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Package folder structure (#1417)

Browse Source 
* Move files

* Replace imports & paths

* Update relative paths

* Update doc symlinks

* Update instructions paths

* Fix imports

* Update grpc files

* Update more instructions

* Downgrade grpc-tools

* Update manifest

* Update more paths

* Update config paths

* Update CI paths

* Update bandit exclusions

* Remove walkthrough section
This commit is contained in:
Simon Alibert
2025-07-01 16:34:46 +02:00
committed by GitHub
parent 483be9aac2
commit d4ee470b00
268 changed files with 862 additions and 890 deletions
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14
docs/source/cameras.mdx
View File

@@ -8,7 +8,7 @@ To instantiate a camera, you need a camera identifier. This identifier might cha
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
python -m lerobot.find_cameras opencv # or realsense for Intel Realsense cameras
```
The output will look something like this if you have two cameras connected:
@@ -44,9 +44,9 @@ Below are two examples, demonstrating how to work with the API.
<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
from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
from lerobot.cameras.opencv.camera_opencv import OpenCVCamera
from lerobot.cameras.configs import ColorMode, Cv2Rotation
# Construct an `OpenCVCameraConfig` with your desired FPS, resolution, color mode, and rotation.
config = OpenCVCameraConfig(
@@ -75,9 +75,9 @@ finally:
<hfoption id="Intel Realsense Camera">
```python
from lerobot.common.cameras.realsense.configuration_realsense import RealSenseCameraConfig
from lerobot.common.cameras.realsense.camera_realsense import RealSenseCamera
from lerobot.common.cameras.configs import ColorMode, Cv2Rotation
from lerobot.cameras.realsense.configuration_realsense import RealSenseCameraConfig
from lerobot.cameras.realsense.camera_realsense import RealSenseCamera
from lerobot.cameras.configs import ColorMode, Cv2Rotation
# Create a `RealSenseCameraConfig` specifying your camera’s serial number and enabling depth.
config = RealSenseCameraConfig(

