Package folder structure (#1417)

* 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

.github/PULL_REQUEST_TEMPLATE.md

@@ -24,7 +24,7 @@ Examples:
 pytest -sx tests/test_stuff.py::test_something
 ```
 ```bash
-python lerobot/scripts/train.py --some.option=true
+python -m lerobot.scripts.train --some.option=true
 ```
 
 ## SECTION TO REMOVE BEFORE SUBMITTING YOUR PR

.github/workflows/nightly-tests.yml

@@ -44,7 +44,7 @@ jobs:
         working-directory: /lerobot
     steps:
       - name: Tests
-        run: pytest -v --cov=./lerobot --disable-warnings tests
+        run: pytest -v --cov=./src/lerobot --disable-warnings tests
 
       - name: Tests end-to-end
         run: make test-end-to-end
@@ -74,7 +74,7 @@ jobs:
         run: nvidia-smi
 
       - name: Test
-        run: pytest -v --cov=./lerobot --cov-report=xml --disable-warnings tests
+        run: pytest -v --cov=./src/lerobot --cov-report=xml --disable-warnings tests
       #   TODO(aliberts): Link with HF Codecov account
       # - name: Upload coverage reports to Codecov with GitHub Action
       #   uses: codecov/codecov-action@v4

.github/workflows/test.yml

@@ -17,7 +17,7 @@ name: Tests
 on:
   pull_request:
     paths:
-      - "lerobot/**"
+      - "src/**"
       - "tests/**"
       - "examples/**"
       - ".github/**"
@@ -29,7 +29,7 @@ on:
     branches:
       - main
     paths:
-      - "lerobot/**"
+      - "src/**"
       - "tests/**"
       - "examples/**"
       - ".github/**"
@@ -73,7 +73,7 @@ jobs:
 
       - name: Test with pytest
         run: |
-          uv run pytest tests -v --cov=./lerobot --durations=0 \
+          uv run pytest tests -v --cov=./src/lerobot --durations=0 \
             -W ignore::DeprecationWarning:imageio_ffmpeg._utils:7 \
             -W ignore::UserWarning:torch.utils.data.dataloader:558 \
             -W ignore::UserWarning:gymnasium.utils.env_checker:247 \
@@ -105,7 +105,7 @@ jobs:
 
       - name: Test with pytest
         run: |
-          uv run pytest tests -v --cov=./lerobot --durations=0 \
+          uv run pytest tests -v --cov=./src/lerobot --durations=0 \
             -W ignore::DeprecationWarning:imageio_ffmpeg._utils:7 \
             -W ignore::UserWarning:torch.utils.data.dataloader:558 \
             -W ignore::UserWarning:gymnasium.utils.env_checker:247 \

CONTRIBUTING.md

@@ -67,7 +67,7 @@ post it.
 
 ## Adding new policies, datasets or environments
 
-Look at our implementations for [datasets](./lerobot/common/datasets/), [policies](./lerobot/common/policies/),
+Look at our implementations for [datasets](./src/lerobot/datasets/), [policies](./src/lerobot/policies/),
 environments ([aloha](https://github.com/huggingface/gym-aloha),
 [xarm](https://github.com/huggingface/gym-xarm),
 [pusht](https://github.com/huggingface/gym-pusht))

MANIFEST.in

@@ -1,2 +1,2 @@
-include lerobot/templates/lerobot_modelcard_template.md
+include src/lerobot/templates/lerobot_modelcard_template.md
-include lerobot/common/datasets/card_template.md
+include src/lerobot/datasets/card_template.md

Makefile

@@ -44,7 +44,7 @@ test-end-to-end:
 	${MAKE} DEVICE=$(DEVICE) test-smolvla-ete-eval
 
 test-act-ete-train:
-	python lerobot/scripts/train.py \
+	python -m lerobot.scripts.train \
 		--policy.type=act \
 		--policy.dim_model=64 \
 		--policy.n_action_steps=20 \
@@ -68,12 +68,12 @@ test-act-ete-train:
 		--output_dir=tests/outputs/act/
 
 test-act-ete-train-resume:
-	python lerobot/scripts/train.py \
+	python -m lerobot.scripts.train \
 		--config_path=tests/outputs/act/checkpoints/000002/pretrained_model/train_config.json \
 		--resume=true
 
 test-act-ete-eval:
-	python lerobot/scripts/eval.py \
+	python -m lerobot.scripts.eval \
 		--policy.path=tests/outputs/act/checkpoints/000004/pretrained_model \
 		--policy.device=$(DEVICE) \
 		--env.type=aloha \
@@ -82,7 +82,7 @@ test-act-ete-eval:
 		--eval.batch_size=1
 
 test-diffusion-ete-train:
-	python lerobot/scripts/train.py \
+	python -m lerobot.scripts.train \
 		--policy.type=diffusion \
 		--policy.down_dims='[64,128,256]' \
 		--policy.diffusion_step_embed_dim=32 \
@@ -106,7 +106,7 @@ test-diffusion-ete-train:
 		--output_dir=tests/outputs/diffusion/
 
 test-diffusion-ete-eval:
-	python lerobot/scripts/eval.py \
+	python -m lerobot.scripts.eval \
 		--policy.path=tests/outputs/diffusion/checkpoints/000002/pretrained_model \
 		--policy.device=$(DEVICE) \
 		--env.type=pusht \
@@ -115,7 +115,7 @@ test-diffusion-ete-eval:
 		--eval.batch_size=1
 
 test-tdmpc-ete-train:
-	python lerobot/scripts/train.py \
+	python -m lerobot.scripts.train \
 		--policy.type=tdmpc \
 		--policy.device=$(DEVICE) \
 		--policy.push_to_hub=false \
@@ -137,7 +137,7 @@ test-tdmpc-ete-train:
 		--output_dir=tests/outputs/tdmpc/
 
 test-tdmpc-ete-eval:
-	python lerobot/scripts/eval.py \
+	python -m lerobot.scripts.eval \
 		--policy.path=tests/outputs/tdmpc/checkpoints/000002/pretrained_model \
 		--policy.device=$(DEVICE) \
 		--env.type=xarm \
@@ -148,7 +148,7 @@ test-tdmpc-ete-eval:
 
 
 test-smolvla-ete-train:
-	python lerobot/scripts/train.py \
+	python -m lerobot.scripts.train \
 		--policy.type=smolvla \
 		--policy.n_action_steps=20 \
 		--policy.chunk_size=20 \
@@ -171,7 +171,7 @@ test-smolvla-ete-train:
 		--output_dir=tests/outputs/smolvla/
 
 test-smolvla-ete-eval:
-	python lerobot/scripts/eval.py \
+	python -m lerobot.scripts.eval \
 		--policy.path=tests/outputs/smolvla/checkpoints/000004/pretrained_model \
 		--policy.device=$(DEVICE) \
 		--env.type=aloha \

README.md

@@ -149,44 +149,20 @@ wandb login
 
 (note: you will also need to enable WandB in the configuration. See below.)
 
-## Walkthrough
-
-```
-.
-├── examples             # contains demonstration examples, start here to learn about LeRobot
-|   └── advanced         # contains even more examples for those who have mastered the basics
-├── lerobot
-|   ├── configs          # contains config classes with all options that you can override in the command line
-|   ├── common           # contains classes and utilities
-|   |   ├── datasets       # various datasets of human demonstrations: aloha, pusht, xarm
-|   |   ├── envs           # various sim environments: aloha, pusht, xarm
-|   |   ├── policies       # various policies: act, diffusion, tdmpc
-|   |   ├── robot_devices  # various real devices: dynamixel motors, opencv cameras, koch robots
-|   |   └── utils          # various utilities
-|   └── scripts          # contains functions to execute via command line
-|       ├── eval.py                 # load policy and evaluate it on an environment
-|       ├── train.py                # train a policy via imitation learning and/or reinforcement learning
-|       ├── control_robot.py        # teleoperate a real robot, record data, run a policy
-|       ├── push_dataset_to_hub.py  # convert your dataset into LeRobot dataset format and upload it to the Hugging Face hub
-|       └── visualize_dataset.py    # load a dataset and render its demonstrations
-├── outputs               # contains results of scripts execution: logs, videos, model checkpoints
-└── tests                 # contains pytest utilities for continuous integration
-```
-
 ### Visualize datasets
 
 Check out [example 1](./examples/1_load_lerobot_dataset.py) that illustrates how to use our dataset class which automatically downloads data from the Hugging Face hub.
 
 You can also locally visualize episodes from a dataset on the hub by executing our script from the command line:
 ```bash
-python lerobot/scripts/visualize_dataset.py \
+python -m lerobot.scripts.visualize_dataset \
     --repo-id lerobot/pusht \
     --episode-index 0
 ```
 
 or from a dataset in a local folder with the `root` option and the `--local-files-only` (in the following case the dataset will be searched for in `./my_local_data_dir/lerobot/pusht`)
 ```bash
-python lerobot/scripts/visualize_dataset.py \
+python -m lerobot.scripts.visualize_dataset \
     --repo-id lerobot/pusht \
     --root ./my_local_data_dir \
     --local-files-only 1 \
@@ -199,7 +175,7 @@ It will open `rerun.io` and display the camera streams, robot states and actions
 https://github-production-user-asset-6210df.s3.amazonaws.com/4681518/328035972-fd46b787-b532-47e2-bb6f-fd536a55a7ed.mov?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAVCODYLSA53PQK4ZA%2F20240505%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20240505T172924Z&X-Amz-Expires=300&X-Amz-Signature=d680b26c532eeaf80740f08af3320d22ad0b8a4e4da1bcc4f33142c15b509eda&X-Amz-SignedHeaders=host&actor_id=24889239&key_id=0&repo_id=748713144
 
 
-Our script can also visualize datasets stored on a distant server. See `python lerobot/scripts/visualize_dataset.py --help` for more instructions.
+Our script can also visualize datasets stored on a distant server. See `python -m lerobot.scripts.visualize_dataset --help` for more instructions.
 
