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Merge branch 'user/aliberts/2025_02_25_refactor_robots' into user/aliberts/2025_04_03_add_hope_jr

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This commit is contained in:
Simon Alibert
2025-06-02 20:04:30 +02:00
committed by GitHub
parent e0d3179b72 adcb07bf62
commit e578e0c2bb
133 changed files with 2125 additions and 3345 deletions
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24
.github/workflows/build-docker-images.yml vendored
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@@ -40,24 +40,24 @@ jobs:
git lfs install
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@v3
uses: docker/setup-buildx-action@b5ca514318bd6ebac0fb2aedd5d36ec1b5c232a2 # v3.10.0
with:
cache-binary: false
- name: Check out code
uses: actions/checkout@v4
uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
with:
lfs: true
persist-credentials: false
- name: Login to DockerHub
uses: docker/login-action@v3
uses: docker/login-action@74a5d142397b4f367a81961eba4e8cd7edddf772 # v3.4.0
with:
username: ${{ secrets.DOCKERHUB_USERNAME }}
password: ${{ secrets.DOCKERHUB_PASSWORD }}
- name: Build and Push CPU
uses: docker/build-push-action@v5
uses: docker/build-push-action@ca052bb54ab0790a636c9b5f226502c73d547a25 # v5.4.0
with:
context: .
file: ./docker/lerobot-cpu/Dockerfile
@@ -78,24 +78,24 @@ jobs:
git lfs install
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@v3
uses: docker/setup-buildx-action@b5ca514318bd6ebac0fb2aedd5d36ec1b5c232a2 # v3.10.0
with:
cache-binary: false
- name: Check out code
uses: actions/checkout@v4
uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
with:
lfs: true
persist-credentials: false
- name: Login to DockerHub
uses: docker/login-action@v3
uses: docker/login-action@74a5d142397b4f367a81961eba4e8cd7edddf772 # v3.4.0
with:
username: ${{ secrets.DOCKERHUB_USERNAME }}
password: ${{ secrets.DOCKERHUB_PASSWORD }}
- name: Build and Push GPU
uses: docker/build-push-action@v5
uses: docker/build-push-action@ca052bb54ab0790a636c9b5f226502c73d547a25 # v5.4.0
with:
context: .
file: ./docker/lerobot-gpu/Dockerfile
@@ -110,23 +110,23 @@ jobs:
group: aws-general-8-plus
steps:
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@v3
uses: docker/setup-buildx-action@b5ca514318bd6ebac0fb2aedd5d36ec1b5c232a2 # v3.10.0
with:
cache-binary: false
- name: Check out code
uses: actions/checkout@v4
uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
with:
persist-credentials: false
- name: Login to DockerHub
uses: docker/login-action@v3
uses: docker/login-action@74a5d142397b4f367a81961eba4e8cd7edddf772 # v3.4.0
with:
username: ${{ secrets.DOCKERHUB_USERNAME }}
password: ${{ secrets.DOCKERHUB_PASSWORD }}
- name: Build and Push GPU dev
uses: docker/build-push-action@v5
uses: docker/build-push-action@ca052bb54ab0790a636c9b5f226502c73d547a25 # v5.4.0
with:
context: .
file: ./docker/lerobot-gpu-dev/Dockerfile

4
.github/workflows/nightly-tests.yml vendored
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@@ -33,7 +33,7 @@ jobs:
runs-on:
group: aws-general-8-plus
container:
image: huggingface/lerobot-cpu:latest
image: huggingface/lerobot-cpu:latest # zizmor: ignore[unpinned-images]
options: --shm-size "16gb"
credentials:
username: ${{ secrets.DOCKERHUB_USERNAME }}
@@ -60,7 +60,7 @@ jobs:
CUDA_VISIBLE_DEVICES: "0"
TEST_TYPE: "single_gpu"
container:
image: huggingface/lerobot-gpu:latest
image: huggingface/lerobot-gpu:latest # zizmor: ignore[unpinned-images]
options: --gpus all --shm-size "16gb"
credentials:
username: ${{ secrets.DOCKERHUB_USERNAME }}

8
.github/workflows/quality.yml vendored
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@@ -33,12 +33,12 @@ jobs:
runs-on: ubuntu-latest
steps:
- name: Checkout Repository
uses: actions/checkout@v4
uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
with:
persist-credentials: false
- name: Set up Python
uses: actions/setup-python@v4
uses: actions/setup-python@7f4fc3e22c37d6ff65e88745f38bd3157c663f7c # v4.9.1
with:
python-version: ${{ env.PYTHON_VERSION }}
@@ -64,9 +64,9 @@ jobs:
runs-on: ubuntu-latest
steps:
- name: Checkout Repository
uses: actions/checkout@v4
uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
with:
persist-credentials: false
- name: typos-action
uses: crate-ci/typos@v1.29.10
uses: crate-ci/typos@db35ee91e80fbb447f33b0e5fbddb24d2a1a884f # v1.29.10

8
.github/workflows/test-docker-build.yml vendored
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@@ -35,7 +35,7 @@ jobs:
matrix: ${{ steps.set-matrix.outputs.matrix }}
steps:
- name: Check out code
uses: actions/checkout@v4
uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
with:
persist-credentials: false
@@ -64,17 +64,17 @@ jobs:
docker-file: ${{ fromJson(needs.get_changed_files.outputs.matrix) }}
steps:
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@v3
uses: docker/setup-buildx-action@b5ca514318bd6ebac0fb2aedd5d36ec1b5c232a2 # v3.10.0
with:
cache-binary: false
- name: Check out code
uses: actions/checkout@v4
uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
with:
persist-credentials: false
- name: Build Docker image
uses: docker/build-push-action@v5
uses: docker/build-push-action@ca052bb54ab0790a636c9b5f226502c73d547a25 # v5.4.0
with:
file: ${{ matrix.docker-file }}
context: .

12
.github/workflows/test.yml vendored
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@@ -50,7 +50,7 @@ jobs:
env:
MUJOCO_GL: egl
steps:
- uses: actions/checkout@v4
- uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
with:
lfs: true # Ensure LFS files are pulled
persist-credentials: false
@@ -62,7 +62,7 @@ jobs:
sudo apt-get install -y libegl1-mesa-dev ffmpeg portaudio19-dev
- name: Install uv and python
uses: astral-sh/setup-uv@v5
uses: astral-sh/setup-uv@d4b2f3b6ecc6e67c4457f6d3e41ec42d3d0fcb86 # v5.4.2
with:
enable-cache: true
version: ${{ env.UV_VERSION }}
@@ -85,7 +85,7 @@ jobs:
env:
MUJOCO_GL: egl
steps:
- uses: actions/checkout@v4
- uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
with:
lfs: true # Ensure LFS files are pulled
persist-credentials: false
@@ -94,7 +94,7 @@ jobs:
run: sudo apt-get update && sudo apt-get install -y ffmpeg
- name: Install uv and python
uses: astral-sh/setup-uv@v5
uses: astral-sh/setup-uv@d4b2f3b6ecc6e67c4457f6d3e41ec42d3d0fcb86 # v5.4.2
with:
enable-cache: true
version: ${{ env.UV_VERSION }}
@@ -117,7 +117,7 @@ jobs:
env:
MUJOCO_GL: egl
steps:
- uses: actions/checkout@v4
- uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
with:
lfs: true # Ensure LFS files are pulled
persist-credentials: false
@@ -129,7 +129,7 @@ jobs:
sudo apt-get install -y libegl1-mesa-dev ffmpeg portaudio19-dev
- name: Install uv and python
uses: astral-sh/setup-uv@v5
uses: astral-sh/setup-uv@d4b2f3b6ecc6e67c4457f6d3e41ec42d3d0fcb86 # v5.4.2
with:
enable-cache: true
version: ${{ env.UV_VERSION }}

4
.github/workflows/trufflehog.yml vendored
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@@ -24,12 +24,12 @@ jobs:
runs-on: ubuntu-latest
steps:
- name: Checkout code
uses: actions/checkout@v4
uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
with:
fetch-depth: 0
persist-credentials: false
- name: Secret Scanning
uses: trufflesecurity/trufflehog@main
uses: trufflesecurity/trufflehog@90694bf9af66e7536abc5824e7a87246dbf933cb # v3.88.35
with:
extra_args: --only-verified

10
.pre-commit-config.yaml
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@@ -37,18 +37,18 @@ repos:
- id: trailing-whitespace
- repo: https://github.com/adhtruong/mirrors-typos
rev: v1.31.1
rev: v1.32.0
hooks:
- id: typos
args: [--force-exclude]
- repo: https://github.com/asottile/pyupgrade
rev: v3.19.1
rev: v3.20.0
hooks:
- id: pyupgrade
- repo: https://github.com/astral-sh/ruff-pre-commit
rev: v0.11.5
rev: v0.11.11
hooks:
- id: ruff
args: [--fix]
@@ -57,12 +57,12 @@ repos:
##### Security #####
- repo: https://github.com/gitleaks/gitleaks
rev: v8.24.3
rev: v8.26.0
hooks:
- id: gitleaks
- repo: https://github.com/woodruffw/zizmor-pre-commit
rev: v1.5.2
rev: v1.8.0
hooks:
- id: zizmor

3
CONTRIBUTING.md
View File

@@ -269,9 +269,6 @@ Follow these steps to start contributing:
the PR as a draft PR. These are useful to avoid duplicated work, and to differentiate
it from PRs ready to be merged;
4. Make sure existing tests pass;
<!-- 5. Add high-coverage tests. No quality testing = no merge.
See an example of a good PR here: https://github.com/huggingface/lerobot/pull/ -->
### Tests

