Replace normalize with register buffer version

changed the motor bus tranformation to degrees but considering the min and max
fixed the kinematics in the so100_follower_end_effector

.github/PULL_REQUEST_TEMPLATE.md

@@ -24,7 +24,7 @@ Examples:
 pytest -sx tests/test_stuff.py::test_something
 ```
 ```bash
-python -m lerobot.scripts.train --some.option=true
+python lerobot/scripts/train.py --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=./src/lerobot --disable-warnings tests
+        run: pytest -v --cov=./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=./src/lerobot --cov-report=xml --disable-warnings tests
+        run: pytest -v --cov=./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:
-      - "src/**"
+      - "lerobot/**"
       - "tests/**"
       - "examples/**"
       - ".github/**"
@@ -29,7 +29,7 @@ on:
     branches:
       - main
     paths:
-      - "src/**"
+      - "lerobot/**"
       - "tests/**"
       - "examples/**"
       - ".github/**"
@@ -73,7 +73,7 @@ jobs:
 
       - name: Test with pytest
         run: |
-          uv run pytest tests -v --cov=./src/lerobot --durations=0 \
+          uv run pytest tests -v --cov=./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=./src/lerobot --durations=0 \
+          uv run pytest tests -v --cov=./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 \

.pre-commit-config.yaml

@@ -37,7 +37,7 @@ repos:
       - id: trailing-whitespace
 
   - repo: https://github.com/adhtruong/mirrors-typos
-    rev: v1.33.1
+    rev: v1.32.0
     hooks:
       - id: typos
         args: [--force-exclude]
@@ -46,9 +46,8 @@ repos:
     rev: v3.20.0
     hooks:
     -   id: pyupgrade
-
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.11.13
+    rev: v0.11.11
     hooks:
       - id: ruff
         args: [--fix]
@@ -57,12 +56,12 @@ repos:
 
   ##### Security #####
   - repo: https://github.com/gitleaks/gitleaks
-    rev: v8.27.2
+    rev: v8.26.0
     hooks:
       - id: gitleaks
 
   - repo: https://github.com/woodruffw/zizmor-pre-commit
-    rev: v1.9.0
+    rev: v1.8.0
     hooks:
       - id: zizmor
 

CONTRIBUTING.md

@@ -67,7 +67,7 @@ post it.
 
 ## Adding new policies, datasets or environments
 
-Look at our implementations for [datasets](./src/lerobot/datasets/), [policies](./src/lerobot/policies/),
+Look at our implementations for [datasets](./lerobot/common/datasets/), [policies](./lerobot/common/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 +0,0 @@
-include src/lerobot/templates/lerobot_modelcard_template.md
-include src/lerobot/datasets/card_template.md

Makefile

@@ -40,17 +40,14 @@ test-end-to-end:
 	${MAKE} DEVICE=$(DEVICE) test-diffusion-ete-eval
 	${MAKE} DEVICE=$(DEVICE) test-tdmpc-ete-train
 	${MAKE} DEVICE=$(DEVICE) test-tdmpc-ete-eval
-	${MAKE} DEVICE=$(DEVICE) test-smolvla-ete-train
-	${MAKE} DEVICE=$(DEVICE) test-smolvla-ete-eval
 
 test-act-ete-train:
-	python -m lerobot.scripts.train \
+	python lerobot/scripts/train.py \
 		--policy.type=act \
 		--policy.dim_model=64 \
 		--policy.n_action_steps=20 \
 		--policy.chunk_size=20 \
 		--policy.device=$(DEVICE) \
-		--policy.push_to_hub=false \
 		--env.type=aloha \
 		--env.episode_length=5 \
 		--dataset.repo_id=lerobot/aloha_sim_transfer_cube_human \
@@ -68,12 +65,12 @@ test-act-ete-train:
 		--output_dir=tests/outputs/act/
 
 test-act-ete-train-resume:
-	python -m lerobot.scripts.train \
+	python lerobot/scripts/train.py \
 		--config_path=tests/outputs/act/checkpoints/000002/pretrained_model/train_config.json \
 		--resume=true
 
 test-act-ete-eval:
-	python -m lerobot.scripts.eval \
+	python lerobot/scripts/eval.py \
 		--policy.path=tests/outputs/act/checkpoints/000004/pretrained_model \
 		--policy.device=$(DEVICE) \
 		--env.type=aloha \
@@ -82,13 +79,12 @@ test-act-ete-eval:
 		--eval.batch_size=1
 
 test-diffusion-ete-train:
-	python -m lerobot.scripts.train \
+	python lerobot/scripts/train.py \
 		--policy.type=diffusion \
 		--policy.down_dims='[64,128,256]' \
 		--policy.diffusion_step_embed_dim=32 \
 		--policy.num_inference_steps=10 \
 		--policy.device=$(DEVICE) \
-		--policy.push_to_hub=false \
 		--env.type=pusht \
 		--env.episode_length=5 \
 		--dataset.repo_id=lerobot/pusht \
@@ -106,7 +102,7 @@ test-diffusion-ete-train:
 		--output_dir=tests/outputs/diffusion/
 
 test-diffusion-ete-eval:
-	python -m lerobot.scripts.eval \
+	python lerobot/scripts/eval.py \
 		--policy.path=tests/outputs/diffusion/checkpoints/000002/pretrained_model \
 		--policy.device=$(DEVICE) \
 		--env.type=pusht \
@@ -115,10 +111,9 @@ test-diffusion-ete-eval:
 		--eval.batch_size=1
 
 test-tdmpc-ete-train:
-	python -m lerobot.scripts.train \
+	python lerobot/scripts/train.py \
 		--policy.type=tdmpc \
 		--policy.device=$(DEVICE) \
-		--policy.push_to_hub=false \
 		--env.type=xarm \
 		--env.task=XarmLift-v0 \
 		--env.episode_length=5 \
@@ -137,7 +132,7 @@ test-tdmpc-ete-train:
 		--output_dir=tests/outputs/tdmpc/
 
 test-tdmpc-ete-eval:
-	python -m lerobot.scripts.eval \
+	python lerobot/scripts/eval.py \
 		--policy.path=tests/outputs/tdmpc/checkpoints/000002/pretrained_model \
 		--policy.device=$(DEVICE) \
 		--env.type=xarm \
@@ -145,36 +140,3 @@ test-tdmpc-ete-eval:
 		--env.task=XarmLift-v0 \
 		--eval.n_episodes=1 \
 		--eval.batch_size=1
-
-
-test-smolvla-ete-train:
-	python -m lerobot.scripts.train \
-		--policy.type=smolvla \
-		--policy.n_action_steps=20 \
-		--policy.chunk_size=20 \
-		--policy.device=$(DEVICE) \
-		--policy.push_to_hub=false \
-		--env.type=aloha \
-		--env.episode_length=5 \
-		--dataset.repo_id=lerobot/aloha_sim_transfer_cube_human \
-		--dataset.image_transforms.enable=true \
-		--dataset.episodes="[0]" \
-		--batch_size=2 \
-		--steps=4 \
-		--eval_freq=2 \
-		--eval.n_episodes=1 \
-		--eval.batch_size=1 \
-		--save_freq=2 \
-		--save_checkpoint=true \
-		--log_freq=1 \
-		--wandb.enable=false \
-		--output_dir=tests/outputs/smolvla/
-
-test-smolvla-ete-eval:
-	python -m lerobot.scripts.eval \
-		--policy.path=tests/outputs/smolvla/checkpoints/000004/pretrained_model \
-		--policy.device=$(DEVICE) \
-		--env.type=aloha \
-		--env.episode_length=5 \
-		--eval.n_episodes=1 \
-		--eval.batch_size=1

README.md

@@ -23,7 +23,7 @@
 </div>
 
 <h2 align="center">
-    <p><a href="https://huggingface.co/docs/lerobot/so101">
+    <p><a href="https://github.com/huggingface/lerobot/blob/main/examples/12_use_so101.md">
         Build Your Own SO-101 Robot!</a></p>
 </h2>
 
@@ -48,11 +48,11 @@
   <p>Train it in minutes with a few simple moves on your laptop.</p>
   <p>Then sit back and watch your creation act autonomously! 🤯</p>
 
-  <p><a href="https://huggingface.co/docs/lerobot/so101">
+  <p><a href="https://github.com/huggingface/lerobot/blob/main/examples/12_use_so101.md">
       See the full SO-101 tutorial here.</a></p>
 
   <p>Want to take it to the next level? Make your SO-101 mobile by building LeKiwi!</p>
-  <p>Check out the <a href="https://huggingface.co/docs/lerobot/lekiwi">LeKiwi tutorial</a> and bring your robot to life on wheels.</p>
+  <p>Check out the <a href="https://github.com/huggingface/lerobot/blob/main/examples/11_use_lekiwi.md">LeKiwi tutorial</a> and bring your robot to life on wheels.</p>
 
   <img src="media/lekiwi/kiwi.webp?raw=true" alt="LeKiwi mobile robot" title="LeKiwi mobile robot" width="50%">
 </div>
@@ -90,7 +90,6 @@
 
 ### Acknowledgment
 
-- The LeRobot team 🤗 for building SmolVLA [Paper](https://arxiv.org/abs/2506.01844), [Blog](https://huggingface.co/blog/smolvla).
 - Thanks to Tony Zhao, Zipeng Fu and colleagues for open sourcing ACT policy, ALOHA environments and datasets. Ours are adapted from [ALOHA](https://tonyzhaozh.github.io/aloha) and [Mobile ALOHA](https://mobile-aloha.github.io).
 - Thanks to Cheng Chi, Zhenjia Xu and colleagues for open sourcing Diffusion policy, Pusht environment and datasets, as well as UMI datasets. Ours are adapted from [Diffusion Policy](https://diffusion-policy.cs.columbia.edu) and [UMI Gripper](https://umi-gripper.github.io).
 - Thanks to Nicklas Hansen, Yunhai Feng and colleagues for open sourcing TDMPC policy, Simxarm environments and datasets. Ours are adapted from [TDMPC](https://github.com/nicklashansen/tdmpc) and [FOWM](https://www.yunhaifeng.com/FOWM).
@@ -130,7 +129,7 @@ pip install -e .
 ```
 
 > **NOTE:** If you encounter build errors, you may need to install additional dependencies (`cmake`, `build-essential`, and `ffmpeg libs`). On Linux, run:
-`sudo apt-get install cmake build-essential python3-dev pkg-config libavformat-dev libavcodec-dev libavdevice-dev libavutil-dev libswscale-dev libswresample-dev libavfilter-dev`. For other systems, see: [Compiling PyAV](https://pyav.org/docs/develop/overview/installation.html#bring-your-own-ffmpeg)
+`sudo apt-get install cmake build-essential python3-dev pkg-config libavformat-dev libavcodec-dev libavdevice-dev libavutil-dev libswscale-dev libswresample-dev libavfilter-dev pkg-config`. For other systems, see: [Compiling PyAV](https://pyav.org/docs/develop/overview/installation.html#bring-your-own-ffmpeg)
 
 For simulations, 🤗 LeRobot comes with gymnasium environments that can be installed as extras:
 - [aloha](https://github.com/huggingface/gym-aloha)
@@ -149,20 +148,44 @@ 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 -m lerobot.scripts.visualize_dataset \
+python lerobot/scripts/visualize_dataset.py \
     --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 -m lerobot.scripts.visualize_dataset \
+python lerobot/scripts/visualize_dataset.py \
     --repo-id lerobot/pusht \
     --root ./my_local_data_dir \
     --local-files-only 1 \
@@ -175,7 +198,7 @@ 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 -m lerobot.scripts.visualize_dataset --help` for more instructions.
+Our script can also visualize datasets stored on a distant server. See `python lerobot/scripts/visualize_dataset.py --help` for more instructions.
 
 ### The `LeRobotDataset` format
 
@@ -228,7 +251,7 @@ 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 -m lerobot.scripts.eval \
+python lerobot/scripts/eval.py \
     --policy.path=lerobot/diffusion_pusht \
     --env.type=pusht \
     --eval.batch_size=10 \
@@ -240,10 +263,10 @@ python -m lerobot.scripts.eval \
 Note: After training your own policy, you can re-evaluate the checkpoints with:
 
 ```bash
-python -m lerobot.scripts.eval --policy.path={OUTPUT_DIR}/checkpoints/last/pretrained_model
+python lerobot/scripts/eval.py --policy.path={OUTPUT_DIR}/checkpoints/last/pretrained_model
 ```
 
-See `python -m lerobot.scripts.eval --help` for more instructions.
+See `python lerobot/scripts/eval.py --help` for more instructions.
 
 ### Train your own policy
 
@@ -255,14 +278,14 @@ 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 -m lerobot.scripts.eval --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 lerobot/scripts/eval.py --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 -m lerobot.scripts.train --config_path=lerobot/diffusion_pusht
+python lerobot/scripts/train.py --config_path=lerobot/diffusion_pusht
 ```
 reproduces SOTA results for Diffusion Policy on the PushT task.
 
@@ -288,7 +311,7 @@ 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/datasets/push_dataset_to_hub/${raw_format}_format.py` by copying examples like [pusht_zarr](https://github.com/huggingface/lerobot/blob/main/lerobot/datasets/push_dataset_to_hub/pusht_zarr_format.py), [umi_zarr](https://github.com/huggingface/lerobot/blob/main/lerobot/datasets/push_dataset_to_hub/umi_zarr_format.py), [aloha_hdf5](https://github.com/huggingface/lerobot/blob/main/lerobot/datasets/push_dataset_to_hub/aloha_hdf5_format.py), or [xarm_pkl](https://github.com/huggingface/lerobot/blob/main/lerobot/datasets/push_dataset_to_hub/xarm_pkl_format.py). -->
+If your dataset format is not supported, implement your own in `lerobot/common/datasets/push_dataset_to_hub/${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). -->
 
 
 ### Add a pretrained policy
@@ -345,15 +368,6 @@ If you want, you can cite this work with:
 ```
 
 Additionally, if you are using any of the particular policy architecture, pretrained models, or datasets, it is recommended to cite the original authors of the work as they appear below:
-- [SmolVLA](https://arxiv.org/abs/2506.01844)
-```bibtex
-@article{shukor2025smolvla,
-  title={SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics},
-  author={Shukor, Mustafa and Aubakirova, Dana and Capuano, Francesco and Kooijmans, Pepijn and Palma, Steven and Zouitine, Adil and Aractingi, Michel and Pascal, Caroline and Russi, Martino and Marafioti, Andres and Alibert, Simon and Cord, Matthieu and Wolf, Thomas and Cadene, Remi},
-  journal={arXiv preprint arXiv:2506.01844},
-  year={2025}
-}
-```
 
 - [Diffusion Policy](https://diffusion-policy.cs.columbia.edu)
 ```bibtex

benchmarks/video/capture_camera_feed.py

@@ -55,7 +55,7 @@ def display_and_save_video_stream(output_dir: Path, fps: int, width: int, height
         if not ret:
             print("Error: Could not read frame.")
             break
-        rr.log("video/stream", rr.Image(frame), static=True)
+        rr.log("video/stream", rr.Image(frame.numpy()), static=True)
         cv2.imwrite(str(capture_dir / f"frame_{frame_index:06d}.png"), frame)
         frame_index += 1
 

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.datasets.lerobot_dataset import LeRobotDataset
-from lerobot.datasets.video_utils import (
+from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.common.datasets.video_utils import (
     decode_video_frames_torchvision,
     encode_video_frames,
 )
-from lerobot.utils.benchmark import TimeBenchmark
+from lerobot.common.utils.benchmark import TimeBenchmark
 
 BASE_ENCODING = OrderedDict(
     [

docker/lerobot-cpu/Dockerfile

@@ -22,7 +22,7 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
 COPY . /lerobot
 WORKDIR /lerobot
 RUN /opt/venv/bin/pip install --upgrade --no-cache-dir pip \
-    && /opt/venv/bin/pip install --no-cache-dir ".[test, aloha, xarm, pusht, smolvla]" \
+    && /opt/venv/bin/pip install --no-cache-dir ".[test, aloha, xarm, pusht, dynamixel]" \
         --extra-index-url https://download.pytorch.org/whl/cpu
 
 # Execute in bash shell rather than python

docker/lerobot-gpu/Dockerfile

@@ -21,4 +21,4 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
 COPY . /lerobot
 WORKDIR /lerobot
 RUN /opt/venv/bin/pip install --upgrade --no-cache-dir pip \
-    && /opt/venv/bin/pip install --no-cache-dir ".[test, aloha, xarm, pusht, dynamixel, smolvla]"
+    && /opt/venv/bin/pip install --no-cache-dir ".[test, aloha, xarm, pusht, dynamixel]"

docs/source/_toctree.yml

@@ -5,23 +5,13 @@
     title: Installation
   title: Get started
 - sections:
-  - local: il_robots
-    title: Imitation Learning for Robots
-  - local: il_sim
-    title: Imitation Learning in Sim
+  - local: getting_started_real_world_robot
+    title: Getting Started with Real-World Robots
   - local: cameras
     title: Cameras
-  - local: integrate_hardware
-    title: Bring Your Own Hardware
   - local: hilserl
-    title: Train a Robot with RL
-  - local: hilserl_sim
-    title: Train RL in Simulation
+    title: Getting Started with Reinforcement Learning
   title: "Tutorials"
-- sections:
-  - local: smolvla
-    title: Finetune SmolVLA
-  title: "Policies"
 - sections:
   - local: so101
     title: SO-101
@@ -32,13 +22,7 @@
   - local: lekiwi
     title: LeKiwi
   title: "Robots"
-- sections:
-  - local: notebooks
-    title: Notebooks
-  title: "Resources"
 - sections:
   - local: contributing
     title: Contribute to LeRobot
-  - local: backwardcomp
-    title: Backward compatibility
-  title: "About"
+  title: "Contribute"

docs/source/backwardcomp.mdx

@@ -1,82 +0,0 @@
-# Backward compatibility
-
-## Hardware API redesign
-
-PR [#777](https://github.com/huggingface/lerobot/pull/777) improves the LeRobot calibration but is **not backward-compatible**. Below is a overview of what changed and how you can continue to work with datasets created before this pull request.
-
-### What changed?
-
-|                                   | Before PR #777                                    | After PR #777                                                               |
-| --------------------------------- | ------------------------------------------------- | --------------------------------------------------------------------------- |
-| **Joint range**                   | Degrees `-180...180°`                              | **Normalised range** Joints: `–100...100` Gripper: `0...100` |
-| **Zero position (SO100 / SO101)** | Arm fully extended horizontally                   | **In middle of the range for each joint**                                        |
-| **Boundary handling**             | Software safeguards to detect ±180 ° wrap-arounds | No wrap-around logic needed due to mid-range zero                           |
-
----
-
-### Impact on existing datasets
-
-* Recorded trajectories created **before** PR #777 will replay incorrectly if loaded directly:
-  * Joint angles are offset and incorrectly normalized.
-* Any models directly finetuned or trained on the old data will need their inputs and outputs converted.
-
-### Using datasets made with the previous calibration system
-We provide a migration example script for replaying an episode recorded with the previous calibration here: `examples/backward_compatibility/replay.py`.
-Below we take you through the modifications that are done in the example script to make the previous calibration datasets work.
-
-```diff
-+   key = f"{name.removeprefix('main_')}.pos"
-    action[key] = action_array[i].item()
-+   action["shoulder_lift.pos"] = -(action["shoulder_lift.pos"] - 90)
-+   action["elbow_flex.pos"] -= 90
-```
-
-Let's break this down.
-New codebase uses `.pos` suffix for the position observations and we have removed `main_` prefix:
-```python
-key = f"{name.removeprefix('main_')}.pos"
-```
-
-For `"shoulder_lift"` (id = 2), the 0 position is changed by -90 degrees and the direction is reversed compared to old calibration/code.
-```python
-action["shoulder_lift.pos"] = -(action["shoulder_lift.pos"] - 90)
-```
-For `"elbow_flex"` (id = 3), the 0 position is changed by -90 degrees compared to old calibration/code.
-```python
-action["elbow_flex.pos"] -= 90
-```
-
-To use degrees normalization we then set the `--robot.use_degrees` option to `true`.
-```diff
-python examples/backward_compatibility/replay.py \
-    --robot.type=so101_follower \
-    --robot.port=/dev/tty.usbmodem5A460814411 \
-    --robot.id=blue \
-+   --robot.use_degrees=true \
-    --dataset.repo_id=my_dataset_id \
-    --dataset.episode=0
-```
-
-### Using policies trained with the previous calibration system
-
-Policies output actions in the same format as the datasets (`torch.Tensors`). Therefore, the same transformations should be applied.
-
-To find these transformations, we recommend to first try and and replay an episode of the dataset your policy was trained on using the section above.
-Then, add these same transformations on your inference script (shown here in the `record.py` script):
-```diff
-action_values = predict_action(
-    observation_frame,
-    policy,
-    get_safe_torch_device(policy.config.device),
-    policy.config.use_amp,
-    task=single_task,
-    robot_type=robot.robot_type,
-    )
-    action = {key: action_values[i].item() for i, key in enumerate(robot.action_features)}
-
-+   action["shoulder_lift.pos"] = -(action["shoulder_lift.pos"] - 90)
-+   action["elbow_flex.pos"] -= 90
-    robot.send_action(action)
-```
-
-If you have questions or run into migration issues, feel free to ask them on [Discord](https://discord.gg/s3KuuzsPFb)

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 -m lerobot.find_cameras opencv # or realsense for Intel Realsense cameras
+python lerobot/find_cameras.py opencv # or realsense for Intel Realsense cameras
 ```
 
 The output will look something like this if you have two cameras connected:
@@ -44,9 +44,9 @@ Below are two examples, demonstrating how to work with the API.
 <hfoption id="Open CV Camera">
 
 ```python
-from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
-from lerobot.cameras.opencv.camera_opencv import OpenCVCamera
-from lerobot.cameras.configs import ColorMode, Cv2Rotation
+from lerobot.common.cameras.opencv.configuration_opencv import OpenCVCameraConfig
+from lerobot.common.cameras.opencv.camera_opencv import OpenCVCamera
+from lerobot.common.cameras.configs import ColorMode, Cv2Rotation
 
 # Construct an `OpenCVCameraConfig` with your desired FPS, resolution, color mode, and rotation.
 config = OpenCVCameraConfig(
@@ -75,13 +75,13 @@ finally:
 <hfoption id="Intel Realsense Camera">
 
 ```python
-from lerobot.cameras.realsense.configuration_realsense import RealSenseCameraConfig
-from lerobot.cameras.realsense.camera_realsense import RealSenseCamera
-from lerobot.cameras.configs import ColorMode, Cv2Rotation
+from lerobot.common.cameras.intel.configuration_realsense import RealSenseCameraConfig
+from lerobot.common.cameras.intel.camera_realsense import RealSenseCamera
+from lerobot.common.cameras.configs import ColorMode, Cv2Rotation
 
 # Create a `RealSenseCameraConfig` specifying your camera’s serial number and enabling depth.
 config = RealSenseCameraConfig(
-    serial_number_or_name="233522074606",
+    serial_number="233522074606",
     fps=15,
     width=640,
     height=480,

docs/source/getting_started_real_world_robot.mdx

@@ -1,4 +1,4 @@
-# Imitation Learning on Real-World Robots
+# Getting Started with Real-World Robots
 
 This tutorial will explain how to train a neural network to control a real robot autonomously.
 
@@ -36,24 +36,22 @@ If you haven't yet set up and calibrated your robot and teleop device, please do
 
 In this example, we’ll demonstrate how to teleoperate the SO101 robot. For each command, we also provide a corresponding API example.
 
-Note that the `id` associated with a robot is used to store the calibration file. It's important to use the same `id` when teleoperating, recording, and evaluating when using the same setup.
-
 <hfoptions id="teleoperate_so101">
 <hfoption id="Command">
 ```bash
 python -m lerobot.teleoperate \
     --robot.type=so101_follower \
     --robot.port=/dev/tty.usbmodem58760431541 \
-    --robot.id=my_awesome_follower_arm \
+    --robot.id=my_red_robot_arm \
     --teleop.type=so101_leader \
     --teleop.port=/dev/tty.usbmodem58760431551 \
-    --teleop.id=my_awesome_leader_arm
+    --teleop.id=my_blue_leader_arm
 ```
 </hfoption>
 <hfoption id="API example">
 ```python
-from lerobot.teleoperators.so101_leader import SO101LeaderConfig, SO101Leader
-from lerobot.robots.so101_follower import SO101FollowerConfig, SO101Follower
+from lerobot.common.teleoperators.so101_leader import SO101LeaderConfig, SO101Leader
+from lerobot.common.robots.so101_follower import SO101FollowerConfig, SO101Follower
 
 robot_config = SO101FollowerConfig(
     port="/dev/tty.usbmodem58760431541",
@@ -95,19 +93,19 @@ With `rerun`, you can teleoperate again while simultaneously visualizing the cam
 python -m lerobot.teleoperate \
     --robot.type=koch_follower \
     --robot.port=/dev/tty.usbmodem58760431541 \
-    --robot.id=my_awesome_follower_arm \
+    --robot.id=my_koch_robot \
     --robot.cameras="{ front: {type: opencv, index_or_path: 0, width: 1920, height: 1080, fps: 30}}" \
     --teleop.type=koch_leader \
     --teleop.port=/dev/tty.usbmodem58760431551 \
-    --teleop.id=my_awesome_leader_arm \
+    --teleop.id=my_koch_teleop \
     --display_data=true
 ```
 </hfoption>
 <hfoption id="API example">
 ```python
-from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
-from lerobot.teleoperators.koch_leader import KochLeaderConfig, KochLeader
-from lerobot.robots.koch_follower import KochFollowerConfig, KochFollower
+from lerobot.common.cameras.opencv.configuration_opencv import OpenCVCameraConfig
+from lerobot.common.teleoperators.koch_leader import KochLeaderConfig, KochLeader
+from lerobot.common.robots.koch_follower import KochFollowerConfig, KochFollower
 
 camera_config = {
     "front": OpenCVCameraConfig(index_or_path=0, width=1920, height=1080, fps=30)
@@ -154,124 +152,21 @@ HF_USER=$(huggingface-cli whoami | head -n 1)
 echo $HF_USER
 ```
 
-Now you can record a dataset. To record 5 episodes and upload your dataset to the hub, adapt the code below for your robot and execute the command or API example.
-
-<hfoptions id="record">
-<hfoption id="Command">
+Now you can record a dataset. To record 2 episodes and upload your dataset to the hub, execute this command tailored to the SO101.
 ```bash
 python -m lerobot.record \
     --robot.type=so101_follower \
     --robot.port=/dev/tty.usbmodem585A0076841 \
-    --robot.id=my_awesome_follower_arm \
+    --robot.id=my_red_robot_arm \
     --robot.cameras="{ front: {type: opencv, index_or_path: 0, width: 1920, height: 1080, fps: 30}}" \
     --teleop.type=so101_leader \
     --teleop.port=/dev/tty.usbmodem58760431551 \
-    --teleop.id=my_awesome_leader_arm \
+    --teleop.id=my_blue_leader_arm \
     --display_data=true \
-    --dataset.repo_id=${HF_USER}/record-test \
-    --dataset.num_episodes=5 \
+    --dataset.repo_id=aliberts/record-test \
+    --dataset.num_episodes=2 \
     --dataset.single_task="Grab the black cube"
 ```
-</hfoption>
-<hfoption id="API example">
-```python
-from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
-from lerobot.datasets.lerobot_dataset import LeRobotDataset
-from lerobot.datasets.utils import hw_to_dataset_features
-from lerobot.robots.so100_follower import SO100Follower, SO100FollowerConfig
-from lerobot.teleoperators.so100_leader.config_so100_leader import SO100LeaderConfig
-from lerobot.teleoperators.so100_leader.so100_leader import SO100Leader
-from lerobot.utils.control_utils import init_keyboard_listener
-from lerobot.utils.utils import log_say
-from lerobot.utils.visualization_utils import _init_rerun
-from lerobot.record import record_loop
-
-NUM_EPISODES = 5
-FPS = 30
-EPISODE_TIME_SEC = 60
-RESET_TIME_SEC = 10
-TASK_DESCRIPTION = "My task description"
-
-# Create the robot and teleoperator configurations
-camera_config = {"front": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=FPS)}
-robot_config = SO100FollowerConfig(
-    port="/dev/tty.usbmodem58760434471", id="my_awesome_follower_arm", cameras=camera_config
-)
-teleop_config = SO100LeaderConfig(port="/dev/tty.usbmodem585A0077581", id="my_awesome_leader_arm")
-
-# Initialize the robot and teleoperator
-robot = SO100Follower(robot_config)
-teleop = SO100Leader(teleop_config)
-
-# Configure the dataset features
-action_features = hw_to_dataset_features(robot.action_features, "action")
-obs_features = hw_to_dataset_features(robot.observation_features, "observation")
-dataset_features = {**action_features, **obs_features}
-
-# Create the dataset
-dataset = LeRobotDataset.create(
-    repo_id="<hf_username>/<dataset_repo_id>",
-    fps=FPS,
-    features=dataset_features,
-    robot_type=robot.name,
-    use_videos=True,
-    image_writer_threads=4,
-)
-
-# Initialize the keyboard listener and rerun visualization
-_, events = init_keyboard_listener()
-_init_rerun(session_name="recording")
-
-# Connect the robot and teleoperator
-robot.connect()
-teleop.connect()
-
-episode_idx = 0
-while episode_idx < NUM_EPISODES and not events["stop_recording"]:
-    log_say(f"Recording episode {episode_idx + 1} of {NUM_EPISODES}")
-
-    record_loop(
-        robot=robot,
-        events=events,
-        fps=FPS,
-        teleop=teleop,
-        dataset=dataset,
-        control_time_s=EPISODE_TIME_SEC,
-        single_task=TASK_DESCRIPTION,
-        display_data=True,
-    )
-
-    # Reset the environment if not stopping or re-recording
-    if not events["stop_recording"] and (episode_idx < NUM_EPISODES - 1 or events["rerecord_episode"]):
-        log_say("Reset the environment")
-        record_loop(
-            robot=robot,
-            events=events,
-            fps=FPS,
-            teleop=teleop,
-            control_time_s=RESET_TIME_SEC,
-            single_task=TASK_DESCRIPTION,
-            display_data=True,
-        )
-
-    if events["rerecord_episode"]:
-        log_say("Re-recording episode")
-        events["rerecord_episode"] = False
-        events["exit_early"] = False
-        dataset.clear_episode_buffer()
-        continue
-
-    dataset.save_episode()
-    episode_idx += 1
-
-# Clean up
-log_say("Stop recording")
-robot.disconnect()
-teleop.disconnect()
-dataset.push_to_hub()
-```
-</hfoption>
-</hfoptions>
 
 #### Dataset upload
 Locally, your dataset is stored in this folder: `~/.cache/huggingface/lerobot/{repo-id}`. At the end of data recording, your dataset will be uploaded on your Hugging Face page (e.g. https://huggingface.co/datasets/cadene/so101_test) that you can obtain by running:
@@ -293,7 +188,7 @@ The `record` function provides a suite of tools for capturing and managing data
 
 ##### 2. Checkpointing and Resuming
 - Checkpoints are automatically created during recording.
-- If an issue occurs, you can resume by re-running the same command with `--resume=true`.
+- If an issue occurs, you can resume by re-running the same command with `--control.resume=true`.
 - To start recording from scratch, **manually delete** the dataset directory.
 
 ##### 3. Recording Parameters
@@ -332,78 +227,50 @@ If you uploaded your dataset to the hub with `--control.push_to_hub=true`, you c
 echo ${HF_USER}/so101_test
 ```
 
+If you didn't upload with `--control.push_to_hub=false`, you can visualize it locally with (via a window in the browser `http://127.0.0.1:9090` with the visualization tool):
+```bash
+python lerobot/scripts/visualize_dataset_html.py \
+  --repo-id ${HF_USER}/so101_test \
+  --local-files-only 1
+```
+
+This will launch a local web server that looks like this:
+<div style="text-align:center;">
+  <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/visualize_dataset_html.webp?raw=true" alt="Koch v1.1 leader and follower arms" title="Koch v1.1 leader and follower arms" width="100%"></img>
+</div>
+
 ## Replay an episode
 
 A useful feature is the `replay` function, which allows you to replay any episode that you've recorded or episodes from any dataset out there. This function helps you test the repeatability of your robot's actions and assess transferability across robots of the same model.
 