30
docs/source/hilserl.mdx
View File

@@ -50,12 +50,12 @@ pip install -e ".[hilserl]"
### Understanding Configuration
The training process begins with proper configuration for the HILSerl environment. The configuration class of interest is `HILSerlRobotEnvConfig` in `lerobot/common/envs/configs.py`. Which is defined as:
The training process begins with proper configuration for the HILSerl environment. The configuration class of interest is `HILSerlRobotEnvConfig` in `lerobot/envs/configs.py`. Which is defined as:
```python
class HILSerlRobotEnvConfig(EnvConfig):
robot: RobotConfig | None = None # Main robot agent (defined in `lerobot/common/robots`)
teleop: TeleoperatorConfig | None = None # Teleoperator agent, e.g., gamepad or leader arm, (defined in `lerobot/common/teleoperators`)
robot: RobotConfig | None = None # Main robot agent (defined in `lerobot/robots`)
teleop: TeleoperatorConfig | None = None # Teleoperator agent, e.g., gamepad or leader arm, (defined in `lerobot/teleoperators`)
wrapper: EnvTransformConfig | None = None # Environment wrapper settings; check `lerobot/scripts/server/gym_manipulator.py`
fps: int = 10 # Control frequency
name: str = "real_robot" # Environment name
@@ -172,7 +172,7 @@ class SO100FollowerEndEffectorConfig(SO100FollowerConfig):
)
```
The `Teleoperator` defines the teleoperation device. You can check the list of available teleoperators in `lerobot/common/teleoperators`.
The `Teleoperator` defines the teleoperation device. You can check the list of available teleoperators in `lerobot/teleoperators`.
**Setting up the Gamepad**
@@ -226,7 +226,7 @@ During the online training, press `space` to take over the policy and `space` ag
Start the recording process, an example of the config file can be found [here](https://huggingface.co/datasets/aractingi/lerobot-example-config-files/blob/main/env_config_so100.json):
```bash
python lerobot/scripts/rl/gym_manipulator.py --config_path lerobot/configs/env_config_so100.json
python -m lerobot.scripts.rl.gym_manipulator --config_path src/lerobot/configs/env_config_so100.json
```
During recording:
@@ -256,7 +256,7 @@ Note: If you already know the crop parameters, you can skip this step and just s
Use the `crop_dataset_roi.py` script to interactively select regions of interest in your camera images:
```bash
python lerobot/scripts/rl/crop_dataset_roi.py --repo-id username/pick_lift_cube
python -m lerobot.scripts.rl.crop_dataset_roi --repo-id username/pick_lift_cube
```
1. For each camera view, the script will display the first frame
@@ -313,7 +313,7 @@ Before training, you need to collect a dataset with labeled examples. The `recor
To collect a dataset, you need to modify some parameters in the environment configuration based on HILSerlRobotEnvConfig.
```bash
python lerobot/scripts/rl/gym_manipulator.py --config_path lerobot/configs/reward_classifier_train_config.json
python -m lerobot.scripts.rl.gym_manipulator --config_path src/lerobot/configs/reward_classifier_train_config.json
```
**Key Parameters for Data Collection**
@@ -387,7 +387,7 @@ Example configuration for training the [reward classifier](https://huggingface.c
To train the classifier, use the `train.py` script with your configuration:
```bash
python lerobot/scripts/train.py --config_path path/to/reward_classifier_train_config.json
python -m lerobot.scripts.train --config_path path/to/reward_classifier_train_config.json
```
**Deploying and Testing the Model**
@@ -410,7 +410,7 @@ or set the argument in the json config file.
Run `gym_manipulator.py` to test the model.
```bash
python lerobot/scripts/rl/gym_manipulator.py --config_path path/to/env_config.json
python -m lerobot.scripts.rl.gym_manipulator --config_path path/to/env_config.json
```
The reward classifier will automatically provide rewards based on the visual input from the robot's cameras.
@@ -422,17 +422,17 @@ The reward classifier will automatically provide rewards based on the visual inp
2. **Collect a dataset**:
```bash
python lerobot/scripts/rl/gym_manipulator.py --config_path lerobot/configs/env_config.json
python -m lerobot.scripts.rl.gym_manipulator --config_path src/lerobot/configs/env_config.json
```
3. **Train the classifier**:
```bash
python lerobot/scripts/train.py --config_path lerobot/configs/reward_classifier_train_config.json
python -m lerobot.scripts.train --config_path src/lerobot/configs/reward_classifier_train_config.json
```
4. **Test the classifier**:
```bash
python lerobot/scripts/rl/gym_manipulator.py --config_path lerobot/configs/env_config.json
python -m lerobot.scripts.rl.gym_manipulator --config_path src/lerobot/configs/env_config.json
```
### Training with Actor-Learner
@@ -446,7 +446,7 @@ Create a training configuration file (example available [here](https://huggingfa
1. Configure the policy settings (`type="sac"`, `device`, etc.)
2. Set `dataset` to your cropped dataset
3. Configure environment settings with crop parameters
4. Check the other parameters related to SAC in [configuration_sac.py](https://github.com/huggingface/lerobot/blob/19bb621a7d0a31c20cd3cc08b1dbab68d3031454/lerobot/common/policies/sac/configuration_sac.py#L79).
4. Check the other parameters related to SAC in [configuration_sac.py](https://github.com/huggingface/lerobot/blob/19bb621a7d0a31c20cd3cc08b1dbab68d3031454/lerobot/policies/sac/configuration_sac.py#L79).
5. Verify that the `policy` config is correct with the right `input_features` and `output_features` for your task.
**Starting the Learner**
@@ -454,7 +454,7 @@ Create a training configuration file (example available [here](https://huggingfa
First, start the learner server process:
```bash
python lerobot/scripts/rl/learner.py --config_path lerobot/configs/train_config_hilserl_so100.json
python -m lerobot.scripts.rl.learner --config_path src/lerobot/configs/train_config_hilserl_so100.json
```
The learner:
@@ -468,7 +468,7 @@ The learner:
In a separate terminal, start the actor process with the same configuration:
```bash
python lerobot/scripts/rl/actor.py --config_path lerobot/configs/train_config_hilserl_so100.json
python -m lerobot.scripts.rl.actor --config_path src/lerobot/configs/train_config_hilserl_so100.json
```
The actor:

8
docs/source/hilserl_sim.mdx
View File

@@ -77,7 +77,7 @@ Important parameters:
To run the environment, set mode to null:
```python
python lerobot/scripts/rl/gym_manipulator.py --config_path path/to/gym_hil_env.json
python -m lerobot.scripts.rl.gym_manipulator --config_path path/to/gym_hil_env.json
```
### Recording a Dataset
@@ -85,7 +85,7 @@ python lerobot/scripts/rl/gym_manipulator.py --config_path path/to/gym_hil_env.j
To collect a dataset, set the mode to `record` whilst defining the repo_id and number of episodes to record:
```python
python lerobot/scripts/rl/gym_manipulator.py --config_path path/to/gym_hil_env.json
python -m lerobot.scripts.rl.gym_manipulator --config_path path/to/gym_hil_env.json
```
### Training a Policy
@@ -93,13 +93,13 @@ python lerobot/scripts/rl/gym_manipulator.py --config_path path/to/gym_hil_env.j
To train a policy, checkout the configuration example available [here](https://huggingface.co/datasets/aractingi/lerobot-example-config-files/blob/main/train_gym_hil_env.json) and run the actor and learner servers:
```python
python lerobot/scripts/rl/actor.py --config_path path/to/train_gym_hil_env.json
python -m lerobot.scripts.rl.actor --config_path path/to/train_gym_hil_env.json
```
In a different terminal, run the learner server:
```python
python lerobot/scripts/rl/learner.py --config_path path/to/train_gym_hil_env.json
python -m lerobot.scripts.rl.learner --config_path path/to/train_gym_hil_env.json
```
The simulation environment provides a safe and repeatable way to develop and test your Human-In-the-Loop reinforcement learning components before deploying to real robots.