 ### The `LeRobotDataset` format
 
@@ -252,7 +228,7 @@ Check out [example 2](./examples/2_evaluate_pretrained_policy.py) that illustrat
 
 We also provide a more capable script to parallelize the evaluation over multiple environments during the same rollout. Here is an example with a pretrained model hosted on [lerobot/diffusion_pusht](https://huggingface.co/lerobot/diffusion_pusht):
 ```bash
-python lerobot/scripts/eval.py \
+python -m lerobot.scripts.eval \
     --policy.path=lerobot/diffusion_pusht \
     --env.type=pusht \
     --eval.batch_size=10 \
@@ -264,10 +240,10 @@ python lerobot/scripts/eval.py \
 Note: After training your own policy, you can re-evaluate the checkpoints with:
 
 ```bash
-python lerobot/scripts/eval.py --policy.path={OUTPUT_DIR}/checkpoints/last/pretrained_model
+python -m lerobot.scripts.eval --policy.path={OUTPUT_DIR}/checkpoints/last/pretrained_model
 ```
 
-See `python lerobot/scripts/eval.py --help` for more instructions.
+See `python -m lerobot.scripts.eval --help` for more instructions.
 
 ### Train your own policy
 
@@ -279,14 +255,14 @@ A link to the wandb logs for the run will also show up in yellow in your termina
 
 ![](media/wandb.png)
 
-Note: For efficiency, during training every checkpoint is evaluated on a low number of episodes. You may use `--eval.n_episodes=500` to evaluate on more episodes than the default. Or, after training, you may want to re-evaluate your best checkpoints on more episodes or change the evaluation settings. See `python lerobot/scripts/eval.py --help` for more instructions.
+Note: For efficiency, during training every checkpoint is evaluated on a low number of episodes. You may use `--eval.n_episodes=500` to evaluate on more episodes than the default. Or, after training, you may want to re-evaluate your best checkpoints on more episodes or change the evaluation settings. See `python -m lerobot.scripts.eval --help` for more instructions.
 
 #### Reproduce state-of-the-art (SOTA)
 
 We provide some pretrained policies on our [hub page](https://huggingface.co/lerobot) that can achieve state-of-the-art performances.
 You can reproduce their training by loading the config from their run. Simply running:
 ```bash
-python lerobot/scripts/train.py --config_path=lerobot/diffusion_pusht
+python -m lerobot.scripts.train --config_path=lerobot/diffusion_pusht
 ```
 reproduces SOTA results for Diffusion Policy on the PushT task.
 
@@ -312,7 +288,7 @@ python lerobot/scripts/push_dataset_to_hub.py \
 
 See `python lerobot/scripts/push_dataset_to_hub.py --help` for more instructions.
 
-If your dataset format is not supported, implement your own in `lerobot/common/datasets/push_dataset_to_hub/${raw_format}_format.py` by copying examples like [pusht_zarr](https://github.com/huggingface/lerobot/blob/main/lerobot/common/datasets/push_dataset_to_hub/pusht_zarr_format.py), [umi_zarr](https://github.com/huggingface/lerobot/blob/main/lerobot/common/datasets/push_dataset_to_hub/umi_zarr_format.py), [aloha_hdf5](https://github.com/huggingface/lerobot/blob/main/lerobot/common/datasets/push_dataset_to_hub/aloha_hdf5_format.py), or [xarm_pkl](https://github.com/huggingface/lerobot/blob/main/lerobot/common/datasets/push_dataset_to_hub/xarm_pkl_format.py). -->
+If your dataset format is not supported, implement your own in `lerobot/datasets/push_dataset_to_hub/${raw_format}_format.py` by copying examples like [pusht_zarr](https://github.com/huggingface/lerobot/blob/main/lerobot/datasets/push_dataset_to_hub/pusht_zarr_format.py), [umi_zarr](https://github.com/huggingface/lerobot/blob/main/lerobot/datasets/push_dataset_to_hub/umi_zarr_format.py), [aloha_hdf5](https://github.com/huggingface/lerobot/blob/main/lerobot/datasets/push_dataset_to_hub/aloha_hdf5_format.py), or [xarm_pkl](https://github.com/huggingface/lerobot/blob/main/lerobot/datasets/push_dataset_to_hub/xarm_pkl_format.py). -->
 
 
 ### Add a pretrained policy

benchmarks/video/run_video_benchmark.py

@@ -35,12 +35,12 @@ import torch
 from skimage.metrics import mean_squared_error, peak_signal_noise_ratio, structural_similarity
 from tqdm import tqdm
 
-from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
-from lerobot.common.datasets.video_utils import (
+from lerobot.datasets.video_utils import (
     decode_video_frames_torchvision,
     encode_video_frames,
 )
-from lerobot.common.utils.benchmark import TimeBenchmark
+from lerobot.utils.benchmark import TimeBenchmark
 
 BASE_ENCODING = OrderedDict(
     [

docs/source/cameras.mdx

@@ -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.cameras.opencv.configuration_opencv import OpenCVCameraConfig
-from lerobot.common.cameras.opencv.camera_opencv import OpenCVCamera
+from lerobot.cameras.opencv.camera_opencv import OpenCVCamera
-from lerobot.common.cameras.configs import ColorMode, Cv2Rotation
+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.cameras.realsense.configuration_realsense import RealSenseCameraConfig
-from lerobot.common.cameras.realsense.camera_realsense import RealSenseCamera
+from lerobot.cameras.realsense.camera_realsense import RealSenseCamera
-from lerobot.common.cameras.configs import ColorMode, Cv2Rotation
+from lerobot.cameras.configs import ColorMode, Cv2Rotation
 
 # Create a `RealSenseCameraConfig` specifying your camera’s serial number and enabling depth.
 config = RealSenseCameraConfig(

docs/source/hilserl.mdx

@@ -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`)
+    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/common/teleoperators`)
+    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:

docs/source/hilserl_sim.mdx

@@ -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.

docs/source/il_robots.mdx

@@ -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.teleoperators.so101_leader import SO101LeaderConfig, SO101Leader
-from lerobot.common.robots.so101_follower import SO101FollowerConfig, SO101Follower
+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.cameras.opencv.configuration_opencv import OpenCVCameraConfig
-from lerobot.common.teleoperators.koch_leader import KochLeaderConfig, KochLeader
+from lerobot.teleoperators.koch_leader import KochLeaderConfig, KochLeader
-from lerobot.common.robots.koch_follower import KochFollowerConfig, KochFollower
+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.cameras.opencv.configuration_opencv import OpenCVCameraConfig
-from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
-from lerobot.common.datasets.utils import hw_to_dataset_features
+from lerobot.datasets.utils import hw_to_dataset_features
-from lerobot.common.robots.so100_follower import SO100Follower, SO100FollowerConfig
+from lerobot.robots.so100_follower import SO100Follower, SO100FollowerConfig
-from lerobot.common.teleoperators.so100_leader.config_so100_leader import SO100LeaderConfig
+from lerobot.teleoperators.so100_leader.config_so100_leader import SO100LeaderConfig
-from lerobot.common.teleoperators.so100_leader.so100_leader import SO100Leader
+from lerobot.teleoperators.so100_leader.so100_leader import SO100Leader
-from lerobot.common.utils.control_utils import init_keyboard_listener
+from lerobot.utils.control_utils import init_keyboard_listener
-from lerobot.common.utils.utils import log_say
+from lerobot.utils.utils import log_say
-from lerobot.common.utils.visualization_utils import _init_rerun
+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.datasets.lerobot_dataset import LeRobotDataset
-from lerobot.common.robots.so100_follower.config_so100_follower import SO100FollowerConfig
+from lerobot.robots.so100_follower.config_so100_follower import SO100FollowerConfig
-from lerobot.common.robots.so100_follower.so100_follower import SO100Follower
+from lerobot.robots.so100_follower.so100_follower import SO100Follower
-from lerobot.common.utils.robot_utils import busy_wait
+from lerobot.utils.robot_utils import busy_wait
-from lerobot.common.utils.utils import log_say
+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.cameras.opencv.configuration_opencv import OpenCVCameraConfig
-from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
-from lerobot.common.datasets.utils import hw_to_dataset_features
+from lerobot.datasets.utils import hw_to_dataset_features
-from lerobot.common.policies.act.modeling_act import ACTPolicy
+from lerobot.policies.act.modeling_act import ACTPolicy
-from lerobot.common.robots.so100_follower.config_so100_follower import SO100FollowerConfig
+from lerobot.robots.so100_follower.config_so100_follower import SO100FollowerConfig
-from lerobot.common.robots.so100_follower.so100_follower import SO100Follower
+from lerobot.robots.so100_follower.so100_follower import SO100Follower
-from lerobot.common.utils.control_utils import init_keyboard_listener
+from lerobot.utils.control_utils import init_keyboard_listener
-from lerobot.common.utils.utils import log_say
+from lerobot.utils.utils import log_say
-from lerobot.common.utils.visualization_utils import _init_rerun
+from lerobot.utils.visualization_utils import _init_rerun
 from lerobot.record import record_loop
 
 NUM_EPISODES = 5

docs/source/il_sim.mdx

@@ -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>

docs/source/integrate_hardware.mdx

@@ -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
+- [`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/common/motors/dynamixel/dynamixel.py) – for controlling Dynamixel 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.
+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/common/robots/so101_follower/so101_follower.py)
+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.cameras import CameraConfig
-from lerobot.common.cameras.opencv import OpenCVCameraConfig
+from lerobot.cameras.opencv import OpenCVCameraConfig
-from lerobot.common.robots import RobotConfig
+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.cameras import make_cameras_from_configs
-from lerobot.common.motors import Motor, MotorNormMode
+from lerobot.motors import Motor, MotorNormMode
-from lerobot.common.motors.feetech import FeetechMotorsBus
+from lerobot.motors.feetech import FeetechMotorsBus
-from lerobot.common.robots import Robot
+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.
 

docs/source/koch.mdx

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

docs/source/lekiwi.mdx

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

docs/source/smolvla.mdx

@@ -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">

docs/source/so100.mdx

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

docs/source/so101.mdx

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

examples/1_load_lerobot_dataset.py

@@ -32,7 +32,7 @@ import torch
 from huggingface_hub import HfApi
 
 import lerobot
-from lerobot.common.datasets.lerobot_dataset import LeRobotDataset, LeRobotDatasetMetadata
+from lerobot.datasets.lerobot_dataset import LeRobotDataset, LeRobotDatasetMetadata
 
 # We ported a number of existing datasets ourselves, use this to see the list:
 print("List of available datasets:")

examples/2_evaluate_pretrained_policy.py

@@ -30,7 +30,7 @@ import imageio
 import numpy
 import torch
 
-from lerobot.common.policies.diffusion.modeling_diffusion import DiffusionPolicy
+from lerobot.policies.diffusion.modeling_diffusion import DiffusionPolicy
 
 # Create a directory to store the video of the evaluation
 output_directory = Path("outputs/eval/example_pusht_diffusion")

examples/3_train_policy.py

@@ -22,11 +22,11 @@ from pathlib import Path
 
 import torch
 
-from lerobot.common.datasets.lerobot_dataset import LeRobotDataset, LeRobotDatasetMetadata
-from lerobot.common.datasets.utils import dataset_to_policy_features
-from lerobot.common.policies.diffusion.configuration_diffusion import DiffusionConfig
-from lerobot.common.policies.diffusion.modeling_diffusion import DiffusionPolicy
 from lerobot.configs.types import FeatureType
+from lerobot.datasets.lerobot_dataset import LeRobotDataset, LeRobotDatasetMetadata
+from lerobot.datasets.utils import dataset_to_policy_features
+from lerobot.policies.diffusion.configuration_diffusion import DiffusionConfig
+from lerobot.policies.diffusion.modeling_diffusion import DiffusionPolicy
 
 
 def main():

examples/4_train_policy_with_script.md

@@ -4,7 +4,7 @@ This tutorial will explain the training script, how to use it, and particularly
 
 ## The training script
 
-LeRobot offers a training script at [`lerobot/scripts/train.py`](../lerobot/scripts/train.py). At a high level it does the following:
+LeRobot offers a training script at [`lerobot/scripts/train.py`](../src/lerobot/scripts/train.py). At a high level it does the following:
 
 - Initialize/load a configuration for the following steps using.
 - Instantiates a dataset.
@@ -21,7 +21,7 @@ In the training script, the main function `train` expects a `TrainPipelineConfig
 def train(cfg: TrainPipelineConfig):
 ```
 