-You can replay the first episode on your robot with either the command below or with the API example:
-
-<hfoptions id="replay">
-<hfoption id="Command">
+You can replay the first episode on your robot with:
 ```bash
 python -m lerobot.replay \
     --robot.type=so101_follower \
     --robot.port=/dev/tty.usbmodem58760431541 \
-    --robot.id=my_awesome_follower_arm \
-    --dataset.repo_id=${HF_USER}/record-test \
-    --dataset.episode=0 # choose the episode you want to replay
+    --robot.id=black \
+    --dataset.repo_id=aliberts/record-test \
+    --dataset.episode=2
 ```
-</hfoption>
-<hfoption id="API example">
-```python
-import time
-
-from lerobot.datasets.lerobot_dataset import LeRobotDataset
-from lerobot.robots.so100_follower.config_so100_follower import SO100FollowerConfig
-from lerobot.robots.so100_follower.so100_follower import SO100Follower
-from lerobot.utils.robot_utils import busy_wait
-from lerobot.utils.utils import log_say
-
-episode_idx = 0
-
-robot_config = SO100FollowerConfig(port="/dev/tty.usbmodem58760434471", id="my_awesome_follower_arm")
-
-robot = SO100Follower(robot_config)
-robot.connect()
-
-dataset = LeRobotDataset("<hf_username>/<dataset_repo_id>", episodes=[episode_idx])
-actions = dataset.hf_dataset.select_columns("action")
-
-log_say(f"Replaying episode {episode_idx}")
-for idx in range(dataset.num_frames):
-    t0 = time.perf_counter()
-
-    action = {
-        name: float(actions[idx]["action"][i]) for i, name in enumerate(dataset.features["action"]["names"])
-    }
-    robot.send_action(action)
-
-    busy_wait(1.0 / dataset.fps - (time.perf_counter() - t0))
-
-robot.disconnect()
-```
-</hfoption>
-</hfoptions>
 
 Your robot should replicate movements similar to those you recorded. For example, check out [this video](https://x.com/RemiCadene/status/1793654950905680090) where we use `replay` on a Aloha robot from [Trossen Robotics](https://www.trossenrobotics.com).
 
 ## Train a policy
 
-To train a policy to control your robot, use the [`python -m lerobot.scripts.train`](../src/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 lerobot/scripts/train.py`](../lerobot/scripts/train.py) script. A few arguments are required. Here is an example command:
 ```bash
-python -m lerobot.scripts.train \
+python lerobot/scripts/train.py \
   --dataset.repo_id=${HF_USER}/so101_test \
   --policy.type=act \
   --output_dir=outputs/train/act_so101_test \
   --job_name=act_so101_test \
   --policy.device=cuda \
-  --wandb.enable=true \
-  --policy.repo_id=${HF_USER}/my_policy
+  --wandb.enable=true
 ```
 
 Let's explain the command:
 1. We provided the dataset as argument with `--dataset.repo_id=${HF_USER}/so101_test`.
-2. We provided the policy with `policy.type=act`. This loads configurations from [`configuration_act.py`](../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.
+2. We provided the policy with `policy.type=act`. This loads configurations from [`configuration_act.py`](../lerobot/common/policies/act/configuration_act.py). Importantly, this policy will automatically adapt to the number of motor states, motor actions and cameras of your robot (e.g. `laptop` and `phone`) which have been saved in your dataset.
 4. We provided `policy.device=cuda` since we are training on a Nvidia GPU, but you could use `policy.device=mps` to train on Apple silicon.
 5. We provided `wandb.enable=true` to use [Weights and Biases](https://docs.wandb.ai/quickstart) for visualizing training plots. This is optional but if you use it, make sure you are logged in by running `wandb login`.
 
@@ -411,18 +278,11 @@ 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 -m lerobot.scripts.train \
+python lerobot/scripts/train.py \
   --config_path=outputs/train/act_so101_test/checkpoints/last/pretrained_model/train_config.json \
   --resume=true
 ```
 
-If you do not want to push your model to the hub after training use `--policy.push_to_hub=false`.
-
-Additionally you can provide extra `tags` or specify a `license` for your model or make the model repo `private` by adding this: `--policy.private=true --policy.tags=\[ppo,rl\] --policy.license=mit`
-
-#### Train using Collab
-If your local computer doesn't have a powerful GPU you could utilize Google Collab to train your model by following the [ACT training notebook](./notebooks#training-act).
-
 #### Upload policy checkpoints
 
 Once training is done, upload the latest checkpoint with:
@@ -438,103 +298,23 @@ huggingface-cli upload ${HF_USER}/act_so101_test${CKPT} \
   outputs/train/act_so101_test/checkpoints/${CKPT}/pretrained_model
 ```
 
-## Run inference and evaluate your policy
+## Evaluate your policy
 
-You can use the `record` script from [`lerobot/record.py`](https://github.com/huggingface/lerobot/blob/main/lerobot/record.py) with a policy checkpoint as input, to run inference and evaluate your policy. For instance, run this command or API example to run inference and record 10 evaluation episodes:
-
-<hfoptions id="eval">
-<hfoption id="Command">
+You can use the `record` script from [`lerobot/record.py`](https://github.com/huggingface/lerobot/blob/main/lerobot/record.py) but with a policy checkpoint as input. For instance, run this command to record 10 evaluation episodes:
 ```bash
 python -m lerobot.record  \
   --robot.type=so100_follower \
   --robot.port=/dev/ttyACM1 \
   --robot.cameras="{ up: {type: opencv, index_or_path: /dev/video10, width: 640, height: 480, fps: 30}, side: {type: intelrealsense, serial_number_or_name: 233522074606, width: 640, height: 480, fps: 30}}" \
-  --robot.id=my_awesome_follower_arm \
+  --robot.id=blue_follower_arm \
+  --teleop.type=so100_leader \
+  --teleop.port=/dev/ttyACM0 \
+  --teleop.id=red_leader_arm \
   --display_data=false \
-  --dataset.repo_id=${HF_USER}/eval_so100 \
+  --dataset.repo_id=$HF_USER/eval_lego_${EPOCHREALTIME/[^0-9]/} \
   --dataset.single_task="Put lego brick into the transparent box" \
-  # <- Teleop optional if you want to teleoperate in between episodes \
-  # --teleop.type=so100_leader \
-  # --teleop.port=/dev/ttyACM0 \
-  # --teleop.id=my_awesome_leader_arm \
-  --policy.path=${HF_USER}/my_policy
+  --policy.path=${HF_USER}/act_johns_arm
 ```
-</hfoption>
-<hfoption id="API example">
-```python
-from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
-from lerobot.datasets.lerobot_dataset import LeRobotDataset
-from lerobot.datasets.utils import hw_to_dataset_features
-from lerobot.policies.act.modeling_act import ACTPolicy
-from lerobot.robots.so100_follower.config_so100_follower import SO100FollowerConfig
-from lerobot.robots.so100_follower.so100_follower import SO100Follower
-from lerobot.utils.control_utils import init_keyboard_listener
-from lerobot.utils.utils import log_say
-from lerobot.utils.visualization_utils import _init_rerun
-from lerobot.record import record_loop
-
-NUM_EPISODES = 5
-FPS = 30
-EPISODE_TIME_SEC = 60
-TASK_DESCRIPTION = "My task description"
-
-# Create the robot configuration
-camera_config = {"front": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=FPS)}
-robot_config = SO100FollowerConfig(
-    port="/dev/tty.usbmodem58760434471", id="my_awesome_follower_arm", cameras=camera_config
-)
-
-# Initialize the robot
-robot = SO100Follower(robot_config)
-
-# Initialize the policy
-policy = ACTPolicy.from_pretrained("<hf_username>/<my_policy_repo_id>")
-
-# Configure the dataset features
-action_features = hw_to_dataset_features(robot.action_features, "action")
-obs_features = hw_to_dataset_features(robot.observation_features, "observation")
-dataset_features = {**action_features, **obs_features}
-
-# Create the dataset
-dataset = LeRobotDataset.create(
-    repo_id="<hf_username>/eval_<dataset_repo_id>",
-    fps=FPS,
-    features=dataset_features,
-    robot_type=robot.name,
-    use_videos=True,
-    image_writer_threads=4,
-)
-
-# Initialize the keyboard listener and rerun visualization
-_, events = init_keyboard_listener()
-_init_rerun(session_name="recording")
-
-# Connect the robot
-robot.connect()
-
-for episode_idx in range(NUM_EPISODES):
-    log_say(f"Running inference, recording eval episode {episode_idx + 1} of {NUM_EPISODES}")
-
-    # Run the policy inference loop
-    record_loop(
-        robot=robot,
-        events=events,
-        fps=FPS,
-        policy=policy,
-        dataset=dataset,
-        control_time_s=EPISODE_TIME_SEC,
-        single_task=TASK_DESCRIPTION,
-        display_data=True,
-    )
-
-    dataset.save_episode()
-
-# Clean up
-robot.disconnect()
-dataset.push_to_hub()
-```
-</hfoption>
-</hfoptions>
 
 As you can see, it's almost the same command as previously used to record your training dataset. Two things changed:
 1. There is an additional `--control.policy.path` argument which indicates the path to your policy checkpoint with  (e.g. `outputs/train/eval_act_so101_test/checkpoints/last/pretrained_model`). You can also use the model repository if you uploaded a model checkpoint to the hub (e.g. `${HF_USER}/act_so101_test`).

docs/source/hilserl.mdx

@@ -1,13 +1,10 @@
-# HIL-SERL Real Robot Training Workflow Guide
+# HilSerl Real Robot Training Workflow Guide
 
-In this tutorial you will go through the full Human-in-the-Loop Sample-Efficient Reinforcement Learning (HIL-SERL) workflow using LeRobot. You will master training a policy with RL on a real robot in just a few hours.
-
-HIL-SERL is a sample-efficient reinforcement learning algorithm that combines human demonstrations with online learning and human interventions. The approach starts from a small set of human demonstrations, uses them to train a reward classifier, and then employs an actor-learner architecture where humans can intervene during policy execution to guide exploration and correct unsafe behaviors. In this tutorial, you'll use a gamepad to provide interventions and control the robot during the learning process.
-
-It combines three key ingredients:
+Human-in-the-Loop Sample-Efficient Reinforcement Learning (HIL-SERL) with LeRobot workflow for taking a policy from “zero” to real-world robot mastery in just a couple of hours.
+It combines three ingredients:
 	1.	**Offline demonstrations & reward classifier:** a handful of human-teleop episodes plus a vision-based success detector give the policy a shaped starting point.
-	2.	**On-robot actor / learner loop with human interventions:** a distributed Soft Actor Critic (SAC) learner updates the policy while an actor explores on the physical robot; the human can jump in at any time to correct dangerous or unproductive behaviour.
-	3.	**Safety & efficiency tools:** joint/end-effector (EE) bounds, crop region of interest (ROI) preprocessing and WandB monitoring keep the data useful and the hardware safe.
+	2.	**On-robot actor / learner loop with human interventions:** a distributed SAC/RLPD learner updates the policy while an actor explores on the physical robot; the human can jump in at any time to correct dangerous or unproductive behaviour.
+	3.	**Safety & efficiency tools:** joint/EE bounds, impedance control, crop-ROI preprocessing and WandB monitoring keep the data useful and the hardware safe.
 
 Together these elements let HIL-SERL reach near-perfect task success and faster cycle times than imitation-only baselines.
 
@@ -19,68 +16,40 @@ Together these elements let HIL-SERL reach near-perfect task success and faster
 
 This guide provides step-by-step instructions for training a robot policy using LeRobot's HilSerl implementation to train on a real robot.
 
-## What do I need?
 
-- A gamepad (recommended) or keyboard to control the robot
-- A Nvidia GPU
-- A real robot with a follower and leader arm (optional if you use the keyboard or the gamepad)
-- A URDF file for the robot for the kinematics package (check `lerobot/common/model/kinematics.py`)
+# 1. Real Robot Training Workflow
 
-## What kind of tasks can I train?
+## 1.1 Understanding Configuration
 
-One can use HIL-SERL to train on a variety of manipulation tasks. Some recommendations:
-- Start with a simple task to understand how the system works.
-  - Push cube to a goal region
-  - Pick and lift cube with the gripper
-- Avoid extremely long horizon tasks. Focus on tasks that can be completed in 5-10 seconds.
-- Once you have a good idea of how the system works, you can try more complex tasks and longer horizons.
-  - Pick and place cube
-  - Bimanual tasks to pick objects with two arms
-  - Hand-over tasks to transfer objects from one arm to another
-  - Go crazy!
-
-## Install LeRobot with HIL-SERL
-
-To install LeRobot with HIL-SERL, you need to install the `hilserl` extra.
-
-```bash
-pip install -e ".[hilserl]"
-```
-
-## Real Robot Training Workflow
-
-### Understanding Configuration
-
-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:
+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:
 
 ```python
 class HILSerlRobotEnvConfig(EnvConfig):
-    robot: RobotConfig | None = None    # Main robot agent (defined in `lerobot/robots`)
-    teleop: TeleoperatorConfig | None = None    # Teleoperator agent, e.g., gamepad or leader arm, (defined in `lerobot/teleoperators`)
-    wrapper: EnvTransformConfig | None = None    # Environment wrapper settings; check `lerobot/scripts/server/gym_manipulator.py`
+    robot: Optional[RobotConfig] = None    # Main robot agent (defined in `lerobot/common/robots`)
+    teleop: Optional[TeleoperatorConfig] = None    # Teleoperator agent, e.g., gamepad or leader arm, (defined in `lerobot/common/teleoperators`)
+    wrapper: Optional[EnvTransformConfig] = None    # Environment wrapper settings; check `lerobot/scripts/server/gym_manipulator.py`
     fps: int = 10    # Control frequency
     name: str = "real_robot"    # Environment name
     mode: str = None    # "record", "replay", or None (for training)
-    repo_id: str | None = None    # LeRobot dataset repository ID
-    dataset_root: str | None = None    # Local dataset root (optional)
+    repo_id: Optional[str] = None    # LeRobot dataset repository ID
+    dataset_root: Optional[str] = None    # Local dataset root (optional)
     task: str = ""    # Task identifier
     num_episodes: int = 10    # Number of episodes for recording
     episode: int = 0    # episode index for replay
     device: str = "cuda"    # Compute device
     push_to_hub: bool = True    # Whether to push the recorded datasets to Hub
-    pretrained_policy_name_or_path: str | None = None    # For policy loading
-    reward_classifier_pretrained_path: str | None = None    # For reward model
-    number_of_steps_after_success: int = 0    # For reward classifier, collect more positive examples after a success to train a classifier
+    pretrained_policy_name_or_path: Optional[str] = None    # For policy loading
+    reward_classifier_pretrained_path: Optional[str] = None    # For reward model
 ```
 
 
-### Finding Robot Workspace Bounds
+## 1.2 Finding Robot Workspace Bounds
 
 Before collecting demonstrations, you need to determine the appropriate operational bounds for your robot.
 
-This helps simplify the problem of learning on the real robot in two ways: 1) by limiting the robot's operational space to a specific region that solves the task and avoids unnecessary or unsafe exploration, and 2) by allowing training in end-effector space rather than joint space. Empirically, learning in joint space for reinforcement learning in manipulation is often a harder problem - some tasks are nearly impossible to learn in joint space but become learnable when the action space is transformed to end-effector coordinates.
+This helps simplifying the problem of learning on the real robot by limiting the robot's operational space to a specific region that solves the task and avoids unnecessary or unsafe exploration.
 
-**Using find_joint_limits.py**
+### 1.2.1 Using find_joint_limits.py
 
 This script helps you find the safe operational bounds for your robot's end-effector. Given that you have a follower and leader arm, you can use the script to find the bounds for the follower arm that will be applied during training.
 Bounding the action space will reduce the redundant exploration of the agent and guarantees safety.
@@ -95,19 +64,19 @@ python -m lerobot.scripts.find_joint_limits \
     --teleop.id=blue
 ```
 
-**Workflow**
+### 1.2.2 Workflow
 
 1. Run the script and move the robot through the space that solves the task
 2. The script will record the minimum and maximum end-effector positions and the joint angles and prints them to the console, for example:
    ```
-   Max ee position [0.2417 0.2012 0.1027]
-   Min ee position [0.1663 -0.0823 0.0336]
+   Max ee position [0.24170487 0.201285   0.10273342]
+   Min ee position [0.16631757 -0.08237468  0.03364977]
    Max joint positions [-20.0, -20.0, -20.0, -20.0, -20.0, -20.0]
    Min joint positions [50.0, 50.0, 50.0, 50.0, 50.0, 50.0]
    ```
-3. Use these values in the configuration of your teleoperation device (TeleoperatorConfig) under the `end_effector_bounds` field
+3. Use these values in the configuration of you teleoperation device (TeleoperatorConfig) under the `end_effector_bounds` field
 
-**Example Configuration**
+### 1.2.3 Example Configuration
 
 ```json
 "end_effector_bounds": {
@@ -116,13 +85,13 @@ python -m lerobot.scripts.find_joint_limits \
 }
 ```
 
-### Collecting Demonstrations
+## 1.3 Collecting Demonstrations
 
 With the bounds defined, you can safely collect demonstrations for training. Training RL with off-policy algorithm allows us to use offline datasets collected in order to improve the efficiency of the learning process.
 
-**Setting Up Record Mode**
+### 1.3.1 Setting Up Record Mode
 
-Create a configuration file for recording demonstrations (or edit an existing one like [env_config_so100.json](https://huggingface.co/datasets/aractingi/lerobot-example-config-files/blob/main/env_config_so100.json)):
+Create a configuration file for recording demonstrations (or edit an existing one like `env_config_so100.json`):
 
 1. Set `mode` to `"record"`
 2. Specify a unique `repo_id` for your dataset (e.g., "username/task_name")
@@ -141,123 +110,46 @@ Example configuration section:
 "push_to_hub": true
 ```
 
-### Using a Teleoperation Device
-
-Along with your robot, you will need a teleoperation device to control it in order to collect datasets of your task and perform interventions during the online training.
-We support using a gamepad or a keyboard or the leader arm of the robot.
-
-HIL-Serl learns actions in the end-effector space of the robot. Therefore, the teleoperation will control the end-effector's x,y,z displacements.
-
-For that we need to define a version of the robot that takes actions in the end-effector space. Check the robot class `SO100FollowerEndEffector` and its configuration `SO100FollowerEndEffectorConfig` for the default parameters related to the end-effector space.
-
-```python
-class SO100FollowerEndEffectorConfig(SO100FollowerConfig):
-    """Configuration for the SO100FollowerEndEffector robot."""
-
-    # Default bounds for the end-effector position (in meters)
-    end_effector_bounds: dict[str, list[float]] = field( # bounds for the end-effector in x,y,z direction
-        default_factory=lambda: {
-            "min": [-1.0, -1.0, -1.0],  # min x, y, z
-            "max": [1.0, 1.0, 1.0],  # max x, y, z
-        }
-    )
-
-    max_gripper_pos: float = 50 # maximum gripper position that the gripper will be open at
-
-    end_effector_step_sizes: dict[str, float] = field( # maximum step size for the end-effector in x,y,z direction
-        default_factory=lambda: {
-            "x": 0.02,
-            "y": 0.02,
-            "z": 0.02,
-        }
-    )
-```
-
-The `Teleoperator` defines the teleoperation device. You can check the list of available teleoperators in `lerobot/teleoperators`.
-
-**Setting up the Gamepad**
-
-The gamepad provides a very convenient way to control the robot and the episode state.
-
-To setup the gamepad, you need to set the `control_mode` to `"gamepad"` and define the `teleop` section in the configuration file.
-
-```json
-    "teleop": {
-        "type": "gamepad",
-        "use_gripper": true
-    },
-```
+### 1.3.2 Gamepad Controls
 
 <p align="center">
   <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/gamepad_guide.jpg?raw=true" alt="Figure shows the control mappings on a Logitech gamepad." title="Gamepad Control Mapping" width="100%"></img>
 </p>
 <p align="center"><i>Gamepad button mapping for robot control and episode management</i></p>
 
-**Setting up the SO101 leader**
 
-The SO101 leader arm has reduced gears that allows it to move and track the follower arm during exploration. Therefore, taking over is much smoother than the gearless SO100.
+### 1.3.3 Recording Demonstrations
 
-To setup the SO101 leader, you need to set the `control_mode` to `"leader"` and define the `teleop` section in the configuration file.
-
-```json
-    "teleop": {
-        "type": "so101_leader",
-        "port": "/dev/tty.usbmodem585A0077921", # check your port number
-        "use_degrees": true
-    },
-```
-
-In order to annotate the success/failure of the episode, **you will need** to use a keyboard to press `s` for success, `esc` for failure.
-During the online training, press `space` to take over the policy and `space` again to give the control back to the policy.
-
-<details>
-<summary><strong>Video: SO101 leader teleoperation</strong></summary>
-
-<div class="video-container">
-  <video controls width="600">
-    <source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so101_leader_tutorial.mp4" type="video/mp4" />
-  </video>
-</div>
-
-<p align="center"><i>SO101 leader teleoperation example, the leader tracks the follower, press `space` to intervene</i></p>
-</details>
-
-**Recording Demonstrations**
-
-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):
+Start the recording process:
 
 ```bash
-python -m lerobot.scripts.rl.gym_manipulator --config_path src/lerobot/configs/env_config_so100.json
+python lerobot/scripts/rl/gym_manipulator.py --config_path lerobot/configs/env_config_so100.json
 ```
 
 During recording:
-1. The robot will reset to the initial position defined in the configuration file `fixed_reset_joint_positions`
-2. Complete the task successfully
-3. The episode ends with a reward of 1 when you press the "success" button
-4. If the time limit is reached, or the fail button is pressed, the episode ends with a reward of 0
-5. You can rerecord an episode by pressing the "rerecord" button
-6. The process automatically continues to the next episode
-7. After recording all episodes, the dataset is pushed to the Hugging Face Hub (optional) and saved locally
+1. The robot will reset to the initial position defined in the configuration file `fixed_reset_position`
+2. Use the gamepad to control the robot by setting `"control_mode"="gamepad"` in the configuration file
+3. Complete the task successfully
+4. The episode ends with a reward of 1 when you press the "success" button
+5. If the time limit is reached, or the fail button is pressed, the episode ends with a reward of 0
+6. You can rerecord an episode by pressing the "rerecord" button
+7. The process automatically continues to the next episode
+8. After recording all episodes, the dataset is pushed to the Hugging Face Hub (optional) and saved locally
 
 
-### Processing the Dataset
+
+## 1.4 Processing the Dataset
 
 After collecting demonstrations, process them to determine optimal camera crops.
 Reinforcement learning is sensitive to background distractions, so it is important to crop the images to the relevant workspace area.
-
-Visual RL algorithms learn directly from pixel inputs, making them vulnerable to irrelevant visual information. Background elements like changing lighting, shadows, people moving, or objects outside the workspace can confuse the learning process. Good ROI selection should:
-- Include only the essential workspace where the task happens
-- Capture the robot's end-effector and all objects involved in the task
-- Exclude unnecessary background elements and distractions
-
 Note: If you already know the crop parameters, you can skip this step and just set the `crop_params_dict` in the configuration file during recording.
 
-**Determining Crop Parameters**
+### 1.4.1 Determining Crop Parameters
 
 Use the `crop_dataset_roi.py` script to interactively select regions of interest in your camera images:
 
 ```bash
-python -m lerobot.scripts.rl.crop_dataset_roi --repo-id username/pick_lift_cube
+python lerobot/scripts/rl/crop_dataset_roi.py --repo-id username/pick_lift_cube
 ```
 
 1. For each camera view, the script will display the first frame
@@ -280,7 +172,7 @@ observation.images.front: [180, 250, 120, 150]
 <p align="center"><i>Interactive cropping tool for selecting regions of interest</i></p>
 
 
-**Updating Configuration**
+### 1.4.2 Updating Configuration
 
 Add these crop parameters to your training configuration:
 
@@ -292,35 +184,106 @@ Add these crop parameters to your training configuration:
 "resize_size": [128, 128]
 ```
 
-**Recommended image resolution**
+## 1.5 Training with Actor-Learner
 
-Most vision-based policies have been validated on square inputs of either **128×128** (default) or **64×64** pixels.  We therefore advise setting the resize_size parameter to [128, 128] – or [64, 64] if you need to save GPU memory and bandwidth.  Other resolutions are possible but have not been extensively tested.
+The LeRobot system uses a distributed actor-learner architecture for training. You will need to start two processes: a learner and an actor.
 
+### 1.5.1 Configuration Setup
 
-### Training a Reward Classifier
+Create a training configuration file (See example `train_config_hilserl_so100.json`). The training config is based on the main `TrainPipelineConfig` class in `lerobot/configs/train.py`.
 
-The reward classifier plays an important role in the HIL-SERL workflow by automating reward assignment and automatically detecting episode success. Instead of manually defining reward functions or relying on human feedback for every timestep, the reward classifier learns to predict success/failure from visual observations. This enables the RL algorithm to learn efficiently by providing consistent and automated reward signals based on the robot's camera inputs.
+1. Set `mode` to `null` (for training mode)
+2. Configure the policy settings (`type`, `device`, etc.)
+3. Set `dataset` to your cropped dataset
+4. Configure environment settings with crop parameters
+5. Check the other parameters related to SAC.
+6. Verify that the `policy` config is correct with the right `input_features` and `output_features` for your task.
 
-This guide explains how to train a reward classifier for human-in-the-loop reinforcement learning implementation of LeRobot. Reward classifiers learn to predict the reward value given a state which can be used in an RL setup to train a policy.
+### 1.5.2 Starting the Learner
+
+First, start the learner server process:
+
+```bash
+python lerobot/scripts/rl/learner.py --config_path lerobot/configs/train_config_hilserl_so100.json
+```
+
+The learner:
+- Initializes the policy network
+- Prepares replay buffers
+- Opens a gRPC server to communicate with actors
+- Processes transitions and updates the policy
+
+### 1.5.3 Starting the Actor
+
+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
+```
+
+The actor:
+- Connects to the learner via gRPC
+- Initializes the environment
+- Execute rollouts of the policy to collect experience
+- Sends transitions to the learner
+- Receives updated policy parameters
+
+### 1.5.4 Training Flow
+
+The training proceeds automatically:
+
+1. The actor executes the policy in the environment
+2. Transitions are collected and sent to the learner
+3. The learner updates the policy based on these transitions
+4. Updated policy parameters are sent back to the actor
+5. The process continues until the specified step limit is reached
+
+### 1.5.5 Human in the Loop
+
+- The key to learning efficiently is to have a human interventions to provide corrective feedback and completing the task to aide the policy learning and exploration.
+- To perform human interventions, you can press the upper right trigger button on the gamepad. This will pause the policy actions and allow you to take over.
+- A successful experiment is one where the human has to intervene at the start but then reduces the amount of interventions as the policy improves. You can monitor the intervention rate in the `wandb` dashboard.
+
+<p align="center">
+  <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/hil_effect.png?raw=true" alt="Figure shows the control mappings on a Logitech gamepad." title="Gamepad Control Mapping" width="100%"></img>
+</p>
+
+<p align="center"><i>Example showing how human interventions help guide policy learning over time</i></p>
+
+- The figure shows the plot of the episodic reward over interaction step. The figure shows the effect of human interventions on the policy learning.
+- The orange curve is an experiment without any human interventions. While the pink and blue curves are experiments with human interventions.
+- We can observe that the number of steps where the policy starts achieving the maximum reward is cut by a quarter when human interventions are present.
+
+#### Guide to Human Interventions
+The strategy to follow is to intervene heavily at the start of training and then reduce the amount of interventions as the training progresses. Some tips and hints:
+- Interevene for almost the length of the entire episode at the first few episodes.
+- When the policy is less chaotic, gradually reduce the intervention time during one episode and let the policy explore for a longer time.
+- Once the policy start guiding the robot towards achieving the task, even if its not perfect, you can limit your interventions to simple quick actions like a grasping command, or grasp and lift command.
+
+## 1.6 Monitoring and Debugging
+
+If you have `wandb.enable` set to `true` in your configuration, you can monitor training progress in real-time through the [Weights & Biases](https://wandb.ai/site/) dashboard.
+
+# 2. Training a Reward Classifier with LeRobot
+
+This guide explains how to train a reward classifier for human-in-the-loop reinforcement learning implementation of  LeRobot. Reward classifiers learn to predict the reward value given a state which can be used in an RL setup to train a policy.
 
-**Note**: Training a reward classifier is optional. You can start the first round of RL experiments by annotating the success manually with your gamepad or keyboard device.
 
 The reward classifier implementation in `modeling_classifier.py` uses a pretrained vision model to process the images. It can output either a single value for binary rewards to predict success/fail cases or multiple values for multi-class settings.
 
-**Collecting a Dataset for the reward classifier**
-
+## 2.1 Collecting a Dataset
 Before training, you need to collect a dataset with labeled examples. The `record_dataset` function in `gym_manipulator.py` enables the process of collecting a dataset of observations, actions, and rewards.
 
-To collect a dataset, you need to modify some parameters in the environment configuration based on HILSerlRobotEnvConfig.
+To collect a dataset, you need to modeify some parameters in the environment configuration based on HILSerlRobotEnvConfig.
 