64
docs/source/il_robots.mdx
View File

@@ -52,8 +52,8 @@ python -m lerobot.teleoperate \
</hfoption>
<hfoption id="API example">
```python
from lerobot.common.teleoperators.so101_leader import SO101LeaderConfig, SO101Leader
from lerobot.common.robots.so101_follower import SO101FollowerConfig, SO101Follower
from lerobot.teleoperators.so101_leader import SO101LeaderConfig, SO101Leader
from lerobot.robots.so101_follower import SO101FollowerConfig, SO101Follower
robot_config = SO101FollowerConfig(
port="/dev/tty.usbmodem58760431541",
@@ -105,9 +105,9 @@ python -m lerobot.teleoperate \
</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
from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
from lerobot.teleoperators.koch_leader import KochLeaderConfig, KochLeader
from lerobot.robots.koch_follower import KochFollowerConfig, KochFollower
camera_config = {
"front": OpenCVCameraConfig(index_or_path=0, width=1920, height=1080, fps=30)
@@ -175,15 +175,15 @@ python -m lerobot.record \
</hfoption>
<hfoption id="API example">
```python
from lerobot.common.cameras.opencv.configuration_opencv import OpenCVCameraConfig
from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
from lerobot.common.datasets.utils import hw_to_dataset_features
from lerobot.common.robots.so100_follower import SO100Follower, SO100FollowerConfig
from lerobot.common.teleoperators.so100_leader.config_so100_leader import SO100LeaderConfig
from lerobot.common.teleoperators.so100_leader.so100_leader import SO100Leader
from lerobot.common.utils.control_utils import init_keyboard_listener
from lerobot.common.utils.utils import log_say
from lerobot.common.utils.visualization_utils import _init_rerun
from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
from lerobot.datasets.lerobot_dataset import LeRobotDataset
from lerobot.datasets.utils import hw_to_dataset_features
from lerobot.robots.so100_follower import SO100Follower, SO100FollowerConfig
from lerobot.teleoperators.so100_leader.config_so100_leader import SO100LeaderConfig
from lerobot.teleoperators.so100_leader.so100_leader import SO100Leader
from lerobot.utils.control_utils import init_keyboard_listener
from lerobot.utils.utils import log_say
from lerobot.utils.visualization_utils import _init_rerun
from lerobot.record import record_loop
NUM_EPISODES = 5
@@ -353,11 +353,11 @@ python -m lerobot.replay \
```python
import time
from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
from lerobot.common.robots.so100_follower.config_so100_follower import SO100FollowerConfig
from lerobot.common.robots.so100_follower.so100_follower import SO100Follower
from lerobot.common.utils.robot_utils import busy_wait
from lerobot.common.utils.utils import log_say
from lerobot.datasets.lerobot_dataset import LeRobotDataset
from lerobot.robots.so100_follower.config_so100_follower import SO100FollowerConfig
from lerobot.robots.so100_follower.so100_follower import SO100Follower
from lerobot.utils.robot_utils import busy_wait
from lerobot.utils.utils import log_say
episode_idx = 0
@@ -389,9 +389,9 @@ Your robot should replicate movements similar to those you recorded. For example
## 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:
To train a policy to control your robot, use the [`python -m lerobot.scripts.train`](../src/lerobot/scripts/train.py) script. A few arguments are required. Here is an example command:
```bash
python lerobot/scripts/train.py \
python -m lerobot.scripts.train \
--dataset.repo_id=${HF_USER}/so101_test \
--policy.type=act \
--output_dir=outputs/train/act_so101_test \
@@ -403,7 +403,7 @@ python lerobot/scripts/train.py \
Let's explain the command:
1. We provided the dataset as argument with `--dataset.repo_id=${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.
2. We provided the policy with `policy.type=act`. This loads configurations from [`configuration_act.py`](../src/lerobot/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`.
@@ -411,7 +411,7 @@ Training should take several hours. You will find checkpoints in `outputs/train/
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 \
python -m lerobot.scripts.train \
--config_path=outputs/train/act_so101_test/checkpoints/last/pretrained_model/train_config.json \
--resume=true
```
@@ -462,15 +462,15 @@ python -m lerobot.record \
</hfoption>
<hfoption id="API example">
```python
from lerobot.common.cameras.opencv.configuration_opencv import OpenCVCameraConfig
from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
from lerobot.common.datasets.utils import hw_to_dataset_features
from lerobot.common.policies.act.modeling_act import ACTPolicy
from lerobot.common.robots.so100_follower.config_so100_follower import SO100FollowerConfig
from lerobot.common.robots.so100_follower.so100_follower import SO100Follower
from lerobot.common.utils.control_utils import init_keyboard_listener
from lerobot.common.utils.utils import log_say
from lerobot.common.utils.visualization_utils import _init_rerun
from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
from lerobot.datasets.lerobot_dataset import LeRobotDataset
from lerobot.datasets.utils import hw_to_dataset_features
from lerobot.policies.act.modeling_act import ACTPolicy
from lerobot.robots.so100_follower.config_so100_follower import SO100FollowerConfig
from lerobot.robots.so100_follower.so100_follower import SO100Follower
from lerobot.utils.control_utils import init_keyboard_listener
from lerobot.utils.utils import log_say
from lerobot.utils.visualization_utils import _init_rerun
from lerobot.record import record_loop
NUM_EPISODES = 5