2
README.md
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@@ -360,7 +360,7 @@ with profile(
If you want, you can cite this work with:
```bibtex
@misc{cadene2024lerobot,
author = {Cadene, Remi and Alibert, Simon and Soare, Alexander and Gallouedec, Quentin and Zouitine, Adil and Wolf, Thomas},
author = {Cadene, Remi and Alibert, Simon and Soare, Alexander and Gallouedec, Quentin and Zouitine, Adil and Palma, Steven and Kooijmans, Pepijn and Aractingi, Michel and Shukor, Mustafa and Aubakirova, Dana and Russi, Martino and Capuano, Francesco and Pascale, Caroline and Choghari, Jade and Moss, Jess and Wolf, Thomas},
title = {LeRobot: State-of-the-art Machine Learning for Real-World Robotics in Pytorch},
howpublished = "\url{https://github.com/huggingface/lerobot}",
year = {2024}

18
docs/source/_toctree.yml
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@@ -5,8 +5,22 @@
title: Installation
title: Get started
- sections:
- local: assemble_so101
title: Assemble SO-101
- local: getting_started_real_world_robot
title: Getting Started with Real-World Robots
- local: cameras
title: Cameras
title: "Tutorials"
- sections:
- local: so101
title: SO-101
- local: so100
title: SO-100
- local: koch
title: Koch v1.1
- local: lekiwi
title: LeKiwi
title: "Robots"
- sections:
- local: contributing
title: Contribute to LeRobot
title: "Contribute"

348
docs/source/assemble_so101.mdx
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@@ -1,348 +0,0 @@
# Assemble SO-101
In the steps below we explain how to assemble our flagship robot, the SO-101.
## 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,
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.
## Install LeRobot
To install LeRobot follow our [Installation Guide](./installation)
## Configure motors
To configure the motors 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.
You now should plug the 5V or 12V power supply to the motor bus. 5V for the STS3215 7.4V motors and 12V for the STS3215 12V motors. Note that the leader arm always uses the 7.4V motors, so watch out that you plug in the right power supply if you have 12V and 7.4V motors, otherwise you might burn your motors! Now, connect the motor bus to your computer via USB. Note that the USB doesn't provide any power, and both the power supply and USB have to be plugged in.
### Find the USB ports associated to each arm
To find the port for each bus servo adapter, run this script:
```bash
python lerobot/scripts/find_motors_bus_port.py
```
##### Example outputs of script
<hfoptions id="example">
<hfoption id="Mac">
Example output leader arm's port: `/dev/tty.usbmodem575E0031751`
```bash
Finding all available ports for the MotorBus.
['/dev/tty.usbmodem575E0032081', '/dev/tty.usbmodem575E0031751']
Remove the usb cable from your MotorsBus and press Enter when done.
[...Disconnect leader arm and press Enter...]
The port of this MotorsBus is /dev/tty.usbmodem575E0031751
Reconnect the usb cable.
```
Example output follower arm port: `/dev/tty.usbmodem575E0032081`
```
Finding all available ports for the MotorBus.
['/dev/tty.usbmodem575E0032081', '/dev/tty.usbmodem575E0031751']
Remove the usb cable from your MotorsBus and press Enter when done.
[...Disconnect follower arm and press Enter...]
The port of this MotorsBus is /dev/tty.usbmodem575E0032081
Reconnect the usb cable.
```
</hfoption>
<hfoption id="Linux">
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
```
Example output leader arm port: `/dev/ttyACM0`
```bash
Finding all available ports for the MotorBus.
['/dev/ttyACM0', '/dev/ttyACM1']
Remove the usb cable from your MotorsBus and press Enter when done.
[...Disconnect leader arm and press Enter...]
The port of this MotorsBus is /dev/ttyACM0
Reconnect the usb cable.
```
Example output follower arm port: `/dev/ttyACM1`
```
Finding all available ports for the MotorBus.
['/dev/ttyACM0', '/dev/ttyACM1']
Remove the usb cable from your MotorsBus and press Enter when done.
[...Disconnect follower arm and press Enter...]
The port of this MotorsBus is /dev/ttyACM1
Reconnect the usb cable.
```
</hfoption>
</hfoptions>
#### Update config file
Now that you have your ports, update the **port** default values of [`SO101RobotConfig`](https://github.com/huggingface/lerobot/blob/main/lerobot/common/robot_devices/robots/configs.py).
You will find a class called `so101` where you can update the `port` values with your actual motor ports:
```diff
@RobotConfig.register_subclass("so101")
@dataclass
class So101RobotConfig(ManipulatorRobotConfig):
calibration_dir: str = ".cache/calibration/so101"
# `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",
+ port="{ADD YOUR LEADER PORT}",
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",
+ port="{ADD YOUR FOLLOWER PORT}",
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"],
},
),
}
)
```
Here is a video of the process:
<div class="video-container">
<video controls width="600">
<source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/lerobot-find-motorbus.mp4" type="video/mp4" />
</video>
</div>
## Step-by-Step Assembly Instructions
The follower arm uses 6x STS3215 motors with 1/345 gearing. The leader however uses three differently geared motors to make sure it can both sustain its own weight and it can be moved without requiring much force. Which motor is needed for which joint is shown in table below.
| Leader-Arm Axis | Motor | Gear Ratio |
|-----------------|:-------:|:----------:|
| Base / Shoulder Yaw | 1 | 1 / 191 |
| Shoulder Pitch | 2 | 1 / 345 |
| Elbow | 3 | 1 / 191 |
| Wrist Roll | 4 | 1 / 147 |
| Wrist Pitch | 5 | 1 / 147 |
| Gripper | 6 | 1 / 147 |
### Set motor IDs
Plug your motor in one of the two ports of the motor bus and run this script to set its ID to 1. Replace the text after --port to the corresponding control board port.
```bash
python lerobot/scripts/configure_motor.py \
--port /dev/tty.usbmodem58760432961 \
--brand feetech \
--model sts3215 \
--baudrate 1000000 \
--ID 1
```
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 this process for all your motors until ID 6. Do the same for the 6 motors of the leader arm, but make sure to change the power supply if you use motors with different voltage and make sure you give the right ID to the right motor according to the table above.
Here is a video of the process:
<div class="video-container">
<video controls width="600">
<source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/lerobot-configure-motor.mp4" type="video/mp4" />
</video>
</div>
### Clean Parts
Remove all support material from the 3D-printed parts, the easiest way to do this is using a small screwdriver to get underneath the support material.
### Joint 1
- Place the first motor into the base.
- Fasten the motor with 4 M2x6mm screws (smallest screws). Two from the top and two from bottom.
- Slide over the first motor holder and fasten it using two M2x6mm screws (one on each side).
- Install both motor horns, securing the top horn with a M3x6mm screw.
- Attach the shoulder part.
- Tighten the shoulder part with 4 M3x6mm screws on top and 4 M3x6mm screws on the bottom
- Add the shoulder motor holder.
<div class="video-container">
<video controls width="600">
<source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/Joint1_v2.mp4" type="video/mp4" />
</video>
</div>
### Joint 2
- Slide the second motor in from the top.
- Fasten the second motor with 4 M2x6mm screws.
- Attach both motor horns to motor 2, again use the M3x6mm horn screw.
- Attach the upper arm with 4 M3x6mm screws on each side.
<div class="video-container">
<video controls width="600">
<source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/Joint2_v2.mp4" type="video/mp4" />
</video>
</div>
### Joint 3
- Insert motor 3 and fasten using 4 M2x6mm screws
- Attach both motor horns to motor 3 and secure one again with a M3x6mm horn screw.
- Connect the forearm to motor 3 using 4 M3x6mm screws on each side.
<div class="video-container">
<video controls width="600">
<source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/Joint3_v2.mp4" type="video/mp4" />
</video>
</div>
### Joint 4
- Slide over motor holder 4.
- Slide in motor 4.
- Fasten motor 4 with 4 M2x6mm screws and attach its motor horns, use a M3x6mm horn screw.
<div class="video-container">
<video controls width="600">
<source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/Joint4_v2.mp4" type="video/mp4" />
</video>
</div>
### Joint 5
- Insert motor 5 into the wrist holder and secure it with 2 M2x6mm front screws.
- Install only one motor horn on the wrist motor and secure it with a M3x6mm horn screw.
- Secure the wrist to motor 4 using 4 M3x6mm screws on both sides.
<div class="video-container">
<video controls width="600">
<source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/Joint5_v2.mp4" type="video/mp4" />
</video>
</div>
### Gripper / Handle
<hfoptions id="assembly">
<hfoption id="Follower">
- Attach the gripper to motor 5, attach it to the motor horn on the wrist using 4 M3x6mm screws.
- Insert the gripper motor and secure it with 2 M2x6mm screws on each side.
- Attach the motor horns and again use a M3x6mm horn screw.
- Install the gripper claw and secure it with 4 M3x6mm screws on both sides.
<div class="video-container">
<video controls width="600">
<source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/Gripper_v2.mp4" type="video/mp4" />
</video>
</div>
</hfoption>
<hfoption id="Leader">
- Mount the leader holder onto the wrist and secure it with 4 M3x6mm screws.
- Attach the handle to motor 5 using 1 M2x6mm screw.
- Insert the gripper motor, secure it with 2 M2x6mm screws on each side, attach a motor horn using a M3x6mm horn screw.
- Attach the follower trigger with 4 M3x6mm screws.
<div class="video-container">
<video controls width="600">
<source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/Leader_v2.mp4" type="video/mp4" />
</video>
</div>
</hfoption>
</hfoptions>
##### Wiring
- Attach the motor controller on the back.
- Then insert all wires, use the wire guides everywhere to make sure the wires don't unplug themselves and stay in place.
<div class="video-container">
<video controls width="600">
<source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/Wiring_v2.mp4" type="video/mp4" />
</video>
</div>
## Calibrate
Next, you'll need to calibrate your SO-101 robot to ensure that the leader and follower arms have the same position values when they are in the same physical position.
The calibration process is very important because it allows a neural network trained on one SO-101 robot to work on another.
#### Manual calibration of follower arm
You will need to move the follower arm to these positions sequentially, note that the rotated position is on the right side of the robot and you have to open the gripper fully.
| 1. Middle position | 2. Zero position | 3. Rotated position | 4. Rest position |
| ------------ |------------------------------------------------------------------------------------------------------------------------------------------------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------ |
| <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/follower_middle.webp?raw=true" alt="SO-101 leader arm middle position" title="SO-101 leader arm middle position" style="width:100%;"> | <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/follower_zero.webp?raw=true" alt="SO-101 leader arm zero position" title="SO-101 leader arm zero position" style="width:100%;"> | <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/follower_rotated.webp?raw=true" alt="SO-101 leader arm rotated position" title="SO-101 leader arm rotated position" style="width:100%;"> | <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/follower_rest.webp?raw=true" alt="SO-101 leader arm rest position" title="SO-101 leader 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=so101 \
--robot.cameras='{}' \
--control.type=calibrate \
--control.arms='["main_follower"]'
```
#### Manual calibration of leader arm
You will also need to move the leader arm to these positions sequentially:
| 1. Middle position | 2. Zero position | 3. Rotated position | 4. Rest position |
| ------------ |------------------------------------------------------------------------------------------------------------------------------------------------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------ |
| <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/leader_middle.webp?raw=true" alt="SO-101 leader arm middle position" title="SO-101 leader arm middle position" style="width:100%;"> | <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/leader_zero.webp?raw=true" alt="SO-101 leader arm zero position" title="SO-101 leader arm zero position" style="width:100%;"> | <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/leader_rotated.webp?raw=true" alt="SO-101 leader arm rotated position" title="SO-101 leader arm rotated position" style="width:100%;"> | <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/leader_rest.webp?raw=true" alt="SO-101 leader arm rest position" title="SO-101 leader arm rest position" style="width:100%;"> |
Run this script to launch manual calibration:
```bash
python lerobot/scripts/control_robot.py \
--robot.type=so101 \
--robot.cameras='{}' \
--control.type=calibrate \
--control.arms='["main_leader"]'
```
Congrats 🎉, your robot is all set to learn a task on its own. Start training it by following this tutorial: [Getting started with real-world robots](./getting_started_real_world_robot)

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# Cameras
LeRobot offers multiple options for video capture, including phone cameras, built-in laptop cameras, external webcams, and Intel RealSense cameras. To efficiently record frames from most cameras, you can use either the `OpenCVCamera` or `RealSenseCamera` class. For additional compatibility details on the `OpenCVCamera` class, refer to the [Video I/O with OpenCV Overview](https://docs.opencv.org/4.x/d0/da7/videoio_overview.html).
### Finding your camera
To instantiate a camera, you need a camera identifier. This identifier might change if you reboot your computer or re-plug your camera, a behavior mostly dependant on your operating system.
To find the camera indices of the cameras plugged into your system, run the following script:
```bash
python lerobot/find_cameras.py opencv # or realsense for Intel Realsense cameras
```
The output will look something like this if you have two cameras connected:
```
--- Detected Cameras ---
Camera #0:
Name: OpenCV Camera @ 0
Type: OpenCV
Id: 0
Backend api: AVFOUNDATION
Default stream profile:
Format: 16.0
Width: 1920
Height: 1080
Fps: 15.0
--------------------
(more cameras ...)
```
> [!WARNING]
> When using Intel RealSense cameras in `macOS`, you could get this [error](https://github.com/IntelRealSense/librealsense/issues/12307): `Error finding RealSense cameras: failed to set power state`, this can be solved by running the same command with `sudo` permissions. Note that using RealSense cameras in `macOS` is unstable.
## Use Cameras
Below are two examples, demonstrating how to work with the API.
- **Asynchronous frame capture** using an OpenCV-based camera
- **Color and depth capture** using an Intel RealSense camera
<hfoptions id="shell_restart">
<hfoption id="Open CV Camera">
```python
from lerobot.common.cameras.opencv.configuration_opencv import OpenCVCameraConfig
from lerobot.common.cameras.opencv.camera_opencv import OpenCVCamera
from lerobot.common.cameras.configs import ColorMode, Cv2Rotation
# Construct an `OpenCVCameraConfig` with your desired FPS, resolution, color mode, and rotation.
config = OpenCVCameraConfig(
index_or_path=0,
fps=15,
width=1920,
height=1080,
color_mode=ColorMode.RGB,
rotation=Cv2Rotation.NO_ROTATION
)
# Instantiate and connect an `OpenCVCamera`, performing a warm-up read (default).
camera = OpenCVCamera(config)
camera.connect()
# Read frames asynchronously in a loop via `async_read(timeout_ms)`
try:
for i in range(10):
frame = camera.async_read(timeout_ms=200)
print(f"Async frame {i} shape:", frame.shape)
finally:
camera.disconnect()
```
</hfoption>
<hfoption id="Intel Realsense Camera">
```python
from lerobot.common.cameras.intel.configuration_realsense import RealSenseCameraConfig
from lerobot.common.cameras.intel.camera_realsense import RealSenseCamera
from lerobot.common.cameras.configs import ColorMode, Cv2Rotation
# Create a `RealSenseCameraConfig` specifying your camera’s serial number and enabling depth.
config = RealSenseCameraConfig(
serial_number="233522074606",
fps=15,
width=640,
height=480,
color_mode=ColorMode.RGB,
use_depth=True,
rotation=Cv2Rotation.NO_ROTATION
)
# Instantiate and connect a `RealSenseCamera` with warm-up read (default).
camera = RealSenseCamera(config)
camera.connect()
# Capture a color frame via `read()` and a depth map via `read_depth()`.
try:
color_frame = camera.read()
depth_map = camera.read_depth()
print("Color frame shape:", color_frame.shape)
print("Depth map shape:", depth_map.shape)
finally:
camera.disconnect()
```
</hfoption>
</hfoptions>
## Use your phone
<hfoptions id="use phone">
<hfoption id="Mac">
To use your iPhone as a camera on macOS, enable the Continuity Camera feature:
- Ensure your Mac is running macOS 13 or later, and your iPhone is on iOS 16 or later.
- Sign in both devices with the same Apple ID.
- Connect your devices with a USB cable or turn on Wi-Fi and Bluetooth for a wireless connection.
For more details, visit [Apple support](https://support.apple.com/en-gb/guide/mac-help/mchl77879b8a/mac).
Your iPhone should be detected automatically when running the camera setup script in the next section.
</hfoption>
<hfoption id="Linux">
If you want to use your phone as a camera on Linux, follow these steps to set up a virtual camera
1. *Install `v4l2loopback-dkms` and `v4l-utils`*. Those packages are required to create virtual camera devices (`v4l2loopback`) and verify their settings with the `v4l2-ctl` utility from `v4l-utils`. Install them using:
```python
sudo apt install v4l2loopback-dkms v4l-utils
```
2. *Install [DroidCam](https://droidcam.app) on your phone*. This app is available for both iOS and Android.
3. *Install [OBS Studio](https://obsproject.com)*. This software will help you manage the camera feed. Install it using [Flatpak](https://flatpak.org):
```python
flatpak install flathub com.obsproject.Studio
```
4. *Install the DroidCam OBS plugin*. This plugin integrates DroidCam with OBS Studio. Install it with:
```python
flatpak install flathub com.obsproject.Studio.Plugin.DroidCam
```
5. *Start OBS Studio*. Launch with:
```python
flatpak run com.obsproject.Studio
```
6. *Add your phone as a source*. Follow the instructions [here](https://droidcam.app/obs/usage). Be sure to set the resolution to `640x480`.
7. *Adjust resolution settings*. In OBS Studio, go to `File > Settings > Video`. Change the `Base(Canvas) Resolution` and the `Output(Scaled) Resolution` to `640x480` by manually typing it in.
8. *Start virtual camera*. In OBS Studio, follow the instructions [here](https://obsproject.com/kb/virtual-camera-guide).
9. *Verify the virtual camera setup*. Use `v4l2-ctl` to list the devices:
```python
v4l2-ctl --list-devices
```
You should see an entry like:
```
VirtualCam (platform:v4l2loopback-000):
/dev/video1
```
10. *Check the camera resolution*. Use `v4l2-ctl` to ensure that the virtual camera output resolution is `640x480`. Change `/dev/video1` to the port of your virtual camera from the output of `v4l2-ctl --list-devices`.
```python
v4l2-ctl -d /dev/video1 --get-fmt-video
```
You should see an entry like:
```
>>> Format Video Capture:
>>> Width/Height : 640/480
>>> Pixel Format : 'YUYV' (YUYV 4:2:2)
```
Troubleshooting: If the resolution is not correct you will have to delete the Virtual Camera port and try again as it cannot be changed.
If everything is set up correctly, you can proceed with the rest of the tutorial.
</hfoption>
</hfoptions>

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../../CONTRIBUTING.md

355
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# Getting Started with Real-World Robots
This tutorial will explain you how to train a neural network to autonomously control a real robot.
This tutorial will explain how to train a neural network to control a real robot autonomously.
**You'll learn:**
1. How to record and visualize your dataset.
2. How to train a policy using your data and prepare it for evaluation.
3. How to evaluate your policy and visualize the results.
By following these steps, you'll be able to replicate tasks like picking up a Lego block and placing it in a bin with a high success rate, as demonstrated in [this video](https://x.com/RemiCadene/status/1814680760592572934).
By following these steps, you'll be able to replicate tasks, such as picking up a Lego block and placing it in a bin with a high success rate, as shown in the video below.
This tutorial is specifically made for the affordable [SO-101](https://github.com/TheRobotStudio/SO-ARM100) robot, but it contains additional information to be easily adapted to various types of robots like [Aloha bimanual robot](https://aloha-2.github.io) by changing some configurations. The SO-101 consists of a leader arm and a follower arm, each with 6 motors. It can work with one or several cameras to record the scene, which serve as visual sensors for the robot.
<details>
<summary><strong>Video: pickup lego block task</strong></summary>
During the data collection phase, you will control the follower arm by moving the leader arm. This process is known as "teleoperation." This technique is used to collect robot trajectories. Afterward, you'll train a neural network to imitate these trajectories and deploy the network to enable your robot to operate autonomously.
<div class="video-container">
<video controls width="600">
<source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/lerobot_task.mp4" type="video/mp4" />
</video>
</div>
If you encounter any issues at any step of the tutorial, feel free to seek help on [Discord](https://discord.com/invite/s3KuuzsPFb) or don't hesitate to iterate with us on the tutorial by creating issues or pull requests.
</details>
## Setup and Calibrate
This tutorial isn’t tied to a specific robot: we walk you through the commands and API snippets you can adapt for any supported platform.
If you haven't yet setup and calibrate the SO-101 follow these steps:
1. [Find ports and update config file](./assemble_so101#find-the-usb-ports-associated-to-each-arm)
2. [Calibrate](./assemble_so101#calibrate)
During data collection, you’ll use a “teloperation” device, such as a leader arm or keyboard to teleoperate the robot and record its motion trajectories.
Once you’ve gathered enough trajectories, you’ll train a neural network to imitate these trajectories and deploy the trained model so your robot can perform the task autonomously.
If you run into any issues at any point, jump into our [Discord community](https://discord.com/invite/s3KuuzsPFb) for support.
## Set up and Calibrate
If you haven't yet set up and calibrated your robot and teleop device, please do so by following the robot-specific tutorial.
## Teleoperate
Run this simple script to teleoperate your robot (it won't connect and display the cameras):
In this example, we’ll demonstrate how to teleoperate the SO101 robot. For each command, we also provide a corresponding API example.
<hfoptions id="teleoperate_so101">
<hfoption id="Command">
```bash
python lerobot/scripts/control_robot.py \
--robot.type=so101 \
--robot.cameras='{}' \
--control.type=teleoperate
python -m lerobot.teleoperate \
--robot.type=so101_follower \
--robot.port=/dev/tty.usbmodem58760431541 \
--robot.id=my_red_robot_arm \
--teleop.type=so101_leader \
--teleop.port=/dev/tty.usbmodem58760431551 \
--teleop.id=my_blue_leader_arm
```
The teleoperate command will automatically:
1. Identify any missing calibrations and initiate the calibration procedure.
2. Connect the robot and start teleoperation.
## Setup Cameras
To connect a camera you have three options:
1. OpenCVCamera which allows us to use any camera: usb, realsense, laptop webcam
2. iPhone camera with MacOS
3. Phone camera on Linux
### Use OpenCVCamera
The [`OpenCVCamera`](../lerobot/common/robot_devices/cameras/opencv.py) class allows you to efficiently record frames from most cameras using the [`opencv2`](https://docs.opencv.org) library. For more details on compatibility, see [Video I/O with OpenCV Overview](https://docs.opencv.org/4.x/d0/da7/videoio_overview.html).
To instantiate an [`OpenCVCamera`](../lerobot/common/robot_devices/cameras/opencv.py), you need a camera index (e.g. `OpenCVCamera(camera_index=0)`). When you only have one camera like a webcam of a laptop, the camera index is usually `0` but it might differ, and the camera index might change if you reboot your computer or re-plug your camera. This behavior depends on your operating system.
To find the camera indices, run the following utility script, which will save a few frames from each detected camera:
```bash
python lerobot/common/robot_devices/cameras/opencv.py \
--images-dir outputs/images_from_opencv_cameras
```
The output will look something like this if you have two cameras connected:
```
Mac or Windows detected. Finding available camera indices through scanning all indices from 0 to 60
[...]
Camera found at index 0
Camera found at index 1
[...]
Connecting cameras
OpenCVCamera(0, fps=30.0, width=1920.0, height=1080.0, color_mode=rgb)
OpenCVCamera(1, fps=24.0, width=1920.0, height=1080.0, color_mode=rgb)
Saving images to outputs/images_from_opencv_cameras
Frame: 0000 Latency (ms): 39.52
[...]
Frame: 0046 Latency (ms): 40.07
Images have been saved to outputs/images_from_opencv_cameras
```
Check the saved images in `outputs/images_from_opencv_cameras` to identify which camera index corresponds to which physical camera (e.g. `0` for `camera_00` or `1` for `camera_01`):
```
camera_00_frame_000000.png
[...]
camera_00_frame_000047.png
camera_01_frame_000000.png
[...]
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.
Now that you have the camera indexes, you should specify the camera's in the config.
### Use your phone
<hfoptions id="use phone">
<hfoption id="Mac">
To use your iPhone as a camera on macOS, enable the Continuity Camera feature:
- Ensure your Mac is running macOS 13 or later, and your iPhone is on iOS 16 or later.
- Sign in both devices with the same Apple ID.
- Connect your devices with a USB cable or turn on Wi-Fi and Bluetooth for a wireless connection.
For more details, visit [Apple support](https://support.apple.com/en-gb/guide/mac-help/mchl77879b8a/mac).
Your iPhone should be detected automatically when running the camera setup script in the next section.
</hfoption>
<hfoption id="Linux">
<hfoption id="API example">
```python
from lerobot.common.teleoperators.so101_leader import SO101LeaderConfig, SO101Leader
from lerobot.common.robots.so101_follower import SO101FollowerConfig, SO101Follower
If you want to use your phone as a camera on Linux, follow these steps to set up a virtual camera
robot_config = SO101FollowerConfig(
port="/dev/tty.usbmodem58760431541",
id="my_red_robot_arm",
)
1. *Install `v4l2loopback-dkms` and `v4l-utils`*. Those packages are required to create virtual camera devices (`v4l2loopback`) and verify their settings with the `v4l2-ctl` utility from `v4l-utils`. Install them using:
```python
sudo apt install v4l2loopback-dkms v4l-utils
```
2. *Install [DroidCam](https://droidcam.app) on your phone*. This app is available for both iOS and Android.
3. *Install [OBS Studio](https://obsproject.com)*. This software will help you manage the camera feed. Install it using [Flatpak](https://flatpak.org):
```python
flatpak install flathub com.obsproject.Studio
```
4. *Install the DroidCam OBS plugin*. This plugin integrates DroidCam with OBS Studio. Install it with:
```python
flatpak install flathub com.obsproject.Studio.Plugin.DroidCam
```
5. *Start OBS Studio*. Launch with:
```python
flatpak run com.obsproject.Studio
```
6. *Add your phone as a source*. Follow the instructions [here](https://droidcam.app/obs/usage). Be sure to set the resolution to `640x480`.
7. *Adjust resolution settings*. In OBS Studio, go to `File > Settings > Video`. Change the `Base(Canvas) Resolution` and the `Output(Scaled) Resolution` to `640x480` by manually typing it in.
8. *Start virtual camera*. In OBS Studio, follow the instructions [here](https://obsproject.com/kb/virtual-camera-guide).
9. *Verify the virtual camera setup*. Use `v4l2-ctl` to list the devices:
```python
v4l2-ctl --list-devices
```
You should see an entry like:
```
VirtualCam (platform:v4l2loopback-000):
/dev/video1
```
10. *Check the camera resolution*. Use `v4l2-ctl` to ensure that the virtual camera output resolution is `640x480`. Change `/dev/video1` to the port of your virtual camera from the output of `v4l2-ctl --list-devices`.
```python
v4l2-ctl -d /dev/video1 --get-fmt-video
```
You should see an entry like:
```
>>> Format Video Capture:
>>> Width/Height : 640/480
>>> Pixel Format : 'YUYV' (YUYV 4:2:2)
```
teleop_config = SO101LeaderConfig(
port="/dev/tty.usbmodem58760431551",
id="my_blue_leader_arm",
)
Troubleshooting: If the resolution is not correct you will have to delete the Virtual Camera port and try again as it cannot be changed.
If everything is set up correctly, you can proceed with the rest of the tutorial.
robot = SO101Follower(robot_config)
teleop_device = SO101Leader(teleop_config)
robot.connect()
teleop_device.connect()
while True:
action = teleop_device.get_action()
robot.send_action(action)
```
</hfoption>
</hfoptions>
The teleoperate command will automatically:
1. Identify any missing calibrations and initiate the calibration procedure.
2. Connect the robot and teleop device and start teleoperation.
## Cameras
To add cameras to your setup, follow this [Guide](./cameras#setup-cameras).
## Teleoperate with cameras
We can now teleoperate again while at the same time visualizing the cameras and joint positions with `rerun`.
With `rerun`, you can teleoperate again while simultaneously visualizing the camera feeds and joint positions. In this example, we’re using the Koch arm.
<hfoptions id="teleoperate_koch_camera">
<hfoption id="Command">
```bash
python lerobot/scripts/control_robot.py \
--robot.type=so101 \
--control.type=teleoperate
--control.display_data=true
python -m lerobot.teleoperate \
--robot.type=koch_follower \
--robot.port=/dev/tty.usbmodem58760431541 \
--robot.id=my_koch_robot \
--robot.cameras="{ front: {type: opencv, index_or_path: 0, width: 1920, height: 1080, fps: 30}}" \
--teleop.type=koch_leader \
--teleop.port=/dev/tty.usbmodem58760431551 \
--teleop.id=my_koch_teleop \
--display_data=true
```
</hfoption>
<hfoption id="API example">
```python
from lerobot.common.cameras.opencv.configuration_opencv import OpenCVCameraConfig
from lerobot.common.teleoperators.koch_leader import KochLeaderConfig, KochLeader
from lerobot.common.robots.koch_follower import KochFollowerConfig, KochFollower
camera_config = {
"front": OpenCVCameraConfig(index_or_path=0, width=1920, height=1080, fps=30)
}
robot_config = KochFollowerConfig(
port="/dev/tty.usbmodem585A0076841",
id="my_red_robot_arm",
cameras=camera_config
)
teleop_config = KochLeaderConfig(
port="/dev/tty.usbmodem58760431551",
id="my_blue_leader_arm",
)
robot = KochFollower(robot_config)
teleop_device = KochLeader(teleop_config)
robot.connect()
teleop_device.connect()
while True:
observation = robot.get_observation()
action = teleop_device.get_action()
robot.send_action(action)
```
</hfoption>
</hfoptions>
## Record a dataset
Once you're familiar with teleoperation, you can record your first dataset with SO-101.
Once you're familiar with teleoperation, you can record your first dataset.
We use the Hugging Face hub features for uploading your dataset. If you haven't previously used the Hub, make sure you can login via the cli using a write-access token, this token can be generated from the [Hugging Face settings](https://huggingface.co/settings/tokens).
Add your token to the cli by running this command:
Add your token to the CLI by running this command:
```bash
huggingface-cli login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential
```
@@ -178,41 +152,24 @@ HF_USER=$(huggingface-cli whoami | head -n 1)
echo $HF_USER
```
Now you can record a dataset, to record 2 episodes and upload your dataset to the hub execute this command:
Now you can record a dataset. To record 2 episodes and upload your dataset to the hub, execute this command tailored to the SO101.
```bash
python lerobot/scripts/control_robot.py \
--robot.type=so101 \
--control.type=record \
--control.fps=30 \
--control.single_task="Grasp a lego block and put it in the bin." \
--control.repo_id=${HF_USER}/so101_test \
--control.tags='["so101","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
python -m lerobot.record \
--robot.type=so101_follower \
--robot.port=/dev/tty.usbmodem585A0076841 \
--robot.id=my_red_robot_arm \
--robot.cameras="{ front: {type: opencv, index_or_path: 0, width: 1920, height: 1080, fps: 30}}" \
--teleop.type=so101_leader \
--teleop.port=/dev/tty.usbmodem58760431551 \
--teleop.id=my_blue_leader_arm \
--display_data=true \
--dataset.repo_id=aliberts/record-test \
--dataset.num_episodes=2 \
--dataset.single_task="Grab the black cube"
```
You will see a lot of lines appearing like this one:
```
INFO 2024-08-10 15:02:58 ol_robot.py:219 dt:33.34 (30.0hz) dtRlead: 5.06 (197.5hz) dtWfoll: 0.25 (3963.7hz) dtRfoll: 6.22 (160.7hz) dtRlaptop: 32.57 (30.7hz) dtRphone: 33.84 (29.5hz)
```
| Field | Meaning |
|:---|:---|
| `2024-08-10 15:02:58` | Timestamp when `print` was called. |
| `ol_robot.py:219` | Source file and line number of the `print` call (`lerobot/scripts/control_robot.py` at line `219`). |
| `dt: 33.34 (30.0 Hz)` | Delta time (ms) between teleop steps (target: 30.0 Hz, `--fps 30`). Yellow if step is too slow. |
| `dtRlead: 5.06 (197.5 Hz)` | Delta time (ms) for reading present position from the **leader arm**. |
| `dtWfoll: 0.25 (3963.7 Hz)` | Delta time (ms) for writing goal position to the **follower arm** (asynchronous). |
| `dtRfoll: 6.22 (160.7 Hz)` | Delta time (ms) for reading present position from the **follower arm**. |
| `dtRlaptop: 32.57 (30.7 Hz)` | Delta time (ms) for capturing an image from the **laptop camera** (async thread). |
| `dtRphone: 33.84 (29.5 Hz)` | Delta time (ms) for capturing an image from the **phone camera** (async thread). |
#### Dataset upload
Locally your dataset is stored in this folder: `~/.cache/huggingface/lerobot/{repo-id}` (e.g. `data/cadene/so101_test`). At the end of data recording, your dataset will be uploaded on your Hugging Face page (e.g. https://huggingface.co/datasets/cadene/so101_test) that you can obtain by running:
Locally, your dataset is stored in this folder: `~/.cache/huggingface/lerobot/{repo-id}`. At the end of data recording, your dataset will be uploaded on your Hugging Face page (e.g. https://huggingface.co/datasets/cadene/so101_test) that you can obtain by running:
```bash
echo https://huggingface.co/datasets/${HF_USER}/so101_test
```
@@ -224,33 +181,26 @@ You can look for other LeRobot datasets on the hub by searching for `LeRobot` [t
The `record` function provides a suite of tools for capturing and managing data during robot operation:
##### 1. Frame Capture and Video Encoding
- Frames from cameras are saved to disk during recording.
- At the end of each episode, frames are encoded into video files.
##### 1. Data Storage
- Data is stored using the `LeRobotDataset` format and is stored on disk during recording.
- By default, the dataset is pushed to your Hugging Face page after recording.
- To disable uploading, use `--dataset.push_to_hub=False`.
##### 2. Data Storage
- Data is stored using the `LeRobotDataset` format.
- By default, the dataset is pushed to your Hugging Face page.
- To disable uploading, use `--control.push_to_hub=false`.
##### 3. Checkpointing and Resuming
##### 2. Checkpointing and Resuming
- Checkpoints are automatically created during recording.
- If an issue occurs, you can resume by re-running the same command with `--control.resume=true`.
- To start recording from scratch, **manually delete** the dataset directory.
##### 4. Recording Parameters
##### 3. Recording Parameters
Set the flow of data recording using command-line arguments:
- `--control.warmup_time_s=10`
Number of seconds before starting data collection (default: **10 seconds**).
Allows devices to warm up and synchronize.
- `--control.episode_time_s=60`
- `--dataset.episode_time_s=60`
Duration of each data recording episode (default: **60 seconds**).
- `--control.reset_time_s=60`
- `--dataset.reset_time_s=60`
Duration for resetting the environment after each episode (default: **60 seconds**).
- `--control.num_episodes=50`
- `--dataset.num_episodes=50`
Total number of episodes to record (default: **50**).
##### 5. Keyboard Controls During Recording
##### 4. Keyboard Controls During Recording
Control the data recording flow using keyboard shortcuts:
- Press **Right Arrow (`→`)**: Early stop the current episode or reset time and move to the next.
- Press **Left Arrow (`←`)**: Cancel the current episode and re-record it.
@@ -264,6 +214,8 @@ In the following sections, you’ll train your neural network. After achieving r
Avoid adding too much variation too quickly, as it may hinder your results.
If you want to dive deeper into this important topic, you can check out the [blog post](https://huggingface.co/blog/lerobot-datasets#what-makes-a-good-dataset) we wrote on what makes a good dataset.
#### Troubleshooting:
- On Linux, if the left and right arrow keys and escape key don't have any effect during data recording, make sure you've set the `$DISPLAY` environment variable. See [pynput limitations](https://pynput.readthedocs.io/en/latest/limitations.html#linux).
@@ -289,16 +241,16 @@ This will launch a local web server that looks like this:
## Replay an episode
A useful feature is the `replay` function, which allows to replay on your robot any episode that you've recorded or episodes from any dataset out there. This function helps you test the repeatability of your robot's actions and assess transferability across robots of the same model.
A useful feature is the `replay` function, which allows you to replay any episode that you've recorded or episodes from any dataset out there. This function helps you test the repeatability of your robot's actions and assess transferability across robots of the same model.
You can replay the first episode on your robot with:
```bash
python lerobot/scripts/control_robot.py \
--robot.type=so101 \
--control.type=replay \
--control.fps=30 \
--control.repo_id=${HF_USER}/so101_test \
--control.episode=0
python -m lerobot.replay \
--robot.type=so101_follower \
--robot.port=/dev/tty.usbmodem58760431541 \
--robot.id=black \
--dataset.repo_id=aliberts/record-test \
--dataset.episode=2
```
Your robot should replicate movements similar to those you recorded. For example, check out [this video](https://x.com/RemiCadene/status/1793654950905680090) where we use `replay` on a Aloha robot from [Trossen Robotics](https://www.trossenrobotics.com).
@@ -348,21 +300,20 @@ huggingface-cli upload ${HF_USER}/act_so101_test${CKPT} \
## 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:
You can use the `record` script from [`lerobot/record.py`](https://github.com/huggingface/lerobot/blob/main/lerobot/record.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=so101 \
--control.type=record \
--control.fps=30 \
--control.single_task="Grasp a lego block and put it in the bin." \
--control.repo_id=${HF_USER}/eval_act_so101_test \
--control.tags='["tutorial"]' \
--control.warmup_time_s=5 \
--control.episode_time_s=30 \
--control.reset_time_s=30 \
--control.num_episodes=10 \
--control.push_to_hub=true \
--control.policy.path=outputs/train/act_so101_test/checkpoints/last/pretrained_model
python -m lerobot.record \
--robot.type=so100_follower \
--robot.port=/dev/ttyACM1 \
--robot.cameras="{ up: {type: opencv, index_or_path: /dev/video10, width: 640, height: 480, fps: 30}, side: {type: intelrealsense, serial_number_or_name: 233522074606, width: 640, height: 480, fps: 30}}" \
--robot.id=blue_follower_arm \
--teleop.type=so100_leader \
--teleop.port=/dev/ttyACM0 \
--teleop.id=red_leader_arm \
--display_data=false \
--dataset.repo_id=$HF_USER/eval_lego_${EPOCHREALTIME/[^0-9]/} \
--dataset.single_task="Put lego brick into the transparent box" \
--policy.path=${HF_USER}/act_johns_arm
```
As you can see, it's almost the same command as previously used to record your training dataset. Two things changed:

52
docs/source/installation.mdx
View File

@@ -2,6 +2,8 @@
## Install LeRobot
Currently only available from source.
Download our source code:
```bash
git clone https://github.com/huggingface/lerobot.git
@@ -13,28 +15,6 @@ Create a virtual environment with Python 3.10, using [`Miniconda`](https://docs.
conda create -y -n lerobot python=3.10
```
Now restart the shell by running:
<hfoptions id="shell_restart">
<hfoption id="Windows">
```bash
source ~/.bashrc
```
</hfoption>
<hfoption id="Mac">
```bash
source ~/.bash_profile
```
</hfoption>
<hfoption id="zshell">
```bash
source ~/.zshrc
```
</hfoption>
</hfoptions>
Then activate your conda environment, you have to do this each time you open a shell to use lerobot:
```bash
conda activate lerobot
@@ -51,14 +31,14 @@ conda install ffmpeg -c conda-forge
> ```bash
> conda install ffmpeg=7.1.1 -c conda-forge
> ```
> - _[On Linux only]_ Install [ffmpeg build dependencies](https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu#GettheDependencies) and [compile ffmpeg from source with libsvtav1](https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu#libsvtav1), and make sure you use the corresponding ffmpeg binary to your install with `which ffmpeg`.
> - _[On Linux only]_ If you want to bring your own ffmpeg: Install [ffmpeg build dependencies](https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu#GettheDependencies) and [compile ffmpeg from source with libsvtav1](https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu#libsvtav1), and make sure you use the corresponding ffmpeg binary to your install with `which ffmpeg`.
Install 🤗 LeRobot:
```bash
cd lerobot && pip install -e ".[feetech]"
pip install -e .
```
## Troubleshooting
### Troubleshooting
If you encounter build errors, you may need to install additional dependencies: `cmake`, `build-essential`, and `ffmpeg libs`.
To install these for linux run:
```bash
@@ -66,18 +46,24 @@ sudo apt-get install cmake build-essential python-dev pkg-config libavformat-dev
```
For other systems, see: [Compiling PyAV](https://pyav.org/docs/develop/overview/installation.html#bring-your-own-ffmpeg)
## Sim
For simulations, 🤗 LeRobot comes with gymnasium environments that can be installed as extras:
- [aloha](https://github.com/huggingface/gym-aloha)
- [xarm](https://github.com/huggingface/gym-xarm)
- [pusht](https://github.com/huggingface/gym-pusht)
## Optional dependencies
For instance, to install 🤗 LeRobot with aloha and pusht, use:
LeRobot provides optional extras for specific functionalities. Multiple extras can be combined (e.g., `.[aloha,feetech]`). For all available extras, refer to `pyproject.toml`.
### Simulations
Install environment packages: `aloha` ([gym-aloha](https://github.com/huggingface/gym-aloha)), `xarm` ([gym-xarm](https://github.com/huggingface/gym-xarm)), or `pusht` ([gym-pusht](https://github.com/huggingface/gym-pusht))
Example:
```bash
pip install -e ".[aloha, pusht]"
pip install -e ".[aloha]" # or "[pusht]" for example
```
## W&B
### Motor Control
For Koch v1.1 install the Dynamixel SDK, for SO100/SO101/Moss install the Feetech SDK.
```bash
pip install -e ".[feetech]" # or "[dynamixel]" for example
```
### Experiment Tracking
To use [Weights and Biases](https://docs.wandb.ai/quickstart) for experiment tracking, log in with
```bash
wandb login

1
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View File

@@ -0,0 +1 @@
../../lerobot/common/robots/koch_follower/koch.mdx

1
docs/source/lekiwi.mdx Symbolic link
View File

@@ -0,0 +1 @@
../../lerobot/common/robots/lekiwi/lekiwi.mdx

1
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View File

@@ -0,0 +1 @@
../../lerobot/common/robots/so100_follower/so100.mdx

1
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View File

@@ -0,0 +1 @@
../../lerobot/common/robots/so101_follower/so101.mdx

28
examples/robots/lekiwi_client_app.py
View File

@@ -16,42 +16,38 @@ 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 OBS_STATE, LeKiwiClient
from lerobot.common.robots.lekiwi.lekiwi_client import LeKiwiClient
from lerobot.common.teleoperators.keyboard import KeyboardTeleop, KeyboardTeleopConfig
from lerobot.common.teleoperators.so100 import SO100Leader, SO100LeaderConfig
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.usbmodem58760434171")
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="192.0.2.42", id="lekiwi")
robot_config = LeKiwiClientConfig(remote_ip="172.18.134.136", id="lekiwi")
robot = LeKiwiClient(robot_config)
logging.info("Creating LeRobot Dataset")
# The observations that we get are expected to be in body frame (x,y,theta)
obs_dict = {f"{OBS_STATE}." + key: value for key, value in robot.state_feature.items()}
# The actions that we send are expected to be in wheel frame (motor encoders)
act_dict = {"action." + key: value for key, value in robot.action_feature.items()}
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}
features_dict = {
**act_dict,
**obs_dict,
**robot.camera_features,
}
dataset = LeRobotDataset.create(
repo_id="user/lekiwi" + str(int(time.time())),
fps=10,
features=features_dict,
features=dataset_features,
robot_type=robot.name,
)
logging.info("Connecting Teleop Devices")
@@ -76,10 +72,10 @@ def main():
observation = robot.get_observation()
frame = {**action_sent, **observation}
frame.update({"task": "Dummy Example Task Dataset"})
task = "Dummy Example Task Dataset"
logging.info("Saved a frame into the dataset")
dataset.add_frame(frame)
dataset.add_frame(frame, task)
i += 1
logging.info("Disconnecting Teleop Devices and LeKiwi Client")

7
lerobot/calibrate.py
View File

@@ -37,18 +37,21 @@ from lerobot.common.robots import ( # noqa: F401
Robot,
RobotConfig,
koch_follower,
lekiwi,
make_robot_from_config,
so100_follower,
so101_follower,
)
from lerobot.common.teleoperators import ( # noqa: F401
Teleoperator,
TeleoperatorConfig,
koch_leader,
make_teleoperator_from_config,
so100_leader,
so101_leader,
)
from lerobot.common.utils.utils import init_logging
from .common.teleoperators import koch_leader, so100_leader # noqa: F401
@dataclass
class CalibrateConfig:

2
lerobot/common/cameras/opencv/camera_opencv.py
View File

@@ -370,7 +370,7 @@ class OpenCVCamera(Camera):
def _read_loop(self):
"""
Internal loop for background thread for asynchronous reading.
Internal loop run by the background thread for asynchronous reading.
On each iteration:
1. Reads a color frame

2
lerobot/common/cameras/realsense/camera_realsense.py
View File

@@ -442,7 +442,7 @@ class RealSenseCamera(Camera):
def _read_loop(self):
"""
Internal loop for background thread for asynchronous reading.
Internal loop run by the background thread for asynchronous reading.
On each iteration:
1. Reads a color frame with 500ms timeout

17
lerobot/common/datasets/utils.py
View File

@@ -400,15 +400,22 @@ def hw_to_dataset_features(
joint_fts = {key: ftype for key, ftype in hw_features.items() if ftype is float}
cam_fts = {key: shape for key, shape in hw_features.items() if isinstance(shape, tuple)}
if joint_fts:
features[f"{prefix}.joints"] = {
if joint_fts and prefix == "action":
features[prefix] = {
"dtype": "float32",
"shape": (len(joint_fts),),
"names": list(joint_fts),
}
if joint_fts and prefix == "observation":
features[f"{prefix}.state"] = {
"dtype": "float32",
"shape": (len(joint_fts),),
"names": list(joint_fts),
}
for key, shape in cam_fts.items():
features[f"{prefix}.cameras.{key}"] = {
features[f"{prefix}.images.{key}"] = {
"dtype": "video" if use_video else "image",
"shape": shape,
"names": ["height", "width", "channels"],
@@ -428,7 +435,7 @@ def build_dataset_frame(
elif ft["dtype"] == "float32" and len(ft["shape"]) == 1:
frame[key] = np.array([values[name] for name in ft["names"]], dtype=np.float32)
elif ft["dtype"] in ["image", "video"]:
frame[key] = values[key.removeprefix(f"{prefix}.cameras.")]
frame[key] = values[key.removeprefix(f"{prefix}.images.")]
return frame
@@ -461,7 +468,7 @@ def dataset_to_policy_features(features: dict[str, dict]) -> dict[str, PolicyFea
type = FeatureType.ENV
elif key.startswith("observation"):
type = FeatureType.STATE
elif key == "action":
elif key.startswith("action"):
type = FeatureType.ACTION
else:
continue

1
lerobot/common/motors/feetech/tables.py
View File

@@ -207,6 +207,7 @@ MODEL_BAUDRATE_TABLE = {
STS_SMS_SERIES_ENCODINGS_TABLE = {
"Homing_Offset": 11,
"Goal_Velocity": 15,
"Present_Velocity": 15,
}
MODEL_ENCODING_TABLE = {

328
lerobot/common/robots/koch_follower/README.md
View File

@@ -1,328 +0,0 @@
# Using the [Koch v1.1](https://github.com/jess-moss/koch-v1-1) with LeRobot
## Table of Contents
- [A. Order and Assemble the parts](#a-order-and-assemble-the-parts)
- [B. Install LeRobot](#b-install-lerobot)
- [C. Configure the Motors](#c-configure-the-motors)
- [D. Calibrate](#d-calibrate)
- [E. Teleoperate](#e-teleoperate)
- [F. Record a dataset](#f-record-a-dataset)
- [G. Visualize a dataset](#g-visualize-a-dataset)
- [H. Replay an episode](#h-replay-an-episode)
- [I. Train a policy](#i-train-a-policy)
- [J. Evaluate your policy](#j-evaluate-your-policy)
- [K. More Information](#k-more-information)
## A. Order and Assemble the parts
Follow the sourcing and assembling instructions provided on the [Koch v1.1 Github page](https://github.com/jess-moss/koch-v1-1). This will guide you through setting up both the follower and leader arms, as shown in the image below.
<div style="text-align:center;">
<img src="../media/tutorial/koch_v1_1_leader_follower.webp?raw=true" alt="Koch v1.1 leader and follower arms" title="Koch v1.1 leader and follower arms" width="50%">
</div>
For a visual walkthrough of the assembly process, you can refer to [this video tutorial](https://youtu.be/8nQIg9BwwTk).
> [!IMPORTANT]
> Since the production of this video, we simplified the configuration phase (detailed in [section C](#c-configure-the-motors)) of the motors.
> Because of this, two things differ from the instructions in that video:
> - Don't plug all the motors cables right away and wait for being instructed to do so in [section C](#c-configure-the-motors).
> - Don't screw in the controller board (PCB) to the base right away and wait for being instructed to do so in [section C](#c-configure-the-motors).
## B. Install LeRobot
> [!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)
Follow instructions on our [README](https://github.com/huggingface/lerobot) to install LeRobot.
In addition to these instructions, you need to install the dynamixel sdk:
```bash
pip install -e ".[dynamixel]"
```
## C. Configure the motors
### 1. Find the USB ports associated to each arm
For each controller board (Waveshare Serial Bus Servo Driver Board, one for the leader arm and one for the follower), connect it first to your computer through usb. To then find the internal port its connected to -which we will need later on- run the utility script:
```bash
python -m lerobot.find_port
```
> [!NOTE]
> Note: 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
> ```
This will first display all currently available ports on your computer. As prompted by the script, unplug the controller board usb cable from your computer. The script will then detect which port has been disconnected and will display it.
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 MotorsBus and press Enter when done.
[...Disconnect leader arm and press Enter...]
The port of this MotorsBus is /dev/tty.usbmodem575E0031751
Reconnect the usb cable.
```
You can now reconnect the usb cable to your computer.
### 2. Set the motors ids and baudrate
Each motor is identified by a unique id on the bus. When brand new, motors usually come with a default id of `1`. For the communication to work properly between the motors and the controller, we first need to set a unique, different id to each motor. Additionally, the speed at which data is transmitted on the bus is determined by the baudrate. In order to talk to each other, the controller and all the motors need to be configured with the same baudrate.
To that end, we first need to connect to each motor individually with the controller in order to set these. Since we will write these parameters in the non-volatile section of the motors' internal memory (EEPROM), we'll only need to do this once.
> [!NOTE]
> Note: If you are repurposing motors from another robot, you will probably also need to perform this step as the ids and baudrate likely won't match.
Connect the usb cable from your computer and the 5V power supply to the leader arm's controller board. Then, run the following command with the port you got from the previous step. You'll also need to give your leader arm a name with the `id` parameter.
```bash
python -m lerobot.setup_motors \
--device.type=so100_leader \
--device.port=/dev/tty.usbmodem575E0031751 # <- paste here the port found at previous step
```
Note that the command above is equivalent to running the following script:
<details>
<summary>Setup script</summary>
```python
from lerobot.common.teleoperators.koch import KochLeader, KochLeaderConfig
config = KochLeaderConfig(
port="/dev/tty.usbmodem575E0031751",
)
leader = KochLeader(config)
leader.setup_motors()
```
</details>
You should see the following instruction
```
Connect the controller board to the 'gripper' motor only and press enter.
```
As instructed, plug the gripper's motor. Make sure it's the only motor connected to the board, and that the motor itself is not yet daisy chained to any other motor. As you press `[Enter]`, the script will automatically set the id and baudrate for that motor.
<details>
<summary>Troubleshooting</summary>
If you get an error at that point, check your cables and make sure they are plugged-in properly:
- Power supply
- USB cable between from your computer to the controller board
- The 3-pin cable from the controller board to the motor.
If you are using a Waveshare controller board, make sure that the two jumpers are set on the `B` channel (USB).
</details>
You should then see the following message:
```
'gripper' motor id set to 6
```
Followed by the next instruction:
```
Connect the controller board to the 'wrist_roll' motor only and press enter.
```
You can disconnect the 3-pin cable from the controller board but you can leave it connected to the gripper motor on the other end as it will already be in the right place. Now, plug-in another 3-pin cable to the wrist roll motor and connect it to the controller board. As with the previous motor, make sure it is the only motor connected to the board and that the motor itself isn't connected to any other one.
Repeat the operation for each motor as instructed.
> [!TIP]
> Check your cabling at each step before pressing Enter. For instance, the power supply cable is not solidly anchored to the board and might disconnect easily as you manipulate the board.
When you are done, the script will simply finish, at which point the motors are ready to be used. You can now plug the 3-pin cable from each motor to the next one, and the cable from the first motor (the 'shoulder pan' with id=1) to the controller board, which can now be attached to the base of the arm.
## D. 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"]'
```
## E. 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
```
## F. 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`.
## G. 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 ${HF_USER}/so100_test \
--local-files-only 1
```
## H. Replay an episode
Now try to replay the first episode on your robot:
```bash
python lerobot/scripts/control_robot.py \
--robot.type=so100 \
--control.type=replay \
--control.fps=30 \
--control.repo_id=${HF_USER}/so100_test \
--control.episode=0
```
## I. 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
```
## J. 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`).
## K. 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).

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@@ -0,0 +1,258 @@
# Koch v1.1
In the steps below, we explain how to assemble the Koch v1.1 robot.
## Order and assemble the parts
Follow the sourcing and assembling instructions provided in this [README](https://github.com/jess-moss/koch-v1-1). This will guide you through setting up both the follower and leader arms, as shown in the image below.
For a visual walkthrough of the assembly process, you can refer to [this video tutorial](https://youtu.be/8nQIg9BwwTk).
> [!WARNING]
> Since the production of this video, we simplified the configuration phase. Because of this, two things differ from the instructions in that video:
> - Don't plug in all the motor cables right away and wait to be instructed to do so in [Configure the motors](#configure-the-motors).
> - Don't screw in the controller board (PCB) to the base right away and wait for being instructed to do so in [Configure the motors](#configure-the-motors).
## Install LeRobot 🤗
To install LeRobot follow, our [Installation Guide](./installation)
In addition to these instructions, you need to install the Dynamixel SDK:
```bash
pip install -e ".[dynamixel]"
```
## Configure the motors
### 1. Find the USB ports associated with each arm
To find the port for each bus servo adapter, run this script:
```bash
python lerobot/find_port.py
```
<hfoptions id="example">
<hfoption id="Mac">
Example output:
```
Finding all available ports for the MotorBus.
['/dev/tty.usbmodem575E0032081', '/dev/tty.usbmodem575E0031751']
Remove the USB cable from your MotorsBus and press Enter when done.
[...Disconnect corresponding leader or follower arm and press Enter...]
The port of this MotorsBus is /dev/tty.usbmodem575E0032081
Reconnect the USB cable.
```
Where the found port is: `/dev/tty.usbmodem575E0032081` corresponding to your leader or follower arm.
</hfoption>
<hfoption id="Linux">
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
```
Example output:
```
Finding all available ports for the MotorBus.
['/dev/ttyACM0', '/dev/ttyACM1']
Remove the usb cable from your MotorsBus and press Enter when done.
[...Disconnect corresponding leader or follower arm and press Enter...]
The port of this MotorsBus is /dev/ttyACM1
Reconnect the USB cable.
```
Where the found port is: `/dev/ttyACM1` corresponding to your leader or follower arm.
</hfoption>
</hfoptions>
### 2. Set the motors ids and baudrates
Each motor is identified by a unique id on the bus. When brand new, motors usually come with a default id of `1`. For the communication to work properly between the motors and the controller, we first need to set a unique, different id to each motor. Additionally, the speed at which data is transmitted on the bus is determined by the baudrate. In order to talk to each other, the controller and all the motors need to be configured with the same baudrate.
To that end, we first need to connect to each motor individually with the controller in order to set these. Since we will write these parameters in the non-volatile section of the motors' internal memory (EEPROM), we'll only need to do this once.
If you are repurposing motors from another robot, you will probably also need to perform this step, as the ids and baudrate likely won't match.
#### Follower
Connect the usb cable from your computer and the 5V power supply to the follower arm's controller board. Then, run the following command or run the API example with the port you got from the previous step. You'll also need to give your leader arm a name with the `id` parameter.
For a visual reference on how to set the motor ids please refer to [this video](https://huggingface.co/docs/lerobot/en/so101#setup-motors-video) where we follow the process for the SO101 arm.
<hfoptions id="setup_motors">
<hfoption id="Command">
```bash
python -m lerobot.setup_motors \
--robot.type=koch_follower \
--robot.port=/dev/tty.usbmodem575E0031751 # <- paste here the port found at previous step
```
</hfoption>
<hfoption id="API example">
```python
from lerobot.common.robots.koch_follower import KochFollower, KochFollowerConfig
config = KochFollowerConfig(
port="/dev/tty.usbmodem575E0031751",
id="my_awesome_follower_arm",
)
follower = KochFollower(config)
follower.setup_motors()
```
</hfoption>
</hfoptions>
You should see the following instruction.
```
Connect the controller board to the 'gripper' motor only and press enter.
```
As instructed, plug the gripper's motor. Make sure it's the only motor connected to the board, and that the motor itself is not yet daisy-chained to any other motor. As you press `[Enter]`, the script will automatically set the id and baudrate for that motor.
<details>
<summary>Troubleshooting</summary>
If you get an error at that point, check your cables and make sure they are plugged in properly:
<ul>
<li>Power supply</li>
<li>USB cable between your computer and the controller board</li>
<li>The 3-pin cable from the controller board to the motor</li>
</ul>
If you are using a Waveshare controller board, make sure that the two jumpers are set on the `B` channel (USB).
</details>
You should then see the following message:
```
'gripper' motor id set to 6
```
Followed by the next instruction:
```
Connect the controller board to the 'wrist_roll' motor only and press enter.
```
You can disconnect the 3-pin cable from the controller board but you can leave it connected to the gripper motor on the other end as it will already be in the right place. Now, plug in another 3-pin cable to the wrist roll motor and connect it to the controller board. As with the previous motor, make sure it is the only motor connected to the board and that the motor itself isn't connected to any other one.
Repeat the operation for each motor as instructed.
> [!TIP]
> Check your cabling at each step before pressing Enter. For instance, the power supply cable might disconnect as you manipulate the board.
When you are done, the script will simply finish, at which point the motors are ready to be used. You can now plug the 3-pin cable from each motor to the next one, and the cable from the first motor (the 'shoulder pan' with id=1) to the controller board, which can now be attached to the base of the arm.
#### Leader
Do the same steps for the leader arm but modify the command or script accordingly.
<hfoptions id="setup_motors">
<hfoption id="Command">
```bash
python -m lerobot.setup_motors \
--teleop.type=koch_leader \
--teleop.port=/dev/tty.usbmodem575E0031751 \ # <- paste here the port found at previous step
```
</hfoption>
<hfoption id="API example">
```python
from lerobot.common.teleoperators.koch_leader import KochLeader, KochLeaderConfig
config = KochLeaderConfig(
port="/dev/tty.usbmodem575E0031751",
id="my_awesome_leader_arm",
)
leader = KochLeader(config)
leader.setup_motors()
```
</hfoption>
</hfoptions>
## Calibrate
Next, you'll need to calibrate your robot to ensure that the leader and follower arms have the same position values when they are in the same physical position.
The calibration process is very important because it allows a neural network trained on one robot to work on another.
#### Follower
Run the following command or API example to calibrate the follower arm:
<hfoptions id="calibrate_follower">
<hfoption id="Command">
```bash
python -m lerobot.calibrate \
--robot.type=koch_follower \
--robot.port=/dev/tty.usbmodem58760431551 \ # <- The port of your robot
--robot.id=my_awesome_follower_arm # <- Give the robot a unique name
```
</hfoption>
<hfoption id="API example">
```python
from lerobot.common.robots.koch_follower import KochFollowerConfig, KochFollower
config = KochFollowerConfig(
port="/dev/tty.usbmodem585A0076891",
id="my_awesome_follower_arm",
)
follower = KochFollower(config)
follower.connect(calibrate=False)
follower.calibrate()
follower.disconnect()
```
</hfoption>
</hfoptions>
We unified the calibration method for most robots. Thus, the calibration steps for this Koch arm are the same as the steps for the SO100 and SO101. First, we have to move the robot to the position where each joint is in the middle of its range, then we press `Enter`. Secondly, we move all joints through their full range of motion. A video of this same process for the SO101 as reference can be found [here](https://huggingface.co/docs/lerobot/en/so101#calibration-video).
#### Leader
Do the same steps to calibrate the leader arm, run the following command or API example:
<hfoptions id="calibrate_leader">
<hfoption id="Command">
```bash
python -m lerobot.calibrate \
--teleop.type=koch_leader \
--teleop.port=/dev/tty.usbmodem58760431551 \ # <- The port of your robot
--teleop.id=my_awesome_leader_arm # <- Give the robot a unique name
```
</hfoption>
<hfoption id="API example">
```python
from lerobot.common.teleoperators.koch_leader import KochLeaderConfig, KochLeader
config = KochLeaderConfig(
port="/dev/tty.usbmodem575E0031751",
id="my_awesome_leader_arm",
)
leader = KochLeader(config)
leader.connect(calibrate=False)
leader.calibrate()
leader.disconnect()
```
</hfoption>
</hfoptions>
Congrats 🎉, your robot is all set to learn a task on its own. Start training it by following this tutorial: [Getting started with real-world robots](./getting_started_real_world_robot)
> [!TIP]
> If you have any questions or need help, please reach out on [Discord](https://discord.com/invite/s3KuuzsPFb).