 ```bash
-python -m lerobot.scripts.rl.gym_manipulator --config_path src/lerobot/configs/reward_classifier_train_config.json
+python lerobot/scripts/rl/gym_manipulator.py --config_path lerobot/configs/reward_classifier_train_config.json
 ```
 
-**Key Parameters for Data Collection**
+### 2.1.1 Key Parameters for Data Collection:
 
-- **mode**: set it to `"record"` to collect a dataset
-- **repo_id**: `"hf_username/dataset_name"`, name of the dataset and repo on the hub
+- **mode**: set it to "record" to collect a dataset
+- **repo_id**: "hf_username/dataset_name", name of the dataset and repo on the hub
 - **num_episodes**: Number of episodes to record
 - **number_of_steps_after_success**: Number of additional frames to record after a success (reward=1) is detected
 - **fps**: Number of frames per second to record
@@ -342,19 +305,19 @@ Example configuration section for data collection:
 }
 ```
 
-**Reward Classifier Configuration**
+## 2.2 Reward Classifier Configuration
 
 The reward classifier is configured using `configuration_classifier.py`. Here are the key parameters:
 
-- **model_name**: Base model architecture (e.g., we mainly use `"helper2424/resnet10"`)
-- **model_type**: `"cnn"` or `"transformer"`
+- **model_name**: Base model architecture (e.g., we mainly use "helper2424/resnet10")
+- **model_type**: "cnn" or "transformer"
 - **num_cameras**: Number of camera inputs
 - **num_classes**: Number of output classes (typically 2 for binary success/failure)
 - **hidden_dim**: Size of hidden representation
 - **dropout_rate**: Regularization parameter
 - **learning_rate**: Learning rate for optimizer
 
-Example configuration for training the [reward classifier](https://huggingface.co/datasets/aractingi/lerobot-example-config-files/blob/main/reward_classifier_train_config.json):
+Example configuration from `reward_classifier_train_config.json`:
 
 ```json
 {
@@ -383,15 +346,15 @@ Example configuration for training the [reward classifier](https://huggingface.c
 }
 ```
 
-**Training the Classifier**
+## 2.3 Training the Classifier
 
 To train the classifier, use the `train.py` script with your configuration:
 
 ```bash
-python -m lerobot.scripts.train --config_path path/to/reward_classifier_train_config.json
+python lerobot/scripts/train.py --config_path lerobot/configs/reward_classifier_train_config.json
 ```
 
-**Deploying and Testing the Model**
+## 2.4 Deploying and Testing the Model
 
 To use your trained reward classifier, configure the `HILSerlRobotEnvConfig` to use your model:
 
@@ -409,135 +372,136 @@ or set the argument in the json config file.
 }
 ```
 
-Run `gym_manipulator.py` to test the model.
+Run gym_manipulator.py to test the model.
 ```bash
-python -m lerobot.scripts.rl.gym_manipulator --config_path path/to/env_config.json
+python lerobot/scripts/rl/gym_manipulator.py --config_path lerobot/configs/env_config.json
 ```
 
 The reward classifier will automatically provide rewards based on the visual input from the robot's cameras.
 
-**Example Workflow for training the reward classifier**
+## 2.5 Example Workflow
 
 1. **Create the configuration files**:
-   Create the necessary json configuration files for the reward classifier and the environment. Check the examples [here](https://huggingface.co/datasets/aractingi/lerobot-example-config-files/tree/main).
+   Create the necessary json configuration files for the reward classifier and the environment. Check the `json_examples` directory for examples.
 
 2. **Collect a dataset**:
    ```bash
-   python -m lerobot.scripts.rl.gym_manipulator --config_path src/lerobot/configs/env_config.json
+   python lerobot/scripts/rl/gym_manipulator.py --config_path lerobot/configs/env_config.json
    ```
 
 3. **Train the classifier**:
    ```bash
-   python -m lerobot.scripts.train --config_path src/lerobot/configs/reward_classifier_train_config.json
+   python lerobot/scripts/train.py --config_path lerobot/configs/reward_classifier_train_config.json
    ```
 
 4. **Test the classifier**:
    ```bash
-   python -m lerobot.scripts.rl.gym_manipulator --config_path src/lerobot/configs/env_config.json
+   python lerobot/scripts/rl/gym_manipulator.py --config_path lerobot/configs/env_config.json
    ```
+# 3. Using gym_hil Simulation Environments with LeRobot
 
-### Training with Actor-Learner
+This guide explains how to use the `gym_hil` simulation environments as an alternative to real robots when working with the LeRobot framework for Human-In-the-Loop (HIL) reinforcement learning.
 
-The LeRobot system uses a distributed actor-learner architecture for training. This architecture decouples robot interactions from the learning process, allowing them to run concurrently without blocking each other. The actor server handles robot observations and actions, sending interaction data to the learner server. The learner server performs gradient descent and periodically updates the actor's policy weights. You will need to start two processes: a learner and an actor.
+`gym_hil` is a package that provides Gymnasium-compatible simulation environments specifically designed for Human-In-the-Loop reinforcement learning. These environments allow you to:
 
-**Configuration Setup**
+- Train policies in simulation to test the RL stack before training on real robots
 
-Create a training configuration file (example available [here](https://huggingface.co/datasets/aractingi/lerobot-example-config-files/blob/main/train_config_hilserl_so100.json)). The training config is based on the main `TrainRLServerPipelineConfig` class in `lerobot/configs/train.py`.
+- Collect demonstrations in sim using external devices like gamepads or keyboards
+- Perform human interventions during policy learning
 
-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/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.
+Currently, the main environment is a Franka Panda robot simulation based on MuJoCo, with tasks like picking up a cube.
 
-**Starting the Learner**
+## 3.1 Installation
 
-First, start the learner server process:
+First, install the `gym_hil` package within the LeRobot environment:
 
 ```bash
-python -m lerobot.scripts.rl.learner --config_path src/lerobot/configs/train_config_hilserl_so100.json
+pip install gym_hil
+
+# Or in LeRobot
+cd lerobot
+pip install -e .[hilserl]
 ```
 
-The learner:
-- Initializes the policy network
-- Prepares replay buffers
-- Opens a `gRPC` server to communicate with actors
-- Processes transitions and updates the policy
+## 3.2 Configuration
 
-**Starting the Actor**
+To use `gym_hil` with LeRobot, you need to create a configuration file. An example is provided in `gym_hil_env.json`. Key configuration sections include:
 
-In a separate terminal, start the actor process with the same configuration:
+### 3.2.1 Environment Type and Task
 
-```bash
-python -m lerobot.scripts.rl.actor --config_path src/lerobot/configs/train_config_hilserl_so100.json
+```json
+{
+    "type": "hil",
+    "name": "franka_sim",
+    "task": "PandaPickCubeGamepad-v0",
+    "device": "cuda"
+}
 ```
 
-The actor:
-- Connects to the learner via `gRPC`
-- Initializes the environment
-- Execute rollouts of the policy to collect experience
-- Sends transitions to the learner
-- Receives updated policy parameters
+Available tasks:
+- `PandaPickCubeBase-v0`: Basic environment
+- `PandaPickCubeGamepad-v0`: With gamepad control
+- `PandaPickCubeKeyboard-v0`: With keyboard control
 
-**Training Flow**
+### 3.2.2 Gym Wrappers Configuration
 
-The training proceeds automatically:
+```json
+"wrapper": {
+    "gripper_penalty": -0.02,
+    "control_time_s": 15.0,
+    "use_gripper": true,
+    "fixed_reset_joint_positions": [0.0, 0.195, 0.0, -2.43, 0.0, 2.62, 0.785],
+    "end_effector_step_sizes": {
+        "x": 0.025,
+        "y": 0.025,
+        "z": 0.025
+    },
+    "control_mode": "gamepad"
+    }
+```
 
-1. The actor executes the policy in the environment
-2. Transitions are collected and sent to the learner
-3. The learner updates the policy based on these transitions
-4. Updated policy parameters are sent back to the actor
-5. The process continues until the specified step limit is reached
+Important parameters:
+- `gripper_penalty`: Penalty for excessive gripper movement
+- `use_gripper`: Whether to enable gripper control
+- `end_effector_step_sizes`: Size of the steps in the x,y,z axes of the end-effector
+- `control_mode`: Set to "gamepad" to use a gamepad controller
 
-**Human in the Loop**
+## 3.3 Running with HIL RL of LeRobot
 
-- The key to learning efficiently is to have human interventions to provide corrective feedback and completing the task to aide the policy learning and exploration.
-- To perform human interventions, you can press the upper right trigger button on the gamepad (or the `space` key on the keyboard). This will pause the policy actions and allow you to take over.
-- A successful experiment is one where the human has to intervene at the start but then reduces the amount of interventions as the policy improves. You can monitor the intervention rate in the `wandb` dashboard.
+### 3.3.1 Basic Usage
 
-<p align="center">
-  <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/hil_effect.png?raw=true" alt="Figure shows the control mappings on a Logitech gamepad." title="Gamepad Control Mapping" width="100%"></img>
-</p>
+To run the environment, set mode to null:
 
-<p align="center"><i>Example showing how human interventions help guide policy learning over time</i></p>
+```python
+python lerobot/scripts/rl/gym_manipulator.py --config_path path/to/gym_hil_env.json
+```
 
-- The figure shows the plot of the episodic reward over interaction step. The figure shows the effect of human interventions on the policy learning.
-- The orange curve is an experiment without any human interventions. While the pink and blue curves are experiments with human interventions.
-- We can observe that the number of steps where the policy starts achieving the maximum reward is cut by a quarter when human interventions are present.
+### 3.3.2 Recording a Dataset
 
-**Monitoring and Debugging**
+To collect a dataset, set the mode to `record` whilst defining the repo_id and number of episodes to record:
 
-If you have `wandb.enable` set to `true` in your configuration, you can monitor training progress in real-time through the [Weights & Biases](https://wandb.ai/site/) dashboard.
+```python
+python lerobot/scripts/rl/gym_manipulator.py --config_path path/to/gym_hil_env.json
+```
 
-### Guide to Human Interventions
-The learning process is very sensitive to the intervention strategy. It will takes a few runs to understand how to intervene effectively. Some tips and hints:
-- Allow the policy to explore for a few episodes at the start of training.
-- Avoid intervening for long periods of time. Try to intervene in situation to correct the robot's behaviour when it goes off track.
-- Once the policy starts achieving the task, even if its not perfect, you can limit your interventions to simple quick actions like a simple grasping commands.
+### 3.3.3 Training a Policy
 
-The ideal behaviour is that your intervention rate should drop gradually during training as shown in the figure below.
+To train a policy, checkout the example json in `train_gym_hil_env.json` and run the actor and learner servers:
 
-<p align="center">
-  <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/intervention_rate_tutorial_rl.png?raw=true" alt="Intervention rate" title="Intervention rate during training" width="100%"></img>
-</p>
+```python
+python lerobot/scripts/rl/actor.py --config_path path/to/train_gym_hil_env.json
+```
 
-<p align="center"><i>Plot of the intervention rate during a training run on a pick and lift cube task</i></p>
+In a different terminal, run the learner server:
 
-### Key hyperparameters to tune
+```python
+python lerobot/scripts/rl/learner.py --config_path path/to/train_gym_hil_env.json
+```
 
-Some configuration values have a disproportionate impact on training stability and speed:
-
-- **`temperature_init`** (`policy.temperature_init`) – initial entropy temperature in SAC. Higher values encourage more exploration; lower values make the policy more deterministic early on. A good starting point is `1e-2`. We observed that setting it too high can make human interventions ineffective and slow down learning.
-- **`policy_parameters_push_frequency`** (`policy.actor_learner_config.policy_parameters_push_frequency`) – interval in *seconds* between two weight pushes from the learner to the actor. The default is `4 s`. Decrease to **1-2 s** to provide fresher weights (at the cost of more network traffic); increase only if your connection is slow, as this will reduce sample efficiency.
-- **`storage_device`** (`policy.storage_device`) – device on which the learner keeps the policy parameters. If you have spare GPU memory, set this to `"cuda"` (instead of the default `"cpu"`). Keeping the weights on-GPU removes CPU→GPU transfer overhead and can significantly increase the number of learner updates per second.
-
-
-Congrats 🎉, you have finished this tutorial!
-
-> [!TIP]
->  If you have any questions or need help, please reach out on [Discord](https://discord.com/invite/s3KuuzsPFb).
+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.
 
 Paper citation:
+
 ```
 @article{luo2024precise,
   title={Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning},

docs/source/hilserl_sim.mdx

@@ -1,120 +0,0 @@
-# Train RL in Simulation
-
-This guide explains how to use the `gym_hil` simulation environments as an alternative to real robots when working with the LeRobot framework for Human-In-the-Loop (HIL) reinforcement learning.
-
-`gym_hil` is a package that provides Gymnasium-compatible simulation environments specifically designed for Human-In-the-Loop reinforcement learning. These environments allow you to:
-
-- Train policies in simulation to test the RL stack before training on real robots
-
-- Collect demonstrations in sim using external devices like gamepads or keyboards
-- Perform human interventions during policy learning
-
-Currently, the main environment is a Franka Panda robot simulation based on MuJoCo, with tasks like picking up a cube.
-
-
-## Installation
-
-First, install the `gym_hil` package within the LeRobot environment:
-
-```bash
-pip install -e ".[hilserl]"
-```
-
-## What do I need?
-
-- A gamepad or keyboard to control the robot
-- A Nvidia GPU
-
-
-
-## Configuration
-
-To use `gym_hil` with LeRobot, you need to create a configuration file. An example is provided [here](https://huggingface.co/datasets/aractingi/lerobot-example-config-files/blob/main/gym_hil_env.json). Key configuration sections include:
-
-### Environment Type and Task
-
-```json
-{
-    "type": "hil",
-    "name": "franka_sim",
-    "task": "PandaPickCubeGamepad-v0",
-    "device": "cuda"
-}
-```
-
-Available tasks:
-- `PandaPickCubeBase-v0`: Basic environment
-- `PandaPickCubeGamepad-v0`: With gamepad control
-- `PandaPickCubeKeyboard-v0`: With keyboard control
-
-### Gym Wrappers Configuration
-
-```json
-"wrapper": {
-    "gripper_penalty": -0.02,
-    "control_time_s": 15.0,
-    "use_gripper": true,
-    "fixed_reset_joint_positions": [0.0, 0.195, 0.0, -2.43, 0.0, 2.62, 0.785],
-    "end_effector_step_sizes": {
-        "x": 0.025,
-        "y": 0.025,
-        "z": 0.025
-    },
-    "control_mode": "gamepad"
-    }
-```
-
-Important parameters:
-- `gripper_penalty`: Penalty for excessive gripper movement
-- `use_gripper`: Whether to enable gripper control
-- `end_effector_step_sizes`: Size of the steps in the x,y,z axes of the end-effector
-- `control_mode`: Set to `"gamepad"` to use a gamepad controller
-
-## Running with HIL RL of LeRobot
-
-### Basic Usage
-
-To run the environment, set mode to null:
-
-```python
-python -m lerobot.scripts.rl.gym_manipulator --config_path path/to/gym_hil_env.json
-```
-
-### Recording a Dataset
-
-To collect a dataset, set the mode to `record` whilst defining the repo_id and number of episodes to record:
-
-```python
-python -m lerobot.scripts.rl.gym_manipulator --config_path path/to/gym_hil_env.json
-```
-
-### Training a Policy
-
-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 -m lerobot.scripts.rl.actor --config_path path/to/train_gym_hil_env.json
-```
-
-In a different terminal, run the learner server:
-
-```python
-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.
-
-Congrats 🎉, you have finished this tutorial!
-
-> [!TIP]
->  If you have any questions or need help, please reach out on [Discord](https://discord.com/invite/s3KuuzsPFb).
-
-Paper citation:
-```
-@article{luo2024precise,
-  title={Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning},
-  author={Luo, Jianlan and Xu, Charles and Wu, Jeffrey and Levine, Sergey},
-  journal={arXiv preprint arXiv:2410.21845},
-  year={2024}
-}
-```

docs/source/il_sim.mdx

@@ -1,152 +0,0 @@
-# Imitation Learning in Sim
-
-This tutorial will explain how to train a neural network to control a robot in simulation with imitation learning.
-
-**You'll learn:**
-1. How to record a dataset in simulation with [gym-hil](https://github.com/huggingface/gym-hil) and visualize the dataset.
-2. How to train a policy using your data.
-3. How to evaluate your policy in simulation and visualize the results.
-
-For the simulation environment we use the same [repo](https://github.com/huggingface/gym-hil) that is also being used by the Human-In-the-Loop (HIL) reinforcement learning algorithm.
-This environment is based on [MuJoCo](https://mujoco.org) and allows you to record datasets in LeRobotDataset format.
-Teleoperation is easiest with a controller like the Logitech F710, but you can also use your keyboard if you are up for the challenge.
-
-## Installation
-
-First, install the `gym_hil` package within the LeRobot environment, go to your LeRobot folder and run this command:
-
-```bash
-pip install -e ".[hilserl]"
-```
-
-## Teleoperate and Record a Dataset
-
-To use `gym_hil` with LeRobot, you need to use a configuration file. An example config file can be found [here](https://huggingface.co/datasets/aractingi/lerobot-example-config-files/blob/main/env_config_gym_hil_il.json).
-
-To teleoperate and collect a dataset, we need to modify this config file and you should add your `repo_id` here: `"repo_id": "il_gym",` and `"num_episodes": 30,` and make sure you set `mode` to `record`, "mode": "record".
-
-If you do not have a Nvidia GPU also change `"device": "cuda"` parameter in the config file (for example to `mps` for MacOS).
-
-By default the config file assumes you use a controller. To use your keyboard please change the envoirment specified at `"task"` in the config file and set it to `"PandaPickCubeKeyboard-v0"`.
-
-Then we can run this command to start:
-
-<hfoptions id="teleop_sim">
-<hfoption id="Linux">
-
-```bash
-python -m lerobot.scripts.rl.gym_manipulator --config_path path/to/env_config_gym_hil_il.json
-```
-
-</hfoption>
-<hfoption id="MacOS">
-
-```bash
-mjpython -m lerobot.scripts.rl.gym_manipulator --config_path path/to/env_config_gym_hil_il.json
-```
-
-</hfoption>
-</hfoptions>
-
-Once rendered you can teleoperate the robot with the gamepad or keyboard, below you can find the gamepad/keyboard controls.
-
-Note that to teleoperate the robot you have to hold the "Human Take Over Pause Policy" Button `RB` to enable control!
-
-**Gamepad Controls**
-
-<p align="center">
-  <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/gamepad_guide.jpg?raw=true" alt="Figure shows the control mappings on a Logitech gamepad." title="Gamepad Control Mapping" width="100%"></img>
-</p>
-<p align="center"><i>Gamepad button mapping for robot control and episode management</i></p>
-
-**Keyboard controls**
-
-For keyboard controls use the `spacebar` to enable control and the following keys to move the robot:
-```bash
-  Arrow keys: Move in X-Y plane
-  Shift and Shift_R: Move in Z axis
-  Right Ctrl and Left Ctrl: Open and close gripper
-  ESC: Exit
-```
-
-## Visualize a dataset
-
-If you uploaded your dataset to the hub you can [visualize your dataset online](https://huggingface.co/spaces/lerobot/visualize_dataset) by copy pasting your repo id.
-
-<p align="center">
-  <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/dataset_visualizer_sim.png" alt="Figure shows the dataset visualizer" title="Dataset visualization" width="100%"></img>
-</p>
-<p align="center"><i>Dataset visualizer</i></p>
-
-
-## Train a policy
-
-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 -m lerobot.scripts.train \
-  --dataset.repo_id=${HF_USER}/il_gym \
-  --policy.type=act \
-  --output_dir=outputs/train/il_sim_test \
-  --job_name=il_sim_test \
-  --policy.device=cuda \
-  --wandb.enable=true
-```
-
-Let's explain the command:
-1. We provided the dataset as argument with `--dataset.repo_id=${HF_USER}/il_gym`.
-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`.
-
-Training should take several hours, 100k steps (which is the default) will take about 1h on Nvidia A100. You will find checkpoints in `outputs/train/il_sim_test/checkpoints`.
-
-#### Train using Collab
-If your local computer doesn't have a powerful GPU you could utilize Google Collab to train your model by following the [ACT training notebook](./notebooks#training-act).
-
-#### Upload policy checkpoints
-
-Once training is done, upload the latest checkpoint with:
-```bash
-huggingface-cli upload ${HF_USER}/il_sim_test \
-  outputs/train/il_sim_test/checkpoints/last/pretrained_model
-```
-
-You can also upload intermediate checkpoints with:
-```bash
-CKPT=010000
-huggingface-cli upload ${HF_USER}/il_sim_test${CKPT} \
-  outputs/train/il_sim_test/checkpoints/${CKPT}/pretrained_model
-```
-
-## Evaluate your policy in Sim
-
-To evaluate your policy we have to use the config file that can be found [here](https://huggingface.co/datasets/aractingi/lerobot-example-config-files/blob/main/eval_config_gym_hil.json).
-
-Make sure to replace the `repo_id` with the dataset you trained on, for example `pepijn223/il_sim_dataset` and replace the `pretrained_policy_name_or_path` with your model id, for example `pepijn223/il_sim_model`
-
-Then you can run this command to visualize your trained policy
-
-<hfoptions id="eval_policy">
-<hfoption id="Linux">
-
-```bash
-python -m lerobot.scripts.rl.eval_policy --config_path=path/to/eval_config_gym_hil.json
-```
-
-</hfoption>
-<hfoption id="MacOS">
-
-```bash
-mjpython -m lerobot.scripts.rl.eval_policy --config_path=path/to/eval_config_gym_hil.json
-```
-
-</hfoption>
-</hfoptions>
-
-> [!WARNING]
-> While the main workflow of training ACT in simulation is straightforward, there is significant room for exploring  how to set up the task, define the initial state of the environment, and determine the type of data required during collection to learn the most effective policy. If your trained policy doesn't perform well, investigate the quality of the dataset it was trained on using our visualizers, as well as the action values and various hyperparameters related to ACT and the simulation.
-
-Congrats 🎉, you have finished this tutorial. If you want to continue with using LeRobot in simulation follow this [Tutorial on reinforcement learning in sim with HIL-SERL](https://huggingface.co/docs/lerobot/hilserl_sim)
-
-> [!TIP]
->  If you have any questions or need help, please reach out on [Discord](https://discord.com/invite/s3KuuzsPFb).

docs/source/installation.mdx

@@ -68,5 +68,3 @@ To use [Weights and Biases](https://docs.wandb.ai/quickstart) for experiment tra
 ```bash
 wandb login
 ```
-
-You can now assemble your robot if it's not ready yet, look for your robot type on the left. Then follow the link below to use Lerobot with your robot.

docs/source/integrate_hardware.mdx

@@ -1,318 +0,0 @@
-# Bring Your Own Hardware
-
-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/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
-
-- Your own robot which exposes a communication interface (e.g. serial, CAN, TCP)
-- A way to read sensor data and send motor commands programmatically, e.g. manufacturer's SDK or API, or your own protocol implementation.
-- LeRobot installed in your environment. Follow our [Installation Guide](./installation).
-
-## Choose your motors
-
-If you're using Feetech or Dynamixel motors, LeRobot provides built-in bus interfaces:
-
-- [`FeetechMotorsBus`](https://github.com/huggingface/lerobot/blob/main/lerobot/motors/feetech/feetech.py) – for controlling Feetech servos
-- [`DynamixelMotorsBus`](https://github.com/huggingface/lerobot/blob/main/lerobot/motors/dynamixel/dynamixel.py) – for controlling Dynamixel servos
-
-Please refer to the [`MotorsBus`](https://github.com/huggingface/lerobot/blob/main/lerobot/motors/motors_bus.py) abstract class to learn about its API.
-For a good example of how it can be used, you can have a look at our own [SO101 follower implementation](https://github.com/huggingface/lerobot/blob/main/lerobot/robots/so101_follower/so101_follower.py)
-
-Use these if compatible. Otherwise, you'll need to find or write a Python interface (not covered in this tutorial):
-- Find an existing SDK in Python (or use bindings to C/C++)
-- Or implement a basic communication wrapper (e.g., via pyserial, socket, or CANopen)
-
-You're not alone—many community contributions use custom boards or firmware!
-
-For Feetech and Dynamixel, we currently support these servos:
-    - Feetech:
-        - STS & SMS series (protocol 0): `sts3215`, `sts3250`, `sm8512bl`
-        - 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/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.
-
-## Step 1: Subclass the `Robot` Interface
-
-You’ll first need to specify the config class and a string identifier (`name`) for your robot. If your robot has special needs that you'd like to be able to change easily, it should go here (e.g. port/address, baudrate).
-
-Here, we'll add the port name and one camera by default for our robot:
-```python
-from dataclasses import dataclass, field
-
-from lerobot.cameras import CameraConfig
-from lerobot.cameras.opencv import OpenCVCameraConfig
-from lerobot.robots import RobotConfig
-
-
-@RobotConfig.register_subclass("my_cool_robot")
-@dataclass
-class MyCoolRobotConfig(RobotConfig):
-    port: str
-    cameras: dict[str, CameraConfig] = field(
-        default_factory={
-            "cam_1": OpenCVCameraConfig(
-                index_or_path=2,
-                fps=30,
-                width=480,
-                height=640,
-            ),
-        }
-    )
-```
-
-Have a look at our [Cameras tutorial](./cameras) to understand how to detect and add your camera.
-
-Next, we'll create our actual robot class which inherits from `Robot`. This abstract class defines a contract you must follow for your robot to be usable with the rest of the LeRobot tools.
-
-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.cameras import make_cameras_from_configs
-from lerobot.motors import Motor, MotorNormMode
-from lerobot.motors.feetech import FeetechMotorsBus
-from lerobot.robots import Robot
-
-class MyCoolRobot(Robot):
-    config_class = MyCoolRobotConfig
-    name = "my_cool_robot"
-
-    def __init__(self, config: MyCoolRobotConfig):
-        super().__init__(config)
-        self.bus = FeetechMotorsBus(
-            port=self.config.port,
-            motors={
-                "joint_1": Motor(1, "sts3250", MotorNormMode.RANGE_M100_100),
-                "joint_2": Motor(2, "sts3215", MotorNormMode.RANGE_M100_100),
-                "joint_3": Motor(3, "sts3215", MotorNormMode.RANGE_M100_100),
-                "joint_4": Motor(4, "sts3215", MotorNormMode.RANGE_M100_100),
-                "joint_5": Motor(5, "sts3215", MotorNormMode.RANGE_M100_100),
-            },
-            calibration=self.calibration,
-        )
-        self.cameras = make_cameras_from_configs(config.cameras)
-```
-
-## Step 2: Define Observation and Action Features
-
-These two properties define the *interface contract* between your robot and tools that consume it (such as data collection or learning pipelines).
-
-> [!WARNING]
-> Note that these properties must be callable even if the robot is not yet connected, so avoid relying on runtime hardware state to define them.
-
-### `observation_features`
-
-This property should return a dictionary describing the structure of sensor outputs from your robot. The keys match what `get_observation()` returns, and the values describe either the shape (for arrays/images) or the type (for simple values).
-
-Example for our 5-DoF arm with one camera:
-```python
-@property
-def _motors_ft(self) -> dict[str, type]:
-    return {
-        "joint_1.pos": float,
-        "joint_2.pos": float,
-        "joint_3.pos": float,
-        "joint_4.pos": float,
-        "joint_5.pos": float,
-    }
-
-@property
-def _cameras_ft(self) -> dict[str, tuple]:
-    return {
-        cam: (self.cameras[cam].height, self.cameras[cam].width, 3) for cam in self.cameras
-    }
-
-@property
-def observation_features(self) -> dict:
-    return {**self._motors_ft, **self._cameras_ft}
-```
-In this case, observations consist of a simple dict storing each motor's position and a camera image.
-
-### `action_features`
-
-This property describes the commands your robot expects via `send_action()`. Again, keys must match the expected input format, and values define the shape/type of each command.
-
-Here, we simply use the same joints proprioceptive features (`self._motors_ft`) as with `observation_features`: the action sent will simply the goal position for each motor.
-```python
-def action_features(self) -> dict:
-    return self._motors_ft
-```
-
-## Step 3: Handle Connection and Disconnection
-
-These methods should handle opening and closing communication with your hardware (e.g. serial ports, CAN interfaces, USB devices, cameras).
-
-### `is_connected`
-
-This property should simply reflect that communication with the robot's hardware is established. When this property is `True`, it should be possible to read and write to the hardware using `get_observation()` and `send_action()`.
-
-```python
-@property
-def is_connected(self) -> bool:
-    return self.bus.is_connected and all(cam.is_connected for cam in self.cameras.values())
-```
-
-### `connect()`
-
-This method should establish communication with the hardware. Moreover, if your robot needs calibration and is not calibrated, it should start a calibration procedure by default. If your robot needs some specific configuration, this should also be called here.
-
-```python
-def connect(self, calibrate: bool = True) -> None:
-    self.bus.connect()
-    if not self.is_calibrated and calibrate:
-        self.calibrate()
-
-    for cam in self.cameras.values():
-        cam.connect()
-
-    self.configure()
-```
-
-### `disconnect()`
-
-This method should gracefully terminate communication with the hardware: free any related resources (threads or processes), close ports, etc.
-
-Here, we already handle this in our `MotorsBus` and `Camera` classes so we just need to call their own `disconnect()` methods:
-```python
-def disconnect(self) -> None:
-    self.bus.disconnect()
-    for cam in self.cameras.values():
-        cam.disconnect()
-```
-
-## Step 4: Support Calibration and Configuration
-
-LeRobot supports saving and loading calibration data automatically. This is useful for joint offsets, zero positions, or sensor alignment.
-
-> Note that depending on your hardware, this may not apply. If that's the case, you can simply leave these methods as no-ops:
-> ```python
-> @property
-> def is_calibrated(self) -> bool:
->    return True
->
-> def calibrate(self) -> None:
->    pass
-> ```
-
-### `is_calibrated`
-
-This should reflect whether your robot has the required calibration loaded.
-
-```python
-@property
-def is_calibrated(self) -> bool:
-    return self.bus.is_calibrated
-```
-
-### `calibrate()`
-
-The goal of the calibration is twofold:
-    - Know the physical range of motion of each motors in order to only send commands within this range.
-    - Normalize raw motors positions to sensible continuous values (e.g. percentages, degrees) instead of arbitrary discrete value dependant on the specific motor used that will not replicate elsewhere.
-
-It should implement the logic for calibration (if relevant) and update the `self.calibration` dictionary. If you are using Feetech or Dynamixel motors, our bus interfaces already include methods to help with this.
-
-```python
-def calibrate(self) -> None:
-    self.bus.disable_torque()
-    for motor in self.bus.motors:
-        self.bus.write("Operating_Mode", motor, OperatingMode.POSITION.value)
-
-    input(f"Move {self} to the middle of its range of motion and press ENTER....")
-    homing_offsets = self.bus.set_half_turn_homings()
-
-    print(
-        "Move all joints sequentially through their entire ranges "
-        "of motion.\nRecording positions. Press ENTER to stop..."
-    )
-    range_mins, range_maxes = self.bus.record_ranges_of_motion()
-
-    self.calibration = {}
-    for motor, m in self.bus.motors.items():
-        self.calibration[motor] = MotorCalibration(
-            id=m.id,
-            drive_mode=0,
-            homing_offset=homing_offsets[motor],
-            range_min=range_mins[motor],
-            range_max=range_maxes[motor],
-        )
-
-    self.bus.write_calibration(self.calibration)
-    self._save_calibration()
-    print("Calibration saved to", self.calibration_fpath)
-```
-
-### `configure()`
-
-Use this to set up any configuration for your hardware (servos control modes, controller gains, etc.). This should usually be run at connection time and be idempotent.
-
-```python
-def configure(self) -> None:
-    with self.bus.torque_disabled():
-        self.bus.configure_motors()
-        for motor in self.bus.motors:
-            self.bus.write("Operating_Mode", motor, OperatingMode.POSITION.value)
-            self.bus.write("P_Coefficient", motor, 16)
-            self.bus.write("I_Coefficient", motor, 0)
-            self.bus.write("D_Coefficient", motor, 32)
-```
-
-## Step 5: Implement Sensors Reading and Action Sending
-
-These are the most important runtime functions: the core I/O loop.
-
-### `get_observation()`
-
-Returns a dictionary of sensor values from the robot. These typically include motor states, camera frames, various sensors, etc. In the LeRobot framework, these observations are what will be fed to a policy in order to predict the actions to take. The dictionary keys and structure must match `observation_features`.
-
-```python
-def get_observation(self) -> dict[str, Any]:
-    if not self.is_connected:
-        raise ConnectionError(f"{self} is not connected.")
-
-    # Read arm position
-    obs_dict = self.bus.sync_read("Present_Position")
-    obs_dict = {f"{motor}.pos": val for motor, val in obs_dict.items()}
-
-    # Capture images from cameras
-    for cam_key, cam in self.cameras.items():
-        obs_dict[cam_key] = cam.async_read()
-
-    return obs_dict
-```
-
-### `send_action()`
-
-Takes a dictionary that matches `action_features`, and sends it to your hardware. You can add safety limits (clipping, smoothing) and return what was actually sent.
-
-For simplicity, we won't be adding any modification of the actions in our example here.
-
-```python
-def send_action(self, action: dict[str, Any]) -> dict[str, Any]:
-    goal_pos = {key.removesuffix(".pos"): val for key, val in action.items()}
-
-    # Send goal position to the arm
-    self.bus.sync_write("Goal_Position", goal_pos)
-
-    return action
-```
-
-## Adding a Teleoperator
-
-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.
-
-## Wrapping Up
-
-Once your robot class is complete, you can leverage the LeRobot ecosystem:
-
-- Control your robot with available teleoperators or integrate directly your teleoperating device
-- Record training data and visualize it
-- Integrate it into RL or imitation learning pipelines
-
-Don't hesitate to reach out to the community for help on our [Discord](https://discord.gg/s3KuuzsPFb) 🤗

docs/source/koch.mdx

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

docs/source/lekiwi.mdx

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

docs/source/notebooks.mdx

@@ -1,29 +0,0 @@
-# 🤗 LeRobot Notebooks
-
-This repository contains example notebooks for using LeRobot. These notebooks demonstrate how to train policies on real or simulation datasets using standardized policies.
-
----
-
-### Training ACT
-
-[ACT](https://huggingface.co/papers/2304.13705) (Action Chunking Transformer) is a transformer-based policy architecture for imitation learning that processes robot states and camera inputs to generate smooth, chunked action sequences.
-
-We provide a ready-to-run Google Colab notebook to help you train ACT policies using datasets from the Hugging Face Hub, with optional logging to Weights & Biases.
-
-| Notebook | Colab |
-|:---------|:------|
-| [Train ACT with LeRobot](https://github.com/huggingface/notebooks/blob/main/lerobot/training-act.ipynb) | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/lerobot/training-act.ipynb) |
-
-Expected training time for 100k steps: ~1.5 hours on an NVIDIA A100 GPU with batch size of `64`.
-
-### Training SmolVLA
-
-[SmolVLA](https://huggingface.co/papers/2506.01844) is a small but efficient Vision-Language-Action model. It is compact in size with 450 M-parameter and is developed by Hugging Face.
-
-We provide a ready-to-run Google Colab notebook to help you train SmolVLA policies using datasets from the Hugging Face Hub, with optional logging to Weights & Biases.
-
-| Notebook                                                                                                        | Colab                                                                                                                                                                                 |
-| :-------------------------------------------------------------------------------------------------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| [Train SmolVLA with LeRobot](https://github.com/huggingface/notebooks/blob/main/lerobot/training-smolvla.ipynb) | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/lerobot/training-smolvla.ipynb) |
-
-Expected training time for 20k steps: ~5 hours on an NVIDIA A100 GPU with batch size of `64`.

docs/source/smolvla.mdx

@@ -1,97 +0,0 @@
-# Finetune SmolVLA
-
-SmolVLA is Hugging Face’s lightweight foundation model for robotics. Designed for easy fine-tuning on LeRobot datasets, it helps accelerate your development!
-
-<p align="center">
-  <img src="https://cdn-uploads.huggingface.co/production/uploads/640e21ef3c82bd463ee5a76d/aooU0a3DMtYmy_1IWMaIM.png" alt="SmolVLA architecture." width="500"/>
-  <br/>
-  <em>Figure 1. SmolVLA takes as input (i) multiple cameras views, (ii) the robot’s current sensorimotor state, and (iii) a natural language instruction, encoded into contextual features used to condition the action expert when generating an action chunk.</em>
-</p>
-
-## Set Up Your Environment
-
-1. Install LeRobot by following our [Installation Guide](./installation).
-2. Install SmolVLA dependencies by running:
-
-   ```bash
-   pip install -e ".[smolvla]"
-   ```
-
-## Collect a dataset
-
-SmolVLA is a base model, so fine-tuning on your own data is required for optimal performance in your setup.
-We recommend recording ~50 episodes of your task as a starting point. Follow our guide to get started: [Recording a Dataset](https://huggingface.co/docs/lerobot/getting_started_real_world_robot#record-a-dataset)
-
-<Tip>
-
-In your dataset, make sure to have enough demonstrations per each variation (e.g. the cube position on the table if it is cube pick-place task) you are introducing.
-
-We recommend checking out the dataset linked below for reference that was used in the [SmolVLA paper](https://huggingface.co/papers/2506.01844):
-
-🔗 [SVLA SO100 PickPlace](https://huggingface.co/spaces/lerobot/visualize_dataset?path=%2Flerobot%2Fsvla_so100_pickplace%2Fepisode_0)
-
-In this dataset, we recorded 50 episodes across 5 distinct cube positions. For each position, we collected 10 episodes of pick-and-place interactions. This structure, repeating each variation several times, helped the model generalize better. We tried similar dataset with 25 episodes, and it was not enough leading to a bad performance. So, the data quality and quantity is definitely a key.
-After you have your dataset available on the Hub, you are good to go to use our finetuning script to adapt SmolVLA to your application.
-</Tip>
-
-## Finetune SmolVLA on your data
-
-Use [`smolvla_base`](https://hf.co/lerobot/smolvla_base), our pretrained 450M model, and fine-tune it on your data.
-Training the model for 20k steps will roughly take ~4 hrs on a single A100 GPU. You should tune the number of steps based on performance and your use-case.
-
-If you don't have a gpu device, you can train using our notebook on [![Google Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/lerobot/training-smolvla.ipynb)
-
-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 -m lerobot.scripts.train \
-  --policy.path=lerobot/smolvla_base \
-  --dataset.repo_id=${HF_USER}/mydataset \
-  --batch_size=64 \
-  --steps=20000 \
-  --output_dir=outputs/train/my_smolvla \
-  --job_name=my_smolvla_training \
-  --policy.device=cuda \
-  --wandb.enable=true
-```
-
-<Tip>
-You can start with a small batch size and increase it incrementally, if the GPU allows it, as long as loading times remain short.
-</Tip>
-
-Fine-tuning is an art. For a complete overview of the options for finetuning, run
-
-```bash
-python -m lerobot.scripts.train --help
-```
-
-<p align="center">
-  <img src="https://cdn-uploads.huggingface.co/production/uploads/640e21ef3c82bd463ee5a76d/S-3vvVCulChREwHDkquoc.gif" alt="Comparison of SmolVLA across task variations." width="500"/>
-  <br/>
-  <em>Figure 2: Comparison of SmolVLA across task variations. From left to right: (1) pick-place cube counting, (2) pick-place cube counting, (3) pick-place cube counting under perturbations, and (4) generalization on pick-and-place of the lego block with real-world SO101.</em>
-</p>
-
-
-## Evaluate the finetuned model and run it in real-time
-
-Similarly for when recording an episode, it is recommended that you are logged in to the HuggingFace Hub. You can follow the corresponding steps: [Record a dataset](./getting_started_real_world_robot#record-a-dataset).
-Once you are logged in, you can run inference in your setup by doing:
-
-```bash
-python -m lerobot.record \
-  --robot.type=so101_follower \
-  --robot.port=/dev/ttyACM0 \ # <- Use your port
-  --robot.id=my_blue_follower_arm \ # <- Use your robot id
-  --robot.cameras="{ front: {type: opencv, index_or_path: 8, width: 640, height: 480, fps: 30}}" \ # <- Use your cameras
-  --dataset.single_task="Grasp a lego block and put it in the bin." \ # <- Use the same task description you used in your dataset recording
-  --dataset.repo_id=${HF_USER}/eval_DATASET_NAME_test \  # <- This will be the dataset name on HF Hub
-  --dataset.episode_time_s=50 \
-  --dataset.num_episodes=10 \
-  # <- Teleop optional if you want to teleoperate in between episodes \
-  # --teleop.type=so100_leader \
-  # --teleop.port=/dev/ttyACM0 \
-  # --teleop.id=my_red_leader_arm \
-  --policy.path=HF_USER/FINETUNE_MODEL_NAME # <- Use your fine-tuned model
-```
-
-Depending on your evaluation setup, you can configure the duration and the number of episodes to record for your evaluation suite.

docs/source/so100.mdx

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

docs/source/so101.mdx

@@ -1 +1 @@
-../../src/lerobot/robots/so101_follower/so101.mdx
+../../lerobot/common/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.datasets.lerobot_dataset import LeRobotDataset, LeRobotDatasetMetadata
+from lerobot.common.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.policies.diffusion.modeling_diffusion import DiffusionPolicy
+from lerobot.common.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`](../src/lerobot/scripts/train.py). At a high level it does the following:
+LeRobot offers a training script at [`lerobot/scripts/train.py`](../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`](../src/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`](../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](../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:
+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:
 ```bash
-python -m lerobot.scripts.train \
+python lerobot/scripts/train.py \
     --dataset.repo_id=lerobot/pusht \
     --policy.type=diffusion \
     --env.type=pusht
@@ -60,12 +60,12 @@ python -m lerobot.scripts.train \
 