-You can inspect the `TrainPipelineConfig` defined in [`lerobot/configs/train.py`](../lerobot/configs/train.py) (which is heavily commented and meant to be a reference to understand any option)
+You can inspect the `TrainPipelineConfig` defined in [`lerobot/configs/train.py`](../src/lerobot/configs/train.py) (which is heavily commented and meant to be a reference to understand any option)
 
 When running the script, inputs for the command line are parsed thanks to the `@parser.wrap()` decorator and an instance of this class is automatically generated. Under the hood, this is done with [Draccus](https://github.com/dlwh/draccus) which is a tool dedicated to this purpose. If you're familiar with Hydra, Draccus can similarly load configurations from config files (.json, .yaml) and also override their values through command line inputs. Unlike Hydra, these configurations are pre-defined in the code through dataclasses rather than being defined entirely in config files. This allows for more rigorous serialization/deserialization, typing, and to manipulate configuration as objects directly in the code and not as dictionaries or namespaces (which enables nice features in an IDE such as autocomplete, jump-to-def, etc.)
 
@@ -50,9 +50,9 @@ By default, every field takes its default value specified in the dataclass. If a
 
 ## Specifying values from the CLI
 
-Let's say that we want to train [Diffusion Policy](../lerobot/common/policies/diffusion) on the [pusht](https://huggingface.co/datasets/lerobot/pusht) dataset, using the [gym_pusht](https://github.com/huggingface/gym-pusht) environment for evaluation. The command to do so would look like this:
+Let's say that we want to train [Diffusion Policy](../src/lerobot/policies/diffusion) on the [pusht](https://huggingface.co/datasets/lerobot/pusht) dataset, using the [gym_pusht](https://github.com/huggingface/gym-pusht) environment for evaluation. The command to do so would look like this:
 ```bash
-python lerobot/scripts/train.py \
+python -m lerobot.scripts.train \
     --dataset.repo_id=lerobot/pusht \
     --policy.type=diffusion \
     --env.type=pusht
@@ -60,12 +60,12 @@ python lerobot/scripts/train.py \
 
 Let's break this down:
 - To specify the dataset, we just need to specify its `repo_id` on the hub which is the only required argument in the `DatasetConfig`. The rest of the fields have default values and in this case we are fine with those so we can just add the option `--dataset.repo_id=lerobot/pusht`.
-- To specify the policy, we can just select diffusion policy using `--policy` appended with `.type`. Here, `.type` is a special argument which allows us to select config classes inheriting from `draccus.ChoiceRegistry` and that have been decorated with the `register_subclass()` method. To have a better explanation of this feature, have a look at this [Draccus demo](https://github.com/dlwh/draccus?tab=readme-ov-file#more-flexible-configuration-with-choice-types). In our code, we use this mechanism mainly to select policies, environments, robots, and some other components like optimizers. The policies available to select are located in [lerobot/common/policies](../lerobot/common/policies)
+- To specify the policy, we can just select diffusion policy using `--policy` appended with `.type`. Here, `.type` is a special argument which allows us to select config classes inheriting from `draccus.ChoiceRegistry` and that have been decorated with the `register_subclass()` method. To have a better explanation of this feature, have a look at this [Draccus demo](https://github.com/dlwh/draccus?tab=readme-ov-file#more-flexible-configuration-with-choice-types). In our code, we use this mechanism mainly to select policies, environments, robots, and some other components like optimizers. The policies available to select are located in [lerobot/policies](../src/lerobot/policies)
-- Similarly, we select the environment with `--env.type=pusht`. The different environment configs are available in [`lerobot/common/envs/configs.py`](../lerobot/common/envs/configs.py)
+- Similarly, we select the environment with `--env.type=pusht`. The different environment configs are available in [`lerobot/envs/configs.py`](../src/lerobot/envs/configs.py)
 
-Let's see another example. Let's say you've been training [ACT](../lerobot/common/policies/act) on [lerobot/aloha_sim_insertion_human](https://huggingface.co/datasets/lerobot/aloha_sim_insertion_human) using the [gym-aloha](https://github.com/huggingface/gym-aloha) environment for evaluation with:
+Let's see another example. Let's say you've been training [ACT](../src/lerobot/policies/act) on [lerobot/aloha_sim_insertion_human](https://huggingface.co/datasets/lerobot/aloha_sim_insertion_human) using the [gym-aloha](https://github.com/huggingface/gym-aloha) environment for evaluation with:
 ```bash
-python lerobot/scripts/train.py \
+python -m lerobot.scripts.train \
     --policy.type=act \
     --dataset.repo_id=lerobot/aloha_sim_insertion_human \
     --env.type=aloha \
@@ -74,9 +74,9 @@ python lerobot/scripts/train.py \
 > Notice we added `--output_dir` to explicitly tell where to write outputs from this run (checkpoints, training state, configs etc.). This is not mandatory and if you don't specify it, a default directory will be created from the current date and time, env.type and policy.type. This will typically look like `outputs/train/2025-01-24/16-10-05_aloha_act`.
 
 We now want to train a different policy for aloha on another task. We'll change the dataset and use [lerobot/aloha_sim_transfer_cube_human](https://huggingface.co/datasets/lerobot/aloha_sim_transfer_cube_human) instead. Of course, we also need to change the task of the environment as well to match this other task.
-Looking at the [`AlohaEnv`](../lerobot/common/envs/configs.py) config, the task is `"AlohaInsertion-v0"` by default, which corresponds to the task we trained on in the command above. The [gym-aloha](https://github.com/huggingface/gym-aloha?tab=readme-ov-file#description) environment also has the `AlohaTransferCube-v0` task which corresponds to this other task we want to train on. Putting this together, we can train this new policy on this different task using:
+Looking at the [`AlohaEnv`](../src/lerobot/envs/configs.py) config, the task is `"AlohaInsertion-v0"` by default, which corresponds to the task we trained on in the command above. The [gym-aloha](https://github.com/huggingface/gym-aloha?tab=readme-ov-file#description) environment also has the `AlohaTransferCube-v0` task which corresponds to this other task we want to train on. Putting this together, we can train this new policy on this different task using:
 ```bash
-python lerobot/scripts/train.py \
+python -m lerobot.scripts.train \
     --policy.type=act \
     --dataset.repo_id=lerobot/aloha_sim_transfer_cube_human \
     --env.type=aloha \
@@ -111,7 +111,7 @@ Now, let's assume that we want to reproduce the run just above. That run has pro
 
 We can then simply load the config values from this file using:
 ```bash
-python lerobot/scripts/train.py \
+python -m lerobot.scripts.train \
     --config_path=outputs/train/act_aloha_transfer/checkpoints/last/pretrained_model/ \
     --output_dir=outputs/train/act_aloha_transfer_2
 ```
@@ -119,7 +119,7 @@ python lerobot/scripts/train.py \
 
 Similarly to Hydra, we can still override some parameters in the CLI if we want to, e.g.:
 ```bash
-python lerobot/scripts/train.py \
+python -m lerobot.scripts.train \
     --config_path=outputs/train/act_aloha_transfer/checkpoints/last/pretrained_model/ \
     --output_dir=outputs/train/act_aloha_transfer_2
     --policy.n_action_steps=80
@@ -128,7 +128,7 @@ python lerobot/scripts/train.py \
 