3
lerobot/common/robots/koch_follower/koch_follower.py
View File

@@ -82,8 +82,7 @@ class KochFollower(Robot):
@property
def is_connected(self) -> bool:
# TODO(aliberts): add cam.is_connected for cam in self.cameras
return self.bus.is_connected
return self.bus.is_connected and all(cam.is_connected for cam in self.cameras.values())
def connect(self, calibrate: bool = True) -> None:
"""

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@@ -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 need 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 [`LeKiwiConfig`](../lerobot/common/robot_devices/robots/configs.py) file. Where you will find something like:
```python
@RobotConfig.register_subclass("lekiwi")
@dataclass
class LeKiwiConfig(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 LeKiwiConfig(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 [`LeKiwiConfig`](../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 states, 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`).

8
lerobot/common/robots/lekiwi/config_lekiwi.py
View File

@@ -14,7 +14,7 @@
from dataclasses import dataclass, field
from lerobot.common.cameras.configs import CameraConfig
from lerobot.common.cameras.configs import CameraConfig, Cv2Rotation
from lerobot.common.cameras.opencv.configuration_opencv import OpenCVCameraConfig
from ..config import RobotConfig
@@ -34,11 +34,9 @@ class LeKiwiConfig(RobotConfig):
cameras: dict[str, CameraConfig] = field(
default_factory=lambda: {
"front": OpenCVCameraConfig(
camera_index="/dev/video0", fps=30, width=640, height=480, rotation=None
),
"front": OpenCVCameraConfig(index_or_path="/dev/video0", fps=30, width=640, height=480),
"wrist": OpenCVCameraConfig(
camera_index="/dev/video2", fps=30, width=640, height=480, rotation=180
index_or_path="/dev/video2", fps=30, width=640, height=480, rotation=Cv2Rotation.ROTATE_90
),
}
)

248
lerobot/common/robots/lekiwi/lekiwi.mdx Normal file
View File

@@ -0,0 +1,248 @@
# LeKiwi
In the steps below, we explain how to assemble the LeKiwi mobile robot.
## 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 advise if it's your first time printing or if you don't own a 3D printer.
### Wired version
If you have the **wired** LeKiwi version, you can skip the installation of the Raspberry Pi and setting up SSH. You can also run all commands directly on your PC for both the LeKiwi scripts and the leader arm scripts for teleoperating.
## Install software on Pi
Now we have to set up the remote PC that will run on the LeKiwi Robot. This is normally a Raspberry Pi, but can be any PC that can run on 5V and has enough usb ports (2 or more) for the cameras and motor control board.
### Install OS
For setting up the Raspberry Pi and its SD-card see: [Setup PI](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 set up [SSH](https://www.raspberrypi.com/news/coding-on-raspberry-pi-remotely-with-visual-studio-code/) (Secure Shell Protocol) so you can log in to the Pi from your laptop without requiring a screen, keyboard, and mouse on the Pi. A great tutorial on how to do this can be found [here](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 Pi 🤗
On your Raspberry Pi install LeRobot using our [Installation Guide](./installation)
In addition to these instructions, you need to install the Feetech sdk on your Pi:
```bash
pip install -e ".[feetech]"
```
## Install LeRobot locally
If you already have installed LeRobot on your laptop/pc you can skip this step; otherwise, please follow along as we do the same steps we did on the Pi.
Follow our [Installation Guide](./installation)
Great :hugs:! You are now done installing LeRobot, and we can begin assembling the SO100/SO101 arms and the mobile base :robot:.
Every time you now want to use LeRobot, you can go to the `~/lerobot` folder where we installed LeRobot and run one of the commands.
# Step-by-Step Assembly Instructions
First, we will assemble the two SO100/SO101 arms. One to attach to the mobile base and one for teleoperation. Then we will assemble the mobile base. The instructions for assembling can be found on these two pages:
- [Assemble SO101](./so101#step-by-step-assembly-instructions)
- [Assemble LeKiwi](https://github.com/SIGRobotics-UIUC/LeKiwi/blob/main/Assembly.md)
### Configure motors
The instructions for configuring the motors can be found in the SO101 [docs](./so101#configure-the-motors). Besides the ids for the arm motors, we also need to set the motor ids for the mobile base. These need to be in a specific order to work. Below an image of the motor ids and motor mounting positions for the mobile base. Note that we only use one Motor Control board on LeKiwi. This means the motor ids for the wheels are 7, 8 and 9.
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/motor_ids.webp" alt="Motor ID's for mobile robot" title="Motor ID's for mobile robot" width="60%">
### Troubleshoot communication
If you are having trouble connecting to the Mobile SO100, follow these steps to diagnose and resolve the issue.
#### 1. Verify IP Address Configuration
Make sure that the correct IP for the Pi is used in the commands or in your code. To check the Raspberry Pi's IP address, run (on the Pi command line):
```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.
### Calibration
Now we have to calibrate the leader arm and the follower arm. The wheel motors don't have to be calibrated.
The calibration process is very important because it allows a neural network trained on one robot to work on another.
### Calibrate follower arm (on mobile base)
Make sure the arm is connected to the Raspberry Pi and run this script or API example (on the Raspberry Pi via SSH) to launch calibration of the follower arm:
<hfoptions id="calibrate_follower">
<hfoption id="Command">
```bash
python -m lerobot.calibrate \
--robot.type=lekiwi \
--robot.port=/dev/ttyACM0 \ # <- The port of your robot
--robot.id=my_awesome_kiwi # <- Give the robot a unique name
```
</hfoption>
<hfoption id="API example">
```python
from lerobot.common.robots.lekiwi import LeKiwiClient, LeKiwiClientConfig
config = LeKiwiClientConfig(
remote_ip="192.168.0.23",
id="my_awesome_kiwi",
)
lekiwi = LeKiwiClient(config)
lekiwi.connect(calibrate=False)
lekiwi.calibrate()
lekiwi.disconnect()
```
</hfoption>
</hfoptions>
We unified the calibration method for most robots, thus, the calibration steps for this SO100 arm are the same as the steps for the Koch and SO101. First, we have to move the robot to the position where each joint is in the middle of its range, then we press `Enter`. Secondly, we move all joints through their full range of motion. A video of this same process for the SO101 as reference can be found [here](https://huggingface.co/docs/lerobot/en/so101#calibration-video).
### Wired version
If you have the **wired** LeKiwi version, please run all commands on your laptop.
### Calibrate leader arm
Then, to calibrate the leader arm (which is attached to the laptop/pc). Run the following command of API example on your laptop:
<hfoptions id="calibrate_leader">
<hfoption id="Command">
```bash
python -m lerobot.calibrate \
--teleop.type=so100_leader \
--teleop.port=/dev/tty.usbmodem58760431551 \ # <- The port of your robot
--teleop.id=my_awesome_leader_arm # <- Give the robot a unique name
```
</hfoption>
<hfoption id="API example">
```python
from lerobot.common.teleoperators.so100_leader import SO100LeaderConfig, SO100Leader
config = SO100LeaderConfig(
port="/dev/tty.usbmodem58760431551",
id="my_awesome_leader_arm",
)
leader = SO100Leader(config)
leader.connect(calibrate=False)
leader.calibrate()
leader.disconnect()
```
</hfoption>
</hfoptions>
## Teleoperate LeKiwi
> [!TIP]
> If you're using a Mac, you might need to give Terminal permission to access your keyboard for teleoperation. 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 command:
```bash
python -m lerobot.common.robots.lekiwi.lekiwi_host
```
Then on your laptop, also run `conda activate lerobot` and this command or API example:
<hfoptions id="teleoperate_koch_camera">
<hfoption id="Command">
```bash
python -m lerobot.teleoperate \
--robot.type=lekiwi \
--robot.port=/dev/tty.usbmodem58760431541 \
--robot.cameras="{}" \
--robot.id=my_lekiwi \
--teleop.type=so101_leader \
--teleop.port=/dev/tty.usbmodem58760431551 \
--teleop.id=my_blue_leader_arm
```
</hfoption>
<hfoption id="API example">
```python
from lerobot.common.teleoperators.keyboard.teleop_keyboard import KeyboardTeleopConfig, KeyboardTeleop
from lerobot.common.teleoperators.so100_leader import SO100LeaderConfig, SO100Leader
from lerobot.common.robots.lekiwi import LeKiwiClient, LeKiwiClientConfig
robot_config = LeKiwiClientConfig(
remote_ip="172.18.133.90",
id="my_red_lekiwi"
)
teleop__arm_config = SO100LeaderConfig(
port="/dev/tty.usbmodem58760431551",
id="my_blue_leader_arm",
)
teleop_keyboard_config = KeyboardTeleopConfig(
id="my_laptop_keyboard",
)
robot = LeKiwiClient(robot_config)
teleop_arm = SO100Leader(teleop__arm_config)
telep_keyboard = KeyboardTeleop(teleop_keyboard_config)
robot.connect()
teleop_arm.connect()
telep_keyboard.connect()
while True:
observation = robot.get_observation()
action_arm = teleop_arm.get_action()
action_base = telep_keyboard.get_action()
robot.send_action(action_arm | action_base)
```
</hfoption>
</hfoptions>
> **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 [`LeKiwiConfig`](../lerobot/common/robot_devices/robots/configs.py).
### Wired version
If you have the **wired** LeKiwi version, please run all commands on your laptop.
Congrats 🎉, your robot is all set to learn a task on its own. Start training it by following this tutorial (you can skip the teleoperation part): [Getting started with real-world robots](./getting_started_real_world_robot)
> [!TIP]
> If you have any questions or need help, please reach out on [Discord](https://discord.com/invite/s3KuuzsPFb).

222
lerobot/common/robots/lekiwi/lekiwi.py
View File

@@ -16,9 +16,12 @@
import logging
import time
from functools import cached_property
from itertools import chain
from typing import Any
import numpy as np
from lerobot.common.cameras.utils import make_cameras_from_configs
from lerobot.common.constants import OBS_IMAGES, OBS_STATE
from lerobot.common.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError
@@ -71,39 +74,39 @@ class LeKiwi(Robot):
self.cameras = make_cameras_from_configs(config.cameras)
@property
def state_feature(self) -> dict:
state_ft = {
"arm_shoulder_pan": {"dtype": "float32"},
"arm_shoulder_lift": {"dtype": "float32"},
"arm_elbow_flex": {"dtype": "float32"},
"arm_wrist_flex": {"dtype": "float32"},
"arm_wrist_roll": {"dtype": "float32"},
"arm_gripper": {"dtype": "float32"},
"base_left_wheel": {"dtype": "float32"},
"base_right_wheel": {"dtype": "float32"},
"base_back_wheel": {"dtype": "float32"},
def _state_ft(self) -> dict[str, type]:
return dict.fromkeys(
(
"arm_shoulder_pan.pos",
"arm_shoulder_lift.pos",
"arm_elbow_flex.pos",
"arm_wrist_flex.pos",
"arm_wrist_roll.pos",
"arm_gripper.pos",
"x.vel",
"y.vel",
"theta.vel",
),
float,
)
@property
def _cameras_ft(self) -> dict[str, tuple]:
return {
cam: (self.config.cameras[cam].height, self.config.cameras[cam].width, 3) for cam in self.cameras
}
return state_ft
@property
def action_feature(self) -> dict:
return self.state_feature
@cached_property
def observation_features(self) -> dict[str, type | tuple]:
return {**self._state_ft, **self._cameras_ft}
@property
def camera_features(self) -> dict[str, dict]:
cam_ft = {}
for cam_key, cam in self.cameras.items():
cam_ft[cam_key] = {
"shape": (cam.height, cam.width, cam.channels),
"names": ["height", "width", "channels"],
"info": None,
}
return cam_ft
@cached_property
def action_features(self) -> dict[str, type]:
return self._state_ft
@property
def is_connected(self) -> bool:
# TODO(aliberts): add cam.is_connected for cam in self.cameras
return self.bus.is_connected
return self.bus.is_connected and all(cam.is_connected for cam in self.cameras.values())
def connect(self, calibrate: bool = True) -> None:
if self.is_connected:
@@ -190,6 +193,142 @@ class LeKiwi(Robot):
self.bus.setup_motor(motor)
print(f"'{motor}' motor id set to {self.bus.motors[motor].id}")
@staticmethod
def _degps_to_raw(degps: float) -> int:
steps_per_deg = 4096.0 / 360.0
speed_in_steps = degps * steps_per_deg
speed_int = int(round(speed_in_steps))
# Cap the value to fit within signed 16-bit range (-32768 to 32767)
if speed_int > 0x7FFF:
speed_int = 0x7FFF # 32767 -> maximum positive value
elif speed_int < -0x8000:
speed_int = -0x8000 # -32768 -> minimum negative value
return speed_int
@staticmethod
def _raw_to_degps(raw_speed: int) -> float:
steps_per_deg = 4096.0 / 360.0
magnitude = raw_speed
degps = magnitude / steps_per_deg
return degps
def _body_to_wheel_raw(
self,
x: float,
y: float,
theta: float,
wheel_radius: float = 0.05,
base_radius: float = 0.125,
max_raw: int = 3000,
) -> dict:
"""
Convert desired body-frame velocities into wheel raw commands.
Parameters:
x_cmd : Linear velocity in x (m/s).
y_cmd : Linear velocity in y (m/s).
theta_cmd : Rotational velocity (deg/s).
wheel_radius: Radius of each wheel (meters).
base_radius : Distance from the center of rotation to each wheel (meters).
max_raw : Maximum allowed raw command (ticks) per wheel.
Returns:
A dictionary with wheel raw commands:
{"base_left_wheel": value, "base_back_wheel": value, "base_right_wheel": value}.
Notes:
- Internally, the method converts theta_cmd to rad/s for the kinematics.
- The raw command is computed from the wheels angular speed in deg/s
using _degps_to_raw(). If any command exceeds max_raw, all commands
are scaled down proportionally.
"""
# Convert rotational velocity from deg/s to rad/s.
theta_rad = theta * (np.pi / 180.0)
# Create the body velocity vector [x, y, theta_rad].
velocity_vector = np.array([x, y, theta_rad])
# Define the wheel mounting angles with a -90° offset.
angles = np.radians(np.array([240, 120, 0]) - 90)
# Build the kinematic matrix: each row maps body velocities to a wheel’s linear speed.
# The third column (base_radius) accounts for the effect of rotation.
m = np.array([[np.cos(a), np.sin(a), base_radius] for a in angles])
# Compute each wheel’s linear speed (m/s) and then its angular speed (rad/s).
wheel_linear_speeds = m.dot(velocity_vector)
wheel_angular_speeds = wheel_linear_speeds / wheel_radius
# Convert wheel angular speeds from rad/s to deg/s.
wheel_degps = wheel_angular_speeds * (180.0 / np.pi)
# Scaling
steps_per_deg = 4096.0 / 360.0
raw_floats = [abs(degps) * steps_per_deg for degps in wheel_degps]
max_raw_computed = max(raw_floats)
if max_raw_computed > max_raw:
scale = max_raw / max_raw_computed
wheel_degps = wheel_degps * scale
# Convert each wheel’s angular speed (deg/s) to a raw integer.
wheel_raw = [self._degps_to_raw(deg) for deg in wheel_degps]
return {
"base_left_wheel": wheel_raw[0],
"base_back_wheel": wheel_raw[1],
"base_right_wheel": wheel_raw[2],
}
def _wheel_raw_to_body(
self,
left_wheel_speed,
back_wheel_speed,
right_wheel_speed,
wheel_radius: float = 0.05,
base_radius: float = 0.125,
) -> dict[str, Any]:
"""
Convert wheel raw command feedback back into body-frame velocities.
Parameters:
wheel_raw : Vector with raw wheel commands ("base_left_wheel", "base_back_wheel", "base_right_wheel").
wheel_radius: Radius of each wheel (meters).
base_radius : Distance from the robot center to each wheel (meters).
Returns:
A dict (x_cmd, y_cmd, theta_cmd) where:
OBS_STATE.x_cmd : Linear velocity in x (m/s).
OBS_STATE.y_cmd : Linear velocity in y (m/s).
OBS_STATE.theta_cmd : Rotational velocity in deg/s.
"""
# Convert each raw command back to an angular speed in deg/s.
wheel_degps = np.array(
[
self._raw_to_degps(left_wheel_speed),
self._raw_to_degps(back_wheel_speed),
self._raw_to_degps(right_wheel_speed),
]
)
# Convert from deg/s to rad/s.
wheel_radps = wheel_degps * (np.pi / 180.0)
# Compute each wheel’s linear speed (m/s) from its angular speed.
wheel_linear_speeds = wheel_radps * wheel_radius
# Define the wheel mounting angles with a -90° offset.
angles = np.radians(np.array([240, 120, 0]) - 90)
m = np.array([[np.cos(a), np.sin(a), base_radius] for a in angles])
# Solve the inverse kinematics: body_velocity = M⁻¹ · wheel_linear_speeds.
m_inv = np.linalg.inv(m)
velocity_vector = m_inv.dot(wheel_linear_speeds)
x, y, theta_rad = velocity_vector
theta = theta_rad * (180.0 / np.pi)
return {
"x.vel": x,
"y.vel": y,
"theta.vel": theta,
} # m/s and deg/s
def get_observation(self) -> dict[str, Any]:
if not self.is_connected:
raise DeviceNotConnectedError(f"{self} is not connected.")
@@ -197,9 +336,19 @@ class LeKiwi(Robot):
# Read actuators position for arm and vel for base
start = time.perf_counter()
arm_pos = self.bus.sync_read("Present_Position", self.arm_motors)
base_vel = self.bus.sync_read("Present_Velocity", self.base_motors)
obs_dict = {**arm_pos, **base_vel}
obs_dict = {f"{OBS_STATE}." + key: value for key, value in obs_dict.items()}
base_wheel_vel = self.bus.sync_read("Present_Velocity", self.base_motors)
base_vel = self._wheel_raw_to_body(
base_wheel_vel["base_left_wheel"],
base_wheel_vel["base_back_wheel"],
base_wheel_vel["base_right_wheel"],
)
arm_state = {f"{k}.pos": v for k, v in arm_pos.items()}
flat_states = {**arm_state, **base_vel}
obs_dict = {f"{OBS_STATE}": flat_states}
dt_ms = (time.perf_counter() - start) * 1e3
logger.debug(f"{self} read state: {dt_ms:.1f}ms")
@@ -228,8 +377,12 @@ class LeKiwi(Robot):
if not self.is_connected:
raise DeviceNotConnectedError(f"{self} is not connected.")
arm_goal_pos = {k: v for k, v in action.items() if k in self.arm_motors}
base_goal_vel = {k: v for k, v in action.items() if k in self.base_motors}
arm_goal_pos = {k: v for k, v in action.items() if k.endswith(".pos")}
base_goal_vel = {k: v for k, v in action.items() if k.endswith(".vel")}
base_wheel_goal_vel = self._body_to_wheel_raw(
base_goal_vel["x.vel"], base_goal_vel["y.vel"], base_goal_vel["theta.vel"]
)
# Cap goal position when too far away from present position.
# /!\ Slower fps expected due to reading from the follower.
@@ -240,8 +393,9 @@ class LeKiwi(Robot):
arm_goal_pos = arm_safe_goal_pos
# Send goal position to the actuators
self.bus.sync_write("Goal_Position", arm_goal_pos)
self.bus.sync_write("Goal_Velocity", base_goal_vel)
arm_goal_pos_raw = {k.replace(".pos", ""): v for k, v in arm_goal_pos.items()}
self.bus.sync_write("Goal_Position", arm_goal_pos_raw)
self.bus.sync_write("Goal_Velocity", base_wheel_goal_vel)
return {**arm_goal_pos, **base_goal_vel}