 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/policies](../src/lerobot/policies)
-- 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)
+- 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)
+- 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)
 
-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:
+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:
 ```bash
-python -m lerobot.scripts.train \
+python lerobot/scripts/train.py \
     --policy.type=act \
     --dataset.repo_id=lerobot/aloha_sim_insertion_human \
     --env.type=aloha \
@@ -74,9 +74,9 @@ python -m lerobot.scripts.train \
 > 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`](../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:
+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:
 ```bash
-python -m lerobot.scripts.train \
+python lerobot/scripts/train.py \
     --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 -m lerobot.scripts.train \
+python lerobot/scripts/train.py \
     --config_path=outputs/train/act_aloha_transfer/checkpoints/last/pretrained_model/ \
     --output_dir=outputs/train/act_aloha_transfer_2
 ```
@@ -119,7 +119,7 @@ python -m lerobot.scripts.train \
 
 Similarly to Hydra, we can still override some parameters in the CLI if we want to, e.g.:
 ```bash
-python -m lerobot.scripts.train \
+python lerobot/scripts/train.py \
     --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 -m lerobot.scripts.train \
 
 `--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 -m lerobot.scripts.train --config_path=lerobot/diffusion_pusht
+python lerobot/scripts/train.py --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 -m lerobot.scripts.train \
+python lerobot/scripts/train.py \
     --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 -m lerobot.scripts.train \
+python lerobot/scripts/train.py \
     --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 -m lerobot.scripts.train \
+python lerobot/scripts/train.py \
     --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 -m lerobot.scripts.train \
+python lerobot/scripts/train.py \
     --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 -m lerobot.scripts.train \
+python lerobot/scripts/train.py \
     --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 -m lerobot.scripts.train \
 
 #### Train a policy from scratch - config file + CLI
 ```bash
-python -m lerobot.scripts.train \
+python lerobot/scripts/train.py \
     --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 -m lerobot.scripts.train \
+python lerobot/scripts/train.py \
     --config_path=checkpoint/pretrained_model/ \
     --resume=true \
     --steps=200000  # <- you can change some training parameters
@@ -259,7 +259,7 @@ python -m lerobot.scripts.train \
 
 #### Fine-tuning
 ```bash
-python -m lerobot.scripts.train \
+python lerobot/scripts/train.py \
     --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/5_control_robot.md

@@ -0,0 +1,998 @@
+# Getting Started with Real-World Robots
+
+This tutorial will guide you through the process of setting up and training a neural network to autonomously control a real robot.
+
+**What You'll Learn:**
+1. How to order and assemble your robot.
+2. How to connect, configure, and calibrate your robot.
+3. How to record and visualize your dataset.
+4. How to train a policy using your data and prepare it for evaluation.
+5. How to evaluate your policy and visualize the results.
+
+By following these steps, you'll be able to replicate tasks like picking up a Lego block and placing it in a bin with a high success rate, as demonstrated in [this video](https://x.com/RemiCadene/status/1814680760592572934).
+
+This tutorial is specifically made for the affordable [Koch v1.1](https://github.com/jess-moss/koch-v1-1) robot, but it contains additional information to be easily adapted to various types of robots like [Aloha bimanual robot](https://aloha-2.github.io) by changing some configurations. The Koch v1.1 consists of a leader arm and a follower arm, each with 6 motors. It can work with one or several cameras to record the scene, which serve as visual sensors for the robot.
+
+During the data collection phase, you will control the follower arm by moving the leader arm. This process is known as "teleoperation." This technique is used to collect robot trajectories. Afterward, you'll train a neural network to imitate these trajectories and deploy the network to enable your robot to operate autonomously.
+
+If you encounter any issues at any step of the tutorial, feel free to seek help on [Discord](https://discord.com/invite/s3KuuzsPFb) or don't hesitate to iterate with us on the tutorial by creating issues or pull requests. Thanks!
+
+## 1. Order and Assemble your Koch v1.1
+
+Follow the sourcing and assembling instructions provided on the [Koch v1.1 Github page](https://github.com/jess-moss/koch-v1-1). This will guide you through setting up both the follower and leader arms, as shown in the image below.
+
+<div style="text-align:center;">
+  <img src="../media/tutorial/koch_v1_1_leader_follower.webp?raw=true" alt="Koch v1.1 leader and follower arms" title="Koch v1.1 leader and follower arms" width="50%">
+</div>
+
+For a visual walkthrough of the assembly process, you can refer to [this video tutorial](https://youtu.be/8nQIg9BwwTk).
+
+## 2. Configure motors, calibrate arms, teleoperate your Koch v1.1
+
+First, install the additional dependencies required for robots built with dynamixel motors like Koch v1.1 by running one of the following commands (make sure gcc is installed).
+
+Using `pip`:
+```bash
+pip install -e ".[dynamixel]"
+```
+
+Using `poetry`:
+```bash
+poetry sync --extras "dynamixel"
+```
+
+Using `uv`:
+```bash
+uv sync --extra "dynamixel"
+```
+
+You are now ready to plug the 5V power supply to the motor bus of the leader arm (the smaller one) since all its motors only require 5V.
+
+Then plug the 12V power supply to the motor bus of the follower arm. It has two motors that need 12V, and the rest will be powered with 5V through the voltage convertor.
+
+Finally, connect both arms to your computer via USB. Note that the USB doesn't provide any power, and both arms need to be plugged in with their associated power supply to be detected by your computer.
+
+Now you are ready to configure your motors for the first time, as detailed in the sections below. In the upcoming sections, you'll learn about our classes and functions by running some python code in an interactive session, or by copy-pasting it in a python file.
+
+If you have already configured your motors the first time, you can streamline the process by directly running the teleoperate script (which is detailed further in the tutorial):
+
+> **NOTE:** To visualize the data, enable `--control.display_data=true`. This streams the data using `rerun`.
+
+```bash
+python lerobot/scripts/control_robot.py \
+  --robot.type=koch \
+  --control.type=teleoperate
+```
+
+It will automatically:
+1. Identify any missing calibrations and initiate the calibration procedure.
+2. Connect the robot and start teleoperation.
+
+### a. Control your motors with DynamixelMotorsBus
+
+You can use the [`DynamixelMotorsBus`](../lerobot/common/robot_devices/motors/dynamixel.py) to communicate with the motors connected as a chain to the corresponding USB bus. This class leverages the Python [Dynamixel SDK](https://emanual.robotis.com/docs/en/software/dynamixel/dynamixel_sdk/sample_code/python_read_write_protocol_2_0/#python-read-write-protocol-20) to facilitate reading from and writing to the motors.
+
+**First Configuration of your motors**
+
+You will need to unplug each motor in turn and run a command the identify the motor. The motor will save its own identification, so you only need to do this once. Start by unplugging all of the motors.
+
+Do the Leader arm first, as all of its motors are of the same type. Plug in your first motor on your leader arm and run this script to set its ID to 1.
+```bash
+python lerobot/scripts/configure_motor.py \
+  --port /dev/tty.usbmodem58760432961 \
+  --brand dynamixel \
+  --model xl330-m288 \
+  --baudrate 1000000 \
+  --id 1
+```
+
+Then unplug your first motor and plug the second motor and set its ID to 2.
+```bash
+python lerobot/scripts/configure_motor.py \
+  --port /dev/tty.usbmodem58760432961 \
+  --brand dynamixel \
+  --model xl330-m288 \
+  --baudrate 1000000 \
+  --id 2
+```
+
+Redo the process for all your motors until ID 6.
+
+The process for the follower arm is almost the same, but the follower arm has two types of motors. For the first two motors, make sure you set the model to `xl430-w250`. _Important: configuring follower motors requires plugging and unplugging power. Make sure you use the 5V power for the XL330s and the 12V power for the XL430s!_
+
+After all of your motors are configured properly, you're ready to plug them all together in a daisy-chain as shown in the original video.
+
+**Instantiate the DynamixelMotorsBus**
+
+To begin, create two instances of the  [`DynamixelMotorsBus`](../lerobot/common/robot_devices/motors/dynamixel.py), one for each arm, using their corresponding USB ports (e.g. `DynamixelMotorsBus(port="/dev/tty.usbmodem575E0031751"`).
+
+To find the correct ports for each arm, run the utility script twice:
+```bash
+python lerobot/scripts/find_motors_bus_port.py
+```
+
+Example output when identifying the leader arm's port (e.g., `/dev/tty.usbmodem575E0031751` on Mac, or possibly `/dev/ttyACM0` on Linux):
+```
+Finding all available ports for the MotorBus.
+['/dev/tty.usbmodem575E0032081', '/dev/tty.usbmodem575E0031751']
+Remove the usb cable from your DynamixelMotorsBus and press Enter when done.
+
+[...Disconnect leader arm and press Enter...]
+
+The port of this DynamixelMotorsBus is /dev/tty.usbmodem575E0031751
+Reconnect the usb cable.
+```
+
+Example output when identifying the follower arm's port (e.g., `/dev/tty.usbmodem575E0032081`, or possibly `/dev/ttyACM1` on Linux):
+```
+Finding all available ports for the MotorBus.
+['/dev/tty.usbmodem575E0032081', '/dev/tty.usbmodem575E0031751']
+Remove the usb cable from your DynamixelMotorsBus and press Enter when done.
+
+[...Disconnect follower arm and press Enter...]
+
+The port of this DynamixelMotorsBus is /dev/tty.usbmodem575E0032081
+Reconnect the usb cable.
+```
+
+Troubleshooting: On Linux, you might need to give access to the USB ports by running this command with your ports:
+```bash
+sudo chmod 666 /dev/tty.usbmodem575E0032081
+sudo chmod 666 /dev/tty.usbmodem575E0031751
+```
+
+*Listing and Configuring Motors*
+
+Next, you'll need to list the motors for each arm, including their name, index, and model. Initially, each motor is assigned the factory default index `1`. Since each motor requires a unique index to function correctly when connected in a chain on a common bus, you'll need to assign different indices. It's recommended to use an ascending index order, starting from `1` (e.g., `1, 2, 3, 4, 5, 6`). These indices will be saved in the persistent memory of each motor during the first connection.
+
+To assign indices to the motors, run this code in an interactive Python session. Replace the `port` values with the ones you identified earlier:
+```python
+from lerobot.common.robot_devices.motors.configs import DynamixelMotorsBusConfig
+from lerobot.common.robot_devices.motors.dynamixel import DynamixelMotorsBus
+
+leader_config = DynamixelMotorsBusConfig(
+    port="/dev/tty.usbmodem575E0031751",
+    motors={
+        # name: (index, model)
+        "shoulder_pan": (1, "xl330-m077"),
+        "shoulder_lift": (2, "xl330-m077"),
+        "elbow_flex": (3, "xl330-m077"),
+        "wrist_flex": (4, "xl330-m077"),
+        "wrist_roll": (5, "xl330-m077"),
+        "gripper": (6, "xl330-m077"),
+    },
+)
+
+follower_config = DynamixelMotorsBusConfig(
+    port="/dev/tty.usbmodem575E0032081",
+    motors={
+        # name: (index, model)
+        "shoulder_pan": (1, "xl430-w250"),
+        "shoulder_lift": (2, "xl430-w250"),
+        "elbow_flex": (3, "xl330-m288"),
+        "wrist_flex": (4, "xl330-m288"),
+        "wrist_roll": (5, "xl330-m288"),
+        "gripper": (6, "xl330-m288"),
+    },
+)
+
+leader_arm = DynamixelMotorsBus(leader_config)
+follower_arm = DynamixelMotorsBus(follower_config)
+```
+
+IMPORTANTLY: Now that you have your ports, update [`KochRobotConfig`](../lerobot/common/robot_devices/robots/configs.py). You will find something like:
+```python
+@RobotConfig.register_subclass("koch")
+@dataclass
+class KochRobotConfig(ManipulatorRobotConfig):
+    calibration_dir: str = ".cache/calibration/koch"
+    # `max_relative_target` limits the magnitude of the relative positional target vector for safety purposes.
+    # Set this to a positive scalar to have the same value for all motors, or a list that is the same length as
+    # the number of motors in your follower arms.
+    max_relative_target: int | None = None
+
+    leader_arms: dict[str, MotorsBusConfig] = field(
+        default_factory=lambda: {
+            "main": DynamixelMotorsBusConfig(
+                port="/dev/tty.usbmodem585A0085511", <-- UPDATE HERE
+                motors={
+                    # name: (index, model)
+                    "shoulder_pan": [1, "xl330-m077"],
+                    "shoulder_lift": [2, "xl330-m077"],
+                    "elbow_flex": [3, "xl330-m077"],
+                    "wrist_flex": [4, "xl330-m077"],
+                    "wrist_roll": [5, "xl330-m077"],
+                    "gripper": [6, "xl330-m077"],
+                },
+            ),
+        }
+    )
+
+    follower_arms: dict[str, MotorsBusConfig] = field(
+        default_factory=lambda: {
+            "main": DynamixelMotorsBusConfig(
+                port="/dev/tty.usbmodem585A0076891", <-- UPDATE HERE
+                motors={
+                    # name: (index, model)
+                    "shoulder_pan": [1, "xl430-w250"],
+                    "shoulder_lift": [2, "xl430-w250"],
+                    "elbow_flex": [3, "xl330-m288"],
+                    "wrist_flex": [4, "xl330-m288"],
+                    "wrist_roll": [5, "xl330-m288"],
+                    "gripper": [6, "xl330-m288"],
+                },
+            ),
+        }
+    )
+```
+
+**Connect and Configure your Motors**
+
+Before you can start using your motors, you'll need to configure them to ensure proper communication. When you first connect the motors, the [`DynamixelMotorsBus`](../lerobot/common/robot_devices/motors/dynamixel.py) automatically detects any mismatch between the current motor indices (factory set to `1`) and the specified indices (e.g., `1, 2, 3, 4, 5, 6`). This triggers a configuration procedure that requires you to unplug the power cord and motors, then reconnect each motor sequentially, starting from the one closest to the bus.
+
+For a visual guide, refer to the [video tutorial of the configuration procedure](https://youtu.be/U78QQ9wCdpY).
+
+To connect and configure the leader arm, run the following code in the same Python interactive session as earlier in the tutorial:
+```python
+leader_arm.connect()
+```
+
+When you connect the leader arm for the first time, you might see an output similar to this:
+```
+Read failed due to communication error on port /dev/tty.usbmodem575E0032081 for group_key ID_shoulder_pan_shoulder_lift_elbow_flex_wrist_flex_wrist_roll_gripper: [TxRxResult] There is no status packet!
+
+/!\ A configuration issue has been detected with your motors:
+If this is the first time you are using these motors, press enter to configure your motors... but before verify that all the cables are connected the proper way. If you find an issue, before making a modification, kill the python process, unplug the power cord to not damage the motors, rewire correctly, then plug the power again and relaunch the script.
+
+Motor indices detected: {9600: [1]}
+
+1. Unplug the power cord
+2. Plug/unplug minimal number of cables to only have the first 1 motor(s) (['shoulder_pan']) connected.
+3. Re-plug the power cord
+Press Enter to continue...
+
+*Follow the procedure*
+
+Setting expected motor indices: [1, 2, 3, 4, 5, 6]
+```
+
+Once the leader arm is configured, repeat the process for the follower arm by running:
+```python
+follower_arm.connect()
+```
+
+Congratulations! Both arms are now properly configured and connected. You won't need to go through the configuration procedure again in the future.
+
+**Troubleshooting**:
+
+If the configuration process fails, you may need to do the configuration process via the Dynamixel Wizard.
+
+Known failure modes:
+- Calling `arm.connect()` raises `OSError: No motor found, but one new motor expected. Verify power cord is plugged in and retry` on Ubuntu 22.
+
+Steps:
+1. Visit https://emanual.robotis.com/docs/en/software/dynamixel/dynamixel_wizard2/#connect-dynamixel.
+2. Follow the software installation instructions in section 3 of the web page.
+3. Launch the software.
+4. Configure the device scanning options in the menu under `Tools` > `Options` > `Scan`. Check only Protocol 2.0, select only the USB port identifier of interest, select all baudrates, set the ID range to `[0, 10]`. _While this step was not strictly necessary, it greatly speeds up scanning_.
+5. For each motor in turn:
+    - Disconnect the power to the driver board.
+    - Connect **only** the motor of interest to the driver board, making sure to disconnect it from any other motors.
+    - Reconnect the power to the driver board.
+    - From the software menu select `Device` > `Scan` and let the scan run. A device should appear.
+    - If the device has an asterisk (*) near it, it means the firmware is indeed outdated. From the software menu, select `Tools` > `Firmware Update`. Follow the prompts.
+    - The main panel should have table with various parameters of the device (refer to the web page, section 5). Select the row with `ID`, and then set the desired ID on the bottom right panel by selecting and clicking `Save`.
+    - Just like you did with the ID, also set the `Baud Rate` to 1 Mbps.
+6. Check everything has been done right:
+   - Rewire the arms in their final configuration and power both of them.
+   - Scan for devices. All 12 motors should appear.
+   - Select the motors one by one and move the arm. Check that the graphical indicator near the top right shows the movement.
+
+** There is a common issue with the Dynamixel XL430-W250 motors where the motors become undiscoverable after upgrading their firmware from Mac and Windows Dynamixel Wizard2 applications.  When this occurs, it is required to do a firmware recovery (Select `DYNAMIXEL Firmware Recovery` and follow the prompts).   There are two known workarounds to conduct this firmware reset:
+  1) Install the Dynamixel Wizard on a linux machine and complete the firmware recovery
+  2) Use the Dynamixel U2D2 in order to perform the reset with Windows or Mac.  This U2D2 can be purchased [here](https://www.robotis.us/u2d2/).
+  For either solution, open DYNAMIXEL Wizard 2.0 and select the appropriate port. You will likely be unable to see the motor in the GUI at this time. Select `Firmware Recovery`, carefully choose the correct model, and wait for the process to complete. Finally, re-scan to confirm the firmware recovery was successful.
+
+**Read and Write with DynamixelMotorsBus**
+
+To get familiar with how `DynamixelMotorsBus` communicates with the motors, you can start by reading data from them. Copy past this code in the same interactive python session:
+```python
+leader_pos = leader_arm.read("Present_Position")
+follower_pos = follower_arm.read("Present_Position")
+print(leader_pos)
+print(follower_pos)
+```
+
+Expected output might look like:
+```
+array([2054,  523, 3071, 1831, 3049, 2441], dtype=int32)
+array([2003, 1601,   56, 2152, 3101, 2283], dtype=int32)
+```
+
+Try moving the arms to various positions and observe how the values change.
+
+Now let's try to enable torque in the follower arm by copy pasting this code:
+```python
+from lerobot.common.robot_devices.motors.dynamixel import TorqueMode
+
+follower_arm.write("Torque_Enable", TorqueMode.ENABLED.value)
+```
+
+With torque enabled, the follower arm will be locked in its current position. Do not attempt to manually move the arm while torque is enabled, as this could damage the motors.
+
+Now, to get more familiar with reading and writing, let's move the arm programmatically copy pasting the following example code:
+```python
+# Get the current position
+position = follower_arm.read("Present_Position")
+
+# Update first motor (shoulder_pan) position by +10 steps
+position[0] += 10
+follower_arm.write("Goal_Position", position)
+
+# Update all motors position by -30 steps
+position -= 30
+follower_arm.write("Goal_Position", position)
+
+# Update gripper by +30 steps
+position[-1] += 30
+follower_arm.write("Goal_Position", position[-1], "gripper")
+```
+
+When you're done playing, you can try to disable the torque, but make sure you hold your robot so that it doesn't fall:
+```python
+follower_arm.write("Torque_Enable", TorqueMode.DISABLED.value)
+```
+
+Finally, disconnect the arms:
+```python
+leader_arm.disconnect()
+follower_arm.disconnect()
+```
+
+Alternatively, you can unplug the power cord, which will automatically disable torque and disconnect the motors.
+
+*/!\ Warning*: These motors tend to overheat, especially under torque or if left plugged in for too long. Unplug after use.
+
+### b. Teleoperate your Koch v1.1 with ManipulatorRobot
+
+**Instantiate the ManipulatorRobot**
+
+Before you can teleoperate your robot, you need to instantiate the  [`ManipulatorRobot`](../lerobot/common/robot_devices/robots/manipulator.py) using the previously defined `leader_config` and `follower_config`.
+
+For the Koch v1.1 robot, we only have one leader, so we refer to it as `"main"` and define it as `leader_arms={"main": leader_config}`. We do the same for the follower arm. For other robots (like the Aloha), which may have two pairs of leader and follower arms, you would define them like this: `leader_arms={"left": left_leader_config, "right": right_leader_config},`. Same thing for the follower arms.
+
+
+Run the following code to instantiate your manipulator robot:
+```python
+from lerobot.common.robot_devices.robots.configs import KochRobotConfig
+from lerobot.common.robot_devices.robots.manipulator import ManipulatorRobot
+
+robot_config = KochRobotConfig(
+    leader_arms={"main": leader_config},
+    follower_arms={"main": follower_config},
+    cameras={},  # We don't use any camera for now
+)
+robot = ManipulatorRobot(robot_config)
+```
+
+The `KochRobotConfig` is used to set the associated settings and calibration process. For instance, we activate the torque of the gripper of the leader Koch v1.1 arm and position it at a 40 degree angle to use it as a trigger.
+
+For the [Aloha bimanual robot](https://aloha-2.github.io), we would use `AlohaRobotConfig` to set different settings such as a secondary ID for shadow joints (shoulder, elbow). Specific to Aloha, LeRobot comes with default calibration files stored in `.cache/calibration/aloha_default`. Assuming the motors have been properly assembled, no manual calibration step is expected for Aloha.
+
+**Calibrate and Connect the ManipulatorRobot**
+
+Next, you'll need to calibrate your Koch robot to ensure that the leader and follower arms have the same position values when they are in the same physical position. This calibration is essential because it allows a neural network trained on one Koch robot to work on another.
+
+When you connect your robot for the first time, the [`ManipulatorRobot`](../lerobot/common/robot_devices/robots/manipulator.py) will detect if the calibration file is missing and trigger the calibration procedure. During this process, you will be guided to move each arm to three different positions.
+
+Here are the positions you'll move the follower arm to:
+
+| 1. Zero position                                                                                                                                                  | 2. Rotated position                                                                                                                                                        | 3. Rest position                                                                                                                                                  |
+| ----------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| <img src="../media/koch/follower_zero.webp?raw=true" alt="Koch v1.1 follower arm zero position" title="Koch v1.1 follower arm zero position" style="width:100%;"> | <img src="../media/koch/follower_rotated.webp?raw=true" alt="Koch v1.1 follower arm rotated position" title="Koch v1.1 follower arm rotated position" style="width:100%;"> | <img src="../media/koch/follower_rest.webp?raw=true" alt="Koch v1.1 follower arm rest position" title="Koch v1.1 follower arm rest position" style="width:100%;"> |
+
+And here are the corresponding positions for the leader arm:
+
+| 1. Zero position                                                                                                                                            | 2. Rotated position                                                                                                                                                  | 3. Rest position                                                                                                                                            |
+| ----------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| <img src="../media/koch/leader_zero.webp?raw=true" alt="Koch v1.1 leader arm zero position" title="Koch v1.1 leader arm zero position" style="width:100%;"> | <img src="../media/koch/leader_rotated.webp?raw=true" alt="Koch v1.1 leader arm rotated position" title="Koch v1.1 leader arm rotated position" style="width:100%;"> | <img src="../media/koch/leader_rest.webp?raw=true" alt="Koch v1.1 leader arm rest position" title="Koch v1.1 leader arm rest position" style="width:100%;"> |
+
+You can watch a [video tutorial of the calibration procedure](https://youtu.be/8drnU9uRY24) for more details.
+
+During calibration, we count the number of full 360-degree rotations your motors have made since they were first used. That's why we ask you to move to this arbitrary "zero" position. We don't actually "set" the zero position, so you don't need to be accurate. After calculating these "offsets" to shift the motor values around 0, we need to assess the rotation direction of each motor, which might differ. That's why we ask you to rotate all motors to roughly 90 degrees, to measure if the values changed negatively or positively.
+
+Finally, the rest position ensures that the follower and leader arms are roughly aligned after calibration, preventing sudden movements that could damage the motors when starting teleoperation.
+
+Importantly, once calibrated, all Koch robots will move to the same positions (e.g. zero and rotated position) when commanded.
+
+Run the following code to calibrate and connect your robot:
+```python
+robot.connect()
+```
+
+The output will look like this:
+```
+Connecting main follower arm
+Connecting main leader arm
+
+Missing calibration file '.cache/calibration/koch/main_follower.json'
+Running calibration of koch main follower...
+Move arm to zero position
+[...]
+Move arm to rotated position
+[...]
+Move arm to rest position
+[...]
+Calibration is done! Saving calibration file '.cache/calibration/koch/main_follower.json'
+
+Missing calibration file '.cache/calibration/koch/main_leader.json'
+Running calibration of koch main leader...
+Move arm to zero position
+[...]
+Move arm to rotated position
+[...]
+Move arm to rest position
+[...]
+Calibration is done! Saving calibration file '.cache/calibration/koch/main_leader.json'
+```
+
+*Verifying Calibration*
+
+Once calibration is complete, you can check the positions of the leader and follower arms to ensure they match. If the calibration was successful, the positions should be very similar.
+
+Run this code to get the positions in degrees:
+```python
+leader_pos = robot.leader_arms["main"].read("Present_Position")
+follower_pos = robot.follower_arms["main"].read("Present_Position")
+
+print(leader_pos)
+print(follower_pos)
+```
+
+Example output:
+```
+array([-0.43945312, 133.94531, 179.82422, -18.984375, -1.9335938, 34.541016], dtype=float32)
+array([-0.58723712, 131.72314, 174.98743, -16.872612, 0.786213, 35.271973], dtype=float32)
+```
+
+These values are in degrees, which makes them easier to interpret and debug. The zero position used during calibration should roughly correspond to 0 degrees for each motor, and the rotated position should roughly correspond to 90 degrees for each motor.
+
+**Teleoperate your Koch v1.1**
+
+You can easily teleoperate your robot by reading the positions from the leader arm and sending them as goal positions to the follower arm.
+
+To teleoperate your robot for 30 seconds at a frequency of approximately 200Hz, run the following code:
+```python
+import tqdm
+seconds = 30
+frequency = 200
+for _ in tqdm.tqdm(range(seconds*frequency)):
+    leader_pos = robot.leader_arms["main"].read("Present_Position")
+    robot.follower_arms["main"].write("Goal_Position", leader_pos)
+```
+
+*Using `teleop_step` for Teleoperation*
+
+Alternatively, you can teleoperate the robot using the `teleop_step` method from [`ManipulatorRobot`](../lerobot/common/robot_devices/robots/manipulator.py).
+
+Run this code to teleoperate:
+```python
+for _ in tqdm.tqdm(range(seconds*frequency)):
+    robot.teleop_step()
+```
+
+*Recording data during Teleoperation*
+
+Teleoperation is particularly useful for recording data. You can use the `teleop_step(record_data=True)` to returns both the follower arm's position as `"observation.state"` and the leader arm's position as `"action"`. This function also converts the numpy arrays into PyTorch tensors. If you're working with a robot that has two leader and two follower arms (like the Aloha), the positions are concatenated.
+
+Run the following code to see how slowly moving the leader arm affects the observation and action:
+```python
+leader_pos = robot.leader_arms["main"].read("Present_Position")
+follower_pos = robot.follower_arms["main"].read("Present_Position")
+observation, action = robot.teleop_step(record_data=True)
+
+print(follower_pos)
+print(observation)
+print(leader_pos)
+print(action)
+```
+
+Expected output:
+```
+array([7.8223, 131.1328, 165.5859, -23.4668, -0.9668, 32.4316], dtype=float32)
+{'observation.state': tensor([7.8223, 131.1328, 165.5859, -23.4668, -0.9668, 32.4316])}
+array([3.4277, 134.1211, 179.8242, -18.5449, -1.5820, 34.7168], dtype=float32)
+{'action': tensor([3.4277, 134.1211, 179.8242, -18.5449, -1.5820, 34.7168])}
+```
+
+*Asynchronous Frame Recording*
+
+Additionally, `teleop_step` can asynchronously record frames from multiple cameras and include them in the observation dictionary as `"observation.images.CAMERA_NAME"`. This feature will be covered in more detail in the next section.
+
+*Disconnecting the Robot*
+
+When you're finished, make sure to disconnect your robot by running:
+```python
+robot.disconnect()
+```
+
+Alternatively, you can unplug the power cord, which will also disable torque.
+
+*/!\ Warning*: These motors tend to overheat, especially under torque or if left plugged in for too long. Unplug after use.
+
+### c. Add your cameras with OpenCVCamera
+
+**(Optional) Use your phone as camera on Linux**
+
+If you want to use your phone as a camera on Linux, follow these steps to set up a virtual camera
+
+1. *Install `v4l2loopback-dkms` and `v4l-utils`*. Those packages are required to create virtual camera devices (`v4l2loopback`) and verify their settings with the `v4l2-ctl` utility from `v4l-utils`. Install them using:
+```python
+sudo apt install v4l2loopback-dkms v4l-utils
+```
+2. *Install [DroidCam](https://droidcam.app) on your phone*. This app is available for both iOS and Android.
+3. *Install [OBS Studio](https://obsproject.com)*. This software will help you manage the camera feed. Install it using [Flatpak](https://flatpak.org):
+```python
+flatpak install flathub com.obsproject.Studio
+```
+4. *Install the DroidCam OBS plugin*. This plugin integrates DroidCam with OBS Studio. Install it with:
+```python
+flatpak install flathub com.obsproject.Studio.Plugin.DroidCam
+```
+5. *Start OBS Studio*. Launch with:
+```python
+flatpak run com.obsproject.Studio
+```
+6. *Add your phone as a source*. Follow the instructions [here](https://droidcam.app/obs/usage). Be sure to set the resolution to `640x480`.
+7. *Adjust resolution settings*. In OBS Studio, go to `File > Settings > Video`. Change the `Base(Canvas) Resolution` and the `Output(Scaled) Resolution` to `640x480` by manually typing it in.
+8. *Start virtual camera*. In OBS Studio, follow the instructions [here](https://obsproject.com/kb/virtual-camera-guide).
+9. *Verify the virtual camera setup*. Use `v4l2-ctl` to list the devices:
+```python
+v4l2-ctl --list-devices
+```
+You should see an entry like:
+```
+VirtualCam (platform:v4l2loopback-000):
+/dev/video1
+```
+10. *Check the camera resolution*. Use `v4l2-ctl` to ensure that the virtual camera output resolution is `640x480`. Change `/dev/video1` to the port of your virtual camera from the output of `v4l2-ctl --list-devices`.
+```python
+v4l2-ctl -d /dev/video1 --get-fmt-video
+```
+You should see an entry like:
+```
+>>> Format Video Capture:
+>>>	Width/Height      : 640/480
+>>>	Pixel Format      : 'YUYV' (YUYV 4:2:2)
+```
+
+Troubleshooting: If the resolution is not correct you will have to delete the Virtual Camera port and try again as it cannot be changed.
+
+If everything is set up correctly, you can proceed with the rest of the tutorial.
+
+**(Optional) Use your iPhone as a camera on MacOS**
+
+To use your iPhone as a camera on macOS, enable the Continuity Camera feature:
+- Ensure your Mac is running macOS 13 or later, and your iPhone is on iOS 16 or later.
+- Sign in both devices with the same Apple ID.
+- Connect your devices with a USB cable or turn on Wi-Fi and Bluetooth for a wireless connection.
+
+For more details, visit [Apple support](https://support.apple.com/en-gb/guide/mac-help/mchl77879b8a/mac).
+
+Your iPhone should be detected automatically when running the camera setup script in the next section.
+
+**Instantiate an OpenCVCamera**
+
+The [`OpenCVCamera`](../lerobot/common/robot_devices/cameras/opencv.py) class allows you to efficiently record frames from most cameras using the [`opencv2`](https://docs.opencv.org) library.  For more details on compatibility, see [Video I/O with OpenCV Overview](https://docs.opencv.org/4.x/d0/da7/videoio_overview.html).
+
+To instantiate an [`OpenCVCamera`](../lerobot/common/robot_devices/cameras/opencv.py), you need a camera index (e.g. `OpenCVCamera(camera_index=0)`). When you only have one camera like a webcam of a laptop, the camera index is usually `0` but it might differ, and the camera index might change if you reboot your computer or re-plug your camera. This behavior depends on your operating system.
+
+To find the camera indices, run the following utility script, which will save a few frames from each detected camera:
+```bash
+python lerobot/common/robot_devices/cameras/opencv.py \
+    --images-dir outputs/images_from_opencv_cameras
+```
+
+The output will look something like this if you have two cameras connected:
+```
+Mac or Windows detected. Finding available camera indices through scanning all indices from 0 to 60
+[...]
+Camera found at index 0
+Camera found at index 1
+[...]
+Connecting cameras
+OpenCVCamera(0, fps=30.0, width=1920.0, height=1080.0, color_mode=rgb)
+OpenCVCamera(1, fps=24.0, width=1920.0, height=1080.0, color_mode=rgb)
+Saving images to outputs/images_from_opencv_cameras
+Frame: 0000	Latency (ms): 39.52
+[...]
+Frame: 0046	Latency (ms): 40.07
+Images have been saved to outputs/images_from_opencv_cameras
+```
+
+Check the saved images in `outputs/images_from_opencv_cameras` to identify which camera index corresponds to which physical camera (e.g. `0` for `camera_00` or `1` for `camera_01`):
+```
+camera_00_frame_000000.png
+[...]
+camera_00_frame_000047.png
+camera_01_frame_000000.png
+[...]
+camera_01_frame_000047.png
+```
+
+Note: Some cameras may take a few seconds to warm up, and the first frame might be black or green.
+
+Finally, run this code to instantiate and connect your camera:
+```python
+from lerobot.common.robot_devices.cameras.configs import OpenCVCameraConfig
+from lerobot.common.robot_devices.cameras.opencv import OpenCVCamera
+
+config = OpenCVCameraConfig(camera_index=0)
+camera = OpenCVCamera(config)
+camera.connect()
+color_image = camera.read()
+
+print(color_image.shape)
+print(color_image.dtype)
+```
+
+Expected output for a laptop camera on MacBookPro:
+```
+(1080, 1920, 3)
+uint8
+```
+
+Or like this if you followed our tutorial to set a virtual camera:
+```
+(480, 640, 3)
+uint8
+```
+
+With certain camera, you can also specify additional parameters like frame rate, resolution, and color mode during instantiation. For instance:
+```python
+config = OpenCVCameraConfig(camera_index=0, fps=30, width=640, height=480)
+```
+
+If the provided arguments are not compatible with the camera, an exception will be raised.
+
+*Disconnecting the camera*
+
+When you're done using the camera, disconnect it by running:
+```python
+camera.disconnect()
+```
+
+**Instantiate your robot with cameras**
+
+Additionally, you can set up your robot to work with your cameras.
+
+Modify the following Python code with the appropriate camera names and configurations:
+```python
+robot = ManipulatorRobot(
+    KochRobotConfig(
+        leader_arms={"main": leader_arm},
+        follower_arms={"main": follower_arm},
+        calibration_dir=".cache/calibration/koch",
+        cameras={
+            "laptop": OpenCVCameraConfig(0, fps=30, width=640, height=480),
+            "phone": OpenCVCameraConfig(1, fps=30, width=640, height=480),
+        },
+    )
+)
+robot.connect()
+```
+
+As a result, `teleop_step(record_data=True` will return a frame for each camera following the pytorch "channel first" convention but we keep images in `uint8` with pixels in range [0,255] to easily save them.
+
+Modify this code with the names of your cameras and run it:
+```python
+observation, action = robot.teleop_step(record_data=True)
+print(observation["observation.images.laptop"].shape)
+print(observation["observation.images.phone"].shape)
+print(observation["observation.images.laptop"].min().item())
+print(observation["observation.images.laptop"].max().item())
+```
+
+The output should look like this:
+```
+torch.Size([3, 480, 640])
+torch.Size([3, 480, 640])
+0
+255
+```
+
+### d. Use `control_robot.py` and our `teleoperate` function
+
+Instead of manually running the python code in a terminal window, you can use [`lerobot/scripts/control_robot.py`](../lerobot/scripts/control_robot.py) to instantiate your robot by providing the robot configurations via command line and control your robot with various modes as explained next.
+
+Try running this code to teleoperate your robot (if you dont have a camera, keep reading):
+```bash
+python lerobot/scripts/control_robot.py \
+  --robot.type=koch \
+  --control.type=teleoperate
+```
+
+You will see a lot of lines appearing like this one:
+```
+INFO 2024-08-10 11:15:03 ol_robot.py:209 dt: 5.12 (195.1hz) dtRlead: 4.93 (203.0hz) dtWfoll: 0.19 (5239.0hz)
+```
+
+It contains
+- `2024-08-10 11:15:03` which is the date and time of the call to the print function.
+- `ol_robot.py:209` which is the end of the file name and the line number where the print function is called  (`lerobot/scripts/control_robot.py` line `209`).
+- `dt: 5.12 (195.1hz)` which is the "delta time" or the number of milliseconds spent between the previous call to `robot.teleop_step()` and the current one, associated with the frequency (5.12 ms equals 195.1 Hz) ; note that you can control the maximum frequency by adding fps as argument such as `--fps 30`.
+- `dtRlead: 4.93 (203.0hz)` which is the number of milliseconds it took to read the position of the leader arm using `leader_arm.read("Present_Position")`.
+- `dtWfoll: 0.22 (4446.9hz)` which is the number of milliseconds it took to set a new goal position for the follower arm using `follower_arm.write("Goal_position", leader_pos)` ; note that writing is done asynchronously so it takes less time than reading.
+
+Importantly: If you don't have any camera, you can remove them dynamically with this [draccus](https://github.com/dlwh/draccus) syntax `--robot.cameras='{}'`:
+```bash
+python lerobot/scripts/control_robot.py \
+  --robot.type=koch \
+  --robot.cameras='{}' \
+  --control.type=teleoperate
+```
+
+We advise to create a new yaml file when the command becomes too long.
+
+## 3. Record your Dataset and Visualize it
+
+Using what you've learned previously, you can now easily record a dataset of states and actions for one episode. You can use `busy_wait` to control the speed of teleoperation and record at a fixed `fps` (frame per seconds).
+
+Try this code to record 30 seconds at 60 fps:
+```python
+import time
+from lerobot.scripts.control_robot import busy_wait
+
+record_time_s = 30
+fps = 60
+
+states = []
+actions = []
+for _ in range(record_time_s * fps):
+    start_time = time.perf_counter()
+    observation, action = robot.teleop_step(record_data=True)
+
+    states.append(observation["observation.state"])
+    actions.append(action["action"])
+
+    dt_s = time.perf_counter() - start_time
+    busy_wait(1 / fps - dt_s)
+
+# Note that observation and action are available in RAM, but
+# you could potentially store them on disk with pickle/hdf5 or
+# our optimized format `LeRobotDataset`. More on this next.
+```
+
+Importantly, many utilities are still missing. For instance, if you have cameras, you will need to save the images on disk to not go out of RAM, and to do so in threads to not slow down communication with your robot. Also, you will need to store your data in a format optimized for training and web sharing like [`LeRobotDataset`](../lerobot/common/datasets/lerobot_dataset.py). More on this in the next section.
+
+### a. Use the `record` function
+
+You can use the `record` function from [`lerobot/scripts/control_robot.py`](../lerobot/scripts/control_robot.py) to achieve efficient data recording. It encompasses many recording utilities:
+1. Frames from cameras are saved on disk in threads, and encoded into videos at the end of each episode recording.
+2. Video streams from cameras are displayed in window so that you can verify them.
+3. Data is stored with [`LeRobotDataset`](../lerobot/common/datasets/lerobot_dataset.py) format which is pushed to your Hugging Face page (unless `--control.push_to_hub=false` is provided).
+4. Checkpoints are done during recording, so if any issue occurs, you can resume recording by re-running the same command again with `--control.resume=true`. You will need to manually delete the dataset directory if you want to start recording from scratch.
+5. Set the flow of data recording using command line arguments:
+   - `--control.warmup_time_s=10` defines the number of seconds before starting data collection. It allows the robot devices to warmup and synchronize (10 seconds by default).
+   - `--control.episode_time_s=60` defines the number of seconds for data recording for each episode (60 seconds by default).
+   - `--control.reset_time_s=60` defines the number of seconds for resetting the environment after each episode (60 seconds by default).
+   - `--control.num_episodes=50` defines the number of episodes to record (50 by default).
+6. Control the flow during data recording using keyboard keys:
+   - Press right arrow `->` at any time during episode recording to early stop and go to resetting. Same during resetting, to early stop and to go to the next episode recording.
+   - Press left arrow `<-` at any time during episode recording or resetting to early stop, cancel the current episode, and re-record it.
+   - Press escape `ESC` at any time during episode recording to end the session early and go straight to video encoding and dataset uploading.
+7. Similarly to `teleoperate`, you can also use the command line to override anything.
+
+Before trying `record`, if you want to push your dataset to the hub, make sure you've logged in using a write-access token, which can be generated from the [Hugging Face settings](https://huggingface.co/settings/tokens):
+```bash
+huggingface-cli login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential
+```
+Also, store your Hugging Face repository name in a variable (e.g. `cadene` or `lerobot`). For instance, run this to use your Hugging Face user name as repository:
+```bash
+HF_USER=$(huggingface-cli whoami | head -n 1)
+echo $HF_USER
+```
+If you don't want to push to hub, use `--control.push_to_hub=false`.
+
+Now run this to record 2 episodes:
+```bash
+python lerobot/scripts/control_robot.py \
+  --robot.type=koch \
+  --control.type=record \
+  --control.single_task="Grasp a lego block and put it in the bin." \
+  --control.fps=30 \
+  --control.repo_id=${HF_USER}/koch_test \
+  --control.tags='["tutorial"]' \
+  --control.warmup_time_s=5 \
+  --control.episode_time_s=30 \
+  --control.reset_time_s=30 \
+  --control.num_episodes=2 \
+  --control.push_to_hub=true
+```
+
+
+This will write your dataset locally to `~/.cache/huggingface/lerobot/{repo-id}` (e.g. `data/cadene/koch_test`) and push it on the hub at `https://huggingface.co/datasets/{HF_USER}/{repo-id}`. Your dataset will be automatically tagged with `LeRobot` for the community to find it easily, and you can also add custom tags (in this case `tutorial` for example).
+
+You can look for other LeRobot datasets on the hub by searching for `LeRobot` tags: https://huggingface.co/datasets?other=LeRobot
+
+You will see a lot of lines appearing like this one:
+```
+INFO 2024-08-10 15:02:58 ol_robot.py:219 dt:33.34 (30.0hz) dtRlead: 5.06 (197.5hz) dtWfoll: 0.25 (3963.7hz) dtRfoll: 6.22 (160.7hz) dtRlaptop: 32.57 (30.7hz) dtRphone: 33.84 (29.5hz)
+```
+It contains:
+- `2024-08-10 15:02:58` which is the date and time of the call to the print function,
+- `ol_robot.py:219` which is the end of the file name and the line number where the print function is called  (`lerobot/scripts/control_robot.py` line `219`).
+- `dt:33.34 (30.0hz)` which is the "delta time" or the number of milliseconds spent between the previous call to `robot.teleop_step(record_data=True)` and the current one, associated with the frequency (33.34 ms equals 30.0 Hz) ; note that we use `--fps 30` so we expect 30.0 Hz ; when a step takes more time, the line appears in yellow.
+- `dtRlead: 5.06 (197.5hz)` which is the delta time of reading the present position of the leader arm.
+- `dtWfoll: 0.25 (3963.7hz)` which is the delta time of writing the goal position on the follower arm ; writing is asynchronous so it takes less time than reading.
+- `dtRfoll: 6.22 (160.7hz)` which is the delta time of reading the present position on the follower arm.
+- `dtRlaptop:32.57 (30.7hz) ` which is the delta time of capturing an image from the laptop camera in the thread running asynchronously.
+- `dtRphone:33.84 (29.5hz)` which is the delta time of capturing an image from the phone camera in the thread running asynchronously.
+
+Troubleshooting:
+- On Linux, if the left and right arrow keys and escape key don't have any effect during data recording, make sure you've set the `$DISPLAY` environment variable. See [pynput limitations](https://pynput.readthedocs.io/en/latest/limitations.html#linux).
+
+At the end of data recording, your dataset will be uploaded on your Hugging Face page (e.g. https://huggingface.co/datasets/cadene/koch_test) that you can obtain by running:
+```bash
+echo https://huggingface.co/datasets/${HF_USER}/koch_test
+```
+
+### b. Advice for recording dataset
+
+Once you're comfortable with data recording, it's time to create a larger dataset for training. A good starting task is grasping an object at different locations and placing it in a bin. We suggest recording at least 50 episodes, with 10 episodes per location. Keep the cameras fixed and maintain consistent grasping behavior throughout the recordings.
+
+In the following sections, you’ll train your neural network. After achieving reliable grasping performance, you can start introducing more variations during data collection, such as additional grasp locations, different grasping techniques, and altering camera positions.
+
+Avoid adding too much variation too quickly, as it may hinder your results.
+
+In the coming months, we plan to release a foundational model for robotics. We anticipate that fine-tuning this model will enhance generalization, reducing the need for strict consistency during data collection.
+
+### c. Visualize all episodes
+
+You can visualize your dataset by running:
+```bash
+python lerobot/scripts/visualize_dataset_html.py \
+  --repo-id ${HF_USER}/koch_test
+```
+
+Note: You might need to add `--local-files-only 1` if your dataset was not uploaded to hugging face hub.
+
+This will launch a local web server that looks like this:
+<div style="text-align:center;">
+  <img src="../media/tutorial/visualize_dataset_html.webp?raw=true" alt="Koch v1.1 leader and follower arms" title="Koch v1.1 leader and follower arms" width="100%">
+</div>
+
+### d. Replay episode on your robot with the `replay` function
+
+A useful feature of [`lerobot/scripts/control_robot.py`](../lerobot/scripts/control_robot.py) is the `replay` function, which allows to replay on your robot any episode that you've recorded or episodes from any dataset out there. This function helps you test the repeatability of your robot's actions and assess transferability across robots of the same model.
+
+To replay the first episode of the dataset you just recorded, run the following command:
+```bash
+python lerobot/scripts/control_robot.py \
+  --robot.type=koch \
+  --control.type=replay \
+  --control.fps=30 \
+  --control.repo_id=${HF_USER}/koch_test \
+  --control.episode=0
+```
+
+Your robot should replicate movements similar to those you recorded. For example, check out [this video](https://x.com/RemiCadene/status/1793654950905680090) where we use `replay` on a Aloha robot from [Trossen Robotics](https://www.trossenrobotics.com).
+
+## 4. Train a policy on your data
+
+### a. Use the `train` script
+
+To train a policy to control your robot, use the [`python lerobot/scripts/train.py`](../lerobot/scripts/train.py) script. A few arguments are required. Here is an example command:
+```bash
+python lerobot/scripts/train.py \
+  --dataset.repo_id=${HF_USER}/koch_test \
+  --policy.type=act \
+  --output_dir=outputs/train/act_koch_test \
+  --job_name=act_koch_test \
+  --policy.device=cuda \
+  --wandb.enable=true
+```
+
+Let's explain it:
+1. We provided the dataset as argument with `--dataset.repo_id=${HF_USER}/koch_test`.
+2. We provided the policy with `policy.type=act`. This loads configurations from [`configuration_act.py`](../lerobot/common/policies/act/configuration_act.py). Importantly, this policy will automatically adapt to the number of motor sates, motor actions and cameras of your robot (e.g. `laptop` and `phone`) which have been saved in your dataset.
+4. We provided `policy.device=cuda` since we are training on a Nvidia GPU, but you could use `policy.device=mps` to train on Apple silicon.
+5. We provided `wandb.enable=true` to use [Weights and Biases](https://docs.wandb.ai/quickstart) for visualizing training plots. This is optional but if you use it, make sure you are logged in by running `wandb login`.
+
+For more information on the `train` script see the previous tutorial: [`examples/4_train_policy_with_script.md`](../examples/4_train_policy_with_script.md)
+
+### b. (Optional) Upload policy checkpoints to the hub
+
+Once training is done, upload the latest checkpoint with:
+```bash
+huggingface-cli upload ${HF_USER}/act_koch_test \
+  outputs/train/act_koch_test/checkpoints/last/pretrained_model
+```
+
+You can also upload intermediate checkpoints with:
+```bash
+CKPT=010000
+huggingface-cli upload ${HF_USER}/act_koch_test_${CKPT} \
+  outputs/train/act_koch_test/checkpoints/${CKPT}/pretrained_model
+```
+
+## 5. Evaluate your policy
+
+Now that you have a policy checkpoint, you can easily control your robot with it using methods from [`ManipulatorRobot`](../lerobot/common/robot_devices/robots/manipulator.py) and the policy.
+
+Try this code for running inference for 60 seconds at 30 fps:
+```python
+from lerobot.common.policies.act.modeling_act import ACTPolicy
+
+inference_time_s = 60
+fps = 30
+device = "cuda"  # TODO: On Mac, use "mps" or "cpu"
+
+ckpt_path = "outputs/train/act_koch_test/checkpoints/last/pretrained_model"
+policy = ACTPolicy.from_pretrained(ckpt_path)
+policy.to(device)
+
+for _ in range(inference_time_s * fps):
+    start_time = time.perf_counter()
+
+    # Read the follower state and access the frames from the cameras
+    observation = robot.capture_observation()
+
+    # Convert to pytorch format: channel first and float32 in [0,1]
+    # with batch dimension
+    for name in observation:
+        if "image" in name:
+            observation[name] = observation[name].type(torch.float32) / 255
+            observation[name] = observation[name].permute(2, 0, 1).contiguous()
+        observation[name] = observation[name].unsqueeze(0)
+        observation[name] = observation[name].to(device)
+
+    # Compute the next action with the policy
+    # based on the current observation
+    action = policy.select_action(observation)
+    # Remove batch dimension
+    action = action.squeeze(0)
+    # Move to cpu, if not already the case
+    action = action.to("cpu")
+    # Order the robot to move
+    robot.send_action(action)
+
+    dt_s = time.perf_counter() - start_time
+    busy_wait(1 / fps - dt_s)
+```
+
+### a. Use our `record` function
+
+Ideally, when controlling your robot with your neural network, you would want to record evaluation episodes and to be able to visualize them later on, or even train on them like in Reinforcement Learning. This pretty much corresponds to recording a new dataset but with a neural network providing the actions instead of teleoperation.
+
+To this end, you can use the `record` function from [`lerobot/scripts/control_robot.py`](../lerobot/scripts/control_robot.py) but with a policy checkpoint as input. For instance, run this command to record 10 evaluation episodes:
+```bash
+python lerobot/scripts/control_robot.py \
+  --robot.type=koch \
+  --control.type=record \
+  --control.fps=30 \
+  --control.repo_id=${HF_USER}/eval_act_koch_test \
+  --control.tags='["tutorial"]' \
+  --control.warmup_time_s=5 \
+  --control.episode_time_s=30 \
+  --control.reset_time_s=30 \
+  --control.num_episodes=10 \
+  --control.push_to_hub=true \
+  --control.policy.path=outputs/train/act_koch_test/checkpoints/last/pretrained_model
+```
+
+As you can see, it's almost the same command as previously used to record your training dataset. Two things changed:
+1. There is an additional `--control.policy.path` argument which indicates the path to your policy checkpoint with  (e.g. `outputs/train/eval_koch_test/checkpoints/last/pretrained_model`). You can also use the model repository if you uploaded a model checkpoint to the hub (e.g. `${HF_USER}/act_koch_test`).
+2. The name of dataset begins by `eval` to reflect that you are running inference (e.g. `${HF_USER}/eval_act_koch_test`).
+
+### b. Visualize evaluation afterwards
+
+You can then visualize your evaluation dataset by running the same command as before but with the new inference dataset as argument:
+```bash
+python lerobot/scripts/visualize_dataset.py \
+  --repo-id ${HF_USER}/eval_act_koch_test
+```
+
+## 6. Next step
+
+Join our [Discord](https://discord.com/invite/s3KuuzsPFb) to collaborate on data collection and help us train a fully open-source foundational models for robotics!