 `--config_path` can also accept the repo_id of a repo on the hub that contains a `train_config.json` file, e.g. running:
 ```bash
-python lerobot/scripts/train.py --config_path=lerobot/diffusion_pusht
+python -m lerobot.scripts.train --config_path=lerobot/diffusion_pusht
 ```
 will start a training run with the same configuration used for training [lerobot/diffusion_pusht](https://huggingface.co/lerobot/diffusion_pusht)
 
@@ -139,7 +139,7 @@ Being able to resume a training run is important in case it crashed or aborted f
 
 Let's reuse the command from the previous run and add a few more options:
 ```bash
-python lerobot/scripts/train.py \
+python -m lerobot.scripts.train \
     --policy.type=act \
     --dataset.repo_id=lerobot/aloha_sim_transfer_cube_human \
     --env.type=aloha \
@@ -155,7 +155,7 @@ INFO 2025-01-24 16:10:56 ts/train.py:263 Checkpoint policy after step 100
 ```
 Now let's simulate a crash by killing the process (hit `ctrl`+`c`). We can then simply resume this run from the last checkpoint available with:
 ```bash
-python lerobot/scripts/train.py \
+python -m lerobot.scripts.train \
     --config_path=outputs/train/run_resumption/checkpoints/last/pretrained_model/ \
     --resume=true
 ```
@@ -164,7 +164,7 @@ You should see from the logging that your training picks up from where it left o
 Another reason for which you might want to resume a run is simply to extend training and add more training steps. The number of training steps is set by the option `--steps`, which is 100 000 by default.
 You could double the number of steps of the previous run with:
 ```bash
-python lerobot/scripts/train.py \
+python -m lerobot.scripts.train \
     --config_path=outputs/train/run_resumption/checkpoints/last/pretrained_model/ \
     --resume=true \
     --steps=200000
@@ -195,7 +195,7 @@ In addition to the features currently in Draccus, we've added a special `.path`
 
 For example, we could fine-tune a [policy pre-trained on the aloha transfer task](https://huggingface.co/lerobot/act_aloha_sim_transfer_cube_human) on the aloha insertion task. We can achieve this with:
 ```bash
-python lerobot/scripts/train.py \
+python -m lerobot.scripts.train \
     --policy.path=lerobot/act_aloha_sim_transfer_cube_human \
     --dataset.repo_id=lerobot/aloha_sim_insertion_human \
     --env.type=aloha \
@@ -236,7 +236,7 @@ We'll summarize here the main use cases to remember from this tutorial.
 
 #### Train a policy from scratch – CLI
 ```bash
-python lerobot/scripts/train.py \
+python -m lerobot.scripts.train \
     --policy.type=act \  # <- select 'act' policy
     --env.type=pusht \  # <- select 'pusht' environment
     --dataset.repo_id=lerobot/pusht  # <- train on this dataset
@@ -244,14 +244,14 @@ python lerobot/scripts/train.py \
 
 #### Train a policy from scratch - config file + CLI
 ```bash
-python lerobot/scripts/train.py \
+python -m lerobot.scripts.train \
     --config_path=path/to/pretrained_model \  # <- can also be a repo_id
     --policy.n_action_steps=80  # <- you may still override values
 ```
 
 #### Resume/continue a training run
 ```bash
-python lerobot/scripts/train.py \
+python -m lerobot.scripts.train \
     --config_path=checkpoint/pretrained_model/ \
     --resume=true \
     --steps=200000  # <- you can change some training parameters
@@ -259,7 +259,7 @@ python lerobot/scripts/train.py \
 