265
lerobot/common/robots/lekiwi/lekiwi_client.py
View File

@@ -15,6 +15,7 @@
import base64
import json
import logging
from functools import cached_property
from typing import Any, Dict, Optional, Tuple
import cv2
@@ -54,8 +55,7 @@ class LeKiwiClient(Robot):
self.last_frames = {}
self.last_remote_arm_state = {}
self.last_remote_base_state = {"base_left_wheel": 0, "base_back_wheel": 0, "base_right_wheel": 0}
self.last_remote_state = {}
# Define three speed levels and a current index
self.speed_levels = [
@@ -68,53 +68,41 @@ class LeKiwiClient(Robot):
self._is_connected = False
self.logs = {}
@property
def state_feature(self) -> dict:
state_ft = {
"arm_shoulder_pan": {"shape": (1,), "info": None, "dtype": "float32"},
"arm_shoulder_lift": {"shape": (1,), "info": None, "dtype": "float32"},
"arm_elbow_flex": {"shape": (1,), "info": None, "dtype": "float32"},
"arm_wrist_flex": {"shape": (1,), "info": None, "dtype": "float32"},
"arm_wrist_roll": {"shape": (1,), "info": None, "dtype": "float32"},
"arm_gripper": {"shape": (1,), "info": None, "dtype": "float32"},
"x_cmd": {"shape": (1,), "info": None, "dtype": "float32"},
"y_cmd": {"shape": (1,), "info": None, "dtype": "float32"},
"theta_cmd": {"shape": (1,), "info": None, "dtype": "float32"},
}
return state_ft
@cached_property
def _state_ft(self) -> dict[str, type]:
return dict.fromkeys(
(
"arm_shoulder_pan.pos",
"arm_shoulder_lift.pos",
"arm_elbow_flex.pos",
"arm_wrist_flex.pos",
"arm_wrist_roll.pos",
"arm_gripper.pos",
"x.vel",
"y.vel",
"theta.vel",
),
float,
)
@property
def action_feature(self) -> dict:
action_ft = {
"arm_shoulder_pan": {"shape": (1,), "info": None, "dtype": "float32"},
"arm_shoulder_lift": {"shape": (1,), "info": None, "dtype": "float32"},
"arm_elbow_flex": {"shape": (1,), "info": None, "dtype": "float32"},
"arm_wrist_flex": {"shape": (1,), "info": None, "dtype": "float32"},
"arm_wrist_roll": {"shape": (1,), "info": None, "dtype": "float32"},
"arm_gripper": {"shape": (1,), "info": None, "dtype": "float32"},
"base_left_wheel": {"shape": (1,), "info": None, "dtype": "float32"},
"base_right_wheel": {"shape": (1,), "info": None, "dtype": "float32"},
"base_back_wheel": {"shape": (1,), "info": None, "dtype": "float32"},
}
return action_ft
@cached_property
def _state_order(self) -> tuple[str, ...]:
return tuple(self._state_ft.keys())
@property
def camera_features(self) -> dict[str, dict]:
cam_ft = {
f"{OBS_IMAGES}.front": {
"shape": (480, 640, 3),
"names": ["height", "width", "channels"],
"info": None,
"dtype": "image",
},
f"{OBS_IMAGES}.wrist": {
"shape": (480, 640, 3),
"names": ["height", "width", "channels"],
"dtype": "image",
"info": None,
},
@cached_property
def _cameras_ft(self) -> dict[str, tuple]:
return {
"front": (480, 640, 3),
"wrist": (640, 480, 3),
}
return cam_ft
@cached_property
def observation_features(self) -> dict[str, type | tuple]:
return {**self._state_ft, **self._cameras_ft}
@cached_property
def action_features(self) -> dict[str, type]:
return self._state_ft
@property
def is_connected(self) -> bool:
@@ -154,130 +142,6 @@ class LeKiwiClient(Robot):
def calibrate(self) -> None:
pass
@staticmethod
def _degps_to_raw(degps: float) -> int:
steps_per_deg = 4096.0 / 360.0
speed_in_steps = degps * steps_per_deg
speed_int = int(round(speed_in_steps))
# Cap the value to fit within signed 16-bit range (-32768 to 32767)
if speed_int > 0x7FFF:
speed_int = 0x7FFF # 32767 -> maximum positive value
elif speed_int < -0x8000:
speed_int = -0x8000 # -32768 -> minimum negative value
return speed_int
@staticmethod
def _raw_to_degps(raw_speed: int) -> float:
steps_per_deg = 4096.0 / 360.0
magnitude = raw_speed
degps = magnitude / steps_per_deg
return degps
def _body_to_wheel_raw(
self,
x_cmd: float,
y_cmd: float,
theta_cmd: float,
wheel_radius: float = 0.05,
base_radius: float = 0.125,
max_raw: int = 3000,
) -> dict:
"""
Convert desired body-frame velocities into wheel raw commands.
Parameters:
x_cmd : Linear velocity in x (m/s).
y_cmd : Linear velocity in y (m/s).
theta_cmd : Rotational velocity (deg/s).
wheel_radius: Radius of each wheel (meters).
base_radius : Distance from the center of rotation to each wheel (meters).
max_raw : Maximum allowed raw command (ticks) per wheel.
Returns:
A dictionary with wheel raw commands:
{"base_left_wheel": value, "base_back_wheel": value, "base_right_wheel": value}.
Notes:
- Internally, the method converts theta_cmd to rad/s for the kinematics.
- The raw command is computed from the wheels angular speed in deg/s
using _degps_to_raw(). If any command exceeds max_raw, all commands
are scaled down proportionally.
"""
# Convert rotational velocity from deg/s to rad/s.
theta_rad = theta_cmd * (np.pi / 180.0)
# Create the body velocity vector [x, y, theta_rad].
velocity_vector = np.array([x_cmd, y_cmd, theta_rad])
# Define the wheel mounting angles with a -90° offset.
angles = np.radians(np.array([240, 120, 0]) - 90)
# Build the kinematic matrix: each row maps body velocities to a wheel’s linear speed.
# The third column (base_radius) accounts for the effect of rotation.
m = np.array([[np.cos(a), np.sin(a), base_radius] for a in angles])
# Compute each wheel’s linear speed (m/s) and then its angular speed (rad/s).
wheel_linear_speeds = m.dot(velocity_vector)
wheel_angular_speeds = wheel_linear_speeds / wheel_radius
# Convert wheel angular speeds from rad/s to deg/s.
wheel_degps = wheel_angular_speeds * (180.0 / np.pi)
# Scaling
steps_per_deg = 4096.0 / 360.0
raw_floats = [abs(degps) * steps_per_deg for degps in wheel_degps]
max_raw_computed = max(raw_floats)
if max_raw_computed > max_raw:
scale = max_raw / max_raw_computed
wheel_degps = wheel_degps * scale
# Convert each wheel’s angular speed (deg/s) to a raw integer.
wheel_raw = [self._degps_to_raw(deg) for deg in wheel_degps]
return {
"base_left_wheel": wheel_raw[0],
"base_back_wheel": wheel_raw[1],
"base_right_wheel": wheel_raw[2],
}
def _wheel_raw_to_body(
self, wheel_raw: dict[str, Any], wheel_radius: float = 0.05, base_radius: float = 0.125
) -> dict[str, Any]:
"""
Convert wheel raw command feedback back into body-frame velocities.
Parameters:
wheel_raw : Vector with raw wheel commands ("base_left_wheel", "base_back_wheel", "base_right_wheel").
wheel_radius: Radius of each wheel (meters).
base_radius : Distance from the robot center to each wheel (meters).
Returns:
A dict (x_cmd, y_cmd, theta_cmd) where:
OBS_STATE.x_cmd : Linear velocity in x (m/s).
OBS_STATE.y_cmd : Linear velocity in y (m/s).
OBS_STATE.theta_cmd : Rotational velocity in deg/s.
"""
# Convert each raw command back to an angular speed in deg/s.
wheel_degps = np.array([LeKiwiClient._raw_to_degps(int(v)) for _, v in wheel_raw.items()])
# Convert from deg/s to rad/s.
wheel_radps = wheel_degps * (np.pi / 180.0)
# Compute each wheel’s linear speed (m/s) from its angular speed.
wheel_linear_speeds = wheel_radps * wheel_radius
# Define the wheel mounting angles with a -90° offset.
angles = np.radians(np.array([240, 120, 0]) - 90)
m = np.array([[np.cos(a), np.sin(a), base_radius] for a in angles])
# Solve the inverse kinematics: body_velocity = M⁻¹ · wheel_linear_speeds.
m_inv = np.linalg.inv(m)
velocity_vector = m_inv.dot(wheel_linear_speeds)
x_cmd, y_cmd, theta_rad = velocity_vector
theta_cmd = theta_rad * (180.0 / np.pi)
return {
f"{OBS_STATE}.x_cmd": x_cmd * 1000,
f"{OBS_STATE}.y_cmd": y_cmd * 1000,
f"{OBS_STATE}.theta_cmd": theta_cmd,
} # Convert to mm/s
def _poll_and_get_latest_message(self) -> Optional[str]:
"""Polls the ZMQ socket for a limited time and returns the latest message string."""
poller = zmq.Poller()
@@ -331,25 +195,24 @@ class LeKiwiClient(Robot):
def _remote_state_from_obs(
self, observation: Dict[str, Any]
) -> Tuple[Dict[str, np.ndarray], Dict[str, Any], Dict[str, Any]]:
"""Extracts frames, speed, and arm state from the parsed observation."""
) -> Tuple[Dict[str, np.ndarray], Dict[str, Any]]:
"""Extracts frames, and state from the parsed observation."""
flat_state = observation[OBS_STATE]
# Separate image and state data
image_observation = {k: v for k, v in observation.items() if k.startswith(OBS_IMAGES)}
state_observation = {k: v for k, v in observation.items() if k.startswith(OBS_STATE)}
state_vec = np.array(
[flat_state.get(k, 0.0) for k in self._state_order],
dtype=np.float32,
)
# Decode images
image_observation = {k: v for k, v in observation.items() if k.startswith(OBS_IMAGES)}
current_frames: Dict[str, np.ndarray] = {}
for cam_name, image_b64 in image_observation.items():
frame = self._decode_image_from_b64(image_b64)
if frame is not None:
current_frames[cam_name] = frame
# Extract state components
current_arm_state = {k: v for k, v in state_observation.items() if k.startswith(f"{OBS_STATE}.arm")}
current_base_state = {k: v for k, v in state_observation.items() if k.startswith(f"{OBS_STATE}.base")}
return current_frames, current_arm_state, current_base_state
return current_frames, {"observation.state": state_vec}
def _get_data(self) -> Tuple[Dict[str, np.ndarray], Dict[str, Any], Dict[str, Any]]:
"""
@@ -365,27 +228,26 @@ class LeKiwiClient(Robot):
# 2. If no message, return cached data
if latest_message_str is None:
return self.last_frames, self.last_remote_arm_state, self.last_remote_base_state
return self.last_frames, self.last_remote_state
# 3. Parse the JSON message
observation = self._parse_observation_json(latest_message_str)
# 4. If JSON parsing failed, return cached data
if observation is None:
return self.last_frames, self.last_remote_arm_state, self.last_remote_base_state
return self.last_frames, self.last_remote_state
# 5. Process the valid observation data
try:
new_frames, new_arm_state, new_base_state = self._remote_state_from_obs(observation)
new_frames, new_state = self._remote_state_from_obs(observation)
except Exception as e:
logging.error(f"Error processing observation data, serving last observation: {e}")
return self.last_frames, self.last_remote_arm_state, self.last_remote_base_state
return self.last_frames, self.last_remote_state
self.last_frames = new_frames
self.last_remote_arm_state = new_arm_state
self.last_remote_base_state = new_base_state
self.last_remote_state = new_state
return new_frames, new_arm_state, new_base_state
return new_frames, new_state
def get_observation(self) -> dict[str, Any]:
"""
@@ -396,13 +258,7 @@ class LeKiwiClient(Robot):
if not self._is_connected:
raise DeviceNotConnectedError("LeKiwiClient is not connected. You need to run `robot.connect()`.")
frames, remote_arm_state, remote_base_state = self._get_data()
remote_body_state = self._wheel_raw_to_body(remote_base_state)
obs_dict = {**remote_arm_state, **remote_body_state}
# TODO(Steven): Remove this when it is possible to record a non-numpy array value
obs_dict = {k: np.array([v], dtype=np.float32) for k, v in obs_dict.items()}
frames, obs_dict = self._get_data()
# Loop over each configured camera
for cam_name, frame in frames.items():
@@ -413,7 +269,7 @@ class LeKiwiClient(Robot):
return obs_dict
def _from_keyboard_to_wheel_action(self, pressed_keys: np.ndarray):
def _from_keyboard_to_base_action(self, pressed_keys: np.ndarray):
# Speed control
if self.teleop_keys["speed_up"] in pressed_keys:
self.speed_index = min(self.speed_index + 1, 2)
@@ -439,7 +295,11 @@ class LeKiwiClient(Robot):
theta_cmd += theta_speed
if self.teleop_keys["rotate_right"] in pressed_keys:
theta_cmd -= theta_speed
return self._body_to_wheel_raw(x_cmd, y_cmd, theta_cmd)
return {
"x.vel": x_cmd,
"y.vel": y_cmd,
"theta.vel": theta_cmd,
}
def configure(self):
pass
@@ -461,26 +321,23 @@ class LeKiwiClient(Robot):
"ManipulatorRobot is not connected. You need to run `robot.connect()`."
)
goal_pos = {}
common_keys = [
key
for key in action
if key in (motor.replace("arm_", "") for motor, _ in self.action_feature.items())
if key in (motor.replace("arm_", "") for motor, _ in self.action_features.items())
]
arm_actions = {"arm_" + arm_motor: action[arm_motor] for arm_motor in common_keys}
goal_pos = arm_actions
keyboard_keys = np.array(list(set(action.keys()) - set(common_keys)))
wheel_actions = self._from_keyboard_to_wheel_action(keyboard_keys)
goal_pos = {**arm_actions, **wheel_actions}
base_actions = self._from_keyboard_to_base_action(keyboard_keys)
goal_pos = {**arm_actions, **base_actions}
self.zmq_cmd_socket.send_string(json.dumps(goal_pos)) # action is in motor space
# TODO(Steven): Remove the np conversion when it is possible to record a non-numpy array value
goal_pos = {"action." + k: np.array([v], dtype=np.float32) for k, v in goal_pos.items()}
return goal_pos
actions = np.array([goal_pos.get(k, 0.0) for k in self._state_order], dtype=np.float32)
return {"action.state": actions}
def disconnect(self):
"""Cleans ZMQ comms"""