examples/advanced/1_add_image_transforms.py

@@ -22,7 +22,7 @@ from pathlib import Path
 
 from torchvision.transforms import ToPILImage, v2
 
-from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.common.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.datasets.lerobot_dataset import LeRobotDataset, LeRobotDatasetMetadata
-from lerobot.policies.diffusion.modeling_diffusion import DiffusionPolicy
+from lerobot.common.datasets.lerobot_dataset import LeRobotDataset, LeRobotDatasetMetadata
+from lerobot.common.policies.diffusion.modeling_diffusion import DiffusionPolicy
 
 
 def main():

examples/backward_compatibility/replay.py

@@ -1,105 +0,0 @@
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-Replays the actions of an episode from a dataset on a robot.
-
-Example:
-
-```shell
-python -m lerobot.replay \
-    --robot.type=so100_follower \
-    --robot.port=/dev/tty.usbmodem58760431541 \
-    --robot.id=black \
-    --dataset.repo_id=aliberts/record-test \
-    --dataset.episode=2
-```
-"""
-
-import logging
-import time
-from dataclasses import asdict, dataclass
-from pathlib import Path
-from pprint import pformat
-
-import draccus
-
-from lerobot.datasets.lerobot_dataset import LeRobotDataset
-from lerobot.robots import (  # noqa: F401
-    Robot,
-    RobotConfig,
-    koch_follower,
-    make_robot_from_config,
-    so100_follower,
-    so101_follower,
-)
-from lerobot.utils.robot_utils import busy_wait
-from lerobot.utils.utils import (
-    init_logging,
-    log_say,
-)
-
-
-@dataclass
-class DatasetReplayConfig:
-    # Dataset identifier. By convention it should match '{hf_username}/{dataset_name}' (e.g. `lerobot/test`).
-    repo_id: str
-    # Episode to replay.
-    episode: int
-    # Root directory where the dataset will be stored (e.g. 'dataset/path').
-    root: str | Path | None = None
-    # Limit the frames per second. By default, uses the policy fps.
-    fps: int = 30
-
-
-@dataclass
-class ReplayConfig:
-    robot: RobotConfig
-    dataset: DatasetReplayConfig
-    # Use vocal synthesis to read events.
-    play_sounds: bool = True
-
-
-@draccus.wrap()
-def replay(cfg: ReplayConfig):
-    init_logging()
-    logging.info(pformat(asdict(cfg)))
-
-    robot = make_robot_from_config(cfg.robot)
-    dataset = LeRobotDataset(cfg.dataset.repo_id, root=cfg.dataset.root, episodes=[cfg.dataset.episode])
-    actions = dataset.hf_dataset.select_columns("action")
-    robot.connect()
-
-    log_say("Replaying episode", cfg.play_sounds, blocking=True)
-    for idx in range(dataset.num_frames):
-        start_episode_t = time.perf_counter()
-
-        action_array = actions[idx]["action"]
-        action = {}
-        for i, name in enumerate(dataset.features["action"]["names"]):
-            key = f"{name.removeprefix('main_')}.pos"
-            action[key] = action_array[i].item()
-
-        action["shoulder_lift.pos"] = -(action["shoulder_lift.pos"] - 90)
-        action["elbow_flex.pos"] -= 90
-        robot.send_action(action)
-
-        dt_s = time.perf_counter() - start_episode_t
-        busy_wait(1 / dataset.fps - dt_s)
-
-    robot.disconnect()
-
-
-if __name__ == "__main__":
-    replay()

examples/lekiwi/evaluate.py

@@ -1,90 +0,0 @@
-from lerobot.datasets.lerobot_dataset import LeRobotDataset
-from lerobot.datasets.utils import hw_to_dataset_features
-from lerobot.policies.act.modeling_act import ACTPolicy
-from lerobot.record import record_loop
-from lerobot.robots.lekiwi import LeKiwiClient, LeKiwiClientConfig
-from lerobot.utils.control_utils import init_keyboard_listener
-from lerobot.utils.utils import log_say
-from lerobot.utils.visualization_utils import _init_rerun
-
-NUM_EPISODES = 2
-FPS = 30
-EPISODE_TIME_SEC = 60
-TASK_DESCRIPTION = "My task description"
-
-# Create the robot and teleoperator configurations
-robot_config = LeKiwiClientConfig(remote_ip="172.18.134.136", id="lekiwi")
-robot = LeKiwiClient(robot_config)
-
-policy = ACTPolicy.from_pretrained("<hf_username>/<policy_repo_id>")
-
-# Configure the dataset features
-action_features = hw_to_dataset_features(robot.action_features, "action")
-obs_features = hw_to_dataset_features(robot.observation_features, "observation")
-dataset_features = {**action_features, **obs_features}
-
-# Create the dataset
-dataset = LeRobotDataset.create(
-    repo_id="<hf_username>/<eval_dataset_repo_id>",
-    fps=FPS,
-    features=dataset_features,
-    robot_type=robot.name,
-    use_videos=True,
-    image_writer_threads=4,
-)
-
-# To connect you already should have this script running on LeKiwi: `python -m lerobot.robots.lekiwi.lekiwi_host --robot.id=my_awesome_kiwi`
-robot.connect()
-
-_init_rerun(session_name="recording")
-
-listener, events = init_keyboard_listener()
-
-if not robot.is_connected:
-    raise ValueError("Robot is not connected!")
-
-recorded_episodes = 0
-while recorded_episodes < NUM_EPISODES and not events["stop_recording"]:
-    log_say(f"Running inference, recording eval episode {recorded_episodes} of {NUM_EPISODES}")
-
-    # Run the policy inference loop
-    record_loop(
-        robot=robot,
-        events=events,
-        fps=FPS,
-        policy=policy,
-        dataset=dataset,
-        control_time_s=EPISODE_TIME_SEC,
-        single_task=TASK_DESCRIPTION,
-        display_data=True,
-    )
-
-    # Logic for reset env
-    if not events["stop_recording"] and (
-        (recorded_episodes < NUM_EPISODES - 1) or events["rerecord_episode"]
-    ):
-        log_say("Reset the environment")
-        record_loop(
-            robot=robot,
-            events=events,
-            fps=FPS,
-            control_time_s=EPISODE_TIME_SEC,
-            single_task=TASK_DESCRIPTION,
-            display_data=True,
-        )
-
-    if events["rerecord_episode"]:
-        log_say("Re-record episode")
-        events["rerecord_episode"] = False
-        events["exit_early"] = False
-        dataset.clear_episode_buffer()
-        continue
-
-    dataset.save_episode()
-    recorded_episodes += 1
-
-# Upload to hub and clean up
-dataset.push_to_hub()
-
-robot.disconnect()
-listener.stop()

examples/lekiwi/record.py

@@ -1,101 +0,0 @@
-from lerobot.datasets.lerobot_dataset import LeRobotDataset
-from lerobot.datasets.utils import hw_to_dataset_features
-from lerobot.record import record_loop
-from lerobot.robots.lekiwi.config_lekiwi import LeKiwiClientConfig
-from lerobot.robots.lekiwi.lekiwi_client import LeKiwiClient
-from lerobot.teleoperators.keyboard import KeyboardTeleop, KeyboardTeleopConfig
-from lerobot.teleoperators.so100_leader import SO100Leader, SO100LeaderConfig
-from lerobot.utils.control_utils import init_keyboard_listener
-from lerobot.utils.utils import log_say
-from lerobot.utils.visualization_utils import _init_rerun
-
-NUM_EPISODES = 3
-FPS = 30
-EPISODE_TIME_SEC = 30
-RESET_TIME_SEC = 10
-TASK_DESCRIPTION = "My task description"
-
-# Create the robot and teleoperator configurations
-robot_config = LeKiwiClientConfig(remote_ip="172.18.134.136", id="lekiwi")
-leader_arm_config = SO100LeaderConfig(port="/dev/tty.usbmodem585A0077581", id="my_awesome_leader_arm")
-keyboard_config = KeyboardTeleopConfig()
-
-robot = LeKiwiClient(robot_config)
-leader_arm = SO100Leader(leader_arm_config)
-keyboard = KeyboardTeleop(keyboard_config)
-
-# Configure the dataset features
-action_features = hw_to_dataset_features(robot.action_features, "action")
-obs_features = hw_to_dataset_features(robot.observation_features, "observation")
-dataset_features = {**action_features, **obs_features}
-
-# Create the dataset
-dataset = LeRobotDataset.create(
-    repo_id="<hf_username>/<dataset_repo_id>",
-    fps=FPS,
-    features=dataset_features,
-    robot_type=robot.name,
-    use_videos=True,
-    image_writer_threads=4,
-)
-
-# To connect you already should have this script running on LeKiwi: `python -m lerobot.robots.lekiwi.lekiwi_host --robot.id=my_awesome_kiwi`
-robot.connect()
-leader_arm.connect()
-keyboard.connect()
-
-_init_rerun(session_name="lekiwi_record")
-
-listener, events = init_keyboard_listener()
-
-if not robot.is_connected or not leader_arm.is_connected or not keyboard.is_connected:
-    raise ValueError("Robot, leader arm of keyboard is not connected!")
-
-recorded_episodes = 0
-while recorded_episodes < NUM_EPISODES and not events["stop_recording"]:
-    log_say(f"Recording episode {recorded_episodes}")
-
-    # Run the record loop
-    record_loop(
-        robot=robot,
-        events=events,
-        fps=FPS,
-        dataset=dataset,
-        teleop=[leader_arm, keyboard],
-        control_time_s=EPISODE_TIME_SEC,
-        single_task=TASK_DESCRIPTION,
-        display_data=True,
-    )
-
-    # Logic for reset env
-    if not events["stop_recording"] and (
-        (recorded_episodes < NUM_EPISODES - 1) or events["rerecord_episode"]
-    ):
-        log_say("Reset the environment")
-        record_loop(
-            robot=robot,
-            events=events,
-            fps=FPS,
-            teleop=[leader_arm, keyboard],
-            control_time_s=RESET_TIME_SEC,
-            single_task=TASK_DESCRIPTION,
-            display_data=True,
-        )
-
-    if events["rerecord_episode"]:
-        log_say("Re-record episode")
-        events["rerecord_episode"] = False
-        events["exit_early"] = False
-        dataset.clear_episode_buffer()
-        continue
-
-    dataset.save_episode()
-    recorded_episodes += 1
-
-# Upload to hub and clean up
-dataset.push_to_hub()
-
-robot.disconnect()
-leader_arm.disconnect()
-keyboard.disconnect()
-listener.stop()