14
docs/source/il_sim.mdx
View File

@@ -35,14 +35,14 @@ Then we can run this command to start:
<hfoption id="Linux">
```bash
python lerobot/scripts/rl/gym_manipulator.py --config_path path/to/env_config_gym_hil_il.json
python -m lerobot.scripts.rl.gym_manipulator --config_path path/to/env_config_gym_hil_il.json
```
</hfoption>
<hfoption id="MacOS">
```bash
mjpython lerobot/scripts/rl/gym_manipulator.py --config_path path/to/env_config_gym_hil_il.json
mjpython -m lerobot.scripts.rl.gym_manipulator --config_path path/to/env_config_gym_hil_il.json
```
</hfoption>
@@ -81,9 +81,9 @@ If you uploaded your dataset to the hub you can [visualize your dataset online](
## 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:
To train a policy to control your robot, use the [`python -m lerobot.scripts.train`](../src/lerobot/scripts/train.py) script. A few arguments are required. Here is an example command:
```bash
python lerobot/scripts/train.py \
python -m lerobot.scripts.train \
--dataset.repo_id=${HF_USER}/il_gym \
--policy.type=act \
--output_dir=outputs/train/il_sim_test \
@@ -94,7 +94,7 @@ python lerobot/scripts/train.py \
Let's explain the command:
1. We provided the dataset as argument with `--dataset.repo_id=${HF_USER}/il_gym`.
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.
2. We provided the policy with `policy.type=act`. This loads configurations from [`configuration_act.py`](../src/lerobot/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`.
@@ -130,14 +130,14 @@ Then you can run this command to visualize your trained policy
<hfoption id="Linux">
```bash
python lerobot/scripts/rl/eval_policy.py --config_path=path/to/eval_config_gym_hil.json
python -m lerobot.scripts.rl.eval_policy --config_path=path/to/eval_config_gym_hil.json
```
</hfoption>
<hfoption id="MacOS">
```bash
mjpython lerobot/scripts/rl/eval_policy.py --config_path=path/to/eval_config_gym_hil.json
mjpython -m lerobot.scripts.rl.eval_policy --config_path=path/to/eval_config_gym_hil.json
```
</hfoption>

28
docs/source/integrate_hardware.mdx
View File

@@ -2,7 +2,7 @@
This tutorial will explain how to integrate your own robot design into the LeRobot ecosystem and have it access all of our tools (data collection, control pipelines, policy training and inference).
To that end, we provide the [`Robot`](https://github.com/huggingface/lerobot/blob/main/lerobot/common/robots/robot.py) base class in the LeRobot which specifies a standard interface for physical robot integration. Let's see how to implement it.
To that end, we provide the [`Robot`](https://github.com/huggingface/lerobot/blob/main/lerobot/robots/robot.py) base class in the LeRobot which specifies a standard interface for physical robot integration. Let's see how to implement it.
## Prerequisites
@@ -14,11 +14,11 @@ To that end, we provide the [`Robot`](https://github.com/huggingface/lerobot/blo
If you're using Feetech or Dynamixel motors, LeRobot provides built-in bus interfaces:
- [`FeetechMotorsBus`](https://github.com/huggingface/lerobot/blob/main/lerobot/common/motors/feetech/feetech.py) – for controlling Feetech servos
- [`DynamixelMotorsBus`](https://github.com/huggingface/lerobot/blob/main/lerobot/common/motors/dynamixel/dynamixel.py) – for controlling Dynamixel servos
- [`FeetechMotorsBus`](https://github.com/huggingface/lerobot/blob/main/lerobot/motors/feetech/feetech.py) – for controlling Feetech servos
- [`DynamixelMotorsBus`](https://github.com/huggingface/lerobot/blob/main/lerobot/motors/dynamixel/dynamixel.py) – for controlling Dynamixel servos
Please refer to the [`MotorsBus`](https://github.com/huggingface/lerobot/blob/main/lerobot/common/motors/motors_bus.py) abstract class to learn about its API.
For a good example of how it can be used, you can have a look at our own [SO101 follower implementation](https://github.com/huggingface/lerobot/blob/main/lerobot/common/robots/so101_follower/so101_follower.py)
Please refer to the [`MotorsBus`](https://github.com/huggingface/lerobot/blob/main/lerobot/motors/motors_bus.py) abstract class to learn about its API.
For a good example of how it can be used, you can have a look at our own [SO101 follower implementation](https://github.com/huggingface/lerobot/blob/main/lerobot/robots/so101_follower/so101_follower.py)
Use these if compatible. Otherwise, you'll need to find or write a Python interface (not covered in this tutorial):
- Find an existing SDK in Python (or use bindings to C/C++)
@@ -32,7 +32,7 @@ For Feetech and Dynamixel, we currently support these servos:
- SCS series (protocol 1): `scs0009`
- Dynamixel (protocol 2.0 only): `xl330-m077`, `xl330-m288`, `xl430-w250`, `xm430-w350`, `xm540-w270`, `xc430-w150`