624
lerobot/common/robots/so100_follower/README.md
View File

@@ -1,624 +0,0 @@
# Using the [SO-100](https://github.com/TheRobotStudio/SO-ARM100) with LeRobot
## Table of Contents
- [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)
- [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)
- [L. More Information](#l-more-information)
## 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,
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.
## B. Install LeRobot
> [!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 :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.
## C. Configure the motors
> [!NOTE]
> 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).
### 1. Find the USB ports associated to each arm
Designate one bus servo adapter and 6 motors for your leader arm, and similarly the other bus servo adapter and 6 motors for the follower arm. 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).
#### 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 MotorsBus and press Enter when done.
[...Disconnect leader arm and press Enter...]
The port of this MotorsBus 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 MotorsBus and press Enter when done.
[...Disconnect follower arm and press Enter...]
The port of this MotorsBus 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, update the **port** default values of [`SO100RobotConfig`](../lerobot/common/robot_devices/robots/configs.py). You will find something like:
```diff
@RobotConfig.register_subclass("so100")
@dataclass
class So100RobotConfig(ManipulatorRobotConfig):
calibration_dir: str = ".cache/calibration/so100"
# `max_relative_target` limits the magnitude of the relative positional target vector for safety purposes.
# Set this to a positive scalar to have the same value for all motors, or a list that is the same length as
# the number of motors in your follower arms.
max_relative_target: int | None = None
leader_arms: dict[str, MotorsBusConfig] = field(
default_factory=lambda: {
"main": FeetechMotorsBusConfig(
- port="/dev/tty.usbmodem58760431091",
+ port="{ADD YOUR LEADER PORT}",
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",
+ port="{ADD YOUR FOLLOWER PORT}",
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"],
},
),
}
)
```
### 2. Assembling the Base
Let's begin with assembling the follower arm base
#### a. Set IDs for all 12 motors
<details>
<summary><strong>Video configuring motor</strong></summary>
<video src="https://github.com/user-attachments/assets/ef9b3317-2e11-4858-b9d3-f0a02fb48ecf"></video>
<video src="https://github.com/user-attachments/assets/f36b5ed5-c803-4ebe-8947-b39278776a0d"></video>
</details>
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:
```bash
python lerobot/scripts/configure_motor.py \
--port /dev/tty.usbmodem58760432961 \
--brand feetech \
--model sts3215 \
--baudrate 1000000 \
--id 1
```
> [!NOTE]
> 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).
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.
#### b. Remove the gears of the 6 leader motors
<details>
<summary><strong>Video removing gears</strong></summary>
<video src="https://github.com/user-attachments/assets/0c95b88c-5b85-413d-ba19-aee2f864f2a7"></video>
</details>
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.
## D. Step-by-Step Assembly Instructions
**Step 1: Clean Parts**
- Remove all support material from the 3D-printed parts.
---
### Additional Guidance
<details>
<summary><strong>Video assembling arms</strong></summary>
<video src="https://github.com/user-attachments/assets/488a39de-0189-4461-9de3-05b015f90cca"></video>
</details>
**Note:**
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.
---
### First Motor
**Step 2: Insert Wires**
- Insert two wires into the first motor.
<img src="../media/tutorial/img1.jpg" style="height:300px;">
**Step 3: Install in Base**
- Place the first motor into the base.
<img src="../media/tutorial/img2.jpg" style="height:300px;">
**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;">
**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.
The calibration process is very important because it allows a neural network trained on one SO-100 robot to work on another.
#### Manual calibration of follower arm
You will need to move the follower arm to these positions sequentially, note that the rotated position is on the right side of the robot and you have to open the gripper fully.
| 1. Middle position | 2. Zero position | 3. Rotated position | 4. Rest position |
| ------------ |------------------------------------------------------------------------------------------------------------------------------------------------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------ |
| <img src="../media/so101/follower_middle.webp?raw=true" alt="SO-101 leader arm middle position" title="SO-101 leader arm middle position" style="width:100%;"> | <img src="../media/so101/follower_zero.webp?raw=true" alt="SO-101 leader arm zero position" title="SO-101 leader arm zero position" style="width:100%;"> | <img src="../media/so101/follower_rotated.webp?raw=true" alt="SO-101 leader arm rotated position" title="SO-101 leader arm rotated position" style="width:100%;"> | <img src="../media/so101/follower_rest.webp?raw=true" alt="SO-101 leader arm rest position" title="SO-101 leader 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"]'
```
#### Manual calibration of leader arm
You will also need to move the leader arm to these positions sequentially:
| 1. Middle position | 2. Zero position | 3. Rotated position | 4. Rest position |
| ------------ |------------------------------------------------------------------------------------------------------------------------------------------------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------ |
| <img src="../media/so101/leader_middle.webp?raw=true" alt="SO-100 leader arm middle position" title="SO-100 leader arm middle position" style="width:100%;"> | <img src="../media/so101/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/so101/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/so101/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 states, 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).

486
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@@ -0,0 +1,486 @@
# SO-100
In the steps below, we explain how to assemble the SO-100 robot.
## Source the parts
Follow this [README](https://github.com/TheRobotStudio/SO-ARM100/blob/main/SO100.md). It contains the bill of materials, with a link to source the parts, as well as the instructions to 3D print the parts. And advise if it's your first time printing or if you don't own a 3D printer.
## Install LeRobot 🤗
To install LeRobot, follow our [Installation Guide](./installation)
In addition to these instructions, you need to install the Feetech SDK:
```bash
pip install -e ".[feetech]"
```
## Step-by-Step Assembly Instructions
## Remove the gears of the 6 leader motors
<details>
<summary><strong>Video removing gears</strong></summary>
<div class="video-container">
<video controls width="600">
<source src="https://github.com/user-attachments/assets/0c95b88c-5b85-413d-ba19-aee2f864f2a7" type="video/mp4" />
</video>
</div>
</details>
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.
### Clean Parts
Remove all support material from the 3D-printed parts. The easiest way to do this is using a small screwdriver to get underneath the support material.
### Additional Guidance
<details>
<summary><strong>Video assembling arms</strong></summary>
<div class="video-container">
<video controls width="600">
<source src="https://github.com/user-attachments/assets/488a39de-0189-4461-9de3-05b015f90cca" type="video/mp4" />
</video>
</div>
</details>
**Note:**
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.
---
### First Motor
**Step 2: Insert Wires**
- Insert two wires into the first motor.
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_1.webp" style="height:300px;"/>
**Step 3: Install in Base**
- Place the first motor into the base.
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_2.webp" style="height:300px;"/>
**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="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_4.webp" style="height:300px;"/>
**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="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_5.webp" 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 towards you (see photo).
- Attach the shoulder part.
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_6.webp" 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="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_8.webp" 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="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_9.webp" style="height:250px;"/>
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_10.webp" style="height:250px;"/>
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_12.webp" 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="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_11.webp" style="height:300px;">
**Step 14: Attach Upper Arm**
- Attach the upper arm with 4 screws on each side.
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_13.webp" 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="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_14.webp" style="height:300px;"/>
**Step 17: Attach Forearm**
- Connect the forearm to motor 3 using 4 screws on each side.
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_15.webp" 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="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_16.webp" style="height:300px;"/>
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_19.webp" 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="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_17.webp" 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="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_18.webp" 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="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_20.webp" 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="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_22.webp" 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="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_23.webp" style="height:300px;"/>
---
### Follower Configuration
**Step 24: Attach Gripper**
- Attach the gripper to motor 5.
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_24.webp" 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="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_25.webp" 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="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_26.webp" style="height:300px;"/>
**Step 27: Mount Controller**
- Attach the motor controller to the back of the robot.
<div style="display: flex;">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_27.webp" style="height:300px;"/>
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_28.webp" 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="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_29.webp" style="height:300px;"/>
**Step 25: Attach Handle**
- Attach the handle to motor 5 using 4 screws.
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_30.webp" 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="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_31.webp" style="height:300px;"/>
**Step 27: Attach Trigger**
- Attach the follower trigger with 4 screws.
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_32.webp" style="height:300px;"/>
**Step 28: Mount Controller**
- Attach the motor controller to the back of the robot.
<div style="display: flex;">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_27.webp" style="height:300px;"/>
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_28.webp" style="height:300px;"/>
</div>
## Configure the motors
### 1. Find the USB ports associated with each arm
To find the port for each bus servo adapter, run this script:
```bash
python lerobot/find_port.py
```
<hfoptions id="example">
<hfoption id="Mac">
Example output:
```
Finding all available ports for the MotorBus.
['/dev/tty.usbmodem575E0032081', '/dev/tty.usbmodem575E0031751']
Remove the USB cable from your MotorsBus and press Enter when done.
[...Disconnect corresponding leader or follower arm and press Enter...]
The port of this MotorsBus is /dev/tty.usbmodem575E0032081
Reconnect the USB cable.
```
Where the found port is: `/dev/tty.usbmodem575E0032081` corresponding to your leader or follower arm.
</hfoption>
<hfoption id="Linux">
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
```
Example output:
```
Finding all available ports for the MotorBus.
['/dev/ttyACM0', '/dev/ttyACM1']
Remove the usb cable from your MotorsBus and press Enter when done.
[...Disconnect corresponding leader or follower arm and press Enter...]
The port of this MotorsBus is /dev/ttyACM1
Reconnect the USB cable.
```
Where the found port is: `/dev/ttyACM1` corresponding to your leader or follower arm.
</hfoption>
</hfoptions>
### 2. Set the motors ids and baudrates
Each motor is identified by a unique id on the bus. When brand new, motors usually come with a default id of `1`. For the communication to work properly between the motors and the controller, we first need to set a unique, different id to each motor. Additionally, the speed at which data is transmitted on the bus is determined by the baudrate. In order to talk to each other, the controller and all the motors need to be configured with the same baudrate.
To that end, we first need to connect to each motor individually with the controller in order to set these. Since we will write these parameters in the non-volatile section of the motors' internal memory (EEPROM), we'll only need to do this once.
If you are repurposing motors from another robot, you will probably also need to perform this step as the ids and baudrate likely won't match.
#### Follower
Connect the usb cable from your computer and the power supply to the follower arm's controller board. Then, run the following command or run the API example with the port you got from the previous step. You'll also need to give your leader arm a name with the `id` parameter.
For a visual reference on how to set the motor ids please refer to [this video](https://huggingface.co/docs/lerobot/en/so101#setup-motors-video) where we follow the process for the SO101 arm.
<hfoptions id="setup_motors">
<hfoption id="Command">
```bash
python -m lerobot.setup_motors \
--robot.type=so100_follower \
--robot.port=/dev/tty.usbmodem585A0076841 # <- paste here the port found at previous step
```
</hfoption>
<hfoption id="API example">
```python
from lerobot.common.robots.so100_follower import SO100Follower, SO100FollowerConfig
config = SO100FollowerConfig(
port="/dev/tty.usbmodem585A0076841",
id="my_awesome_follower_arm",
)
follower = SO100Follower(config)
follower.setup_motors()
```
</hfoption>
</hfoptions>
You should see the following instruction
```
Connect the controller board to the 'gripper' motor only and press enter.
```
As instructed, plug the gripper's motor. Make sure it's the only motor connected to the board, and that the motor itself is not yet daisy-chained to any other motor. As you press `[Enter]`, the script will automatically set the id and baudrate for that motor.
<details>
<summary>Troubleshooting</summary>
If you get an error at that point, check your cables and make sure they are plugged in properly:
<ul>
<li>Power supply</li>
<li>USB cable between your computer and the controller board</li>
<li>The 3-pin cable from the controller board to the motor</li>
</ul>
If you are using a Waveshare controller board, make sure that the two jumpers are set on the `B` channel (USB).
</details>
You should then see the following message:
```
'gripper' motor id set to 6
```
Followed by the next instruction:
```
Connect the controller board to the 'wrist_roll' motor only and press enter.
```
You can disconnect the 3-pin cable from the controller board, but you can leave it connected to the gripper motor on the other end, as it will already be in the right place. Now, plug in another 3-pin cable to the wrist roll motor and connect it to the controller board. As with the previous motor, make sure it is the only motor connected to the board and that the motor itself isn't connected to any other one.
Repeat the operation for each motor as instructed.
> [!TIP]
> Check your cabling at each step before pressing Enter. For instance, the power supply cable might disconnect as you manipulate the board.
When you are done, the script will simply finish, at which point the motors are ready to be used. You can now plug the 3-pin cable from each motor to the next one, and the cable from the first motor (the 'shoulder pan' with id=1) to the controller board, which can now be attached to the base of the arm.
#### Leader
Do the same steps for the leader arm.
<hfoptions id="setup_motors">
<hfoption id="Command">
```bash
python -m lerobot.setup_motors \
--teleop.type=so100_leader \
--teleop.port=/dev/tty.usbmodem575E0031751 # <- paste here the port found at previous step
```
</hfoption>
<hfoption id="API example">
```python
from lerobot.common.teleoperators.so100_leader import SO100Leader, SO100LeaderConfig
config = SO100LeaderConfig(
port="/dev/tty.usbmodem585A0076841",
id="my_awesome_leader_arm",
)
leader = SO100Leader(config)
leader.setup_motors()
```
</hfoption>
</hfoptions>
## Calibrate
Next, you'll need to calibrate your robot to ensure that the leader and follower arms have the same position values when they are in the same physical position.
The calibration process is very important because it allows a neural network trained on one robot to work on another.
#### Follower
Run the following command or API example to calibrate the follower arm:
<hfoptions id="calibrate_follower">
<hfoption id="Command">
```bash
python -m lerobot.calibrate \
--robot.type=so100_follower \
--robot.port=/dev/tty.usbmodem58760431551 \ # <- The port of your robot
--robot.id=my_awesome_follower_arm # <- Give the robot a unique name
```
</hfoption>
<hfoption id="API example">
```python
from lerobot.common.robots.so100_follower import SO100FollowerConfig, SO100Follower
config = SO100FollowerConfig(
port="/dev/tty.usbmodem585A0076891",
id="my_awesome_follower_arm",
)
follower = SO100Follower(config)
follower.connect(calibrate=False)
follower.calibrate()
follower.disconnect()
```
</hfoption>
</hfoptions>
We unified the calibration method for most robots. Thus, the calibration steps for this SO100 arm are the same as the steps for the Koch and SO101. First, we have to move the robot to the position where each joint is in the middle of its range, then we press `Enter`. Secondly, we move all joints through their full range of motion. A video of this same process for the SO101 as reference can be found [here](https://huggingface.co/docs/lerobot/en/so101#calibration-video)
#### Leader
Do the same steps to calibrate the leader arm, run the following command or API example:
<hfoptions id="calibrate_leader">
<hfoption id="Command">
```bash
python -m lerobot.calibrate \
--teleop.type=so100_leader \
--teleop.port=/dev/tty.usbmodem58760431551 \ # <- The port of your robot
--teleop.id=my_awesome_leader_arm # <- Give the robot a unique name
```
</hfoption>
<hfoption id="API example">
```python
from lerobot.common.teleoperators.so100_leader import SO100LeaderConfig, SO100Leader
config = SO100LeaderConfig(
port="/dev/tty.usbmodem58760431551",
id="my_awesome_leader_arm",
)
leader = SO100Leader(config)
leader.connect(calibrate=False)
leader.calibrate()
leader.disconnect()
```
</hfoption>
</hfoptions>
Congrats 🎉, your robot is all set to learn a task on its own. Start training it by following this tutorial: [Getting started with real-world robots](./getting_started_real_world_robot)
> [!TIP]
> If you have any questions or need help, please reach out on [Discord](https://discord.com/invite/s3KuuzsPFb).

4
lerobot/common/robots/so100_follower/so100_follower.py
View File

@@ -79,8 +79,7 @@ class SO100Follower(Robot):
@property
def is_connected(self) -> bool:
# TODO(aliberts): add cam.is_connected for cam in self.cameras
return self.bus.is_connected
return self.bus.is_connected and all(cam.is_connected for cam in self.cameras.values())
def connect(self, calibrate: bool = True) -> None:
"""
@@ -94,7 +93,6 @@ class SO100Follower(Robot):
if not self.is_calibrated and calibrate:
self.calibrate()
# Connect the cameras
for cam in self.cameras.values():
cam.connect()

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lerobot/common/robots/so101_follower/README.md
View File