examples/lekiwi/replay.py

@@ -1,33 +0,0 @@
-import time
-
-from lerobot.datasets.lerobot_dataset import LeRobotDataset
-from lerobot.robots.lekiwi.config_lekiwi import LeKiwiClientConfig
-from lerobot.robots.lekiwi.lekiwi_client import LeKiwiClient
-from lerobot.utils.robot_utils import busy_wait
-from lerobot.utils.utils import log_say
-
-EPISODE_IDX = 0
-
-robot_config = LeKiwiClientConfig(remote_ip="172.18.134.136", id="lekiwi")
-robot = LeKiwiClient(robot_config)
-
-dataset = LeRobotDataset("<hf_username>/<dataset_repo_id>", episodes=[EPISODE_IDX])
-actions = dataset.hf_dataset.select_columns("action")
-
-robot.connect()
-
-if not robot.is_connected:
-    raise ValueError("Robot is not connected!")
-
-log_say(f"Replaying episode {EPISODE_IDX}")
-for idx in range(dataset.num_frames):
-    t0 = time.perf_counter()
-
-    action = {
-        name: float(actions[idx]["action"][i]) for i, name in enumerate(dataset.features["action"]["names"])
-    }
-    robot.send_action(action)
-
-    busy_wait(max(1.0 / dataset.fps - (time.perf_counter() - t0), 0.0))
-
-robot.disconnect()

examples/lekiwi/teleoperate.py

@@ -1,47 +0,0 @@
-import time
-
-from lerobot.robots.lekiwi import LeKiwiClient, LeKiwiClientConfig
-from lerobot.teleoperators.keyboard.teleop_keyboard import KeyboardTeleop, KeyboardTeleopConfig
-from lerobot.teleoperators.so100_leader import SO100Leader, SO100LeaderConfig
-from lerobot.utils.robot_utils import busy_wait
-from lerobot.utils.visualization_utils import _init_rerun, log_rerun_data
-
-FPS = 30
-
-# Create the robot and teleoperator configurations
-robot_config = LeKiwiClientConfig(remote_ip="172.18.134.136", id="my_lekiwi")
-teleop_arm_config = SO100LeaderConfig(port="/dev/tty.usbmodem585A0077581", id="my_awesome_leader_arm")
-keyboard_config = KeyboardTeleopConfig(id="my_laptop_keyboard")
-
-robot = LeKiwiClient(robot_config)
-leader_arm = SO100Leader(teleop_arm_config)
-keyboard = KeyboardTeleop(keyboard_config)
-
-# To connect you already should have this script running on LeKiwi: `python -m lerobot.robots.lekiwi.lekiwi_host --robot.id=my_awesome_kiwi`
-robot.connect()
-leader_arm.connect()
-keyboard.connect()
-
-_init_rerun(session_name="lekiwi_teleop")
-
-if not robot.is_connected or not leader_arm.is_connected or not keyboard.is_connected:
-    raise ValueError("Robot, leader arm of keyboard is not connected!")
-
-while True:
-    t0 = time.perf_counter()
-
-    observation = robot.get_observation()
-
-    arm_action = leader_arm.get_action()
-    arm_action = {f"arm_{k}": v for k, v in arm_action.items()}
-
-    keyboard_keys = keyboard.get_action()
-    base_action = robot._from_keyboard_to_base_action(keyboard_keys)
-
-    log_rerun_data(observation, {**arm_action, **base_action})
-
-    action = {**arm_action, **base_action} if len(base_action) > 0 else arm_action
-
-    robot.send_action(action)
-
-    busy_wait(max(1.0 / FPS - (time.perf_counter() - t0), 0.0))

examples/robots/lekiwi_client_app.py

@@ -0,0 +1,94 @@
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import logging
+import time
+
+from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.common.datasets.utils import hw_to_dataset_features
+from lerobot.common.robots.lekiwi.config_lekiwi import LeKiwiClientConfig
+from lerobot.common.robots.lekiwi.lekiwi_client import LeKiwiClient
+from lerobot.common.teleoperators.keyboard import KeyboardTeleop, KeyboardTeleopConfig
+from lerobot.common.teleoperators.so100_leader import SO100Leader, SO100LeaderConfig
+
+NB_CYCLES_CLIENT_CONNECTION = 250
+
+
+def main():
+    logging.info("Configuring Teleop Devices")
+    leader_arm_config = SO100LeaderConfig(port="/dev/tty.usbmodem58760433331")
+    leader_arm = SO100Leader(leader_arm_config)
+
+    keyboard_config = KeyboardTeleopConfig()
+    keyboard = KeyboardTeleop(keyboard_config)
+
+    logging.info("Configuring LeKiwi Client")
+    robot_config = LeKiwiClientConfig(remote_ip="172.18.134.136", id="lekiwi")
+    robot = LeKiwiClient(robot_config)
+
+    logging.info("Creating LeRobot Dataset")
+
+    action_features = hw_to_dataset_features(robot.action_features, "action")
+    obs_features = hw_to_dataset_features(robot.observation_features, "observation")
+    dataset_features = {**action_features, **obs_features}
+
+    dataset = LeRobotDataset.create(
+        repo_id="user/lekiwi" + str(int(time.time())),
+        fps=10,
+        features=dataset_features,
+        robot_type=robot.name,
+    )
+
+    logging.info("Connecting Teleop Devices")
+    leader_arm.connect()
+    keyboard.connect()
+
+    logging.info("Connecting remote LeKiwi")
+    robot.connect()
+
+    if not robot.is_connected or not leader_arm.is_connected or not keyboard.is_connected:
+        logging.error("Failed to connect to all devices")
+        return
+
+    logging.info("Starting LeKiwi teleoperation")
+    i = 0
+    while i < NB_CYCLES_CLIENT_CONNECTION:
+        arm_action = leader_arm.get_action()
+        base_action = keyboard.get_action()
+        action = {**arm_action, **base_action} if len(base_action) > 0 else arm_action
+
+        action_sent = robot.send_action(action)
+        observation = robot.get_observation()
+
+        frame = {**action_sent, **observation}
+        task = "Dummy Example Task Dataset"
+
+        logging.info("Saved a frame into the dataset")
+        dataset.add_frame(frame, task)
+        i += 1
+
+    logging.info("Disconnecting Teleop Devices and LeKiwi Client")
+    robot.disconnect()
+    leader_arm.disconnect()
+    keyboard.disconnect()
+
+    logging.info("Uploading dataset to the hub")
+    dataset.save_episode()
+    dataset.push_to_hub()
+
+    logging.info("Finished LeKiwi cleanly")
+
+
+if __name__ == "__main__":
+    main()

lerobot/__init__.py

@@ -167,10 +167,15 @@ available_datasets = sorted(
     set(itertools.chain(*available_datasets_per_env.values(), available_real_world_datasets))
 )
 
-# lists all available policies from `lerobot/policies`
-available_policies = ["act", "diffusion", "tdmpc", "vqbet"]
+# lists all available policies from `lerobot/common/policies`
+available_policies = [
+    "act",
+    "diffusion",
+    "tdmpc",
+    "vqbet",
+]
 
-# lists all available robots from `lerobot/robot_devices/robots`
+# lists all available robots from `lerobot/common/robot_devices/robots`
 available_robots = [
     "koch",
     "koch_bimanual",
@@ -179,13 +184,13 @@ available_robots = [
     "so101",
 ]
 
-# lists all available cameras from `lerobot/robot_devices/cameras`
+# lists all available cameras from `lerobot/common/robot_devices/cameras`
 available_cameras = [
     "opencv",
     "intelrealsense",
 ]
 
-# lists all available motors from `lerobot/robot_devices/motors`
+# lists all available motors from `lerobot/common/robot_devices/motors`
 available_motors = [
     "dynamixel",
     "feetech",

lerobot/calibrate.py

@@ -31,18 +31,18 @@ from pprint import pformat
 
 import draccus
 
-from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig  # noqa: F401
-from lerobot.cameras.realsense.configuration_realsense import RealSenseCameraConfig  # noqa: F401
-from lerobot.robots import (  # noqa: F401
+from lerobot.common.cameras.opencv.configuration_opencv import OpenCVCameraConfig  # noqa: F401
+from lerobot.common.cameras.realsense.configuration_realsense import RealSenseCameraConfig  # noqa: F401
+from lerobot.common.robots import (  # noqa: F401
     Robot,
     RobotConfig,
     koch_follower,
     lekiwi,
     make_robot_from_config,
     so100_follower,
-    so101_follower,
+    so100_follower_end_effector,
 )
-from lerobot.teleoperators import (  # noqa: F401
+from lerobot.common.teleoperators import (  # noqa: F401
     Teleoperator,
     TeleoperatorConfig,
     koch_leader,
@@ -50,7 +50,7 @@ from lerobot.teleoperators import (  # noqa: F401
     so100_leader,
     so101_leader,
 )
-from lerobot.utils.utils import init_logging
+from lerobot.common.utils.utils import init_logging
 
 
 @dataclass

lerobot/common/cameras/opencv/camera_opencv.py

@@ -27,7 +27,7 @@ from typing import Any, Dict, List
 import cv2
 import numpy as np
 
-from lerobot.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError
+from lerobot.common.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.cameras.opencv import OpenCVCamera
-        from lerobot.cameras.configuration_opencv import OpenCVCameraConfig, ColorMode, Cv2Rotation
+        from lerobot.common.cameras.opencv import OpenCVCamera
+        from lerobot.common.cameras.configuration_opencv import OpenCVCameraConfig, ColorMode, Cv2Rotation
 
         # Basic usage with camera index 0
         config = OpenCVCameraConfig(index_or_path=0)
@@ -124,9 +124,10 @@ class OpenCVCamera(Camera):
         self.backend: int = get_cv2_backend()
 
         if self.height and self.width:
-            self.capture_width, self.capture_height = self.width, self.height
             if self.rotation in [cv2.ROTATE_90_CLOCKWISE, cv2.ROTATE_90_COUNTERCLOCKWISE]:
                 self.capture_width, self.capture_height = self.height, self.width
+            else:
+                self.capture_width, self.capture_height = self.width, self.height
 
     def __str__(self) -> str:
         return f"{self.__class__.__name__}({self.index_or_path})"
@@ -205,11 +206,12 @@ class OpenCVCamera(Camera):
         default_height = int(round(self.videocapture.get(cv2.CAP_PROP_FRAME_HEIGHT)))
 
         if self.width is None or self.height is None:
-            self.width, self.height = default_width, default_height
-            self.capture_width, self.capture_height = default_width, default_height
             if self.rotation in [cv2.ROTATE_90_CLOCKWISE, cv2.ROTATE_90_COUNTERCLOCKWISE]:
                 self.width, self.height = default_height, default_width
                 self.capture_width, self.capture_height = default_width, default_height
+            else:
+                self.width, self.height = default_width, default_height
+                self.capture_width, self.capture_height = default_width, default_height
         else:
             self._validate_width_and_height()
 
@@ -225,19 +227,16 @@ class OpenCVCamera(Camera):
     def _validate_width_and_height(self) -> None:
         """Validates and sets the camera's frame capture width and height."""
 
-        width_success = self.videocapture.set(cv2.CAP_PROP_FRAME_WIDTH, float(self.capture_width))
-        height_success = self.videocapture.set(cv2.CAP_PROP_FRAME_HEIGHT, float(self.capture_height))
-
+        success = self.videocapture.set(cv2.CAP_PROP_FRAME_WIDTH, float(self.capture_width))
         actual_width = int(round(self.videocapture.get(cv2.CAP_PROP_FRAME_WIDTH)))
-        if not width_success or self.capture_width != actual_width:
-            raise RuntimeError(
-                f"{self} failed to set capture_width={self.capture_width} ({actual_width=}, {width_success=})."
-            )
+        if not success or self.capture_width != actual_width:
+            raise RuntimeError(f"{self} failed to set capture_width={self.capture_width} ({actual_width=}).")
 
+        success = self.videocapture.set(cv2.CAP_PROP_FRAME_HEIGHT, float(self.capture_height))
         actual_height = int(round(self.videocapture.get(cv2.CAP_PROP_FRAME_HEIGHT)))
-        if not height_success or self.capture_height != actual_height:
+        if not success or self.capture_height != actual_height:
             raise RuntimeError(
-                f"{self} failed to set capture_height={self.capture_height} ({actual_height=}, {height_success=})."
+                f"{self} failed to set capture_height={self.capture_height} ({actual_height})."
             )
 
     @staticmethod

lerobot/common/cameras/realsense/camera_realsense.py

@@ -29,7 +29,7 @@ try:
 except Exception as e:
     logging.info(f"Could not import realsense: {e}")
 
-from lerobot.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError
+from lerobot.common.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError
 
 from ..camera import Camera
 from ..configs import ColorMode
@@ -63,8 +63,8 @@ class RealSenseCamera(Camera):
 
     Example:
         ```python
-        from lerobot.cameras.realsense import RealSenseCamera, RealSenseCameraConfig
-        from lerobot.cameras import ColorMode, Cv2Rotation
+        from lerobot.common.cameras.realsense import RealSenseCamera, RealSenseCameraConfig
+        from lerobot.common.cameras import ColorMode, Cv2Rotation
 
         # Basic usage with serial number
         config = RealSenseCameraConfig(serial_number_or_name="0123456789") # Replace with actual SN
@@ -138,9 +138,10 @@ class RealSenseCamera(Camera):
         self.rotation: int | None = get_cv2_rotation(config.rotation)
 
         if self.height and self.width:
-            self.capture_width, self.capture_height = self.width, self.height
             if self.rotation in [cv2.ROTATE_90_CLOCKWISE, cv2.ROTATE_90_COUNTERCLOCKWISE]:
                 self.capture_width, self.capture_height = self.height, self.width
+            else:
+                self.capture_width, self.capture_height = self.width, self.height
 
     def __str__(self) -> str:
         return f"{self.__class__.__name__}({self.serial_number})"

lerobot/common/constants.py

@@ -22,14 +22,8 @@ OBS_STATE = "observation.state"
 OBS_IMAGE = "observation.image"
 OBS_IMAGES = "observation.images"
 ACTION = "action"
-OBS_IMAGE_2 = "observation.image2"
-OBS_IMAGE_3 = "observation.image3"
-OBS_IMAGE_4 = "observation.image4"
-REWARD = "next.reward"
 
 ROBOTS = "robots"
-TASK = "task"
-ROBOT_TYPE = "robot_type"
 TELEOPERATORS = "teleoperators"
 