 #### Fine-tuning
 ```bash
-python lerobot/scripts/train.py \
+python -m lerobot.scripts.train \
     --policy.path=lerobot/act_aloha_sim_transfer_cube_human \  # <- can also be a local path to a checkpoint
     --dataset.repo_id=lerobot/aloha_sim_insertion_human \
     --env.type=aloha \

examples/advanced/1_add_image_transforms.py

@@ -22,7 +22,7 @@ from pathlib import Path
 
 from torchvision.transforms import ToPILImage, v2
 
-from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
 
 dataset_repo_id = "lerobot/aloha_static_screw_driver"
 

examples/advanced/2_calculate_validation_loss.py

@@ -26,8 +26,8 @@ import math
 
 import torch
 
-from lerobot.common.datasets.lerobot_dataset import LeRobotDataset, LeRobotDatasetMetadata
+from lerobot.datasets.lerobot_dataset import LeRobotDataset, LeRobotDatasetMetadata
-from lerobot.common.policies.diffusion.modeling_diffusion import DiffusionPolicy
+from lerobot.policies.diffusion.modeling_diffusion import DiffusionPolicy
 
 
 def main():

examples/backward_compatibility/replay.py

@@ -35,8 +35,8 @@ from pprint import pformat
 
 import draccus
 
-from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
-from lerobot.common.robots import (  # noqa: F401
+from lerobot.robots import (  # noqa: F401
     Robot,
     RobotConfig,
     koch_follower,
@@ -44,8 +44,8 @@ from lerobot.common.robots import (  # noqa: F401
     so100_follower,
     so101_follower,
 )
-from lerobot.common.utils.robot_utils import busy_wait
+from lerobot.utils.robot_utils import busy_wait
-from lerobot.common.utils.utils import (
+from lerobot.utils.utils import (
     init_logging,
     log_say,
 )

examples/lekiwi/evaluate.py

@@ -1,8 +1,8 @@
-from lerobot.common.datasets.utils import build_dataset_frame, hw_to_dataset_features
+from lerobot.datasets.utils import build_dataset_frame, hw_to_dataset_features
-from lerobot.common.policies.act.modeling_act import ACTPolicy
+from lerobot.policies.act.modeling_act import ACTPolicy
-from lerobot.common.robots.lekiwi import LeKiwiClient, LeKiwiClientConfig
+from lerobot.robots.lekiwi import LeKiwiClient, LeKiwiClientConfig
-from lerobot.common.utils.control_utils import predict_action
+from lerobot.utils.control_utils import predict_action
-from lerobot.common.utils.utils import get_safe_torch_device
+from lerobot.utils.utils import get_safe_torch_device
 
 NB_CYCLES_CLIENT_CONNECTION = 1000
 

examples/lekiwi/record.py

@@ -1,11 +1,11 @@
 import time
 
-from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
-from lerobot.common.datasets.utils import hw_to_dataset_features
+from lerobot.datasets.utils import hw_to_dataset_features
-from lerobot.common.robots.lekiwi.config_lekiwi import LeKiwiClientConfig
+from lerobot.robots.lekiwi.config_lekiwi import LeKiwiClientConfig
-from lerobot.common.robots.lekiwi.lekiwi_client import LeKiwiClient
+from lerobot.robots.lekiwi.lekiwi_client import LeKiwiClient
-from lerobot.common.teleoperators.keyboard import KeyboardTeleop, KeyboardTeleopConfig
+from lerobot.teleoperators.keyboard import KeyboardTeleop, KeyboardTeleopConfig
-from lerobot.common.teleoperators.so100_leader import SO100Leader, SO100LeaderConfig
+from lerobot.teleoperators.so100_leader import SO100Leader, SO100LeaderConfig
 
 NB_CYCLES_CLIENT_CONNECTION = 250
 

examples/lekiwi/replay.py

@@ -1,9 +1,9 @@
 import time
 
-from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
-from lerobot.common.robots.lekiwi.config_lekiwi import LeKiwiClientConfig
+from lerobot.robots.lekiwi.config_lekiwi import LeKiwiClientConfig
-from lerobot.common.robots.lekiwi.lekiwi_client import LeKiwiClient
+from lerobot.robots.lekiwi.lekiwi_client import LeKiwiClient
-from lerobot.common.utils.robot_utils import busy_wait
+from lerobot.utils.robot_utils import busy_wait
 
 robot_config = LeKiwiClientConfig(remote_ip="172.18.134.136", id="lekiwi")
 robot = LeKiwiClient(robot_config)

examples/lekiwi/teleoperate.py

@@ -1,6 +1,6 @@
-from lerobot.common.robots.lekiwi import LeKiwiClient, LeKiwiClientConfig
+from lerobot.robots.lekiwi import LeKiwiClient, LeKiwiClientConfig
-from lerobot.common.teleoperators.keyboard.teleop_keyboard import KeyboardTeleop, KeyboardTeleopConfig
+from lerobot.teleoperators.keyboard.teleop_keyboard import KeyboardTeleop, KeyboardTeleopConfig
-from lerobot.common.teleoperators.so100_leader import SO100Leader, SO100LeaderConfig
+from lerobot.teleoperators.so100_leader import SO100Leader, SO100LeaderConfig
 
 robot_config = LeKiwiClientConfig(remote_ip="172.18.134.136", id="my_lekiwi")
 

lerobot/common/transport/services_pb2.py

@@ -1,45 +0,0 @@
-# Generated by the protocol buffer compiler.  DO NOT EDIT!
-# NO CHECKED-IN PROTOBUF GENCODE
-# source: lerobot/common/transport/services.proto
-# Protobuf Python Version: 5.29.0
-"""Generated protocol buffer code."""
-from google.protobuf import descriptor as _descriptor
-from google.protobuf import descriptor_pool as _descriptor_pool
-from google.protobuf import runtime_version as _runtime_version
-from google.protobuf import symbol_database as _symbol_database
-from google.protobuf.internal import builder as _builder
-_runtime_version.ValidateProtobufRuntimeVersion(
-    _runtime_version.Domain.PUBLIC,
-    5,
-    29,
-    0,
-    '',
-    'lerobot/common/transport/services.proto'
-)
-# @@protoc_insertion_point(imports)
-
-_sym_db = _symbol_database.Default()
-
-
-
-
-DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\'lerobot/common/transport/services.proto\x12\ttransport\"L\n\nTransition\x12\x30\n\x0etransfer_state\x18\x01 \x01(\x0e\x32\x18.transport.TransferState\x12\x0c\n\x04\x64\x61ta\x18\x02 \x01(\x0c\"L\n\nParameters\x12\x30\n\x0etransfer_state\x18\x01 \x01(\x0e\x32\x18.transport.TransferState\x12\x0c\n\x04\x64\x61ta\x18\x02 \x01(\x0c\"T\n\x12InteractionMessage\x12\x30\n\x0etransfer_state\x18\x01 \x01(\x0e\x32\x18.transport.TransferState\x12\x0c\n\x04\x64\x61ta\x18\x02 \x01(\x0c\"\x07\n\x05\x45mpty*`\n\rTransferState\x12\x14\n\x10TRANSFER_UNKNOWN\x10\x00\x12\x12\n\x0eTRANSFER_BEGIN\x10\x01\x12\x13\n\x0fTRANSFER_MIDDLE\x10\x02\x12\x10\n\x0cTRANSFER_END\x10\x03\x32\x81\x02\n\x0eLearnerService\x12=\n\x10StreamParameters\x12\x10.transport.Empty\x1a\x15.transport.Parameters0\x01\x12<\n\x0fSendTransitions\x12\x15.transport.Transition\x1a\x10.transport.Empty(\x01\x12\x45\n\x10SendInteractions\x12\x1d.transport.InteractionMessage\x1a\x10.transport.Empty(\x01\x12+\n\x05Ready\x12\x10.transport.Empty\x1a\x10.transport.Emptyb\x06proto3')
-
-_globals = globals()
-_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
-_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'lerobot.common.transport.services_pb2', _globals)
-if not _descriptor._USE_C_DESCRIPTORS:
-  DESCRIPTOR._loaded_options = None
-  _globals['_TRANSFERSTATE']._serialized_start=305
-  _globals['_TRANSFERSTATE']._serialized_end=401
-  _globals['_TRANSITION']._serialized_start=54
-  _globals['_TRANSITION']._serialized_end=130
-  _globals['_PARAMETERS']._serialized_start=132
-  _globals['_PARAMETERS']._serialized_end=208
-  _globals['_INTERACTIONMESSAGE']._serialized_start=210
-  _globals['_INTERACTIONMESSAGE']._serialized_end=294
-  _globals['_EMPTY']._serialized_start=296
-  _globals['_EMPTY']._serialized_end=303
-  _globals['_LEARNERSERVICE']._serialized_start=404
-  _globals['_LEARNERSERVICE']._serialized_end=661
-# @@protoc_insertion_point(module_scope)

pyproject.toml

@@ -123,10 +123,10 @@ select = ["E4", "E7", "E9", "F", "I", "N", "B", "C4", "SIM"]
 exclude_dirs = [
     "tests",
     "benchmarks",
-    "lerobot/common/datasets/push_dataset_to_hub",
+    "src/lerobot/datasets/push_dataset_to_hub",
-    "lerobot/common/datasets/v2/convert_dataset_v1_to_v2",
+    "src/lerobot/datasets/v2/convert_dataset_v1_to_v2",
-    "lerobot/common/policies/pi0/conversion_scripts",
+    "src/lerobot/policies/pi0/conversion_scripts",
-    "lerobot/scripts/push_dataset_to_hub.py",
+    "src/lerobot/scripts/push_dataset_to_hub.py",
 ]
 skips = ["B101", "B311", "B404", "B603"]
 

src/lerobot/__init__.py

@@ -167,10 +167,10 @@ available_datasets = sorted(
     set(itertools.chain(*available_datasets_per_env.values(), available_real_world_datasets))
 )
 
-# lists all available policies from `lerobot/common/policies`
+# lists all available policies from `lerobot/policies`
 available_policies = ["act", "diffusion", "tdmpc", "vqbet"]
 
-# lists all available robots from `lerobot/common/robot_devices/robots`
+# lists all available robots from `lerobot/robot_devices/robots`
 available_robots = [
     "koch",
     "koch_bimanual",
@@ -179,13 +179,13 @@ available_robots = [
     "so101",
 ]
 
-# lists all available cameras from `lerobot/common/robot_devices/cameras`
+# lists all available cameras from `lerobot/robot_devices/cameras`
 available_cameras = [
     "opencv",
     "intelrealsense",
 ]
 
-# lists all available motors from `lerobot/common/robot_devices/motors`
+# lists all available motors from `lerobot/robot_devices/motors`
 available_motors = [
     "dynamixel",
     "feetech",

src/lerobot/calibrate.py

@@ -31,9 +31,9 @@ from pprint import pformat
 
 import draccus
 
-from lerobot.common.cameras.opencv.configuration_opencv import OpenCVCameraConfig  # noqa: F401
+from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig  # noqa: F401
-from lerobot.common.cameras.realsense.configuration_realsense import RealSenseCameraConfig  # noqa: F401
+from lerobot.cameras.realsense.configuration_realsense import RealSenseCameraConfig  # noqa: F401
-from lerobot.common.robots import (  # noqa: F401
+from lerobot.robots import (  # noqa: F401
     Robot,
     RobotConfig,
     koch_follower,
@@ -42,7 +42,7 @@ from lerobot.common.robots import (  # noqa: F401
     so100_follower,
     so101_follower,
 )
-from lerobot.common.teleoperators import (  # noqa: F401
+from lerobot.teleoperators import (  # noqa: F401
     Teleoperator,
     TeleoperatorConfig,
     koch_leader,
@@ -50,7 +50,7 @@ from lerobot.common.teleoperators import (  # noqa: F401
     so100_leader,
     so101_leader,
 )
-from lerobot.common.utils.utils import init_logging
+from lerobot.utils.utils import init_logging
 
 
 @dataclass

src/lerobot/cameras/opencv/camera_opencv.py

@@ -27,7 +27,7 @@ from typing import Any, Dict, List
 import cv2
 import numpy as np
 
-from lerobot.common.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError
+from lerobot.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError
 
 from ..camera import Camera
 from ..utils import get_cv2_backend, get_cv2_rotation
@@ -64,8 +64,8 @@ class OpenCVCamera(Camera):
 
     Example:
         ```python
-        from lerobot.common.cameras.opencv import OpenCVCamera