@@ -1,711 +0,0 @@
# Assemble and use SO-101
In the steps below we explain how to assemble and use our flagship robot, the SO-101 with LeRobot 🤗.
## 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,
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.
## Install LeRobot
> [!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)
Download our source code:
```bash
git clone https://github.com/huggingface/lerobot.git
cd lerobot
```
Create a virtual environment with Python 3.10 and activate it, e.g. with [`miniconda`](https://docs.anaconda.com/miniconda/install/#quick-command-line-install):
```bash
conda create -y -n lerobot python=3.10
```
Now restart the shell by running:
##### Windows:
```bash
`source ~/.bashrc`
```
##### Mac:
```bash
`source ~/.bash_profile`
```
##### zshell:
```bash
`source ~/.zshrc`
```
Then activate your conda environment, you have to do this each time you open a shell to use lerobot:
```bash
conda activate lerobot
```
When using `miniconda`, install `ffmpeg` in your environment:
```bash
conda install ffmpeg -c conda-forge
```
> [!NOTE]
> This usually installs `ffmpeg 7.X` for your platform compiled with the `libsvtav1` encoder. If `libsvtav1` is not supported (check supported encoders with `ffmpeg -encoders`), you can:
> - _[On any platform]_ Explicitly install `ffmpeg 7.X` using:
> ```bash
> conda install ffmpeg=7.1.1 -c conda-forge
> ```
> - _[On Linux only]_ Install [ffmpeg build dependencies](https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu#GettheDependencies) and [compile ffmpeg from source with libsvtav1](https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu#libsvtav1), and make sure you use the corresponding ffmpeg binary to your install with `which ffmpeg`.
Install 🤗 LeRobot:
```bash
cd lerobot && pip install -e ".[feetech]"
```
> [!NOTE]
> If you encounter build errors, you may need to install additional dependencies (`cmake`, `build-essential`, and `ffmpeg libs`). On Linux, run: `sudo apt-get install cmake build-essential python3-dev pkg-config libavformat-dev libavcodec-dev libavdevice-dev libavutil-dev libswscale-dev libswresample-dev libavfilter-dev pkg-config`. For other systems, see: [Compiling PyAV](https://pyav.org/docs/develop/overview/installation.html#bring-your-own-ffmpeg)
## Configure motors
To configure the motors 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.
You now should plug the 5V or 12V power supply to the motor bus. 5V for the STS3215 7.4V motors and 12V for the STS3215 12V motors. Note that the leader arm always uses the 7.4V motors, so watch out that you plug in the right power supply if you have 12V and 7.4V motors, otherwise you might burn your motors! Now, connect the motor bus to your computer via USB. Note that the USB doesn't provide any power, and both the power supply and USB have to be plugged in.
### Find the USB ports associated to each arm
To find the port for each bus servo adapter, run this script:
```bash
python lerobot/scripts/find_motors_bus_port.py
```
#### Example outputs of script
##### Mac:
Example output leader arm's port: `/dev/tty.usbmodem575E0031751`
```bash
Finding all available ports for the MotorBus.
['/dev/tty.usbmodem575E0032081', '/dev/tty.usbmodem575E0031751']
Remove the usb cable from your MotorsBus and press Enter when done.
[...Disconnect leader arm and press Enter...]
The port of this MotorsBus is /dev/tty.usbmodem575E0031751
Reconnect the usb cable.
```
Example output follower arm port: `/dev/tty.usbmodem575E0032081`
```
Finding all available ports for the MotorBus.
['/dev/tty.usbmodem575E0032081', '/dev/tty.usbmodem575E0031751']
Remove the usb cable from your MotorsBus and press Enter when done.
[...Disconnect follower arm and press Enter...]
The port of this MotorsBus is /dev/tty.usbmodem575E0032081
Reconnect the usb cable.
```
##### Linux:
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
```
Example output leader arm port: `/dev/ttyACM0`
```bash
Finding all available ports for the MotorBus.
['/dev/ttyACM0', '/dev/ttyACM1']
Remove the usb cable from your MotorsBus and press Enter when done.
[...Disconnect leader arm and press Enter...]
The port of this MotorsBus is /dev/ttyACM0
Reconnect the usb cable.
```
Example output follower arm port: `/dev/ttyACM1`
```
Finding all available ports for the MotorBus.
['/dev/ttyACM0', '/dev/ttyACM1']
Remove the usb cable from your MotorsBus and press Enter when done.
[...Disconnect follower arm and press Enter...]
The port of this MotorsBus is /dev/ttyACM1
Reconnect the usb cable.
```
#### Update config file
Now that you have your ports, update the **port** default values of [`SO101RobotConfig`](https://github.com/huggingface/lerobot/blob/main/lerobot/common/robot_devices/robots/configs.py).
You will find a class called `so101` where you can update the `port` values with your actual motor ports:
```diff
@RobotConfig.register_subclass("so101")
@dataclass
class So101RobotConfig(ManipulatorRobotConfig):
calibration_dir: str = ".cache/calibration/so101"
# `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",
+ port="{ADD YOUR LEADER PORT}",
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",
+ port="{ADD YOUR FOLLOWER PORT}",
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"],
},
),
}
)
```
Here is a video of the process:
<video controls width="640" src="https://github.com/user-attachments/assets/fc45d756-31bb-4a61-b973-a87d633d08a7" type="video/mp4"></video>
### Set motor IDs
Now we need to set the motor ID for each motor. Plug your motor in only one of the two ports of the motor bus and run this script to set its ID to 1. Replace the text after --port to the corresponding control board port.
```bash
python lerobot/scripts/configure_motor.py \
--port /dev/tty.usbmodem58760432961 \
--brand feetech \
--model sts3215 \
--baudrate 1000000 \
--ID 1
```
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 this process for all your motors until ID 6. Do the same for the 6 motors of the leader arm, but make sure to change the power supply if you use motors with different voltage.
Here is a video of the process:
<video controls width="640" src="https://github.com/user-attachments/assets/b31c115f-e706-4dcd-b7f1-4535da62416d" type="video/mp4"></video>
## Step-by-Step Assembly Instructions
The follower arm uses 6x STS3215 motors with 1/345 gearing. The leader however uses three differently geared motors to make sure it can both sustain its own weight and it can be moved without requiring much force. Which motor is needed for which joint is shown in table below.
| Leader-Arm Axis | Motor | Gear Ratio |
|-----------------|:-------:|:----------:|
| Base / Shoulder Yaw | 1 | 1 / 191 |
| Shoulder Pitch | 2 | 1 / 345 |
| Elbow | 3 | 1 / 191 |
| Wrist Roll | 4 | 1 / 147 |
| Wrist Pitch | 5 | 1 / 147 |
| Gripper | 6 | 1 / 147 |
### Clean Parts
Remove all support material from the 3D-printed parts.
### Joint 1
- Place the first motor into the base.
- Fasten the motor with 4 M2x6mm screws (smallest screws). Two from the top and two from bottom.
- Slide over the first motor holder and fasten it using two M2x6mm screws (one on each side).
- Install both motor horns, securing the top horn with a M3x6mm screw.
- Attach the shoulder part.
- Tighten the shoulder part with 4 M3x6mm screws on top and 4 M3x6mm screws on the bottom
- Add the shoulder motor holder.
<video controls width="640" src="https://github.com/user-attachments/assets/b0ee9dee-a2d0-445b-8489-02ebecb3d639" type="video/mp4"></video>
### Joint 2
- Slide the second motor in from the top.
- Fasten the second motor with 4 M2x6mm screws.
- Attach both motor horns to motor 2, again use the M3x6mm horn screw.
- Attach the upper arm with 4 M3x6mm screws on each side.
<video controls width="640" src="https://github.com/user-attachments/assets/32453dc2-5006-4140-9f56-f0d78eae5155" type="video/mp4"></video>
### Joint 3
- Insert motor 3 and fasten using 4 M2x6mm screws
- Attach both motor horns to motor 3 and secure one again with a M3x6mm horn screw.
- Connect the forearm to motor 3 using 4 M3x6mm screws on each side.
<video controls width="640" src="https://github.com/user-attachments/assets/7384b9a7-a946-440c-b292-91391bcc4d6b" type="video/mp4"></video>
### Joint 4
- Slide over motor holder 4.
- Slide in motor 4.
- Fasten motor 4 with 4 M2x6mm screws and attach its motor horns, use a M3x6mm horn screw.
<video controls width="640" src="https://github.com/user-attachments/assets/dca78ad0-7c36-4bdf-8162-c9ac42a1506f" type="video/mp4"></video>
### Joint 5
- Insert motor 5 into the wrist holder and secure it with 2 M2x6mm front screws.
- Install only one motor horn on the wrist motor and secure it with a M3x6mm horn screw.
- Secure the wrist to motor 4 using 4 M3x6mm screws on both sides.
<video controls width="640" src="https://github.com/user-attachments/assets/55f5d245-976d-49ff-8b4a-59843c441b12" type="video/mp4"></video>
### Gripper / Handle
#### Follower:
- Attach the gripper to motor 5, attach it to the motor horn on the wrist using 4 M3x6mm screws.
- Insert the gripper motor and secure it with 2 M2x6mm screws on each side.
- Attach the motor horns and again use a M3x6mm horn screw.
- Install the gripper claw and secure it with 4 M3x6mm screws on both sides.
<video controls width="640" src="https://github.com/user-attachments/assets/6f766aa9-cfae-4388-89e7-0247f198c086" type="video/mp4"></video>
#### Leader:
- Mount the leader holder onto the wrist and secure it with 4 M3x6mm screws.
- Attach the handle to motor 5 using 1 M2x6mm screw.
- Insert the gripper motor, secure it with 2 M2x6mm screws on each side, attach a motor horn using a M3x6mm horn screw.
- Attach the follower trigger with 4 M3x6mm screws.
<video controls width="640" src="https://github.com/user-attachments/assets/1308c93d-2ef1-4560-8e93-a3812568a202" type="video/mp4"></video>
##### Wiring
- Attach the motor controller on the back.
- Then insert all wires, use the wire guides everywhere to make sure the wires don't unplug themselves and stay in place.
<video controls width="640" src="https://github.com/user-attachments/assets/4c2cacfd-9276-4ee4-8bf2-ba2492667b78" type="video/mp4"></video>
## Calibrate
Next, you'll need to calibrate your SO-101 robot to ensure that the leader and follower arms have the same position values when they are in the same physical position.
The calibration process is very important because it allows a neural network trained on one SO-101 robot to work on another.
#### Manual calibration of follower arm
You will need to move the follower arm to these positions sequentially, note that the rotated position is on the right side of the robot and you have to open the gripper fully.
| 1. Middle position | 2. Zero position | 3. Rotated position | 4. Rest position |
| ------------ |------------------------------------------------------------------------------------------------------------------------------------------------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------ |
| <img src="../media/so101/follower_middle.webp?raw=true" alt="SO-101 leader arm middle position" title="SO-101 leader arm middle position" style="width:100%;"> | <img src="../media/so101/follower_zero.webp?raw=true" alt="SO-101 leader arm zero position" title="SO-101 leader arm zero position" style="width:100%;"> | <img src="../media/so101/follower_rotated.webp?raw=true" alt="SO-101 leader arm rotated position" title="SO-101 leader arm rotated position" style="width:100%;"> | <img src="../media/so101/follower_rest.webp?raw=true" alt="SO-101 leader arm rest position" title="SO-101 leader 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=so101 \
--robot.cameras='{}' \
--control.type=calibrate \
--control.arms='["main_follower"]'
```
#### Manual calibration of leader arm
You will also need to move the leader arm to these positions sequentially:
| 1. Middle position | 2. Zero position | 3. Rotated position | 4. Rest position |
| ------------ |------------------------------------------------------------------------------------------------------------------------------------------------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------ |
| <img src="../media/so101/leader_middle.webp?raw=true" alt="SO-101 leader arm middle position" title="SO-101 leader arm middle position" style="width:100%;"> | <img src="../media/so101/leader_zero.webp?raw=true" alt="SO-101 leader arm zero position" title="SO-101 leader arm zero position" style="width:100%;"> | <img src="../media/so101/leader_rotated.webp?raw=true" alt="SO-101 leader arm rotated position" title="SO-101 leader arm rotated position" style="width:100%;"> | <img src="../media/so101/leader_rest.webp?raw=true" alt="SO-101 leader arm rest position" title="SO-101 leader arm rest position" style="width:100%;"> |
Run this script to launch manual calibration:
```bash
python lerobot/scripts/control_robot.py \
--robot.type=so101 \
--robot.cameras='{}' \
--control.type=calibrate \
--control.arms='["main_leader"]'
```
## Control your robot
Congrats 🎉, your robot is all set to learn a task on its own. Next we will explain to you how to train a neural network to autonomously control a real robot.
**You'll learn to:**
1. How to record and visualize your dataset.
2. How to train a policy using your data and prepare it for evaluation.
3. How to evaluate your policy and visualize the results.
By following these steps, you'll be able to replicate tasks like picking up a Lego block and placing it in a bin with a high success rate, as demonstrated in [this video](https://x.com/RemiCadene/status/1814680760592572934).
This tutorial is specifically made for the affordable [SO-101](https://github.com/TheRobotStudio/SO-ARM100) robot, but it contains additional information to be easily adapted to various types of robots like [Aloha bimanual robot](https://aloha-2.github.io) by changing some configurations. The SO-101 consists of a leader arm and a follower arm, each with 6 motors. It can work with one or several cameras to record the scene, which serve as visual sensors for the robot.
During the data collection phase, you will control the follower arm by moving the leader arm. This process is known as "teleoperation." This technique is used to collect robot trajectories. Afterward, you'll train a neural network to imitate these trajectories and deploy the network to enable your robot to operate autonomously.
If you encounter any issues at any step of the tutorial, feel free to seek help on [Discord](https://discord.com/invite/s3KuuzsPFb) or don't hesitate to iterate with us on the tutorial by creating issues or pull requests.
## Teleoperate
Run this simple script to teleoperate your robot (it won't connect and display the cameras):
```bash
python lerobot/scripts/control_robot.py \
--robot.type=so101 \
--robot.cameras='{}' \
--control.type=teleoperate
```
The teleoperate command will automatically:
1. Identify any missing calibrations and initiate the calibration procedure.
2. Connect the robot and start teleoperation.
## Setup Cameras
To connect a camera you have three options:
1. OpenCVCamera which allows us to use any camera: usb, realsense, laptop webcam
2. iPhone camera with MacOS
3. Phone camera on Linux
### Use OpenCVCamera
The [`OpenCVCamera`](../lerobot/common/robot_devices/cameras/opencv.py) class allows you to efficiently record frames from most cameras using the [`opencv2`](https://docs.opencv.org) library. For more details on compatibility, see [Video I/O with OpenCV Overview](https://docs.opencv.org/4.x/d0/da7/videoio_overview.html).
To instantiate an [`OpenCVCamera`](../lerobot/common/robot_devices/cameras/opencv.py), you need a camera index (e.g. `OpenCVCamera(camera_index=0)`). When you only have one camera like a webcam of a laptop, the camera index is usually `0` but it might differ, and the camera index might change if you reboot your computer or re-plug your camera. This behavior depends on your operating system.
To find the camera indices, run the following utility script, which will save a few frames from each detected camera:
```bash
python lerobot/common/robot_devices/cameras/opencv.py \
--images-dir outputs/images_from_opencv_cameras
```
The output will look something like this if you have two cameras connected:
```
Mac or Windows detected. Finding available camera indices through scanning all indices from 0 to 60
[...]
Camera found at index 0
Camera found at index 1
[...]
Connecting cameras
OpenCVCamera(0, fps=30.0, width=1920.0, height=1080.0, color_mode=rgb)
OpenCVCamera(1, fps=24.0, width=1920.0, height=1080.0, color_mode=rgb)
Saving images to outputs/images_from_opencv_cameras
Frame: 0000 Latency (ms): 39.52
[...]
Frame: 0046 Latency (ms): 40.07
Images have been saved to outputs/images_from_opencv_cameras
```
Check the saved images in `outputs/images_from_opencv_cameras` to identify which camera index corresponds to which physical camera (e.g. `0` for `camera_00` or `1` for `camera_01`):
```
camera_00_frame_000000.png
[...]
camera_00_frame_000047.png
camera_01_frame_000000.png
[...]
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.
Now that you have the camera indexes, you should change them in the config. You can also change the fps, width or height of the camera.
The camera config is defined per robot, can be found here [`RobotConfig`](https://github.com/huggingface/lerobot/blob/main/lerobot/common/robot_devices/robots/configs.py) and looks like this:
```python
cameras: dict[str, CameraConfig] = field(
default_factory=lambda: {
"wrist": OpenCVCameraConfig(
camera_index=0, <-- UPDATE HERE
fps=30,
width=640,
height=480,
),
"base": OpenCVCameraConfig(
camera_index=1, <-- UPDATE HERE
fps=30,
width=640,
height=480,
),
}
)
```
### Use your phone
#### Mac:
To use your iPhone as a camera on macOS, enable the Continuity Camera feature:
- Ensure your Mac is running macOS 13 or later, and your iPhone is on iOS 16 or later.
- Sign in both devices with the same Apple ID.
- Connect your devices with a USB cable or turn on Wi-Fi and Bluetooth for a wireless connection.
For more details, visit [Apple support](https://support.apple.com/en-gb/guide/mac-help/mchl77879b8a/mac).
Your iPhone should be detected automatically when running the camera setup script in the next section.
#### Linux:
If you want to use your phone as a camera on Linux, follow these steps to set up a virtual camera
1. *Install `v4l2loopback-dkms` and `v4l-utils`*. Those packages are required to create virtual camera devices (`v4l2loopback`) and verify their settings with the `v4l2-ctl` utility from `v4l-utils`. Install them using:
```python
sudo apt install v4l2loopback-dkms v4l-utils
```
2. *Install [DroidCam](https://droidcam.app) on your phone*. This app is available for both iOS and Android.
3. *Install [OBS Studio](https://obsproject.com)*. This software will help you manage the camera feed. Install it using [Flatpak](https://flatpak.org):
```python
flatpak install flathub com.obsproject.Studio
```
4. *Install the DroidCam OBS plugin*. This plugin integrates DroidCam with OBS Studio. Install it with:
```python
flatpak install flathub com.obsproject.Studio.Plugin.DroidCam
```
5. *Start OBS Studio*. Launch with:
```python
flatpak run com.obsproject.Studio
```
6. *Add your phone as a source*. Follow the instructions [here](https://droidcam.app/obs/usage). Be sure to set the resolution to `640x480`.
7. *Adjust resolution settings*. In OBS Studio, go to `File > Settings > Video`. Change the `Base(Canvas) Resolution` and the `Output(Scaled) Resolution` to `640x480` by manually typing it in.
8. *Start virtual camera*. In OBS Studio, follow the instructions [here](https://obsproject.com/kb/virtual-camera-guide).
9. *Verify the virtual camera setup*. Use `v4l2-ctl` to list the devices:
```python
v4l2-ctl --list-devices
```
You should see an entry like:
```
VirtualCam (platform:v4l2loopback-000):
/dev/video1
```
10. *Check the camera resolution*. Use `v4l2-ctl` to ensure that the virtual camera output resolution is `640x480`. Change `/dev/video1` to the port of your virtual camera from the output of `v4l2-ctl --list-devices`.
```python
v4l2-ctl -d /dev/video1 --get-fmt-video
```
You should see an entry like:
```
>>> Format Video Capture:
>>> Width/Height : 640/480
>>> Pixel Format : 'YUYV' (YUYV 4:2:2)
```
Troubleshooting: If the resolution is not correct you will have to delete the Virtual Camera port and try again as it cannot be changed.
If everything is set up correctly, you can proceed with the rest of the tutorial.
### Add wrist camera
If you have an additional camera you can add a wrist camera to the SO101. There are already many premade wrist camera holders that you can find in the SO101 repo: [Wrist camera's](https://github.com/TheRobotStudio/SO-ARM100#wrist-cameras)
## Teleoperate with cameras
We can now teleoperate again while at the same time visualizing the cameras and joint positions with `rerun`.
```bash
python lerobot/scripts/control_robot.py \
--robot.type=so101 \
--control.type=teleoperate \
--control.display_data=true
```
## Record a dataset
Once you're familiar with teleoperation, you can record your first dataset with SO-101.
We use the Hugging Face hub features for uploading your dataset. If you haven't previously used the Hub, make sure you can login via the cli using a write-access token, this token can be generated from the [Hugging Face settings](https://huggingface.co/settings/tokens).
Add your token to the cli by running this command:
```bash
huggingface-cli login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential
```
Then store your Hugging Face repository name in a variable:
```bash
HF_USER=$(huggingface-cli whoami | head -n 1)
echo $HF_USER
```
Now you can record a dataset, to record 2 episodes and upload your dataset to the hub execute this command:
```bash
python lerobot/scripts/control_robot.py \
--robot.type=so101 \
--control.type=record \
--control.fps=30 \
--control.single_task="Grasp a lego block and put it in the bin." \
--control.repo_id=${HF_USER}/so101_test \
--control.tags='["so101","tutorial"]' \
--control.warmup_time_s=5 \
--control.episode_time_s=30 \
--control.reset_time_s=30 \
--control.num_episodes=2 \
--control.display_data=true \
--control.push_to_hub=true
```
You will see a lot of lines appearing like this one:
```
INFO 2024-08-10 15:02:58 ol_robot.py:219 dt:33.34 (30.0hz) dtRlead: 5.06 (197.5hz) dtWfoll: 0.25 (3963.7hz) dtRfoll: 6.22 (160.7hz) dtRlaptop: 32.57 (30.7hz) dtRphone: 33.84 (29.5hz)
```
It contains:
- `2024-08-10 15:02:58` which is the date and time of the call to the print function,
- `ol_robot.py:219` which is the end of the file name and the line number where the print function is called (`lerobot/scripts/control_robot.py` line `219`).
- `dt:33.34 (30.0hz)` which is the "delta time" or the number of milliseconds spent between the previous call to `robot.teleop_step(record_data=True)` and the current one, associated with the frequency (33.34 ms equals 30.0 Hz) ; note that we use `--fps 30` so we expect 30.0 Hz ; when a step takes more time, the line appears in yellow.
- `dtRlead: 5.06 (197.5hz)` which is the delta time of reading the present position of the leader arm.
- `dtWfoll: 0.25 (3963.7hz)` which is the delta time of writing the goal position on the follower arm ; writing is asynchronous so it takes less time than reading.
- `dtRfoll: 6.22 (160.7hz)` which is the delta time of reading the present position on the follower arm.
- `dtRlaptop:32.57 (30.7hz) ` which is the delta time of capturing an image from the laptop camera in the thread running asynchronously.
- `dtRphone:33.84 (29.5hz)` which is the delta time of capturing an image from the phone camera in the thread running asynchronously.
#### Dataset upload
Locally your dataset is stored in this folder: `~/.cache/huggingface/lerobot/{repo-id}` (e.g. `data/cadene/so101_test`). At the end of data recording, your dataset will be uploaded on your Hugging Face page (e.g. https://huggingface.co/datasets/cadene/so101_test) that you can obtain by running:
```bash
echo https://huggingface.co/datasets/${HF_USER}/so101_test
```
Your dataset will be automatically tagged with `LeRobot` for the community to find it easily, and you can also add custom tags (in this case `tutorial` for example).
You can look for other LeRobot datasets on the hub by searching for `LeRobot` [tags](https://huggingface.co/datasets?other=LeRobot).
#### Record function
The `record` function provides a suite of tools for capturing and managing data during robot operation:
1. Set the flow of data recording using command line arguments:
- `--control.warmup_time_s=10` defines the number of seconds before starting data collection. It allows the robot devices to warmup and synchronize (10 seconds by default).
- `--control.episode_time_s=60` defines the number of seconds for data recording for each episode (60 seconds by default).
- `--control.reset_time_s=60` defines the number of seconds for resetting the environment after each episode (60 seconds by default).
- `--control.num_episodes=50` defines the number of episodes to record (50 by default).
2. Control the flow during data recording using keyboard keys:
- Press right arrow `->` at any time during episode recording to early stop and go to resetting. Same during resetting, to early stop and to go to the next episode recording.
- Press left arrow `<-` at any time during episode recording or resetting to early stop, cancel the current episode, and re-record it.
- Press escape `ESC` at any time during episode recording to end the session early and go straight to video encoding and dataset uploading.
3. Checkpoints are done set during recording, so if any issue occurs, you can resume recording by re-running the same command again with `--control.resume=true`. You will need to manually delete the dataset directory if you want to start recording from scratch.
#### Tips for gathering data
Once you're comfortable with data recording, you can create a larger dataset for training. A good starting task is grasping an object at different locations and placing it in a bin. We suggest recording at least 50 episodes, with 10 episodes per location. Keep the cameras fixed and maintain consistent grasping behavior throughout the recordings. Also make sure the object you are manipulating is visible on the camera's. A good rule of thumb is you should be able to do the task yourself by only looking at the camera images.
In the following sections, you’ll train your neural network. After achieving reliable grasping performance, you can start introducing more variations during data collection, such as additional grasp locations, different grasping techniques, and altering camera positions.
Avoid adding too much variation too quickly, as it may hinder your results.
#### Troubleshooting:
- On Linux, if the left and right arrow keys and escape key don't have any effect during data recording, make sure you've set the `$DISPLAY` environment variable. See [pynput limitations](https://pynput.readthedocs.io/en/latest/limitations.html#linux).
## 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}/so101_test
```
If you didn't upload with `--control.push_to_hub=false`, you can visualize it locally with (via a window in the browser `http://127.0.0.1:9090` with the visualization tool):
```bash
python lerobot/scripts/visualize_dataset_html.py \
--repo-id ${HF_USER}/so101_test \
--local-files-only 1
```
This will launch a local web server that looks like this:
<div style="text-align:center;">
<img src="../media/tutorial/visualize_dataset_html.webp?raw=true" alt="Koch v1.1 leader and follower arms" title="Koch v1.1 leader and follower arms" width="100%"></img>
</div>
## Replay an episode
A useful feature is the `replay` function, which allows to replay on your robot any episode that you've recorded or episodes from any dataset out there. This function helps you test the repeatability of your robot's actions and assess transferability across robots of the same model.
You can replay the first episode on your robot with:
```bash
python lerobot/scripts/control_robot.py \
--robot.type=so101 \
--control.type=replay \
--control.fps=30 \
--control.repo_id=${HF_USER}/so101_test \
--control.episode=0
```
Your robot should replicate movements similar to those you recorded. For example, check out [this video](https://x.com/RemiCadene/status/1793654950905680090) where we use `replay` on a Aloha robot from [Trossen Robotics](https://www.trossenrobotics.com).
## 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=${HF_USER}/so101_test \
--policy.type=act \
--output_dir=outputs/train/act_so101_test \
--job_name=act_so101_test \
--policy.device=cuda \
--wandb.enable=true
```
Let's explain the command:
1. We provided the dataset as argument with `--dataset.repo_id=${HF_USER}/so101_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 states, 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_so101_test/checkpoints`.
To resume training from a checkpoint, below is an example command to resume from `last` checkpoint of the `act_so101_test` policy:
```bash
python lerobot/scripts/train.py \
--config_path=outputs/train/act_so101_test/checkpoints/last/pretrained_model/train_config.json \
--resume=true
```
#### Upload policy checkpoints
Once training is done, upload the latest checkpoint with:
```bash
huggingface-cli upload ${HF_USER}/act_so101_test \
outputs/train/act_so101_test/checkpoints/last/pretrained_model
```
You can also upload intermediate checkpoints with:
```bash
CKPT=010000
huggingface-cli upload ${HF_USER}/act_so101_test${CKPT} \
outputs/train/act_so101_test/checkpoints/${CKPT}/pretrained_model
```
## Evaluate your policy
You can use the `record` function from [`lerobot/scripts/control_robot.py`](../lerobot/scripts/control_robot.py) but with a policy checkpoint as input. For instance, run this command to record 10 evaluation episodes:
```bash
python lerobot/scripts/control_robot.py \
--robot.type=so101 \
--control.type=record \
--control.fps=30 \
--control.single_task="Grasp a lego block and put it in the bin." \
--control.repo_id=${HF_USER}/eval_act_so101_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_so101_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_so101_test/checkpoints/last/pretrained_model`). You can also use the model repository if you uploaded a model checkpoint to the hub (e.g. `${HF_USER}/act_so101_test`).
2. The name of dataset begins by `eval` to reflect that you are running inference (e.g. `${HF_USER}/eval_act_so101_test`).