 # files & directories

lerobot/common/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 -m lerobot.datasets.v2.convert_dataset_v1_to_v2 \\
+python lerobot/common/datasets/v2/convert_dataset_v1_to_v2.py \\
     --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 -m lerobot.datasets.v21.convert_dataset_v20_to_v21 --repo-id={repo_id}
+python lerobot/common/datasets/v21/convert_dataset_v20_to_v21.py --repo-id={repo_id}
 ```
 
 If you encounter a problem, contact LeRobot maintainers on [Discord](https://discord.com/invite/s3KuuzsPFb)

lerobot/common/datasets/compute_stats.py

@@ -15,7 +15,7 @@
 # limitations under the License.
 import numpy as np
 
-from lerobot.datasets.utils import load_image_as_numpy
+from lerobot.common.datasets.utils import load_image_as_numpy
 
 
 def estimate_num_samples(
@@ -125,30 +125,9 @@ def _assert_type_and_shape(stats_list: list[dict[str, dict]]):
 
 def aggregate_feature_stats(stats_ft_list: list[dict[str, dict]]) -> dict[str, dict[str, np.ndarray]]:
     """Aggregates stats for a single feature."""
-    # Filter out stats that don't have required keys
-    valid_stats = []
-    for s in stats_ft_list:
-        if all(key in s for key in ["mean", "std", "count", "min", "max"]):
-            valid_stats.append(s)
-        else:
-            # If count is missing, add it with a default value
-            if "count" not in s:
-                s["count"] = np.array([1])  # Default count
-            valid_stats.append(s)
-    
-    if not valid_stats:
-        # If no valid stats, return empty stats
-        return {
-            "min": np.array([0]),
-            "max": np.array([0]),
-            "mean": np.array([0]),
-            "std": np.array([0]),
-            "count": np.array([0]),
-        }
-    
-    means = np.stack([s["mean"] for s in valid_stats])
-    variances = np.stack([s["std"] ** 2 for s in valid_stats])
-    counts = np.stack([s["count"] for s in valid_stats])
+    means = np.stack([s["mean"] for s in stats_ft_list])
+    variances = np.stack([s["std"] ** 2 for s in stats_ft_list])
+    counts = np.stack([s["count"] for s in stats_ft_list])
     total_count = counts.sum(axis=0)
 
     # Prepare weighted mean by matching number of dimensions
@@ -163,13 +142,12 @@ def aggregate_feature_stats(stats_ft_list: list[dict[str, dict]]) -> dict[str, d
     delta_means = means - total_mean
     weighted_variances = (variances + delta_means**2) * counts
     total_variance = weighted_variances.sum(axis=0) / total_count
-    total_std = np.sqrt(total_variance)
 
     return {
-        "min": np.min(np.stack([s["min"] for s in valid_stats]), axis=0),
-        "max": np.max(np.stack([s["max"] for s in valid_stats]), axis=0),
+        "min": np.min(np.stack([s["min"] for s in stats_ft_list]), axis=0),
+        "max": np.max(np.stack([s["max"] for s in stats_ft_list]), axis=0),
         "mean": total_mean,
-        "std": total_std,
+        "std": np.sqrt(total_variance),
         "count": total_count,
     }
 

lerobot/common/datasets/factory.py

@@ -18,22 +18,20 @@ from pprint import pformat
 
 import torch
 
-from lerobot.configs.policies import PreTrainedConfig
-from lerobot.configs.train import TrainPipelineConfig
-from lerobot.datasets.lerobot_dataset import (
+from lerobot.common.datasets.lerobot_dataset import (
     LeRobotDataset,
     LeRobotDatasetMetadata,
     MultiLeRobotDataset,
 )
-from lerobot.datasets.transforms import ImageTransforms
+from lerobot.common.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)
     "std": [[[0.229]], [[0.224]], [[0.225]]],  # (c,1,1)
 }
 
-from lerobot.datasets.utils_must import EPISODES_DATASET_MAPPING, FEATURE_KEYS_MAPPING
-
 
 def resolve_delta_timestamps(
     cfg: PreTrainedConfig, ds_meta: LeRobotDatasetMetadata
@@ -83,87 +81,37 @@ def make_dataset(cfg: TrainPipelineConfig) -> LeRobotDataset | MultiLeRobotDatas
     image_transforms = (
         ImageTransforms(cfg.dataset.image_transforms) if cfg.dataset.image_transforms.enable else None
     )
-    if "," in cfg.dataset.repo_id:
-        repo_id = cfg.dataset.repo_id.split(",")
-        repo_id = [r for r in repo_id if r]
-    else:
-        repo_id = cfg.dataset.repo_id
-    sampling_weights = cfg.dataset.sampling_weights.split(",") if cfg.dataset.sampling_weights else None
-    feature_keys_mapping = FEATURE_KEYS_MAPPING
-    if isinstance(repo_id, str):
-        revision = getattr(cfg.dataset, "revision", None)
+
+    if isinstance(cfg.dataset.repo_id, str):
         ds_meta = LeRobotDatasetMetadata(
-            cfg.dataset.repo_id,
-            feature_keys_mapping=feature_keys_mapping,
-            revision=revision,
+            cfg.dataset.repo_id, root=cfg.dataset.root, revision=cfg.dataset.revision
         )
         delta_timestamps = resolve_delta_timestamps(cfg.policy, ds_meta)
         dataset = LeRobotDataset(
             cfg.dataset.repo_id,
-            root=getattr(cfg.dataset, "root", None),
+            root=cfg.dataset.root,
             episodes=cfg.dataset.episodes,
             delta_timestamps=delta_timestamps,
             image_transforms=image_transforms,
-            revision=revision,
+            revision=cfg.dataset.revision,
             video_backend=cfg.dataset.video_backend,
-            download_videos=True,
-            feature_keys_mapping=feature_keys_mapping,
-            max_action_dim=cfg.dataset.max_action_dim,
-            max_state_dim=cfg.dataset.max_state_dim,
-            max_num_images=cfg.dataset.max_num_images,
-            max_image_dim=cfg.dataset.max_image_dim,
         )
     else:
-        delta_timestamps = {}
-        episodes = {}
-        for i in range(len(repo_id)):
-            ds_meta = LeRobotDatasetMetadata(
-                repo_id[i],
-                feature_keys_mapping=feature_keys_mapping,
-            )  # FIXME(mshukor): ?
-            delta_timestamps[repo_id[i]] = resolve_delta_timestamps(cfg.policy, ds_meta)
-            episodes[repo_id[i]] = EPISODES_DATASET_MAPPING.get(repo_id[i], cfg.dataset.episodes)
-        # training_features = TRAINING_FEATURES.get(cfg.dataset.features_version, None)
-        # FIXME: (jadechoghari): check support for training features
-        training_features = None
+        raise NotImplementedError("The MultiLeRobotDataset isn't supported for now.")
         dataset = MultiLeRobotDataset(
-            repo_id,
+            cfg.dataset.repo_id,
             # TODO(aliberts): add proper support for multi dataset
-            episodes=episodes,
-            delta_timestamps=delta_timestamps,
+            # delta_timestamps=delta_timestamps,
             image_transforms=image_transforms,
             video_backend=cfg.dataset.video_backend,
-            download_videos=True,
-            sampling_weights=sampling_weights,
-            feature_keys_mapping=feature_keys_mapping,
-            max_action_dim=cfg.policy.max_action_dim,
-            max_state_dim=cfg.policy.max_state_dim,
-            max_num_images=cfg.dataset.max_num_images,
-            max_image_dim=cfg.dataset.max_image_dim,
-            train_on_all_features=cfg.dataset.train_on_all_features,
-            training_features=training_features,
-            discard_first_n_frames=cfg.dataset.discard_first_n_frames,
-            min_fps=cfg.dataset.min_fps,
-            max_fps=cfg.dataset.max_fps,
-            discard_first_idle_frames=cfg.dataset.discard_first_idle_frames,
-            motion_threshold=cfg.dataset.motion_threshold,
-            motion_window_size=cfg.dataset.motion_window_size,
-            motion_buffer=cfg.dataset.motion_buffer,
         )
         logging.info(
             "Multiple datasets were provided. Applied the following index mapping to the provided datasets: "
             f"{pformat(dataset.repo_id_to_index, indent=2)}"
         )
+
     if cfg.dataset.use_imagenet_stats:
-        # Initialize stats structure if it doesn't exist
-        if dataset.meta.stats is None:
-            dataset.meta.stats = {}
-        
         for key in dataset.meta.camera_keys:
-            # Initialize stats for this camera key if it doesn't exist
-            if key not in dataset.meta.stats or dataset.meta.stats[key] is None:
-                dataset.meta.stats[key] = {}
-                
             for stats_type, stats in IMAGENET_STATS.items():
                 dataset.meta.stats[key][stats_type] = torch.tensor(stats, dtype=torch.float32)
 

lerobot/common/datasets/lerobot_dataset.py

@@ -15,7 +15,6 @@
 # limitations under the License.
 import contextlib
 import logging
-import os
 import shutil
 from pathlib import Path
 from typing import Callable
@@ -31,25 +30,17 @@ from huggingface_hub import HfApi, snapshot_download
 from huggingface_hub.constants import REPOCARD_NAME
 from huggingface_hub.errors import RevisionNotFoundError
 
-from lerobot.constants import (
-    ACTION,
-    HF_LEROBOT_HOME,
-    OBS_ENV_STATE,
-    OBS_STATE,
-)
-from lerobot.datasets.compute_stats import (  # aggregate_stats_per_robot_type,
-    aggregate_stats,
-    compute_episode_stats,
-)
-from lerobot.datasets.image_writer import AsyncImageWriter, write_image
-from lerobot.datasets.utils import (
+from lerobot.common.constants import HF_LEROBOT_HOME
+from lerobot.common.datasets.compute_stats import aggregate_stats, compute_episode_stats
+from lerobot.common.datasets.image_writer import AsyncImageWriter, write_image
+from lerobot.common.datasets.utils import (
     DEFAULT_FEATURES,
     DEFAULT_IMAGE_PATH,
     INFO_PATH,
     TASKS_PATH,
-    _validate_feature_names,
     append_jsonlines,
     backward_compatible_episodes_stats,
+    check_delta_timestamps,
     check_timestamps_sync,
     check_version_compatibility,
     create_empty_dataset_info,
@@ -66,36 +57,14 @@ from lerobot.datasets.utils import (
     load_info,
     load_stats,
     load_tasks,
-    map_dict_keys,
     validate_episode_buffer,
     validate_frame,
     write_episode,
     write_episode_stats,
     write_info,
     write_json,
-    # keep_datasets_with_the_same_features_per_robot_type,
-    # map_dict_pad_keys,
-    # keep_datasets_with_valid_fps,
-    # find_start_of_motion,
 )
-
-# mustafa stuff here
-from lerobot.datasets.utils_must import (
-    OBS_IMAGE,
-    OBS_IMAGE_2,
-    OBS_IMAGE_3,
-    ROBOT_TYPE_KEYS_MAPPING,
-    TASKS_KEYS_MAPPING,
-    aggregate_stats_per_robot_type,
-    create_padded_features,
-    find_start_of_motion,
-    keep_datasets_with_the_same_features_per_robot_type,
-    keep_datasets_with_valid_fps,
-    map_dict_keys,
-    pad_tensor,
-    reshape_features_to_max_dim,
-)
-from lerobot.datasets.video_utils import (
+from lerobot.common.datasets.video_utils import (
     VideoFrame,
     decode_video_frames,
     encode_video_frames,
@@ -104,15 +73,6 @@ from lerobot.datasets.video_utils import (
 )
 
 CODEBASE_VERSION = "v2.1"
-LEROBOT_HOME = Path(os.getenv("LEROBOT_HOME", "~/.cache/huggingface/lerobot")).expanduser()
-
-
-def find_start_of_motion(velocities, window_size, threshold, motion_buffer):
-    for t in range(len(velocities) - window_size):
-        window_mean = velocities[t : t + window_size].mean()
-        if window_mean > threshold:
-            return max(0, t - motion_buffer)  # include slight context before motion
-    return 0
 
 
 class LeRobotDatasetMetadata:
@@ -120,13 +80,10 @@ class LeRobotDatasetMetadata:
         self,
         repo_id: str,
         root: str | Path | None = None,
-        local_files_only: bool = False,
-        feature_keys_mapping: dict[str, str] | None = None,
         revision: str | None = None,
         force_cache_sync: bool = False,
     ):
         self.repo_id = repo_id
-        self.local_files_only = local_files_only
         self.revision = revision if revision else CODEBASE_VERSION
         self.root = Path(root) if root is not None else HF_LEROBOT_HOME / repo_id
 
@@ -141,27 +98,18 @@ class LeRobotDatasetMetadata:
             (self.root / "meta").mkdir(exist_ok=True, parents=True)
             self.pull_from_repo(allow_patterns="meta/")
             self.load_metadata()
-        # added by mshukor
-        self.feature_keys_mapping = feature_keys_mapping.get(repo_id, None) if feature_keys_mapping else None
-        self.inverse_feature_keys_mapping = (
-            {v: k for k, v in self.feature_keys_mapping.items() if v} if self.feature_keys_mapping else {}
-        )
-        self.info["features"] = map_dict_keys(
-            self.info["features"], feature_keys_mapping=self.feature_keys_mapping
-        )
 
     def load_metadata(self):
         self.info = load_info(self.root)
         check_version_compatibility(self.repo_id, self._version, CODEBASE_VERSION)
         self.tasks, self.task_to_task_index = load_tasks(self.root)
         self.episodes = load_episodes(self.root)
-        # Force all datasets to use v2.1 format (episodes_stats.jsonl) to avoid missing stats.json issues, because I converted all the datasets to v2.1 format. 
-        # if self._version < packaging.version.parse("v2.1"):
-        #     self.stats = load_stats(self.root)
-        #     self.episodes_stats = backward_compatible_episodes_stats(self.stats, self.episodes)
-        # else:
-        self.episodes_stats = load_episodes_stats(self.root)
-        self.stats = aggregate_stats(list(self.episodes_stats.values()))
+        if self._version < packaging.version.parse("v2.1"):
+            self.stats = load_stats(self.root)
+            self.episodes_stats = backward_compatible_episodes_stats(self.stats, self.episodes)
+        else:
+            self.episodes_stats = load_episodes_stats(self.root)
+            self.stats = aggregate_stats(list(self.episodes_stats.values()))
 
     def pull_from_repo(
         self,
@@ -228,15 +176,7 @@ class LeRobotDatasetMetadata:
     @property
     def video_keys(self) -> list[str]:
         """Keys to access visual modalities stored as videos."""
-        # changed
-        keys = []
-        for key, ft in self.features.items():
-            key_ = (
-                self.inverse_feature_keys_mapping.get(key, key) if self.inverse_feature_keys_mapping else key
-            )
-            if ft["dtype"] == "video":
-                keys.append(key_)
-        return keys
+        return [key for key, ft in self.features.items() if ft["dtype"] == "video"]
 
     @property
     def camera_keys(self) -> list[str]:
@@ -374,9 +314,23 @@ class LeRobotDatasetMetadata:
 
         obj.root.mkdir(parents=True, exist_ok=False)
 
+        # if robot is not None:
+        #     features = get_features_from_robot(robot, use_videos)
+        #     robot_type = robot.robot_type
+        #     if not all(cam.fps == fps for cam in robot.cameras.values()):
+        #         logging.warning(
+        #             f"Some cameras in your {robot.robot_type} robot don't have an fps matching the fps of your dataset."
+        #             "In this case, frames from lower fps cameras will be repeated to fill in the blanks."
+        #         )
+
         # TODO(aliberts, rcadene): implement sanity check for features
         features = {**features, **DEFAULT_FEATURES}
-        _validate_feature_names(features)
+
+        # check if none of the features contains a "/" in their names,
+        # as this would break the dict flattening in the stats computation, which uses '/' as separator
+        for key in features:
+            if "/" in key:
+                raise ValueError(f"Feature names should not contain '/'. Found '/' in feature '{key}'.")
 
         obj.tasks, obj.task_to_task_index = {}, {}
         obj.episodes_stats, obj.stats, obj.episodes = {}, {}, {}
@@ -401,19 +355,6 @@ class LeRobotDataset(torch.utils.data.Dataset):
         force_cache_sync: bool = False,
         download_videos: bool = True,
         video_backend: str | None = None,
-        local_files_only: bool = False,
-        # new thing by M
-        feature_keys_mapping: dict[str, str] | None = None,
-        max_action_dim: int = None,
-        max_state_dim: int = None,
-        max_num_images: int = None,
-        max_image_dim: int = None,
-        training_features: list | None = None,
-        discard_first_n_frames: int = 0,
-        discard_first_idle_frames: bool = False,
-        motion_threshold: float = 5e-2,
-        motion_window_size: int = 10,
-        motion_buffer: int = 3,
     ):
         """
         2 modes are available for instantiating this class, depending on 2 different use cases:
@@ -429,7 +370,7 @@ 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/datasets/v2/convert_dataset_v1_to_v2.py.
+              lerobot/common/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
@@ -527,35 +468,15 @@ class LeRobotDataset(torch.utils.data.Dataset):
         self.video_backend = video_backend if video_backend else get_safe_default_codec()
         self.delta_indices = None
 
-        # by mshukor
-        self.training_features = training_features
-        self.discard_first_n_frames = discard_first_n_frames
-        self.discard_first_idle_frames = discard_first_idle_frames
-        self.motion_threshold = motion_threshold
-        self.motion_window_size = motion_window_size
-        self.motion_buffer = motion_buffer
-
         # Unused attributes
         self.image_writer = None
         self.episode_buffer = None
 
         self.root.mkdir(exist_ok=True, parents=True)
 
-        # more mshukor
-        self.feature_keys_mapping = feature_keys_mapping.get(repo_id, None) if feature_keys_mapping else None
-        self.inverse_feature_keys_mapping = (
-            {v: k for k, v in self.feature_keys_mapping.items() if v} if self.feature_keys_mapping else {}
-        )
-
         # Load metadata
-        # TODO: change
         self.meta = LeRobotDatasetMetadata(
-            self.repo_id,
-            self.root,
-            local_files_only=local_files_only,
-            revision=self.revision,
-            force_cache_sync=force_cache_sync,
-            feature_keys_mapping=feature_keys_mapping,
+            self.repo_id, self.root, self.revision, force_cache_sync=force_cache_sync
         )
         if self.episodes is not None and self.meta._version >= packaging.version.parse("v2.1"):
             episodes_stats = [self.meta.episodes_stats[ep_idx] for ep_idx in self.episodes]
@@ -574,74 +495,17 @@ class LeRobotDataset(torch.utils.data.Dataset):
 
         self.episode_data_index = get_episode_data_index(self.meta.episodes, self.episodes)
 
-        # mustafa code
-        if self.discard_first_n_frames > 0:
-            print("Discarding first n frames:", self.discard_first_n_frames)
-            self.subset_frame_ids = []
-            for ep_idx in range(self.num_episodes):
-                from_ = self.episode_data_index["from"][ep_idx]
-                to_ = self.episode_data_index["to"][ep_idx]
-                # TODO implement advanced strategy
-                self.subset_frame_ids += [
-                    frame_idx for frame_idx in range(from_ + int(self.fps * self.discard_first_n_frames), to_)
-                ]
-        elif self.discard_first_idle_frames:
-            print(
-                f"Discarding first idle frames: motion_threshold={self.motion_threshold}, motion_window_size={self.motion_window_size}, motion_buffer={self.motion_buffer}"
-            )
-            self.robot_states = torch.stack(self.hf_dataset[OBS_STATE]).numpy()  # shape: [T, D]
-            self.subset_frame_ids = []
-            for ep_idx in range(self.num_episodes):
-                from_ = self.episode_data_index["from"][ep_idx]
-                to_ = self.episode_data_index["to"][ep_idx]
-                ep_states = self.robot_states[from_:to_]
-                velocities = np.linalg.norm(np.diff(ep_states, axis=0), axis=1)
-                velocities = np.concatenate([[0.0], velocities])
-                start_idx = find_start_of_motion(
-                    velocities, self.motion_window_size, self.motion_threshold, self.motion_buffer
-                )
-                self.subset_frame_ids += list(range(from_ + start_idx, to_))
-
         # Check timestamps
-        # commented TODO: check why
-        # timestamps = torch.stack(self.hf_dataset["timestamp"]).numpy()
-        # episode_indices = torch.stack(self.hf_dataset["episode_index"]).numpy()
-        # ep_data_index_np = {k: t.numpy() for k, t in self.episode_data_index.items()}
-        # check_timestamps_sync(timestamps, episode_indices, ep_data_index_np, self.fps, self.tolerance_s)
+        timestamps = torch.stack(self.hf_dataset["timestamp"]).numpy()
+        episode_indices = torch.stack(self.hf_dataset["episode_index"]).numpy()
+        ep_data_index_np = {k: t.numpy() for k, t in self.episode_data_index.items()}
+        check_timestamps_sync(timestamps, episode_indices, ep_data_index_np, self.fps, self.tolerance_s)
 
         # Setup delta_indices
         if self.delta_timestamps is not None:
-            # TODO: check why commented
-            # check_delta_timestamps(self.delta_timestamps, self.fps, self.tolerance_s)
+            check_delta_timestamps(self.delta_timestamps, self.fps, self.tolerance_s)
             self.delta_indices = get_delta_indices(self.delta_timestamps, self.fps)
 
-        # Mustafa
-        self.meta.info["features"] = map_dict_keys(
-            self.meta.info["features"],
-            feature_keys_mapping=self.feature_keys_mapping,
-            training_features=self.training_features,
-        )
-        self.keys_to_max_dim = {
-            ACTION: max_action_dim,
-            OBS_ENV_STATE: max_state_dim,
-            OBS_STATE: max_state_dim,
-            OBS_IMAGE: max_image_dim,
-            OBS_IMAGE_2: max_image_dim,
-            OBS_IMAGE_3: max_image_dim,
-        }
-        self.meta.info["features"] = reshape_features_to_max_dim(
-            self.meta.info["features"], reshape_dim=-1, keys_to_max_dim=self.keys_to_max_dim
-        )
-        self.meta.stats = map_dict_keys(
-            self.meta.stats,
-            feature_keys_mapping=self.feature_keys_mapping,
-            training_features=self.training_features,
-        )
-        self.robot_type = self.meta.info.get("robot_type", "")
-        # Override tasks
-        print(TASKS_KEYS_MAPPING.get(self.repo_id, self.meta.tasks), "previous", self.meta.tasks)
-        self.meta.tasks = TASKS_KEYS_MAPPING.get(self.repo_id, self.meta.tasks)
-
     def push_to_hub(
         self,
         branch: str | None = None,
@@ -790,18 +654,12 @@ class LeRobotDataset(torch.utils.data.Dataset):
             return get_hf_features_from_features(self.features)
 
     def _get_query_indices(self, idx: int, ep_idx: int) -> tuple[dict[str, list[int | bool]]]:
-        # Bounds check to prevent IndexError when episode_index is out of range
-        if ep_idx >= len(self.episode_data_index["from"]):
-            # Fall back to the last valid episode
-            ep_idx = len(self.episode_data_index["from"]) - 1
-            
         ep_start = self.episode_data_index["from"][ep_idx]
         ep_end = self.episode_data_index["to"][ep_idx]
         query_indices = {
             key: [max(ep_start.item(), min(ep_end.item() - 1, idx + delta)) for delta in delta_idx]
             for key, delta_idx in self.delta_indices.items()
         }
-        # FIXME(mshukor): what if we train on multiple datasets with different features
         padding = {  # Pad values outside of current episode range
             f"{key}_is_pad": torch.BoolTensor(
                 [(idx + delta < ep_start.item()) | (idx + delta >= ep_end.item()) for delta in delta_idx]
@@ -825,21 +683,12 @@ class LeRobotDataset(torch.utils.data.Dataset):
 
         return query_timestamps
 
-    # TODO: changed by mustafa
     def _query_hf_dataset(self, query_indices: dict[str, list[int]]) -> dict:
-        queries = {}
-        for key, q_idx in query_indices.items():
-            if (
-                key not in self.meta.video_keys
-                and self.inverse_feature_keys_mapping.get(key, key) not in self.meta.video_keys
-            ):
-                key_ = (
-                    self.inverse_feature_keys_mapping.get(key, key)
-                    if self.inverse_feature_keys_mapping
-                    else key
-                )
-                queries[key] = torch.stack(self.hf_dataset.select(q_idx)[key_])
-        return queries
+        return {
+            key: torch.stack(self.hf_dataset.select(q_idx)[key])
+            for key, q_idx in query_indices.items()
+            if key not in self.meta.video_keys
+        }
 
     def _query_videos(self, query_timestamps: dict[str, list[float]], ep_idx: int) -> dict[str, torch.Tensor]:
         """Note: When using data workers (e.g. DataLoader with num_workers>0), do not call this function
@@ -863,12 +712,8 @@ class LeRobotDataset(torch.utils.data.Dataset):
     def __len__(self):
         return self.num_frames
 
-    # changed by mshukor
     def __getitem__(self, idx) -> dict:
-        if self.discard_first_n_frames > 0 or self.discard_first_idle_frames:
-            idx = self.subset_frame_ids[idx]
         item = self.hf_dataset[idx]
-        item = map_dict_keys(item, feature_keys_mapping=self.feature_keys_mapping)
         ep_idx = item["episode_index"].item()
 
         query_indices = None
@@ -885,27 +730,15 @@ class LeRobotDataset(torch.utils.data.Dataset):
             video_frames = self._query_videos(query_timestamps, ep_idx)
             item = {**video_frames, **item}
 
+        if self.image_transforms is not None:
+            image_keys = self.meta.camera_keys
+            for cam in image_keys:
+                item[cam] = self.image_transforms(item[cam])
+
         # Add task as a string
         task_idx = item["task_index"].item()
-        try:
-            item["task"] = self.meta.tasks[task_idx]
-        except:
-            print(self.meta.tasks, task_idx, self.repo_id)
-        if "robot_type" not in item:
-            item["robot_type"] = self.robot_type
-        item = map_dict_keys(
-            item, feature_keys_mapping=self.feature_keys_mapping, training_features=self.training_features
-        )
-        # Add padded features
-        # item = self._add_padded_features(item, self.training_features)
-        if self.image_transforms is not None:
-            for cam in item:
-                if cam in self.meta.camera_keys or ("image" in cam and "is_pad" not in cam):
-                    item[cam] = self.image_transforms(item[cam])
-        # Map pad keys
-        # print(item.keys(), "before")
-        # item = map_dict_pad_keys(item, feature_keys_mapping=self.feature_keys_mapping, training_features=self.training_features)
-        # print(item.keys())
+        item["task"] = self.meta.tasks[task_idx]
+
         return item
 
     def __repr__(self):
@@ -1165,7 +998,6 @@ class LeRobotDataset(torch.utils.data.Dataset):
         )
         obj.repo_id = obj.meta.repo_id
         obj.root = obj.meta.root
-        obj.local_files_only = obj.meta.local_files_only
         obj.revision = None
         obj.tolerance_s = tolerance_s
         obj.image_writer = None
@@ -1186,106 +1018,6 @@ class LeRobotDataset(torch.utils.data.Dataset):
         return obj
 
 
-class MultiLeRobotDatasetMeta:
-    def __init__(
-        self,
-        datasets: list[LeRobotDataset],
-        repo_ids: list[str],
-        keys_to_max_dim: dict[str, int],
-        train_on_all_features: bool = False,
-    ):
-        self.repo_ids = repo_ids
-        self.keys_to_max_dim = keys_to_max_dim
-        self.train_on_all_features = train_on_all_features
-        self.robot_types = [ds.meta.info["robot_type"] for ds in datasets]
-
-        # assign robot_type if missing
-        for ds in datasets:
-            ds.meta.info["robot_type"] = ROBOT_TYPE_KEYS_MAPPING.get(ds.repo_id, ds.meta.info["robot_type"])
-            ds.robot_type = ds.meta.info["robot_type"]
-
-        # step 1: compute disabled features
-        self.disabled_features = set()
-        if not self.train_on_all_features:
-            intersection = set(datasets[0].features)
-            for ds in datasets:
-                intersection.intersection_update(ds.features)
-            if not intersection:
-                raise RuntimeError("No common features across datasets.")
-            for repo_id, ds in zip(repo_ids, datasets, strict=False):
-                extra = set(ds.features) - intersection
-                logging.warning(f"Disabling {extra} for repo {repo_id}")
-                self.disabled_features.update(extra)
-
-        # step 2: build union_features excluding disabled
-        self.union_features = {}
-        for ds in datasets:
-            for k, v in ds.features.items():
-                if k not in self.disabled_features:
-                    self.union_features[k] = v
-
-        # step 3: reshape feature schema
-        self.features = reshape_features_to_max_dim(
-            self.union_features, reshape_dim=-1, keys_to_max_dim=self.keys_to_max_dim
-        )
-
-        # step 4: aggregate stats
-        self.stats = aggregate_stats_per_robot_type(datasets)
-        for robot_type_, stats_ in self.stats.items():
-            for feat_key, feat_stats in stats_.items():
-                if feat_key in [ACTION, OBS_ENV_STATE, OBS_STATE]:
-                    for k, v in feat_stats.items():
-                        pad_value = 0 if k in ["min", "mean"] else 1
-                        self.stats[robot_type_][feat_key][k] = pad_tensor(
-                            v,
-                            max_size=self.keys_to_max_dim.get(feat_key, -1),
-                            pad_dim=-1,
-                            pad_value=pad_value,
-                        )
-
-        # step 5: episodes & tasks
-        self.episodes = {repo_id: ds.meta.episodes for repo_id, ds in zip(repo_ids, datasets, strict=False)}
-        self.tasks = {repo_id: ds.meta.tasks for repo_id, ds in zip(repo_ids, datasets, strict=False)}
-        self.info = {repo_id: ds.meta.info for repo_id, ds in zip(repo_ids, datasets, strict=False)}
-
-
-class MultiLeRobotDatasetCleaner:
-    def __init__(
-        self,
-        datasets: list[LeRobotDataset],
-        repo_ids: list[str],
-        sampling_weights: list[float],
-        datasets_repo_ids: list[str],
-        min_fps: int = 1,
-        max_fps: int = 100,
-    ):
-        self.original_datasets = datasets
-        self.original_repo_ids = repo_ids
-        self.original_weights = sampling_weights
-        self.original_datasets_repo_ids = datasets_repo_ids
-
-        # step 1: remove datasets with invalid fps
-        valid_fps_datasets = keep_datasets_with_valid_fps(datasets, min_fps=min_fps, max_fps=max_fps)
-
-        # step 2: keep datasets with same features per robot type
-        consistent_datasets, keep_mask = keep_datasets_with_the_same_features_per_robot_type(
-            valid_fps_datasets
-        )
-
-        self.cleaned_datasets = consistent_datasets
-        self.keep_mask = keep_mask
-        self.cleaned_weights = [sampling_weights[i] for i in range(len(valid_fps_datasets)) if keep_mask[i]]
-        self.cleaned_repo_ids = [repo_ids[i] for i in range(len(valid_fps_datasets)) if keep_mask[i]]
-        self.cleaned_datasets_repo_ids = [
-            datasets_repo_ids[i] for i in range(len(valid_fps_datasets)) if keep_mask[i]
-        ]
-
-        self.cumulative_sizes = np.array(
-            [0] + list(torch.cumsum(torch.tensor([len(d) for d in consistent_datasets]), dim=0))
-        )
-        self.cleaned_weights = np.array(self.cleaned_weights, dtype=np.float32)
-
-
 class MultiLeRobotDataset(torch.utils.data.Dataset):
     """A dataset consisting of multiple underlying `LeRobotDataset`s.
 
@@ -1302,24 +1034,7 @@ class MultiLeRobotDataset(torch.utils.data.Dataset):
         delta_timestamps: dict[list[float]] | None = None,
         tolerances_s: dict | None = None,
         download_videos: bool = True,
-        local_files_only: bool = False,
         video_backend: str | None = None,
-        # add
-        sampling_weights: list[float] | None = None,
-        feature_keys_mapping: dict[str, dict[str, str]] | None = None,
-        max_action_dim: int = None,
-        max_state_dim: int = None,
-        max_num_images: int = None,
-        max_image_dim: int = None,
-        train_on_all_features: bool = False,
-        training_features: list | None = None,
-        discard_first_n_frames: int = 0,
-        min_fps: int = 1,
-        max_fps: int = 100,
-        discard_first_idle_frames: bool = False,
-        motion_threshold: float = 0.05,
-        motion_window_size: int = 10,
-        motion_buffer: int = 3,
     ):
         super().__init__()
         self.repo_ids = repo_ids
@@ -1327,89 +1042,46 @@ class MultiLeRobotDataset(torch.utils.data.Dataset):
         self.tolerances_s = tolerances_s if tolerances_s else dict.fromkeys(repo_ids, 0.0001)
         # Construct the underlying datasets passing everything but `transform` and `delta_timestamps` which
         # are handled by this class.
-        _datasets = []
-        datasets_repo_ids = []
-        self.sampling_weights = []
-        self.training_features = training_features
-
-        sampling_weights = sampling_weights if sampling_weights is not None else [1] * len(repo_ids)
-        assert len(sampling_weights) == len(repo_ids), (
-            "The number of sampling weights must match the number of datasets. "
-            f"Got {len(sampling_weights)} weights for {len(repo_ids)} datasets."
-        )
-        for i, repo_id in enumerate(repo_ids):
-            try:
-                # delta_timestamps = resolve_delta_timestamps(cfg.policy, ds_meta)
-                _datasets.append(
-                    LeRobotDataset(
-                        repo_id,
-                        root=self.root / repo_id,
-                        episodes=episodes.get(repo_id, None) if episodes else None,
-                        image_transforms=image_transforms,
-                        delta_timestamps=delta_timestamps.get(repo_id, None) if delta_timestamps else None,
-                        tolerance_s=self.tolerances_s[repo_id],
-                        download_videos=download_videos,
-                        video_backend=video_backend,
-                        feature_keys_mapping=feature_keys_mapping,
-                        training_features=training_features,
-                        discard_first_n_frames=discard_first_n_frames,
-                        discard_first_idle_frames=discard_first_idle_frames,
-                        motion_threshold=motion_threshold,
-                        motion_window_size=motion_window_size,
-                        motion_buffer=motion_buffer,
-                    )
-                )
-                datasets_repo_ids.append(repo_id)
-                self.sampling_weights.append(float(sampling_weights[i]))
-            except Exception as e:
-                print(f"Failed to load dataset: {repo_id} due to Exception: {e}")
-        print(
-            f"Finish loading {len(_datasets)} datasets, with sampling weights: {self.sampling_weights} corresponding to: {datasets_repo_ids}"
-        )
+        self._datasets = [
+            LeRobotDataset(
+                repo_id,
+                root=self.root / repo_id,
+                episodes=episodes[repo_id] if episodes else None,
+                image_transforms=image_transforms,
+                delta_timestamps=delta_timestamps,
+                tolerance_s=self.tolerances_s[repo_id],
+                download_videos=download_videos,
+                video_backend=video_backend,
+            )
+            for repo_id in repo_ids
+        ]
 
         # Disable any data keys that are not common across all of the datasets. Note: we may relax this
         # restriction in future iterations of this class. For now, this is necessary at least for being able
         # to use PyTorch's default DataLoader collate function.
-        # FIXME(mshukor): apply mapping to unify used keys
-        # FIXME(mshukor): pad based on types in case we have more than one state?
+        self.disabled_features = set()
+        intersection_features = set(self._datasets[0].features)
+        for ds in self._datasets:
+            intersection_features.intersection_update(ds.features)
+        if len(intersection_features) == 0:
+            raise RuntimeError(
+                "Multiple datasets were provided but they had no keys common to all of them. "
+                "The multi-dataset functionality currently only keeps common keys."
+            )
+        for repo_id, ds in zip(self.repo_ids, self._datasets, strict=True):
+            extra_keys = set(ds.features).difference(intersection_features)
+            logging.warning(
+                f"keys {extra_keys} of {repo_id} were disabled as they are not contained in all the "
+                "other datasets."
+            )
+            self.disabled_features.update(extra_keys)
+
         self.image_transforms = image_transforms
-        self.delta_timestamps = (
-            delta_timestamps.get(repo_id, None) if delta_timestamps else None
-        )  # delta_timestamps # FIXME(mshukor): last repo?
-        # In case datasets with the same robot_type have different features
-        cleaner = MultiLeRobotDatasetCleaner(
-            datasets=_datasets,
-            repo_ids=repo_ids,
-            sampling_weights=self.sampling_weights,
-            datasets_repo_ids=datasets_repo_ids,
-            min_fps=min_fps,
-            max_fps=max_fps,
-        )
-        self._datasets = cleaner.cleaned_datasets
-        self.sampling_weights = cleaner.cleaned_weights
-        self.repo_ids = cleaner.cleaned_repo_ids
-        self.datasets_repo_ids = cleaner.cleaned_datasets_repo_ids
-        self.cumulative_sizes = cleaner.cumulative_sizes
-        # self.meta = copy.deepcopy(self._datasets[0].meta)  # FIXME(mshukor): aggregate meta from all datasets
-        # self.meta.info = {
-        #     repo_id: ds.meta.info for repo_id, ds in zip(self.repo_ids, self._datasets, strict=False)
-        # }
-        # self.meta.info["features"] = self._datasets[0].meta.info["features"] # Assume all datasets have the same features
-        self.meta = MultiLeRobotDatasetMeta(
-            datasets=self._datasets,
-            repo_ids=self.repo_ids,
-            keys_to_max_dim={
-                ACTION: max_action_dim,
-                OBS_ENV_STATE: max_state_dim,
-                OBS_STATE: max_state_dim,
-                OBS_IMAGE: max_image_dim,
-                OBS_IMAGE_2: max_image_dim,
-                OBS_IMAGE_3: max_image_dim,
-            },
-            train_on_all_features=train_on_all_features,
-        )
-        self.disabled_features = self.meta.disabled_features
-        self.stats = self.meta.stats
+        self.delta_timestamps = delta_timestamps
+        # TODO(rcadene, aliberts): We should not perform this aggregation for datasets
+        # with multiple robots of different ranges. Instead we should have one normalization
+        # per robot.
+        self.stats = aggregate_stats([dataset.meta.stats for dataset in self._datasets])
 
     @property
     def repo_id_to_index(self):
@@ -1497,14 +1169,23 @@ class MultiLeRobotDataset(torch.utils.data.Dataset):
     def __getitem__(self, idx: int) -> dict[str, torch.Tensor]:
         if idx >= len(self):
             raise IndexError(f"Index {idx} out of bounds.")
-        dataset_idx = np.searchsorted(self.cumulative_sizes, idx, side="right").item() - 1
-        local_idx = (idx - self.cumulative_sizes[dataset_idx]).item()
-        item = self._datasets[dataset_idx][local_idx]
+        # Determine which dataset to get an item from based on the index.
+        start_idx = 0
+        dataset_idx = 0
+        for dataset in self._datasets:
+            if idx >= start_idx + dataset.num_frames:
+                start_idx += dataset.num_frames
+                dataset_idx += 1
+                continue
+            break
+        else:
+            raise AssertionError("We expect the loop to break out as long as the index is within bounds.")
+        item = self._datasets[dataset_idx][idx - start_idx]
         item["dataset_index"] = torch.tensor(dataset_idx)
-        item = create_padded_features(item, self.meta.features)
-        for data_key in self.disabled_features:  # FIXME(mshukor): not in getitem?
+        for data_key in self.disabled_features:
             if data_key in item:
                 del item[data_key]
+
         return item
 
     def __repr__(self):

lerobot/common/datasets/online_buffer.py

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

lerobot/common/datasets/push_dataset_to_hub/utils.py

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

lerobot/common/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.configs.types import DictLike, FeatureType, PolicyFeature
-from lerobot.datasets.backward_compatibility import (
+from lerobot.common.datasets.backward_compatibility import (
     V21_MESSAGE,
     BackwardCompatibilityError,
     ForwardCompatibilityError,
 )
-from lerobot.robots import Robot
-from lerobot.utils.utils import is_valid_numpy_dtype_string
+from lerobot.common.robots import Robot
+from lerobot.common.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 src/lerobot/datasets/card_template.md.
+    Keyword arguments will be used to replace values in ./lerobot/common/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.datasets") / "card_template.md").read_text()
+    card_template = (importlib.resources.files("lerobot.common.datasets") / "card_template.md").read_text()
 
     return DatasetCard.from_template(
         card_data=card_data,
@@ -858,21 +858,3 @@ def validate_episode_buffer(episode_buffer: dict, total_episodes: int, features:
             f"In episode_buffer not in features: {buffer_keys - set(features)}"
             f"In features not in episode_buffer: {set(features) - buffer_keys}"
         )
-
-
-def map_dict_keys(
-    item: dict, feature_keys_mapping: dict, training_features: list = None, pad_key: str = "is_pad"
-) -> dict:
-    """Maps feature keys from the dataset to the keys used in the model."""
-    if feature_keys_mapping is None:
-        return item
-    features = {}
-    for key in item:
-        if key in feature_keys_mapping:
-            if feature_keys_mapping[key] is not None:
-                if training_features is None or feature_keys_mapping[key] in training_features:
-                    features[feature_keys_mapping[key]] = item[key]
-        else:
-            if training_features is None or key in training_features or pad_key in key:
-                features[key] = item[key]
-    return features

lerobot/common/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.datasets.v2.convert_dataset_v1_to_v2 import convert_dataset
-from lerobot.robots.aloha.configuration_aloha import AlohaRobotConfig
+from lerobot.common.datasets.v2.convert_dataset_v1_to_v2 import convert_dataset
+from lerobot.common.robots.aloha.configuration_aloha import AlohaRobotConfig
 
 LOCAL_DIR = Path("data/")
 
@@ -36,7 +36,7 @@ ALOHA_MOBILE_INFO = {
     "robot_config": AlohaRobotConfig(),
     "license": "mit",
     "url": "https://mobile-aloha.github.io/",
-    "paper": "https://huggingface.co/papers/2401.02117",
+    "paper": "https://arxiv.org/abs/2401.02117",
     "citation_bibtex": dedent(r"""
         @inproceedings{fu2024mobile,
             author    = {Fu, Zipeng and Zhao, Tony Z. and Finn, Chelsea},
@@ -49,7 +49,7 @@ ALOHA_STATIC_INFO = {
     "robot_config": AlohaRobotConfig(),
     "license": "mit",
     "url": "https://tonyzhaozh.github.io/aloha/",
-    "paper": "https://huggingface.co/papers/2304.13705",
+    "paper": "https://arxiv.org/abs/2304.13705",
     "citation_bibtex": dedent(r"""
         @article{Zhao2023LearningFB,
             title={Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware},
@@ -57,13 +57,13 @@ ALOHA_STATIC_INFO = {
             journal={RSS},
             year={2023},
             volume={abs/2304.13705},
-            url={https://huggingface.co/papers/2304.13705}
+            url={https://arxiv.org/abs/2304.13705}
         }""").lstrip(),
 }
 PUSHT_INFO = {
     "license": "mit",
     "url": "https://diffusion-policy.cs.columbia.edu/",
-    "paper": "https://huggingface.co/papers/2303.04137",
+    "paper": "https://arxiv.org/abs/2303.04137v5",
     "citation_bibtex": dedent(r"""
         @article{chi2024diffusionpolicy,
             author = {Cheng Chi and Zhenjia Xu and Siyuan Feng and Eric Cousineau and Yilun Du and Benjamin Burchfiel and Russ Tedrake and Shuran Song},
@@ -75,7 +75,7 @@ PUSHT_INFO = {
 XARM_INFO = {
     "license": "mit",
     "url": "https://www.nicklashansen.com/td-mpc/",
-    "paper": "https://huggingface.co/papers/2203.04955",
+    "paper": "https://arxiv.org/abs/2203.04955",
     "citation_bibtex": dedent(r"""
         @inproceedings{Hansen2022tdmpc,
             title={Temporal Difference Learning for Model Predictive Control},
@@ -244,7 +244,7 @@ DATASETS = {
         "tasks_col": "language_instruction",
         "license": "mit",
         "url": "https://ut-austin-rpl.github.io/BUDS-website/",
-        "paper": "https://huggingface.co/papers/2109.13841",
+        "paper": "https://arxiv.org/abs/2109.13841",
         "citation_bibtex": dedent(r"""
             @article{zhu2022bottom,
                 title={Bottom-Up Skill Discovery From Unsegmented Demonstrations for Long-Horizon Robot Manipulation},
@@ -261,7 +261,7 @@ DATASETS = {
         "tasks_col": "language_instruction",
         "license": "mit",
         "url": "https://ut-austin-rpl.github.io/sailor/",
-        "paper": "https://huggingface.co/papers/2210.11435",
+        "paper": "https://arxiv.org/abs/2210.11435",
         "citation_bibtex": dedent(r"""
             @inproceedings{nasiriany2022sailor,
                 title={Learning and Retrieval from Prior Data for Skill-based Imitation Learning},
@@ -274,7 +274,7 @@ DATASETS = {
         "tasks_col": "language_instruction",
         "license": "mit",
         "url": "https://ut-austin-rpl.github.io/sirius/",
-        "paper": "https://huggingface.co/papers/2211.08416",
+        "paper": "https://arxiv.org/abs/2211.08416",
         "citation_bibtex": dedent(r"""
             @inproceedings{liu2022robot,
                 title = {Robot Learning on the Job: Human-in-the-Loop Autonomy and Learning During Deployment},
@@ -298,14 +298,14 @@ DATASETS = {
         "tasks_col": "language_instruction",
         "license": "cc-by-4.0",
         "url": "https://sites.google.com/view/cablerouting/home",
-        "paper": "https://huggingface.co/papers/2307.08927",
+        "paper": "https://arxiv.org/abs/2307.08927",
         "citation_bibtex": dedent(r"""
             @article{luo2023multistage,
                 author    = {Jianlan Luo and Charles Xu and Xinyang Geng and Gilbert Feng and Kuan Fang and Liam Tan and Stefan Schaal and Sergey Levine},
                 title     = {Multi-Stage Cable Routing through Hierarchical Imitation Learning},
                 journal   = {arXiv pre-print},
                 year      = {2023},
-                url       = {https://huggingface.co/papers/2307.08927},
+                url       = {https://arxiv.org/abs/2307.08927},
             }""").lstrip(),
     },
     "berkeley_fanuc_manipulation": {
@@ -322,7 +322,7 @@ DATASETS = {
     "berkeley_gnm_cory_hall": {
         "tasks_col": "language_instruction",
         "license": "mit",
-        "paper": "https://huggingface.co/papers/1709.10489",
+        "paper": "https://arxiv.org/abs/1709.10489",
         "citation_bibtex": dedent(r"""
             @inproceedings{kahn2018self,
                 title={Self-supervised deep reinforcement learning with generalized computation graphs for robot navigation},
@@ -337,7 +337,7 @@ DATASETS = {
         "tasks_col": "language_instruction",
         "license": "mit",
         "url": "https://sites.google.com/view/recon-robot",
-        "paper": "https://huggingface.co/papers/2104.05859",
+        "paper": "https://arxiv.org/abs/2104.05859",
         "citation_bibtex": dedent(r"""
             @inproceedings{shah2021rapid,
                 title={Rapid Exploration for Open-World Navigation with Latent Goal Models},
@@ -351,7 +351,7 @@ DATASETS = {
         "tasks_col": "language_instruction",
         "license": "mit",
         "url": "https://sites.google.com/view/SACSoN-review",
-        "paper": "https://huggingface.co/papers/2306.01874",
+        "paper": "https://arxiv.org/abs/2306.01874",
         "citation_bibtex": dedent(r"""
             @article{hirose2023sacson,
                 title={SACSoN: Scalable Autonomous Data Collection for Social Navigation},
@@ -363,7 +363,7 @@ DATASETS = {
     "berkeley_mvp": {
         "tasks_col": "language_instruction",
         "license": "mit",
-        "paper": "https://huggingface.co/papers/2203.06173",
+        "paper": "https://arxiv.org/abs/2203.06173",
         "citation_bibtex": dedent(r"""
             @InProceedings{Radosavovic2022,
                 title = {Real-World Robot Learning with Masked Visual Pre-training},
@@ -375,7 +375,7 @@ DATASETS = {
     "berkeley_rpt": {
         "tasks_col": "language_instruction",
         "license": "mit",
-        "paper": "https://huggingface.co/papers/2306.10007",
+        "paper": "https://arxiv.org/abs/2306.10007",
         "citation_bibtex": dedent(r"""
             @article{Radosavovic2023,
                 title={Robot Learning with Sensorimotor Pre-training},
@@ -388,7 +388,7 @@ DATASETS = {
         "tasks_col": "language_instruction",
         "license": "mit",
         "url": "https://human-world-model.github.io/",
-        "paper": "https://huggingface.co/papers/2308.10901",
+        "paper": "https://arxiv.org/abs/2308.10901",
         "citation_bibtex": dedent(r"""
             @inproceedings{mendonca2023structured,
                 title={Structured World Models from Human Videos},
@@ -401,7 +401,7 @@ DATASETS = {
         "tasks_col": "language_instruction",
         "license": "mit",
         "url": "https://play-fusion.github.io/",
-        "paper": "https://huggingface.co/papers/2312.04549",
+        "paper": "https://arxiv.org/abs/2312.04549",
         "citation_bibtex": dedent(r"""
             @inproceedings{chen2023playfusion,
                 title={PlayFusion: Skill Acquisition via Diffusion from Language-Annotated Play},
@@ -414,7 +414,7 @@ DATASETS = {
         "tasks_col": "language_instruction",
         "license": "mit",
         "url": "https://robo-affordances.github.io/",
-        "paper": "https://huggingface.co/papers/2304.08488",
+        "paper": "https://arxiv.org/abs/2304.08488",
         "citation_bibtex": dedent(r"""
             @inproceedings{bahl2023affordances,
                 title={Affordances from Human Videos as a Versatile Representation for Robotics},
@@ -433,7 +433,7 @@ DATASETS = {
         "tasks_col": "language_instruction",
         "license": "mit",
         "url": "https://diffusion-policy.cs.columbia.edu/",
-        "paper": "https://huggingface.co/papers/2303.04137",
+        "paper": "https://arxiv.org/abs/2303.04137v5",
         "citation_bibtex": dedent(r"""
             @inproceedings{chi2023diffusionpolicy,
                 title={Diffusion Policy: Visuomotor Policy Learning via Action Diffusion},
@@ -505,7 +505,7 @@ DATASETS = {
         "tasks_col": "language_instruction",
         "license": "mit",
         "url": "https://droid-dataset.github.io/",
-        "paper": "https://huggingface.co/papers/2403.12945",
+        "paper": "https://arxiv.org/abs/2403.12945",
         "citation_bibtex": dedent(r"""
             @article{khazatsky2024droid,
                 title   = {DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset},
@@ -517,7 +517,7 @@ DATASETS = {
         "tasks_col": "language_instruction",
         "license": "cc-by-4.0",
         "url": "https://functional-manipulation-benchmark.github.io/",
-        "paper": "https://huggingface.co/papers/2401.08553",
+        "paper": "https://arxiv.org/abs/2401.08553",
         "citation_bibtex": dedent(r"""
             @article{luo2024fmb,
                 title={FMB: a Functional Manipulation Benchmark for Generalizable Robotic Learning},
@@ -530,7 +530,7 @@ DATASETS = {
         "tasks_col": "language_instruction",
         "license": "mit",
         "url": "https://openreview.net/forum?id=WuBv9-IGDUA",
-        "paper": "https://huggingface.co/papers/2401.14502",
+        "paper": "https://arxiv.org/abs/2401.14502",
         "citation_bibtex": dedent(r"""
             @inproceedings{saxena2023multiresolution,
                 title={Multi-Resolution Sensing for Real-Time Control with Vision-Language Models},
@@ -575,7 +575,7 @@ DATASETS = {
         "tasks_col": "language_instruction",
         "license": "mit",
         "url": "https://jyopari.github.io/VINN/",
-        "paper": "https://huggingface.co/papers/2112.01511",
+        "paper": "https://arxiv.org/abs/2112.01511",
         "citation_bibtex": dedent(r"""
             @misc{pari2021surprising,
                 title={The Surprising Effectiveness of Representation Learning for Visual Imitation},
@@ -590,7 +590,7 @@ DATASETS = {
         "tasks_col": "language_instruction",
         "license": "mit",
         "url": "https://play-to-policy.github.io/",
-        "paper": "https://huggingface.co/papers/2210.10047",
+        "paper": "https://arxiv.org/abs/2210.10047",
         "citation_bibtex": dedent(r"""
             @article{cui2022play,
                 title   = {From Play to Policy: Conditional Behavior Generation from Uncurated Robot Data},
@@ -603,7 +603,7 @@ DATASETS = {
         "tasks_col": "language_instruction",
         "license": "mit",
         "url": "https://rot-robot.github.io/",
-        "paper": "https://huggingface.co/papers/2206.15469",
+        "paper": "https://arxiv.org/abs/2206.15469",
         "citation_bibtex": dedent(r"""
             @inproceedings{haldar2023watch,
                 title={Watch and match: Supercharging imitation with regularized optimal transport},
@@ -633,7 +633,7 @@ DATASETS = {
         "tasks_col": "language_instruction",
         "license": "mit",
         "url": "https://sites.google.com/view/hydra-il-2023",
-        "paper": "https://huggingface.co/papers/2306.17237",
+        "paper": "https://arxiv.org/abs/2306.17237",
         "citation_bibtex": dedent(r"""
             @article{belkhale2023hydra,
                 title={HYDRA: Hybrid Robot Actions for Imitation Learning},
@@ -646,21 +646,21 @@ DATASETS = {
         "tasks_col": "language_instruction",
         "license": "mit",
         "url": "https://sites.google.com/view/visionandtouch",
-        "paper": "https://huggingface.co/papers/1810.10191",
+        "paper": "https://arxiv.org/abs/1810.10191",
         "citation_bibtex": dedent(r"""
             @inproceedings{lee2019icra,
                 title={Making sense of vision and touch: Self-supervised learning of multimodal representations for contact-rich tasks},
                 author={Lee, Michelle A and Zhu, Yuke and Srinivasan, Krishnan and Shah, Parth and Savarese, Silvio and Fei-Fei, Li and  Garg, Animesh and Bohg, Jeannette},
                 booktitle={2019 IEEE International Conference on Robotics and Automation (ICRA)},
                 year={2019},
-                url={https://huggingface.co/papers/1810.10191}
+                url={https://arxiv.org/abs/1810.10191}
             }""").lstrip(),
     },
     "stanford_robocook": {
         "tasks_col": "language_instruction",
         "license": "mit",
         "url": "https://hshi74.github.io/robocook/",
-        "paper": "https://huggingface.co/papers/2306.14447",
+        "paper": "https://arxiv.org/abs/2306.14447",
         "citation_bibtex": dedent(r"""
             @article{shi2023robocook,
                 title={RoboCook: Long-Horizon Elasto-Plastic Object Manipulation with Diverse Tools},
@@ -673,7 +673,7 @@ DATASETS = {
         "tasks_col": "language_instruction",
         "license": "cc-by-4.0",
         "url": "https://www.kaggle.com/datasets/oiermees/taco-robot",
-        "paper": "https://huggingface.co/papers/2209.08959, https://huggingface.co/papers/2210.01911",
+        "paper": "https://arxiv.org/abs/2209.08959, https://arxiv.org/abs/2210.01911",
         "citation_bibtex": dedent(r"""
             @inproceedings{rosete2022tacorl,
                 author = {Erick Rosete-Beas and Oier Mees and Gabriel Kalweit and Joschka Boedecker and Wolfram Burgard},
@@ -693,7 +693,7 @@ DATASETS = {
         "tasks_col": "language_instruction",
         "license": "mit",
         "url": "URL",
-        "paper": "https://huggingface.co/papers/2107.05842",
+        "paper": "https://arxiv.org/abs/2107.05842",
         "citation_bibtex": dedent(r"""
             @Article{Osa22,
                 author  = {Takayuki Osa},
@@ -709,7 +709,7 @@ DATASETS = {
         "tasks_col": "language_instruction",
         "license": "mit",
         "url": "https://toto-benchmark.org/",
-        "paper": "https://huggingface.co/papers/2306.00942",
+        "paper": "https://arxiv.org/abs/2306.00942",
         "citation_bibtex": dedent(r"""
             @inproceedings{zhou2023train,
                 author={Zhou, Gaoyue and Dean, Victoria and Srirama, Mohan Kumar and Rajeswaran, Aravind and Pari, Jyothish and Hatch, Kyle and Jain, Aryan and Yu, Tianhe and Abbeel, Pieter and Pinto, Lerrel and Finn, Chelsea and Gupta, Abhinav},
@@ -733,7 +733,7 @@ DATASETS = {
         "tasks_col": "language_instruction",
         "license": "mit",
         "url": "https://owmcorl.github.io/#",
-        "paper": "https://huggingface.co/papers/2310.16029",
+        "paper": "https://arxiv.org/abs/2310.16029",
         "citation_bibtex": dedent(r"""
             @preprint{Feng2023Finetuning,
                 title={Finetuning Offline World Models in the Real World},
@@ -745,7 +745,7 @@ DATASETS = {
         "tasks_col": "language_instruction",
         "license": "mit",
         "url": "https://robopil.github.io/d3fields/",
-        "paper": "https://huggingface.co/papers/2309.16118",
+        "paper": "https://arxiv.org/abs/2309.16118",
         "citation_bibtex": dedent(r"""
             @article{wang2023d3field,
                 title={D^3Field: Dynamic 3D Descriptor Fields for Generalizable Robotic Manipulation},
@@ -758,7 +758,7 @@ DATASETS = {
         "tasks_col": "language_instruction",
         "license": "mit",
         "url": "https://uscresl.github.io/dmfd/",
-        "paper": "https://huggingface.co/papers/2207.10148",
+        "paper": "https://arxiv.org/abs/2207.10148",
         "citation_bibtex": dedent(r"""
             @article{salhotra2022dmfd,
                 author={Salhotra, Gautam and Liu, I-Chun Arthur and Dominguez-Kuhne, Marcus and Sukhatme, Gaurav S.},
@@ -775,7 +775,7 @@ DATASETS = {
         "tasks_col": "language_instruction",
         "license": "mit",
         "url": "https://ut-austin-rpl.github.io/MUTEX/",
-        "paper": "https://huggingface.co/papers/2309.14320",
+        "paper": "https://arxiv.org/abs/2309.14320",
         "citation_bibtex": dedent(r"""
             @inproceedings{shah2023mutex,
                 title={{MUTEX}: Learning Unified Policies from Multimodal Task Specifications},
@@ -811,7 +811,7 @@ DATASETS = {
         "tasks_col": "language_instruction",
         "license": "mit",
         "url": "https://saytap.github.io/",
-        "paper": "https://huggingface.co/papers/2306.07580",
+        "paper": "https://arxiv.org/abs/2306.07580",
         "citation_bibtex": dedent(r"""
             @article{saytap2023,
                 author = {Yujin Tang and Wenhao Yu and Jie Tan and Heiga Zen and Aleksandra Faust and
@@ -847,7 +847,7 @@ DATASETS = {
         "tasks_col": "language_instruction",
         "license": "mit",
         "url": "https://ut-austin-rpl.github.io/VIOLA/",
-        "paper": "https://huggingface.co/papers/2210.11339",
+        "paper": "https://arxiv.org/abs/2210.11339",
         "citation_bibtex": dedent(r"""
             @article{zhu2022viola,
                 title={VIOLA: Imitation Learning for Vision-Based Manipulation with Object Proposal Priors},

lerobot/common/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 -m lerobot.datasets.v2.convert_dataset_v1_to_v2 \
+python lerobot/common/datasets/v2/convert_dataset_v1_to_v2.py \
     --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 -m lerobot.datasets.v2.convert_dataset_v1_to_v2 \
 ```
 
 ```bash
-python -m lerobot.datasets.v2.convert_dataset_v1_to_v2 \
+python lerobot/common/datasets/v2/convert_dataset_v1_to_v2.py \
     --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 -m lerobot.datasets.v2.convert_dataset_v1_to_v2 \
+    python lerobot/common/datasets/v2/convert_dataset_v1_to_v2.py \
         --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 -m lerobot.datasets.v2.convert_dataset_v1_to_v2 \
+python lerobot/common/datasets/v2/convert_dataset_v1_to_v2.py \
     --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.datasets.utils import (
+from lerobot.common.datasets.utils import (
     DEFAULT_CHUNK_SIZE,
     DEFAULT_PARQUET_PATH,
     DEFAULT_VIDEO_PATH,
@@ -136,12 +136,12 @@ from lerobot.datasets.utils import (
     write_json,
     write_jsonlines,
 )
-from lerobot.datasets.video_utils import (
+from lerobot.common.datasets.video_utils import (
     VideoFrame,  # noqa: F401
     get_image_pixel_channels,
     get_video_info,
 )
-from lerobot.robots import RobotConfig
+from lerobot.common.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.robots.koch_follower import KochFollowerConfig
+        from lerobot.common.robots.koch_follower import KochFollowerConfig
 
         return KochFollowerConfig(**kwargs)
     elif robot_type == "so100_follower":
-        from lerobot.robots.so100_follower import SO100FollowerConfig
+        from lerobot.common.robots.so100_follower import SO100FollowerConfig
 
         return SO100FollowerConfig(**kwargs)
     elif robot_type == "stretch":
-        from lerobot.robots.stretch3 import Stretch3RobotConfig
+        from lerobot.common.robots.stretch3 import Stretch3RobotConfig
 
         return Stretch3RobotConfig(**kwargs)
     elif robot_type == "lekiwi":
-        from lerobot.robots.lekiwi import LeKiwiConfig
+        from lerobot.common.robots.lekiwi import LeKiwiConfig
 
         return LeKiwiConfig(**kwargs)
     else:

lerobot/common/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.datasets.lerobot_dataset import LeRobotDatasetMetadata
-from lerobot.datasets.utils import INFO_PATH, write_info
-from lerobot.datasets.v21.convert_dataset_v20_to_v21 import V20, SuppressWarnings
+from lerobot.common.datasets.lerobot_dataset import LeRobotDatasetMetadata
+from lerobot.common.datasets.utils import INFO_PATH, write_info
+from lerobot.common.datasets.v21.convert_dataset_v20_to_v21 import V20, SuppressWarnings
 
 LOCAL_DIR = Path("data/")
 

lerobot/common/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.datasets.v21.convert_dataset_v20_to_v21 import V21, convert_dataset
+from lerobot.common.datasets.v21.convert_dataset_v20_to_v21 import V21, convert_dataset
 
 LOCAL_DIR = Path("data/")
 

lerobot/common/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 -m lerobot.datasets.v21.convert_dataset_v20_to_v21 \
+python lerobot/common/datasets/v21/convert_dataset_v20_to_v21.py \
     --repo-id=aliberts/koch_tutorial
 ```
 