+        from lerobot.cameras.opencv import OpenCVCamera
-        from lerobot.common.cameras.configuration_opencv import OpenCVCameraConfig, ColorMode, Cv2Rotation
+        from lerobot.cameras.configuration_opencv import OpenCVCameraConfig, ColorMode, Cv2Rotation
 
         # Basic usage with camera index 0
         config = OpenCVCameraConfig(index_or_path=0)

src/lerobot/cameras/realsense/camera_realsense.py

@@ -29,7 +29,7 @@ try:
 except Exception as e:
     logging.info(f"Could not import realsense: {e}")
 
-from lerobot.common.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError
+from lerobot.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError
 
 from ..camera import Camera
 from ..configs import ColorMode
@@ -63,8 +63,8 @@ class RealSenseCamera(Camera):
 
     Example:
         ```python
-        from lerobot.common.cameras.realsense import RealSenseCamera, RealSenseCameraConfig
+        from lerobot.cameras.realsense import RealSenseCamera, RealSenseCameraConfig
-        from lerobot.common.cameras import ColorMode, Cv2Rotation
+        from lerobot.cameras import ColorMode, Cv2Rotation
 
         # Basic usage with serial number
         config = RealSenseCameraConfig(serial_number_or_name="0123456789") # Replace with actual SN

src/lerobot/configs/default.py

@@ -16,11 +16,11 @@
 
 from dataclasses import dataclass, field
 
-from lerobot.common import (
+from lerobot import (
     policies,  # noqa: F401
 )
-from lerobot.common.datasets.transforms import ImageTransformsConfig
+from lerobot.datasets.transforms import ImageTransformsConfig
-from lerobot.common.datasets.video_utils import get_safe_default_codec
+from lerobot.datasets.video_utils import get_safe_default_codec
 
 
 @dataclass

src/lerobot/configs/eval.py

@@ -17,7 +17,7 @@ import logging
 from dataclasses import dataclass, field
 from pathlib import Path
 
-from lerobot.common import envs, policies  # noqa: F401
+from lerobot import envs, policies  # noqa: F401
 from lerobot.configs import parser
 from lerobot.configs.default import EvalConfig
 from lerobot.configs.policies import PreTrainedConfig

src/lerobot/configs/parser.py

@@ -22,7 +22,7 @@ from typing import Sequence
 
 import draccus
 
-from lerobot.common.utils.utils import has_method
+from lerobot.utils.utils import has_method
 
 PATH_KEY = "path"
 PLUGIN_DISCOVERY_SUFFIX = "discover_packages_path"

src/lerobot/configs/policies.py

@@ -23,11 +23,11 @@ from huggingface_hub import hf_hub_download
 from huggingface_hub.constants import CONFIG_NAME
 from huggingface_hub.errors import HfHubHTTPError
 
-from lerobot.common.optim.optimizers import OptimizerConfig
-from lerobot.common.optim.schedulers import LRSchedulerConfig
-from lerobot.common.utils.hub import HubMixin
-from lerobot.common.utils.utils import auto_select_torch_device, is_amp_available, is_torch_device_available
 from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature
+from lerobot.optim.optimizers import OptimizerConfig
+from lerobot.optim.schedulers import LRSchedulerConfig
+from lerobot.utils.hub import HubMixin
+from lerobot.utils.utils import auto_select_torch_device, is_amp_available, is_torch_device_available
 
 # Generic variable that is either PreTrainedConfig or a subclass thereof
 T = TypeVar("T", bound="PreTrainedConfig")

src/lerobot/configs/train.py

@@ -21,13 +21,13 @@ import draccus
 from huggingface_hub import hf_hub_download
 from huggingface_hub.errors import HfHubHTTPError
 
-from lerobot.common import envs
+from lerobot import envs
-from lerobot.common.optim import OptimizerConfig
-from lerobot.common.optim.schedulers import LRSchedulerConfig
-from lerobot.common.utils.hub import HubMixin
 from lerobot.configs import parser
 from lerobot.configs.default import DatasetConfig, EvalConfig, WandBConfig
 from lerobot.configs.policies import PreTrainedConfig
+from lerobot.optim import OptimizerConfig
+from lerobot.optim.schedulers import LRSchedulerConfig
+from lerobot.utils.hub import HubMixin
 
 TRAIN_CONFIG_NAME = "train_config.json"
 

src/lerobot/datasets/backward_compatibility.py

@@ -20,7 +20,7 @@ The dataset you requested ({repo_id}) is in {version} format.
 We introduced a new format since v2.0 which is not backward compatible with v1.x.
 Please, use our conversion script. Modify the following command with your own task description:
 ```
-python lerobot/common/datasets/v2/convert_dataset_v1_to_v2.py \\
+python -m lerobot.datasets.v2.convert_dataset_v1_to_v2 \\
     --repo-id {repo_id} \\
     --single-task "TASK DESCRIPTION."  # <---- /!\\ Replace TASK DESCRIPTION /!\\
 ```
@@ -40,7 +40,7 @@ The dataset you requested ({repo_id}) is in {version} format.
 While current version of LeRobot is backward-compatible with it, the version of your dataset still uses global
 stats instead of per-episode stats. Update your dataset stats to the new format using this command:
 ```
-python lerobot/common/datasets/v21/convert_dataset_v20_to_v21.py --repo-id={repo_id}
+python -m lerobot.datasets.v21.convert_dataset_v20_to_v21 --repo-id={repo_id}
 ```
 
 If you encounter a problem, contact LeRobot maintainers on [Discord](https://discord.com/invite/s3KuuzsPFb)

src/lerobot/datasets/compute_stats.py

@@ -15,7 +15,7 @@
 # limitations under the License.
 import numpy as np
 
-from lerobot.common.datasets.utils import load_image_as_numpy
+from lerobot.datasets.utils import load_image_as_numpy
 
 
 def estimate_num_samples(

src/lerobot/datasets/factory.py

@@ -18,14 +18,14 @@ from pprint import pformat
 
 import torch
 
-from lerobot.common.datasets.lerobot_dataset import (
+from lerobot.configs.policies import PreTrainedConfig
+from lerobot.configs.train import TrainPipelineConfig
+from lerobot.datasets.lerobot_dataset import (
     LeRobotDataset,
     LeRobotDatasetMetadata,
     MultiLeRobotDataset,
 )
-from lerobot.common.datasets.transforms import ImageTransforms
+from lerobot.datasets.transforms import ImageTransforms
-from lerobot.configs.policies import PreTrainedConfig
-from lerobot.configs.train import TrainPipelineConfig
 
 IMAGENET_STATS = {
     "mean": [[[0.485]], [[0.456]], [[0.406]]],  # (c,1,1)

src/lerobot/datasets/lerobot_dataset.py

@@ -30,10 +30,10 @@ from huggingface_hub import HfApi, snapshot_download
 from huggingface_hub.constants import REPOCARD_NAME
 from huggingface_hub.errors import RevisionNotFoundError
 
-from lerobot.common.constants import HF_LEROBOT_HOME
+from lerobot.constants import HF_LEROBOT_HOME
-from lerobot.common.datasets.compute_stats import aggregate_stats, compute_episode_stats
+from lerobot.datasets.compute_stats import aggregate_stats, compute_episode_stats
-from lerobot.common.datasets.image_writer import AsyncImageWriter, write_image
+from lerobot.datasets.image_writer import AsyncImageWriter, write_image
-from lerobot.common.datasets.utils import (
+from lerobot.datasets.utils import (
     DEFAULT_FEATURES,
     DEFAULT_IMAGE_PATH,
     INFO_PATH,
@@ -65,7 +65,7 @@ from lerobot.common.datasets.utils import (
     write_info,
     write_json,
 )
-from lerobot.common.datasets.video_utils import (
+from lerobot.datasets.video_utils import (
     VideoFrame,
     decode_video_frames,
     encode_video_frames,
@@ -357,7 +357,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
               the dataset from that address and load it, pending your dataset is compliant with
               codebase_version v2.0. If your dataset has been created before this new format, you will be
               prompted to convert it using our conversion script from v1.6 to v2.0, which you can find at
-              lerobot/common/datasets/v2/convert_dataset_v1_to_v2.py.
+              lerobot/datasets/v2/convert_dataset_v1_to_v2.py.
 
 
         2. Your dataset doesn't already exists (either on local disk or on the Hub): you can create an empty

src/lerobot/datasets/online_buffer.py

@@ -28,7 +28,7 @@ from typing import Any
 import numpy as np
 import torch
 
-from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
 
 
 def _make_memmap_safe(**kwargs) -> np.memmap:

src/lerobot/datasets/push_dataset_to_hub/utils.py

@@ -23,7 +23,7 @@ import numpy
 import PIL
 import torch
 
-from lerobot.common.datasets.video_utils import encode_video_frames
+from lerobot.datasets.video_utils import encode_video_frames
 
 
 def concatenate_episodes(ep_dicts):

src/lerobot/datasets/utils.py

@@ -35,14 +35,14 @@ from huggingface_hub.errors import RevisionNotFoundError
 from PIL import Image as PILImage
 from torchvision import transforms
 
-from lerobot.common.datasets.backward_compatibility import (
+from lerobot.configs.types import DictLike, FeatureType, PolicyFeature
+from lerobot.datasets.backward_compatibility import (
     V21_MESSAGE,
     BackwardCompatibilityError,
     ForwardCompatibilityError,
 )
-from lerobot.common.robots import Robot
+from lerobot.robots import Robot
-from lerobot.common.utils.utils import is_valid_numpy_dtype_string
+from lerobot.utils.utils import is_valid_numpy_dtype_string
-from lerobot.configs.types import DictLike, FeatureType, PolicyFeature
 
 DEFAULT_CHUNK_SIZE = 1000  # Max number of episodes per chunk
 
@@ -664,7 +664,7 @@ def create_lerobot_dataset_card(
     **kwargs,
 ) -> DatasetCard:
     """
-    Keyword arguments will be used to replace values in ./lerobot/common/datasets/card_template.md.
+    Keyword arguments will be used to replace values in src/lerobot/datasets/card_template.md.
     Note: If specified, license must be one of https://huggingface.co/docs/hub/repositories-licenses.
     """
     card_tags = ["LeRobot"]
@@ -687,7 +687,7 @@ def create_lerobot_dataset_card(
         ],
     )
 
-    card_template = (importlib.resources.files("lerobot.common.datasets") / "card_template.md").read_text()
+    card_template = (importlib.resources.files("lerobot.datasets") / "card_template.md").read_text()
 
     return DatasetCard.from_template(
         card_data=card_data,

src/lerobot/datasets/v2/batch_convert_dataset_v1_to_v2.py

@@ -26,8 +26,8 @@ from pathlib import Path
 from textwrap import dedent
 
 from lerobot import available_datasets
-from lerobot.common.datasets.v2.convert_dataset_v1_to_v2 import convert_dataset
+from lerobot.datasets.v2.convert_dataset_v1_to_v2 import convert_dataset
-from lerobot.common.robots.aloha.configuration_aloha import AlohaRobotConfig
+from lerobot.robots.aloha.configuration_aloha import AlohaRobotConfig
 
 LOCAL_DIR = Path("data/")
 

src/lerobot/datasets/v2/convert_dataset_v1_to_v2.py

@@ -38,7 +38,7 @@ If your dataset contains a single task, you can simply provide it directly via t
 Examples:
 
