action, **base_action} if len(base_action) > 0 else arm_action
|
||||
|
||||
action_sent = robot.send_action(action)
|
||||
observation = robot.get_observation()
|
||||
|
||||
frame = {**action_sent, **observation}
|
||||
task = "Dummy Example Task Dataset"
|
||||
|
||||
logging.info("Saved a frame into the dataset")
|
||||
dataset.add_frame(frame, task)
|
||||
i += 1
|
||||
|
||||
logging.info("Disconnecting Teleop Devices and LeKiwi Client")
|
||||
robot.disconnect()
|
||||
leader_arm.disconnect()
|
||||
keyboard.disconnect()
|
||||
|
||||
logging.info("Uploading dataset to the hub")
|
||||
dataset.save_episode()
|
||||
dataset.push_to_hub()
|
||||
|
||||
logging.info("Finished LeKiwi cleanly")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user