381
lerobot/common/robots/so101_follower/so101.mdx Normal file
View File

@@ -0,0 +1,381 @@
# SO-101
In the steps below, we explain how to assemble our flagship robot, the SO-101.
## 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.
And advise if it's your first time printing or if you don't own a 3D printer.
## Install LeRobot 🤗
To install LeRobot, follow our [Installation Guide](./installation)
In addition to these instructions, you need to install the Feetech SDK:
```bash
pip install -e ".[feetech]"
```
## Step-by-Step Assembly Instructions
The follower arm uses 6x STS3215 motors with 1/345 gearing. The leader, however, uses three differently geared motors to make sure it can both sustain its own weight and it can be moved without requiring much force. Which motor is needed for which joint is shown in the table below.
| Leader-Arm Axis | Motor | Gear Ratio |
|-----------------|:-------:|:----------:|
| Base / Shoulder Yaw | 1 | 1 / 191 |
| Shoulder Pitch | 2 | 1 / 345 |
| Elbow | 3 | 1 / 191 |
| Wrist Roll | 4 | 1 / 147 |
| Wrist Pitch | 5 | 1 / 147 |
| Gripper | 6 | 1 / 147 |
### Clean Parts
Remove all support material from the 3D-printed parts. The easiest way to do this is using a small screwdriver to get underneath the support material.
### Joint 1
- Place the first motor into the base.
- Fasten the motor with 4 M2x6mm screws (smallest screws). Two from the top and two from the bottom.
- Slide over the first motor holder and fasten it using two M2x6mm screws (one on each side).
- Install both motor horns, securing the top horn with a M3x6mm screw.
- Attach the shoulder part.
- Tighten the shoulder part with 4 M3x6mm screws on top and 4 M3x6mm screws on the bottom
- Add the shoulder motor holder.
<div class="video-container">
<video controls width="600">
<source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/Joint1_v2.mp4" type="video/mp4" />
</video>
</div>
### Joint 2
- Slide the second motor in from the top.
- Fasten the second motor with 4 M2x6mm screws.
- Attach both motor horns to motor 2, again use the M3x6mm horn screw.
- Attach the upper arm with 4 M3x6mm screws on each side.
<div class="video-container">
<video controls width="600">
<source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/Joint2_v2.mp4" type="video/mp4" />
</video>
</div>
### Joint 3
- Insert motor 3 and fasten using 4 M2x6mm screws
- Attach both motor horns to motor 3 and secure one again with a M3x6mm horn screw.
- Connect the forearm to motor 3 using 4 M3x6mm screws on each side.
<div class="video-container">
<video controls width="600">
<source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/Joint3_v2.mp4" type="video/mp4" />
</video>
</div>
### Joint 4
- Slide over motor holder 4.
- Slide in motor 4.
- Fasten motor 4 with 4 M2x6mm screws and attach its motor horns, use a M3x6mm horn screw.
<div class="video-container">
<video controls width="600">
<source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/Joint4_v2.mp4" type="video/mp4" />
</video>
</div>
### Joint 5
- Insert motor 5 into the wrist holder and secure it with 2 M2x6mm front screws.
- Install only one motor horn on the wrist motor and secure it with a M3x6mm horn screw.
- Secure the wrist to motor 4 using 4 M3x6mm screws on both sides.
<div class="video-container">
<video controls width="600">
<source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/Joint5_v2.mp4" type="video/mp4" />
</video>
</div>
### Gripper / Handle
<hfoptions id="assembly">
<hfoption id="Follower">
- Attach the gripper to motor 5, attach it to the motor horn on the wrist using 4 M3x6mm screws.
- Insert the gripper motor and secure it with 2 M2x6mm screws on each side.
- Attach the motor horns and again use a M3x6mm horn screw.
- Install the gripper claw and secure it with 4 M3x6mm screws on both sides.
<div class="video-container">
<video controls width="600">
<source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/Gripper_v2.mp4" type="video/mp4" />
</video>
</div>
</hfoption>
<hfoption id="Leader">
- Mount the leader holder onto the wrist and secure it with 4 M3x6mm screws.
- Attach the handle to motor 5 using 1 M2x6mm screw.
- Insert the gripper motor, secure it with 2 M2x6mm screws on each side, attach a motor horn using a M3x6mm horn screw.
- Attach the follower trigger with 4 M3x6mm screws.
<div class="video-container">
<video controls width="600">
<source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/Leader_v2.mp4" type="video/mp4" />
</video>
</div>
</hfoption>
</hfoptions>
## Configure the motors
### 1. Find the USB ports associated with each arm
To find the port for each bus servo adapter, run this script:
```bash
python lerobot/find_port.py
```
<hfoptions id="example">
<hfoption id="Mac">
Example output:
```
Finding all available ports for the MotorBus.
['/dev/tty.usbmodem575E0032081', '/dev/tty.usbmodem575E0031751']
Remove the USB cable from your MotorsBus and press Enter when done.
[...Disconnect corresponding leader or follower arm and press Enter...]
The port of this MotorsBus is /dev/tty.usbmodem575E0032081
Reconnect the USB cable.
```
Where the found port is: `/dev/tty.usbmodem575E0032081` corresponding to your leader or follower arm.
</hfoption>
<hfoption id="Linux">
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
```
Example output:
```
Finding all available ports for the MotorBus.
['/dev/ttyACM0', '/dev/ttyACM1']
Remove the usb cable from your MotorsBus and press Enter when done.
[...Disconnect corresponding leader or follower arm and press Enter...]
The port of this MotorsBus is /dev/ttyACM1
Reconnect the USB cable.
```
Where the found port is: `/dev/ttyACM1` corresponding to your leader or follower arm.
</hfoption>
</hfoptions>
### 2. Set the motors ids and baudrates
Each motor is identified by a unique id on the bus. When brand new, motors usually come with a default id of `1`. For the communication to work properly between the motors and the controller, we first need to set a unique, different id to each motor. Additionally, the speed at which data is transmitted on the bus is determined by the baudrate. In order to talk to each other, the controller and all the motors need to be configured with the same baudrate.
To that end, we first need to connect to each motor individually with the controller in order to set these. Since we will write these parameters in the non-volatile section of the motors' internal memory (EEPROM), we'll only need to do this once.
If you are repurposing motors from another robot, you will probably also need to perform this step as the ids and baudrate likely won't match.
The video below shows the sequence of steps for setting the motor ids.
##### Setup motors video
<div class="video-container">
<video controls width="600">
<source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/setup_motors_so101_2.mp4" type="video/mp4" />
</video>
</div>
#### Follower
Connect the usb cable from your computer and the power supply to the follower arm's controller board. Then, run the following command or run the API example with the port you got from the previous step. You'll also need to give your leader arm a name with the `id` parameter.
<hfoptions id="setup_motors">
<hfoption id="Command">
```bash
python -m lerobot.setup_motors \
--robot.type=so101_follower \
--robot.port=/dev/tty.usbmodem585A0076841 # <- paste here the port found at previous step
```
</hfoption>
<hfoption id="API example">
```python
from lerobot.common.robots.so101_follower import SO101Follower, SO101FollowerConfig
config = SO101FollowerConfig(
port="/dev/tty.usbmodem585A0076841",
id="my_awesome_follower_arm",
)
follower = SO101Follower(config)
follower.setup_motors()
```
</hfoption>
</hfoptions>
You should see the following instruction
```bash
Connect the controller board to the 'gripper' motor only and press enter.
```
As instructed, plug the gripper's motor. Make sure it's the only motor connected to the board, and that the motor itself is not yet daisy-chained to any other motor. As you press `[Enter]`, the script will automatically set the id and baudrate for that motor.
<details>
<summary>Troubleshooting</summary>
If you get an error at that point, check your cables and make sure they are plugged in properly:
<ul>
<li>Power supply</li>
<li>USB cable between your computer and the controller board</li>
<li>The 3-pin cable from the controller board to the motor</li>
</ul>
If you are using a Waveshare controller board, make sure that the two jumpers are set on the `B` channel (USB).
</details>
You should then see the following message:
```bash
'gripper' motor id set to 6
```
Followed by the next instruction:
```bash
Connect the controller board to the 'wrist_roll' motor only and press enter.
```
You can disconnect the 3-pin cable from the controller board, but you can leave it connected to the gripper motor on the other end, as it will already be in the right place. Now, plug in another 3-pin cable to the wrist roll motor and connect it to the controller board. As with the previous motor, make sure it is the only motor connected to the board and that the motor itself isn't connected to any other one.
Repeat the operation for each motor as instructed.
> [!TIP]
> Check your cabling at each step before pressing Enter. For instance, the power supply cable might disconnect as you manipulate the board.
When you are done, the script will simply finish, at which point the motors are ready to be used. You can now plug the 3-pin cable from each motor to the next one, and the cable from the first motor (the 'shoulder pan' with id=1) to the controller board, which can now be attached to the base of the arm.
#### Leader
Do the same steps for the leader arm.
<hfoptions id="setup_motors">
<hfoption id="Command">
```bash
python -m lerobot.setup_motors \
--teleop.type=so101_leader \
--teleop.port=/dev/tty.usbmodem575E0031751 # <- paste here the port found at previous step
```
</hfoption>
<hfoption id="API example">
```python
from lerobot.common.teleoperators.so101_leader import SO101Leader, SO101LeaderConfig
config = SO101LeaderConfig(
port="/dev/tty.usbmodem585A0076841",
id="my_awesome_leader_arm",
)
leader = SO101Leader(config)
leader.setup_motors()
```
</hfoption>
</hfoptions>
## Calibrate
Next, you'll need to calibrate your robot to ensure that the leader and follower arms have the same position values when they are in the same physical position.
The calibration process is very important because it allows a neural network trained on one robot to work on another.
#### Follower
Run the following command or API example to calibrate the follower arm:
<hfoptions id="calibrate_follower">
<hfoption id="Command">
```bash
python -m lerobot.calibrate \
--robot.type=so101_follower \
--robot.port=/dev/tty.usbmodem58760431551 \ # <- The port of your robot
--robot.id=my_awesome_follower_arm # <- Give the robot a unique name
```
</hfoption>
<hfoption id="API example">
```python
from lerobot.common.robots.so101_follower import SO101FollowerConfig, SO101Follower
config = SO101FollowerConfig(
port="/dev/tty.usbmodem585A0076891",
id="my_awesome_follower_arm",
)
follower = SO101Follower(config)
follower.connect(calibrate=False)
follower.calibrate()
follower.disconnect()
```
</hfoption>
</hfoptions>
The video below shows how to perform the calibration. First you need to move the robot to the position where all joints are in the middle of their ranges. Then after pressing enter you have to move each joint through its full range of motion.
##### Calibration video
<div class="video-container">
<video controls width="600">
<source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/calibrate_so101_2.mp4" type="video/mp4" />
</video>
</div>
#### Leader
Do the same steps to calibrate the leader arm, run the following command or API example:
<hfoptions id="calibrate_leader">
<hfoption id="Command">
```bash
python -m lerobot.calibrate \
--teleop.type=so101_leader \
--teleop.port=/dev/tty.usbmodem58760431551 \ # <- The port of your robot
--teleop.id=my_awesome_leader_arm # <- Give the robot a unique name
```
</hfoption>
<hfoption id="API example">
```python
from lerobot.common.teleoperators.so101_leader import SO101LeaderConfig, SO101Leader
config = SO101LeaderConfig(
port="/dev/tty.usbmodem58760431551",
id="my_awesome_leader_arm",
)
leader = SO101Leader(config)
leader.connect(calibrate=False)
leader.calibrate()
leader.disconnect()
```
</hfoption>
</hfoptions>
Congrats 🎉, your robot is all set to learn a task on its own. Start training it by following this tutorial: [Getting started with real-world robots](./getting_started_real_world_robot)
> [!TIP]
> If you have any questions or need help, please reach out on [Discord](https://discord.com/invite/s3KuuzsPFb).

4
lerobot/common/robots/so101_follower/so101_follower.py
View File

@@ -79,8 +79,7 @@ class SO101Follower(Robot):
@property
def is_connected(self) -> bool:
# TODO(aliberts): add cam.is_connected for cam in self.cameras
return self.bus.is_connected
return self.bus.is_connected and all(cam.is_connected for cam in self.cameras.values())
def connect(self, calibrate: bool = True) -> None:
"""
@@ -94,7 +93,6 @@ class SO101Follower(Robot):
if not self.is_calibrated and calibrate:
self.calibrate()
# Connect the cameras
for cam in self.cameras.values():
cam.connect()

3
lerobot/common/robots/viperx/viperx.py
View File

@@ -83,8 +83,7 @@ class ViperX(Robot):
@property
def is_connected(self) -> bool:
# TODO(aliberts): add cam.is_connected for cam in self.cameras
return self.bus.is_connected
return self.bus.is_connected and all(cam.is_connected for cam in self.cameras.values())
def connect(self, calibrate: bool = True) -> None:
"""

3
lerobot/common/teleoperators/koch_leader/koch_leader.py
View File

@@ -140,7 +140,8 @@ class KochLeader(Teleoperator):
self.bus.write("Operating_Mode", "gripper", OperatingMode.CURRENT_POSITION.value)
# Set gripper's goal pos in current position mode so that we can use it as a trigger.
self.bus.enable_torque("gripper")
self.bus.write("Goal_Position", "gripper", self.config.gripper_open_pos)
if self.is_calibrated:
self.bus.write("Goal_Position", "gripper", self.config.gripper_open_pos)
def setup_motors(self) -> None:
for motor in reversed(self.bus.motors):

152
lerobot/common/utils/control_utils.py
View File

@@ -18,25 +18,20 @@
import logging
import time
import traceback
from contextlib import nullcontext
from copy import copy
from functools import cache
import numpy as np
import rerun as rr
import torch
from deepdiff import DeepDiff
from termcolor import colored
from lerobot.common.datasets.image_writer import safe_stop_image_writer
from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
from lerobot.common.datasets.utils import DEFAULT_FEATURES
from lerobot.common.policies.pretrained import PreTrainedPolicy
from lerobot.common.robots import Robot
from lerobot.common.utils.robot_utils import busy_wait
from lerobot.common.utils.utils import get_safe_torch_device, has_method
def log_control_info(robot: Robot, dt_s, episode_index=None, frame_index=None, fps=None):
@@ -112,6 +107,7 @@ def predict_action(
):
# Convert to pytorch format: channel first and float32 in [0,1] with batch dimension
for name in observation:
observation[name] = torch.from_numpy(observation[name])
if "image" in name:
observation[name] = observation[name].type(torch.float32) / 255
observation[name] = observation[name].permute(2, 0, 1).contiguous()
@@ -172,152 +168,6 @@ def init_keyboard_listener():
return listener, events
def warmup_record(
robot,
events,
enable_teleoperation,
warmup_time_s,
display_data,
fps,
):
control_loop(
robot=robot,
control_time_s=warmup_time_s,
display_data=display_data,
events=events,
fps=fps,
teleoperate=enable_teleoperation,
)
def record_episode(
robot,
dataset,
events,
episode_time_s,
display_data,
policy,
fps,
single_task,
):
control_loop(
robot=robot,
control_time_s=episode_time_s,
display_data=display_data,
dataset=dataset,
events=events,
policy=policy,
fps=fps,
teleoperate=policy is None,
single_task=single_task,
)
@safe_stop_image_writer
def control_loop(
robot,
control_time_s=None,
teleoperate=False,
display_data=False,
dataset: LeRobotDataset | None = None,
events=None,
policy: PreTrainedPolicy = None,
fps: int | None = None,
single_task: str | None = None,
):
# TODO(rcadene): Add option to record logs
if not robot.is_connected:
robot.connect()
if events is None:
events = {"exit_early": False}
if control_time_s is None:
control_time_s = float("inf")
if teleoperate and policy is not None:
raise ValueError("When `teleoperate` is True, `policy` should be None.")
if dataset is not None and single_task is None:
raise ValueError("You need to provide a task as argument in `single_task`.")
if dataset is not None and fps is not None and dataset.fps != fps:
raise ValueError(f"The dataset fps should be equal to requested fps ({dataset['fps']} != {fps}).")
timestamp = 0
start_episode_t = time.perf_counter()
# Controls starts, if policy is given it needs cleaning up
if policy is not None:
policy.reset()
while timestamp < control_time_s:
start_loop_t = time.perf_counter()
if teleoperate:
observation, action = robot.teleop_step(record_data=True)
else:
observation = robot.capture_observation()
action = None
if policy is not None:
pred_action = predict_action(
observation, policy, get_safe_torch_device(policy.config.device), policy.config.use_amp
)
# Action can eventually be clipped using `max_relative_target`,
# so action actually sent is saved in the dataset.
action = robot.send_action(pred_action)
action = {"action": action}
if dataset is not None:
frame = {**observation, **action, "task": single_task}
dataset.add_frame(frame)
# TODO(Steven): This should be more general (for RemoteRobot instead of checking the name, but anyways it will change soon)
if (display_data and not is_headless()) or (display_data and robot.robot_type.startswith("lekiwi")):
if action is not None:
for k, v in action.items():
for i, vv in enumerate(v):
rr.log(f"sent_{k}_{i}", rr.Scalar(vv.numpy()))
image_keys = [key for key in observation if "image" in key]
for key in image_keys:
rr.log(key, rr.Image(observation[key].numpy()), static=True)
if fps is not None:
dt_s = time.perf_counter() - start_loop_t
busy_wait(1 / fps - dt_s)
dt_s = time.perf_counter() - start_loop_t
log_control_info(robot, dt_s, fps=fps)
timestamp = time.perf_counter() - start_episode_t
if events["exit_early"]:
events["exit_early"] = False
break
def reset_environment(robot, events, reset_time_s, fps):
# TODO(rcadene): refactor warmup_record and reset_environment
if has_method(robot, "teleop_safety_stop"):
robot.teleop_safety_stop()
control_loop(
robot=robot,
control_time_s=reset_time_s,
events=events,
fps=fps,
teleoperate=True,
)
def stop_recording(robot, listener, display_data):
robot.disconnect()
if not is_headless() and listener is not None:
listener.stop()
def sanity_check_dataset_name(repo_id, policy_cfg):
_, dataset_name = repo_id.split("/")
# either repo_id doesnt start with "eval_" and there is no policy

9
lerobot/record.py
View File

@@ -57,6 +57,7 @@ from lerobot.common.robots import ( # noqa: F401
koch_follower,
make_robot_from_config,
so100_follower,
so101_follower,
)
from lerobot.common.teleoperators import ( # noqa: F401
Teleoperator,
@@ -80,7 +81,7 @@ from lerobot.common.utils.visualization_utils import _init_rerun
from lerobot.configs import parser
from lerobot.configs.policies import PreTrainedConfig
from .common.teleoperators import koch_leader, so100_leader # noqa: F401
from .common.teleoperators import koch_leader, so100_leader, so101_leader # noqa: F401
@dataclass
@@ -130,8 +131,6 @@ class RecordConfig:
teleop: TeleoperatorConfig | None = None
# Whether to control the robot with a policy
policy: PreTrainedConfig | None = None
# Number of seconds before starting data collection. It allows the robot devices to warmup and synchronize.
warmup_time_s: int | float = 10
# Display all cameras on screen
display_data: bool = False
# Use vocal synthesis to read events.
@@ -186,9 +185,10 @@ def record_loop(
observation_frame = build_dataset_frame(dataset.features, observation, prefix="observation")
if policy is not None:
action = predict_action(
action_values = predict_action(
observation_frame, policy, get_safe_torch_device(policy.config.device), policy.config.use_amp
)
action = {key: action_values[i] for i, key in enumerate(robot.action_features)}
else:
action = teleop.get_action()
@@ -322,7 +322,6 @@ def record(cfg: RecordConfig) -> LeRobotDataset:
single_task=cfg.dataset.single_task,
display_data=cfg.display_data,
)
# reset_environment(robot, events, cfg.dataset.reset_time_s, cfg.dataset.fps)
if events["rerecord_episode"]:
log_say("Re-record episode", cfg.play_sounds)

7
lerobot/replay.py
View File

@@ -42,6 +42,7 @@ from lerobot.common.robots import ( # noqa: F401
koch_follower,
make_robot_from_config,
so100_follower,
so101_follower,
)
from lerobot.common.utils.robot_utils import busy_wait
from lerobot.common.utils.utils import (
@@ -77,16 +78,16 @@ def replay(cfg: ReplayConfig):
robot = make_robot_from_config(cfg.robot)
dataset = LeRobotDataset(cfg.dataset.repo_id, root=cfg.dataset.root, episodes=[cfg.dataset.episode])
actions = dataset.hf_dataset.select_columns("action.joints")
actions = dataset.hf_dataset.select_columns("action")
robot.connect()
log_say("Replaying episode", cfg.play_sounds, blocking=True)
for idx in range(dataset.num_frames):
start_episode_t = time.perf_counter()
action_array = actions[idx]["action.joints"]
action_array = actions[idx]["action"]
action = {}
for i, name in enumerate(dataset.features["action.joints"]["names"]):
for i, name in enumerate(dataset.features["action"]["names"]):
action[name] = action_array[i]
robot.send_action(action)

13
lerobot/setup_motors.py
View File

@@ -28,12 +28,20 @@ from dataclasses import dataclass
import draccus
from .common.robots import RobotConfig, koch_follower, make_robot_from_config, so100_follower # noqa: F401
from .common.robots import ( # noqa: F401
RobotConfig,
koch_follower,
lekiwi,
make_robot_from_config,
so100_follower,
so101_follower,
)
from .common.teleoperators import ( # noqa: F401
TeleoperatorConfig,
koch_leader,
make_teleoperator_from_config,
so100_leader,
so101_leader,
)
COMPATIBLE_DEVICES = [
@@ -41,6 +49,9 @@ COMPATIBLE_DEVICES = [
"koch_leader",
"so100_follower",
"so100_leader",
"so101_follower",
"so101_leader",
"lekiwi",
]

12
lerobot/teleoperate.py
View File

@@ -19,13 +19,14 @@ Example:
```shell
python -m lerobot.teleoperate \
--robot.type=so100_follower \
--robot.type=so101_follower \
--robot.port=/dev/tty.usbmodem58760431541 \
--robot.cameras="{laptop: {type: opencv, camera_index: 0}}" \
--robot.cameras="{ front: {type: opencv, index_or_path: 0, width: 1920, height: 1080, fps: 30}}" \
--robot.id=black \
--teleop.type=so100_leader \
--teleop.type=so101_leader \
--teleop.port=/dev/tty.usbmodem58760431551 \
--teleop.id=blue
--teleop.id=blue \
--display_data=true
```
"""
@@ -46,6 +47,7 @@ from lerobot.common.robots import ( # noqa: F401
koch_follower,
make_robot_from_config,
so100_follower,
so101_follower,
)
from lerobot.common.teleoperators import (
Teleoperator,
@@ -55,7 +57,7 @@ from lerobot.common.teleoperators import (
from lerobot.common.utils.utils import init_logging, move_cursor_up
from lerobot.common.utils.visualization_utils import _init_rerun
from .common.teleoperators import koch_leader, so100_leader # noqa: F401
from .common.teleoperators import koch_leader, so100_leader, so101_leader # noqa: F401
@dataclass

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