 ```bash
-python lerobot/common/datasets/v2/convert_dataset_v1_to_v2.py \
+python -m lerobot.datasets.v2.convert_dataset_v1_to_v2 \
     --repo-id lerobot/aloha_sim_insertion_human_image \
     --single-task "Insert the peg into the socket." \
     --robot-config lerobot/configs/robot/aloha.yaml \
@@ -46,7 +46,7 @@ python lerobot/common/datasets/v2/convert_dataset_v1_to_v2.py \
 ```
 
 ```bash
-python lerobot/common/datasets/v2/convert_dataset_v1_to_v2.py \
+python -m lerobot.datasets.v2.convert_dataset_v1_to_v2 \
     --repo-id aliberts/koch_tutorial \
     --single-task "Pick the Lego block and drop it in the box on the right." \
     --robot-config lerobot/configs/robot/koch.yaml \
@@ -63,7 +63,7 @@ If your dataset is a multi-task dataset, you have two options to provide the tas
     Example:
 
     ```bash
-    python lerobot/common/datasets/v2/convert_dataset_v1_to_v2.py \
+    python -m lerobot.datasets.v2.convert_dataset_v1_to_v2 \
         --repo-id lerobot/stanford_kuka_multimodal_dataset \
         --tasks-col "language_instruction" \
         --local-dir data
@@ -92,7 +92,7 @@ parquet file, and you must provide this column's name with the '--tasks-col' arg
 Example:
 
 ```bash
-python lerobot/common/datasets/v2/convert_dataset_v1_to_v2.py \
+python -m lerobot.datasets.v2.convert_dataset_v1_to_v2 \
     --repo-id lerobot/stanford_kuka_multimodal_dataset \
     --tasks-col "language_instruction" \
     --local-dir data
@@ -119,7 +119,7 @@ from huggingface_hub import HfApi
 from huggingface_hub.errors import EntryNotFoundError, HfHubHTTPError
 from safetensors.torch import load_file
 
-from lerobot.common.datasets.utils import (
+from lerobot.datasets.utils import (
     DEFAULT_CHUNK_SIZE,
     DEFAULT_PARQUET_PATH,
     DEFAULT_VIDEO_PATH,
@@ -136,12 +136,12 @@ from lerobot.common.datasets.utils import (
     write_json,
     write_jsonlines,
 )
-from lerobot.common.datasets.video_utils import (
+from lerobot.datasets.video_utils import (
     VideoFrame,  # noqa: F401
     get_image_pixel_channels,
     get_video_info,
 )
-from lerobot.common.robots import RobotConfig
+from lerobot.robots import RobotConfig
 
 V16 = "v1.6"
 V20 = "v2.0"
@@ -602,19 +602,19 @@ def make_robot_config(robot_type: str, **kwargs) -> RobotConfig:
         raise NotImplementedError  # TODO
 
     elif robot_type == "koch_follower":
-        from lerobot.common.robots.koch_follower import KochFollowerConfig
+        from lerobot.robots.koch_follower import KochFollowerConfig
 
         return KochFollowerConfig(**kwargs)
     elif robot_type == "so100_follower":
-        from lerobot.common.robots.so100_follower import SO100FollowerConfig
+        from lerobot.robots.so100_follower import SO100FollowerConfig
 
         return SO100FollowerConfig(**kwargs)
     elif robot_type == "stretch":
-        from lerobot.common.robots.stretch3 import Stretch3RobotConfig
+        from lerobot.robots.stretch3 import Stretch3RobotConfig
 
         return Stretch3RobotConfig(**kwargs)
     elif robot_type == "lekiwi":
-        from lerobot.common.robots.lekiwi import LeKiwiConfig
+        from lerobot.robots.lekiwi import LeKiwiConfig
 
         return LeKiwiConfig(**kwargs)
     else:

src/lerobot/datasets/v21/_remove_language_instruction.py

@@ -20,9 +20,9 @@ from datasets import get_dataset_config_info
 from huggingface_hub import HfApi
 
 from lerobot import available_datasets
-from lerobot.common.datasets.lerobot_dataset import LeRobotDatasetMetadata
+from lerobot.datasets.lerobot_dataset import LeRobotDatasetMetadata
-from lerobot.common.datasets.utils import INFO_PATH, write_info
+from lerobot.datasets.utils import INFO_PATH, write_info
-from lerobot.common.datasets.v21.convert_dataset_v20_to_v21 import V20, SuppressWarnings
+from lerobot.datasets.v21.convert_dataset_v20_to_v21 import V20, SuppressWarnings
 
 LOCAL_DIR = Path("data/")
 

src/lerobot/datasets/v21/batch_convert_dataset_v20_to_v21.py

@@ -24,7 +24,7 @@ from pathlib import Path
 from huggingface_hub import HfApi
 
 from lerobot import available_datasets
-from lerobot.common.datasets.v21.convert_dataset_v20_to_v21 import V21, convert_dataset
+from lerobot.datasets.v21.convert_dataset_v20_to_v21 import V21, convert_dataset
 
 LOCAL_DIR = Path("data/")
 

src/lerobot/datasets/v21/convert_dataset_v20_to_v21.py

@@ -25,7 +25,7 @@ This script will help you convert any LeRobot dataset already pushed to the hub
 Usage:
 