If you are using Feetech or Dynamixel servos that are not in this list, you can add those in the [Feetech table](https://github.com/huggingface/lerobot/blob/main/lerobot/common/motors/feetech/tables.py) or [Dynamixel table](https://github.com/huggingface/lerobot/blob/main/lerobot/common/motors/dynamixel/tables.py). Depending on the model, this will require you to add model-specific information. In most cases though, there shouldn't be a lot of additions to do.
If you are using Feetech or Dynamixel servos that are not in this list, you can add those in the [Feetech table](https://github.com/huggingface/lerobot/blob/main/lerobot/motors/feetech/tables.py) or [Dynamixel table](https://github.com/huggingface/lerobot/blob/main/lerobot/motors/dynamixel/tables.py). Depending on the model, this will require you to add model-specific information. In most cases though, there shouldn't be a lot of additions to do.
In the next sections, we'll use a `FeetechMotorsBus` as the motors interface for the examples. Replace it and adapt to your motors if necessary.
@@ -44,9 +44,9 @@ Here, we'll add the port name and one camera by default for our robot:
```python
from dataclasses import dataclass, field
from lerobot.common.cameras import CameraConfig
from lerobot.common.cameras.opencv import OpenCVCameraConfig
from lerobot.common.robots import RobotConfig
from lerobot.cameras import CameraConfig
from lerobot.cameras.opencv import OpenCVCameraConfig
from lerobot.robots import RobotConfig
@RobotConfig.register_subclass("my_cool_robot")
@@ -72,10 +72,10 @@ Next, we'll create our actual robot class which inherits from `Robot`. This abst
Here we'll create a simple 5-DoF robot with one camera. It could be a simple arm but notice that the `Robot` abstract class does not assume anything on your robot's form factor. You can let you imagination run wild when designing new robots!
```python
from lerobot.common.cameras import make_cameras_from_configs
from lerobot.common.motors import Motor, MotorNormMode
from lerobot.common.motors.feetech import FeetechMotorsBus
from lerobot.common.robots import Robot
from lerobot.cameras import make_cameras_from_configs
from lerobot.motors import Motor, MotorNormMode
from lerobot.motors.feetech import FeetechMotorsBus
from lerobot.robots import Robot
class MyCoolRobot(Robot):
config_class = MyCoolRobotConfig
@@ -303,7 +303,7 @@ def send_action(self, action: dict[str, Any]) -> dict[str, Any]:
## Adding a Teleoperator
For implementing teleoperation devices, we also provide a [`Teleoperator`](https://github.com/huggingface/lerobot/blob/main/lerobot/common/teleoperators/teleoperator.py) base class. This class is very similar to the `Robot` base class and also doesn't assume anything on form factor.
For implementing teleoperation devices, we also provide a [`Teleoperator`](https://github.com/huggingface/lerobot/blob/main/lerobot/teleoperators/teleoperator.py) base class. This class is very similar to the `Robot` base class and also doesn't assume anything on form factor.
The main differences are in the I/O functions: a teleoperator allows you to produce action via `get_action` and can receive feedback actions via `send_feedback`. Feedback could be anything controllable on the teleoperation device that could help the person controlling it understand the consequences of the actions sent. Think motion/force feedback on a leader arm, vibrations on a gamepad controller for example. To implement a teleoperator, you can follow this same tutorial and adapt it for these two methods.

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

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@@ -1 +1 @@
../../lerobot/common/robots/lekiwi/lekiwi.mdx
../../src/lerobot/robots/lekiwi/lekiwi.mdx

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docs/source/smolvla.mdx
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@@ -44,7 +44,7 @@ If you don't have a gpu device, you can train using our notebook on [![Google Co
Pass your dataset to the training script using `--dataset.repo_id`. If you want to test your installation, run the following command where we use one of the datasets we collected for the [SmolVLA Paper](https://huggingface.co/papers/2506.01844).
```bash
cd lerobot && python lerobot/scripts/train.py \
cd lerobot && python -m lerobot.scripts.train \
--policy.path=lerobot/smolvla_base \
--dataset.repo_id=${HF_USER}/mydataset \
--batch_size=64 \
@@ -62,7 +62,7 @@ You can start with a small batch size and increase it incrementally, if the GPU
Fine-tuning is an art. For a complete overview of the options for finetuning, run
```bash
python lerobot/scripts/train.py --help
python -m lerobot.scripts.train --help
```
<p align="center">

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../../lerobot/common/robots/so100_follower/so100.mdx
../../src/lerobot/robots/so100_follower/so100.mdx

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../../lerobot/common/robots/so101_follower/so101.mdx
../../src/lerobot/robots/so101_follower/so101.mdx