 ```bash
-python lerobot/common/datasets/v21/convert_dataset_v20_to_v21.py \
+python -m lerobot.datasets.v21.convert_dataset_v20_to_v21 \
     --repo-id=aliberts/koch_tutorial
 ```
 
@@ -36,9 +36,9 @@ import logging
 
 from huggingface_hub import HfApi
 
-from lerobot.common.datasets.lerobot_dataset import CODEBASE_VERSION, LeRobotDataset
+from lerobot.datasets.lerobot_dataset import CODEBASE_VERSION, LeRobotDataset
-from lerobot.common.datasets.utils import EPISODES_STATS_PATH, STATS_PATH, load_stats, write_info
+from lerobot.datasets.utils import EPISODES_STATS_PATH, STATS_PATH, load_stats, write_info
-from lerobot.common.datasets.v21.convert_stats import check_aggregate_stats, convert_stats
+from lerobot.datasets.v21.convert_stats import check_aggregate_stats, convert_stats
 
 V20 = "v2.0"
 V21 = "v2.1"

src/lerobot/datasets/v21/convert_stats.py

@@ -17,9 +17,9 @@ from concurrent.futures import ThreadPoolExecutor, as_completed
 import numpy as np
 from tqdm import tqdm
 
-from lerobot.common.datasets.compute_stats import aggregate_stats, get_feature_stats, sample_indices
+from lerobot.datasets.compute_stats import aggregate_stats, get_feature_stats, sample_indices
-from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
-from lerobot.common.datasets.utils import write_episode_stats
+from lerobot.datasets.utils import write_episode_stats
 
 
 def sample_episode_video_frames(dataset: LeRobotDataset, episode_index: int, ft_key: str) -> np.ndarray:

src/lerobot/envs/configs.py

@@ -18,10 +18,10 @@ from typing import Any, Optional
 
 import draccus
 
-from lerobot.common.constants import ACTION, OBS_ENV_STATE, OBS_IMAGE, OBS_IMAGES, OBS_STATE
-from lerobot.common.robots import RobotConfig
-from lerobot.common.teleoperators.config import TeleoperatorConfig
 from lerobot.configs.types import FeatureType, PolicyFeature
+from lerobot.constants import ACTION, OBS_ENV_STATE, OBS_IMAGE, OBS_IMAGES, OBS_STATE
+from lerobot.robots import RobotConfig
+from lerobot.teleoperators.config import TeleoperatorConfig
 
 
 @dataclass

src/lerobot/envs/factory.py

@@ -17,7 +17,7 @@ import importlib
 
 import gymnasium as gym
 
-from lerobot.common.envs.configs import AlohaEnv, EnvConfig, HILEnvConfig, PushtEnv, XarmEnv
+from lerobot.envs.configs import AlohaEnv, EnvConfig, HILEnvConfig, PushtEnv, XarmEnv
 
 
 def make_env_config(env_type: str, **kwargs) -> EnvConfig:

src/lerobot/envs/utils.py

@@ -22,9 +22,9 @@ import numpy as np
 import torch
 from torch import Tensor
 
-from lerobot.common.envs.configs import EnvConfig
-from lerobot.common.utils.utils import get_channel_first_image_shape
 from lerobot.configs.types import FeatureType, PolicyFeature
+from lerobot.envs.configs import EnvConfig
+from lerobot.utils.utils import get_channel_first_image_shape
 
 
 def preprocess_observation(observations: dict[str, np.ndarray]) -> dict[str, Tensor]:

src/lerobot/find_cameras.py

@@ -37,11 +37,11 @@ from typing import Any, Dict, List
 import numpy as np
 from PIL import Image
 
-from lerobot.common.cameras.configs import ColorMode
+from lerobot.cameras.configs import ColorMode
-from lerobot.common.cameras.opencv.camera_opencv import OpenCVCamera
+from lerobot.cameras.opencv.camera_opencv import OpenCVCamera
-from lerobot.common.cameras.opencv.configuration_opencv import OpenCVCameraConfig
+from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
-from lerobot.common.cameras.realsense.camera_realsense import RealSenseCamera
+from lerobot.cameras.realsense.camera_realsense import RealSenseCamera
-from lerobot.common.cameras.realsense.configuration_realsense import RealSenseCameraConfig
+from lerobot.cameras.realsense.configuration_realsense import RealSenseCameraConfig
 
 logger = logging.getLogger(__name__)
 

src/lerobot/motors/dynamixel/dynamixel.py

@@ -22,7 +22,7 @@ import logging
 from copy import deepcopy
 from enum import Enum
 
-from lerobot.common.utils.encoding_utils import decode_twos_complement, encode_twos_complement
+from lerobot.utils.encoding_utils import decode_twos_complement, encode_twos_complement
 
 from ..motors_bus import Motor, MotorCalibration, MotorsBus, NameOrID, Value, get_address
 from .tables import (

src/lerobot/motors/feetech/feetech.py

@@ -17,7 +17,7 @@ from copy import deepcopy
 from enum import Enum
 from pprint import pformat
 
-from lerobot.common.utils.encoding_utils import decode_sign_magnitude, encode_sign_magnitude
+from lerobot.utils.encoding_utils import decode_sign_magnitude, encode_sign_magnitude
 
 from ..motors_bus import Motor, MotorCalibration, MotorsBus, NameOrID, Value, get_address
 from .tables import (

src/lerobot/motors/motors_bus.py

@@ -32,8 +32,8 @@ import serial
 from deepdiff import DeepDiff
 from tqdm import tqdm
 
-from lerobot.common.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError
+from lerobot.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError
-from lerobot.common.utils.utils import enter_pressed, move_cursor_up
+from lerobot.utils.utils import enter_pressed, move_cursor_up
 
 NameOrID: TypeAlias = str | int
 Value: TypeAlias = int | float
@@ -446,7 +446,7 @@ class MotorsBus(abc.ABC):
         except (FileNotFoundError, OSError, serial.SerialException) as e:
             raise ConnectionError(
                 f"\nCould not connect on port '{self.port}'. Make sure you are using the correct port."
-                "\nTry running `python lerobot/find_port.py`\n"
+                "\nTry running `python -m lerobot.find_port`\n"
             ) from e
 
     @abc.abstractmethod

src/lerobot/optim/factory.py

@@ -18,8 +18,8 @@
 from torch.optim import Optimizer
 from torch.optim.lr_scheduler import LRScheduler
 
-from lerobot.common.policies.pretrained import PreTrainedPolicy
 from lerobot.configs.train import TrainPipelineConfig
+from lerobot.policies.pretrained import PreTrainedPolicy
 
 
 def make_optimizer_and_scheduler(

src/lerobot/optim/optimizers.py

@@ -22,12 +22,12 @@ import draccus
 import torch
 from safetensors.torch import load_file, save_file
 
-from lerobot.common.constants import (
+from lerobot.constants import (
     OPTIMIZER_PARAM_GROUPS,
     OPTIMIZER_STATE,
 )
-from lerobot.common.datasets.utils import flatten_dict, unflatten_dict, write_json
+from lerobot.datasets.utils import flatten_dict, unflatten_dict, write_json
-from lerobot.common.utils.io_utils import deserialize_json_into_object
+from lerobot.utils.io_utils import deserialize_json_into_object
 
 
 @dataclass

src/lerobot/optim/schedulers.py

@@ -22,9 +22,9 @@ import draccus
 from torch.optim import Optimizer
 from torch.optim.lr_scheduler import LambdaLR, LRScheduler
 
-from lerobot.common.constants import SCHEDULER_STATE
+from lerobot.constants import SCHEDULER_STATE
-from lerobot.common.datasets.utils import write_json
+from lerobot.datasets.utils import write_json
-from lerobot.common.utils.io_utils import deserialize_json_into_object
+from lerobot.utils.io_utils import deserialize_json_into_object
 
 
 @dataclass

src/lerobot/policies/act/configuration_act.py

@@ -15,9 +15,9 @@
 # limitations under the License.
 from dataclasses import dataclass, field
 
-from lerobot.common.optim.optimizers import AdamWConfig
 from lerobot.configs.policies import PreTrainedConfig
 from lerobot.configs.types import NormalizationMode
+from lerobot.optim.optimizers import AdamWConfig
 
 
 @PreTrainedConfig.register_subclass("act")

src/lerobot/policies/act/modeling_act.py

@@ -33,10 +33,10 @@ from torch import Tensor, nn
 from torchvision.models._utils import IntermediateLayerGetter
 from torchvision.ops.misc import FrozenBatchNorm2d
 
-from lerobot.common.constants import ACTION, OBS_IMAGES
+from lerobot.constants import ACTION, OBS_IMAGES
-from lerobot.common.policies.act.configuration_act import ACTConfig
+from lerobot.policies.act.configuration_act import ACTConfig
-from lerobot.common.policies.normalize import Normalize, Unnormalize
+from lerobot.policies.normalize import Normalize, Unnormalize
-from lerobot.common.policies.pretrained import PreTrainedPolicy
+from lerobot.policies.pretrained import PreTrainedPolicy
 
 
 class ACTPolicy(PreTrainedPolicy):

src/lerobot/policies/diffusion/configuration_diffusion.py

@@ -16,10 +16,10 @@
 # limitations under the License.
 from dataclasses import dataclass, field
 
-from lerobot.common.optim.optimizers import AdamConfig
-from lerobot.common.optim.schedulers import DiffuserSchedulerConfig
 from lerobot.configs.policies import PreTrainedConfig
 from lerobot.configs.types import NormalizationMode
+from lerobot.optim.optimizers import AdamConfig
+from lerobot.optim.schedulers import DiffuserSchedulerConfig
 
 
 @PreTrainedConfig.register_subclass("diffusion")

src/lerobot/policies/diffusion/modeling_diffusion.py

@@ -33,11 +33,11 @@ from diffusers.schedulers.scheduling_ddim import DDIMScheduler
 from diffusers.schedulers.scheduling_ddpm import DDPMScheduler
 from torch import Tensor, nn
 
-from lerobot.common.constants import ACTION, OBS_ENV_STATE, OBS_IMAGES, OBS_STATE
+from lerobot.constants import ACTION, OBS_ENV_STATE, OBS_IMAGES, OBS_STATE
-from lerobot.common.policies.diffusion.configuration_diffusion import DiffusionConfig
+from lerobot.policies.diffusion.configuration_diffusion import DiffusionConfig
-from lerobot.common.policies.normalize import Normalize, Unnormalize
+from lerobot.policies.normalize import Normalize, Unnormalize
-from lerobot.common.policies.pretrained import PreTrainedPolicy
+from lerobot.policies.pretrained import PreTrainedPolicy
-from lerobot.common.policies.utils import (
+from lerobot.policies.utils import (
     get_device_from_parameters,
     get_dtype_from_parameters,
     get_output_shape,

src/lerobot/policies/factory.py

@@ -18,60 +18,60 @@ import logging
 
 from torch import nn
 
-from lerobot.common.datasets.lerobot_dataset import LeRobotDatasetMetadata
-from lerobot.common.datasets.utils import dataset_to_policy_features
-from lerobot.common.envs.configs import EnvConfig
-from lerobot.common.envs.utils import env_to_policy_features
-from lerobot.common.policies.act.configuration_act import ACTConfig
-from lerobot.common.policies.diffusion.configuration_diffusion import DiffusionConfig
-from lerobot.common.policies.pi0.configuration_pi0 import PI0Config
-from lerobot.common.policies.pi0fast.configuration_pi0fast import PI0FASTConfig
-from lerobot.common.policies.pretrained import PreTrainedPolicy
-from lerobot.common.policies.sac.configuration_sac import SACConfig
-from lerobot.common.policies.sac.reward_model.configuration_classifier import RewardClassifierConfig
-from lerobot.common.policies.smolvla.configuration_smolvla import SmolVLAConfig
-from lerobot.common.policies.tdmpc.configuration_tdmpc import TDMPCConfig
-from lerobot.common.policies.vqbet.configuration_vqbet import VQBeTConfig
 from lerobot.configs.policies import PreTrainedConfig
 from lerobot.configs.types import FeatureType
+from lerobot.datasets.lerobot_dataset import LeRobotDatasetMetadata
+from lerobot.datasets.utils import dataset_to_policy_features
+from lerobot.envs.configs import EnvConfig
+from lerobot.envs.utils import env_to_policy_features
+from lerobot.policies.act.configuration_act import ACTConfig
+from lerobot.policies.diffusion.configuration_diffusion import DiffusionConfig
+from lerobot.policies.pi0.configuration_pi0 import PI0Config
+from lerobot.policies.pi0fast.configuration_pi0fast import PI0FASTConfig
+from lerobot.policies.pretrained import PreTrainedPolicy
+from lerobot.policies.sac.configuration_sac import SACConfig
+from lerobot.policies.sac.reward_model.configuration_classifier import RewardClassifierConfig
+from lerobot.policies.smolvla.configuration_smolvla import SmolVLAConfig
+from lerobot.policies.tdmpc.configuration_tdmpc import TDMPCConfig
+from lerobot.policies.vqbet.configuration_vqbet import VQBeTConfig
 
 
 def get_policy_class(name: str) -> PreTrainedPolicy:
     """Get the policy's class and config class given a name (matching the policy class' `name` attribute)."""
     if name == "tdmpc":
-        from lerobot.common.policies.tdmpc.modeling_tdmpc import TDMPCPolicy
+        from lerobot.policies.tdmpc.modeling_tdmpc import TDMPCPolicy
 
         return TDMPCPolicy
     elif name == "diffusion":
-        from lerobot.common.policies.diffusion.modeling_diffusion import DiffusionPolicy
+        from lerobot.policies.diffusion.modeling_diffusion import DiffusionPolicy
 
         return DiffusionPolicy
     elif name == "act":
-        from lerobot.common.policies.act.modeling_act import ACTPolicy
+        from lerobot.policies.act.modeling_act import ACTPolicy
 
         return ACTPolicy
     elif name == "vqbet":
-        from lerobot.common.policies.vqbet.modeling_vqbet import VQBeTPolicy
+        from lerobot.policies.vqbet.modeling_vqbet import VQBeTPolicy
 
         return VQBeTPolicy
     elif name == "pi0":
-        from lerobot.common.policies.pi0.modeling_pi0 import PI0Policy
+        from lerobot.policies.pi0.modeling_pi0 import PI0Policy
 
         return PI0Policy
     elif name == "pi0fast":
-        from lerobot.common.policies.pi0fast.modeling_pi0fast import PI0FASTPolicy
+        from lerobot.policies.pi0fast.modeling_pi0fast import PI0FASTPolicy
 
         return PI0FASTPolicy
     elif name == "sac":
-        from lerobot.common.policies.sac.modeling_sac import SACPolicy
+        from lerobot.policies.sac.modeling_sac import SACPolicy
 
         return SACPolicy
     elif name == "reward_classifier":
-        from lerobot.common.policies.sac.reward_model.modeling_classifier import Classifier
+        from lerobot.policies.sac.reward_model.modeling_classifier import Classifier
 
         return Classifier
     elif name == "smolvla":
-        from lerobot.common.policies.smolvla.modeling_smolvla import SmolVLAPolicy
+        from lerobot.policies.smolvla.modeling_smolvla import SmolVLAPolicy
 
         return SmolVLAPolicy
     else:

src/lerobot/policies/pi0/configuration_pi0.py

@@ -14,12 +14,12 @@
 
 from dataclasses import dataclass, field
 
-from lerobot.common.optim.optimizers import AdamWConfig
-from lerobot.common.optim.schedulers import (
-    CosineDecayWithWarmupSchedulerConfig,
-)
 from lerobot.configs.policies import PreTrainedConfig
 from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature
+from lerobot.optim.optimizers import AdamWConfig
+from lerobot.optim.schedulers import (
+    CosineDecayWithWarmupSchedulerConfig,
+)
 
 
 @PreTrainedConfig.register_subclass("pi0")

src/lerobot/policies/pi0/conversion_scripts/benchmark.py

@@ -14,9 +14,9 @@
 
 import torch
 
-from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
-from lerobot.common.policies.factory import make_policy
 from lerobot.configs.policies import PreTrainedConfig
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.policies.factory import make_policy
 
 torch.backends.cudnn.benchmark = True
 

src/lerobot/policies/pi0/conversion_scripts/compare_with_jax.py

@@ -18,9 +18,9 @@ from pathlib import Path
 
 import torch
 
-from lerobot.common.datasets.lerobot_dataset import LeRobotDatasetMetadata
-from lerobot.common.policies.factory import make_policy
 from lerobot.configs.policies import PreTrainedConfig
+from lerobot.datasets.lerobot_dataset import LeRobotDatasetMetadata
+from lerobot.policies.factory import make_policy
 
 
 def display(tensor: torch.Tensor):
@@ -97,7 +97,7 @@ def main():
 
     noise = torch.from_numpy(noise).to(device=device, dtype=torch.float32)
 
-    from lerobot.common import policies  # noqa
+    from lerobot import policies  # noqa
 
     cfg = PreTrainedConfig.from_pretrained(ckpt_torch_dir)
     cfg.pretrained_path = ckpt_torch_dir