@@ -36,9 +36,9 @@ import logging
 
 from huggingface_hub import HfApi
 
-from lerobot.datasets.lerobot_dataset import CODEBASE_VERSION, LeRobotDataset
-from lerobot.datasets.utils import EPISODES_STATS_PATH, STATS_PATH, load_stats, write_info
-from lerobot.datasets.v21.convert_stats import check_aggregate_stats, convert_stats
+from lerobot.common.datasets.lerobot_dataset import CODEBASE_VERSION, LeRobotDataset
+from lerobot.common.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
 
 V20 = "v2.0"
 V21 = "v2.1"

lerobot/common/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.datasets.compute_stats import aggregate_stats, get_feature_stats, sample_indices
-from lerobot.datasets.lerobot_dataset import LeRobotDataset
-from lerobot.datasets.utils import write_episode_stats
+from lerobot.common.datasets.compute_stats import aggregate_stats, get_feature_stats, sample_indices
+from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.common.datasets.utils import write_episode_stats
 
 
 def sample_episode_video_frames(dataset: LeRobotDataset, episode_index: int, ft_key: str) -> np.ndarray:
@@ -43,32 +43,14 @@ def convert_episode_stats(dataset: LeRobotDataset, ep_idx: int):
         else:
             ep_ft_data = np.array(ep_data[key])
 
-        if ft["dtype"] in ["image", "video"]:
-            # Handle variable dimensions for image/video data
-            # Expected formats: (frames, channels, height, width) or (channels, height, width)
-            if ep_ft_data.ndim == 4:
-                # Standard case: (frames, channels, height, width)
-                axes_to_reduce = (0, 2, 3)  # reduce over frames, height, width
-            elif ep_ft_data.ndim == 3:
-                # Squeezed case: (channels, height, width) - single frame
-                axes_to_reduce = (1, 2)  # reduce over height, width
-            else:
-                raise ValueError(f"Unexpected dimensions for {ft['dtype']} data: {ep_ft_data.shape}")
-            keepdims = True
-        else:
-            axes_to_reduce = 0
-            keepdims = ep_ft_data.ndim == 1
+        axes_to_reduce = (0, 2, 3) if ft["dtype"] in ["image", "video"] else 0
+        keepdims = True if ft["dtype"] in ["image", "video"] else ep_ft_data.ndim == 1
         ep_stats[key] = get_feature_stats(ep_ft_data, axis=axes_to_reduce, keepdims=keepdims)
 
         if ft["dtype"] in ["image", "video"]:  # remove batch dim
-            if ep_ft_data.ndim == 4:
-                # For 4D data, squeeze the first axis (batch/frames)
-                ep_stats[key] = {
-                    k: v if k == "count" else np.squeeze(v, axis=0) for k, v in ep_stats[key].items()
-                }
-            elif ep_ft_data.ndim == 3:
-                # For 3D data, the stats already have correct shape (channels,)
-                pass
+            ep_stats[key] = {
+                k: v if k == "count" else np.squeeze(v, axis=0) for k, v in ep_stats[key].items()
+            }
 
     dataset.meta.episodes_stats[ep_idx] = ep_stats
 

lerobot/common/envs/configs.py

@@ -14,14 +14,14 @@
 
 import abc
 from dataclasses import dataclass, field
-from typing import Any, Optional
+from typing import Any, Dict, Optional, Tuple
 
 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
@@ -169,6 +169,17 @@ class VideoRecordConfig:
     trajectory_name: str = "trajectory"
 
 
+# @dataclass
+# class EEActionSpaceConfig:
+#     """Configuration parameters for end-effector action space."""
+
+#     x_step_size: float
+#     y_step_size: float
+#     z_step_size: float
+#     bounds: Dict[str, Any]  # Contains 'min' and 'max' keys with position bounds
+#     control_mode: str = "gamepad"
+
+
 @dataclass
 class EnvTransformConfig:
     """Configuration for environment wrappers."""
@@ -179,12 +190,12 @@ class EnvTransformConfig:
     add_joint_velocity_to_observation: bool = False
     add_current_to_observation: bool = False
     add_ee_pose_to_observation: bool = False
-    crop_params_dict: Optional[dict[str, tuple[int, int, int, int]]] = None
-    resize_size: Optional[tuple[int, int]] = None
+    crop_params_dict: Optional[Dict[str, Tuple[int, int, int, int]]] = None
+    resize_size: Optional[Tuple[int, int]] = None
     control_time_s: float = 20.0
     fixed_reset_joint_positions: Optional[Any] = None
     reset_time_s: float = 5.0
-    use_gripper: bool = True
+    use_gripper: bool = False
     gripper_quantization_threshold: float | None = 0.8
     gripper_penalty: float = 0.0
     gripper_penalty_in_reward: bool = False
@@ -260,8 +271,6 @@ class HILEnvConfig(EnvConfig):
     device: str = "cuda"
     push_to_hub: bool = True
     pretrained_policy_name_or_path: Optional[str] = None
-    # For the reward classifier, to record more positive examples after a success
-    number_of_steps_after_success: int = 0
     ############################
 
     @property

lerobot/common/envs/factory.py

@@ -17,7 +17,7 @@ import importlib
 
 import gymnasium as gym
 
-from lerobot.envs.configs import AlohaEnv, EnvConfig, HILEnvConfig, PushtEnv, XarmEnv
+from lerobot.common.envs.configs import AlohaEnv, EnvConfig, HILEnvConfig, PushtEnv, XarmEnv
 
 
 def make_env_config(env_type: str, **kwargs) -> EnvConfig:
@@ -67,5 +67,8 @@ def make_env(cfg: EnvConfig, n_envs: int = 1, use_async_envs: bool = False) -> g
     env = env_cls(
         [lambda: gym.make(gym_handle, disable_env_checker=True, **cfg.gym_kwargs) for _ in range(n_envs)]
     )
+    # TODO: add observation processor wrapper and remove preprocess_observation in the codebase
+    # https://github.com/Farama-Foundation/Gymnasium/blob/main/gymnasium/wrappers/vector/vectorize_observation.py#L19,
+    # env = ObservationProcessorWrapper(env=env)
 
     return env

lerobot/common/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]:

lerobot/common/model/kinematics.py

@@ -0,0 +1,589 @@
+# ruff: noqa: N806, N815, N803
+
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import numpy as np
+from scipy.spatial.transform import Rotation
+
+
+def skew_symmetric(w):
+    """Creates the skew-symmetric matrix from a 3D vector."""
+    return np.array([[0, -w[2], w[1]], [w[2], 0, -w[0]], [-w[1], w[0], 0]])
+
+
+def rodrigues_rotation(w, theta):
+    """Computes the rotation matrix using Rodrigues' formula."""
+    w_hat = skew_symmetric(w)
+    return np.eye(3) + np.sin(theta) * w_hat + (1 - np.cos(theta)) * w_hat @ w_hat
+
+
+def screw_axis_to_transform(S, theta):
+    """Converts a screw axis to a 4x4 transformation matrix."""
+    S_w = S[:3]
+    S_v = S[3:]
+    if np.allclose(S_w, 0) and np.linalg.norm(S_v) == 1:  # Pure translation
+        T = np.eye(4)
+        T[:3, 3] = S_v * theta
+    elif np.linalg.norm(S_w) == 1:  # Rotation and translation
+        w_hat = skew_symmetric(S_w)
+        R = np.eye(3) + np.sin(theta) * w_hat + (1 - np.cos(theta)) * w_hat @ w_hat
+        t = (np.eye(3) * theta + (1 - np.cos(theta)) * w_hat + (theta - np.sin(theta)) * w_hat @ w_hat) @ S_v
+        T = np.eye(4)
+        T[:3, :3] = R
+        T[:3, 3] = t
+    else:
+        raise ValueError("Invalid screw axis parameters")
+    return T
+
+
+def pose_difference_se3(pose1, pose2):
+    """
+    Calculates the SE(3) difference between two 4x4 homogeneous transformation matrices.
+    SE(3) (Special Euclidean Group) represents rigid body transformations in 3D space, combining rotation (SO(3)) and translation.
+    Each 4x4 matrix has the following structure, a 3x3 rotation matrix in the top-left and a 3x1 translation vector in the top-right:
+
+    [R11 R12 R13 tx]
+    [R21 R22 R23 ty]
+    [R31 R32 R33 tz]
+    [ 0   0   0   1]
+
+    where Rij is the 3x3 rotation matrix and [tx,ty,tz] is the translation vector.
+
+    pose1 - pose2
+
+    Args:
+        pose1: A 4x4 numpy array representing the first pose.
+        pose2: A 4x4 numpy array representing the second pose.
+
+    Returns:
+        A tuple (translation_diff, rotation_diff) where:
+        - translation_diff is a 3x1 numpy array representing the translational difference.
+        - rotation_diff is a 3x1 numpy array representing the rotational difference in axis-angle representation.
+    """
+
+    # Extract rotation matrices from poses
+    R1 = pose1[:3, :3]
+    R2 = pose2[:3, :3]
+
+    # Calculate translational difference
+    translation_diff = pose1[:3, 3] - pose2[:3, 3]
+
+    # Calculate rotational difference using scipy's Rotation library
+    R_diff = Rotation.from_matrix(R1 @ R2.T)
+    rotation_diff = R_diff.as_rotvec()  # Convert to axis-angle representation
+
+    return np.concatenate([translation_diff, rotation_diff])
+
+
+def se3_error(target_pose, current_pose):
+    pos_error = target_pose[:3, 3] - current_pose[:3, 3]
+    R_target = target_pose[:3, :3]
+    R_current = current_pose[:3, :3]
+    R_error = R_target @ R_current.T
+    rot_error = Rotation.from_matrix(R_error).as_rotvec()
+    return np.concatenate([pos_error, rot_error])
+
+
+class RobotKinematics:
+    """Robot kinematics class supporting multiple robot models."""
+
+    # Robot measurements dictionary
+    ROBOT_MEASUREMENTS = {
+        "koch": {
+            "gripper": [0.239, -0.001, 0.024],
+            "wrist": [0.209, 0, 0.024],
+            "forearm": [0.108, 0, 0.02],
+            "humerus": [0, 0, 0.036],
+            "shoulder": [0, 0, 0],
+            "base": [0, 0, 0.02],
+        },
+        "so100": {
+            "gripper": [0.320, 0, 0.050],
+            "wrist": [0.278, 0, 0.050],
+            "forearm": [0.143, 0, 0.044],
+            "humerus": [0.031, 0, 0.072],
+            "shoulder": [0, 0, 0],
+            "base": [0, 0, 0.02],
+        },
+        "moss": {
+            "gripper": [0.246, 0.013, 0.111],
+            "wrist": [0.245, 0.002, 0.064],
+            "forearm": [0.122, 0, 0.064],
+            "humerus": [0.001, 0.001, 0.063],
+            "shoulder": [0, 0, 0],
+            "base": [0, 0, 0.02],
+        },
+        "so101": {
+            "gripper": [0.33, 0.0, 0.285],
+            "wrist": [0.30, 0.0, 0.267],
+            "forearm": [0.25, 0.0, 0.266],
+            "humerus": [0.06, 0.0, 0.264],
+            "shoulder": [0.0, 0.0, 0.238],
+            "base": [0.0, 0.0, 0.12],
+        },
+    }
+
+    def __init__(self, robot_type="so100"):
+        """Initialize kinematics for the specified robot type.
+
+        Args:
+            robot_type: String specifying the robot model ("koch", "so100", or "moss")
+        """
+        if robot_type not in self.ROBOT_MEASUREMENTS:
+            raise ValueError(
+                f"Unknown robot type: {robot_type}. Available types: {list(self.ROBOT_MEASUREMENTS.keys())}"
+            )
+
+        self.robot_type = robot_type
+        self.measurements = self.ROBOT_MEASUREMENTS[robot_type]
+
+        # Initialize all transformation matrices and screw axes
+        self._setup_transforms()
+
+    def _create_translation_matrix(self, x=0, y=0, z=0):
+        """Create a 4x4 translation matrix."""
+        return np.array([[1, 0, 0, x], [0, 1, 0, y], [0, 0, 1, z], [0, 0, 0, 1]])
+
+    def _setup_transforms(self):
+        """Setup all transformation matrices and screw axes for the robot."""
+        # Set up rotation matrices (constant across robot types)
+
+        # Gripper orientation
+        self.gripper_X0 = np.array(
+            [
+                [1, 0, 0, 0],
+                [0, 0, 1, 0],
+                [0, -1, 0, 0],
+                [0, 0, 0, 1],
+            ]
+        )
+
+        # Wrist orientation
+        self.wrist_X0 = np.array(
+            [
+                [0, -1, 0, 0],
+                [1, 0, 0, 0],
+                [0, 0, 1, 0],
+                [0, 0, 0, 1],
+            ]
+        )
+
+        # Base orientation
+        self.base_X0 = np.array(
+            [
+                [0, 0, 1, 0],
+                [1, 0, 0, 0],
+                [0, 1, 0, 0],
+                [0, 0, 0, 1],
+            ]
+        )
+
+        # Gripper
+        # Screw axis of gripper frame wrt base frame
+        self.S_BG = np.array(
+            [
+                1,
+                0,
+                0,
+                0,
+                self.measurements["gripper"][2],
+                -self.measurements["gripper"][1],
+            ]
+        )
+
+        # Gripper origin to centroid transform
+        self.X_GoGc = self._create_translation_matrix(x=0.07)
+
+        # Gripper origin to tip transform
+        self.X_GoGt = self._create_translation_matrix(x=0.12)
+
+        # 0-position gripper frame pose wrt base
+        self.X_BoGo = self._create_translation_matrix(
+            x=self.measurements["gripper"][0],
+            y=self.measurements["gripper"][1],
+            z=self.measurements["gripper"][2],
+        )
+
+        # Wrist
+        # Screw axis of wrist frame wrt base frame
+        self.S_BR = np.array([0, 1, 0, -self.measurements["wrist"][2], 0, self.measurements["wrist"][0]])
+
+        # 0-position origin to centroid transform
+        self.X_RoRc = self._create_translation_matrix(x=0.0035, y=-0.002)
+
+        # 0-position wrist frame pose wrt base
+        self.X_BR = self._create_translation_matrix(
+            x=self.measurements["wrist"][0],
+            y=self.measurements["wrist"][1],
+            z=self.measurements["wrist"][2],
+        )
+
+        # Forearm
+        # Screw axis of forearm frame wrt base frame
+        self.S_BF = np.array(
+            [
+                0,
+                1,
+                0,
+                -self.measurements["forearm"][2],
+                0,
+                self.measurements["forearm"][0],
+            ]
+        )
+
+        # Forearm origin + centroid transform
+        self.X_FoFc = self._create_translation_matrix(x=0.036)  # spellchecker:disable-line
+
+        # 0-position forearm frame pose wrt base
+        self.X_BF = self._create_translation_matrix(
+            x=self.measurements["forearm"][0],
+            y=self.measurements["forearm"][1],
+            z=self.measurements["forearm"][2],
+        )
+
+        # Humerus
+        # Screw axis of humerus frame wrt base frame
+        self.S_BH = np.array(
+            [
+                0,
+                -1,
+                0,
+                self.measurements["humerus"][2],
+                0,
+                -self.measurements["humerus"][0],
+            ]
+        )
+
+        # Humerus origin to centroid transform
+        self.X_HoHc = self._create_translation_matrix(x=0.0475)
+
+        # 0-position humerus frame pose wrt base
+        self.X_BH = self._create_translation_matrix(
+            x=self.measurements["humerus"][0],
+            y=self.measurements["humerus"][1],
+            z=self.measurements["humerus"][2],
+        )
+
+        # Shoulder
+        # Screw axis of shoulder frame wrt Base frame
+        self.S_BS = np.array([0, 0, -1, 0, 0, 0])
+
+        # Shoulder origin to centroid transform
+        self.X_SoSc = self._create_translation_matrix(x=-0.017, z=0.0235)
+
+        # 0-position shoulder frame pose wrt base
+        self.X_BS = self._create_translation_matrix(
+            x=self.measurements["shoulder"][0],
+            y=self.measurements["shoulder"][1],
+            z=self.measurements["shoulder"][2],
+        )
+
+        # Base
+        # Base origin to centroid transform
+        self.X_BoBc = self._create_translation_matrix(y=0.015)
+
+        # World to base transform
+        self.X_WoBo = self._create_translation_matrix(
+            x=self.measurements["base"][0],
+            y=self.measurements["base"][1],
+            z=self.measurements["base"][2],
+        )
+
+        # Pre-compute gripper post-multiplication matrix
+        self._fk_gripper_post = self.X_GoGc @ self.X_BoGo @ self.gripper_X0
+
+    def fk_base(self):
+        """Forward kinematics for the base frame."""
+        return self.X_WoBo @ self.X_BoBc @ self.base_X0
+
+    def fk_shoulder(self, robot_pos_deg):
+        """Forward kinematics for the shoulder frame."""
+        robot_pos_rad = robot_pos_deg / 180 * np.pi
+        return self.X_WoBo @ screw_axis_to_transform(self.S_BS, robot_pos_rad[0]) @ self.X_SoSc @ self.X_BS
+
+    def fk_humerus(self, robot_pos_deg):
+        """Forward kinematics for the humerus frame."""
+        robot_pos_rad = robot_pos_deg / 180 * np.pi
+
+        theta_shoulder_pan = robot_pos_rad[0]
+        # NOTE: Negate shoulder lift angle for all robot types
+        theta_shoulder_lift = -robot_pos_rad[1]
+
+        return (
+            self.X_WoBo
+            @ screw_axis_to_transform(self.S_BS, theta_shoulder_pan)
+            @ screw_axis_to_transform(self.S_BH, theta_shoulder_lift)
+            @ self.X_HoHc
+            @ self.X_BH
+        )
+
+    def fk_forearm(self, robot_pos_deg):
+        """Forward kinematics for the forearm frame."""
+        robot_pos_rad = robot_pos_deg / 180 * np.pi
+
+        theta_shoulder_pan = robot_pos_rad[0]
+        # NOTE: Negate shoulder lift angle for all robot types
+        theta_shoulder_lift = -robot_pos_rad[1]
+        theta_elbow_flex = robot_pos_rad[2]
+
+        return (
+            self.X_WoBo
+            @ screw_axis_to_transform(self.S_BS, theta_shoulder_pan)
+            @ screw_axis_to_transform(self.S_BH, theta_shoulder_lift)
+            @ screw_axis_to_transform(self.S_BF, theta_elbow_flex)
+            @ self.X_FoFc  # spellchecker:disable-line
+            @ self.X_BF
+        )
+
+    def fk_wrist(self, robot_pos_deg):
+        """Forward kinematics for the wrist frame."""
+        robot_pos_rad = robot_pos_deg / 180 * np.pi
+
+        theta_shoulder_pan = robot_pos_rad[0]
+        # NOTE: Negate shoulder lift angle for all robot types
+        theta_shoulder_lift = -robot_pos_rad[1]
+        theta_elbow_flex = robot_pos_rad[2]
+        theta_wrist_flex = robot_pos_rad[3]
+
+        return (
+            self.X_WoBo
+            @ screw_axis_to_transform(self.S_BS, theta_shoulder_pan)
+            @ screw_axis_to_transform(self.S_BH, theta_shoulder_lift)
+            @ screw_axis_to_transform(self.S_BF, theta_elbow_flex)
+            @ screw_axis_to_transform(self.S_BR, theta_wrist_flex)
+            @ self.X_RoRc
+            @ self.X_BR
+            @ self.wrist_X0
+        )
+
+    def fk_gripper(self, robot_pos_deg):
+        """Forward kinematics for the gripper frame."""
+        robot_pos_rad = robot_pos_deg / 180 * np.pi
+
+        theta_shoulder_pan = robot_pos_rad[0]
+        # NOTE: Negate shoulder lift angle for all robot types
+        theta_shoulder_lift = -robot_pos_rad[1]
+        theta_elbow_flex = robot_pos_rad[2]
+        theta_wrist_flex = robot_pos_rad[3]
+        theta_wrist_roll = robot_pos_rad[4]
+
+        return (
+            self.X_WoBo
+            @ screw_axis_to_transform(self.S_BS, theta_shoulder_pan)
+            @ screw_axis_to_transform(self.S_BH, theta_shoulder_lift)
+            @ screw_axis_to_transform(self.S_BF, theta_elbow_flex)
+            @ screw_axis_to_transform(self.S_BR, theta_wrist_flex)
+            @ screw_axis_to_transform(self.S_BG, theta_wrist_roll)
+            @ self._fk_gripper_post
+        )
+
+    def fk_gripper_tip(self, robot_pos_deg):
+        """Forward kinematics for the gripper tip frame."""
+        robot_pos_rad = robot_pos_deg / 180 * np.pi
+
+        theta_shoulder_pan = robot_pos_rad[0]
+        # Negate shoulder lift angle for all robot types
+        theta_shoulder_lift = -robot_pos_rad[1]
+        theta_elbow_flex = robot_pos_rad[2]
+        theta_wrist_flex = robot_pos_rad[3]
+        theta_wrist_roll = robot_pos_rad[4]
+
+        return (
+            self.X_WoBo
+            @ screw_axis_to_transform(self.S_BS, theta_shoulder_pan)
+            @ screw_axis_to_transform(self.S_BH, theta_shoulder_lift)
+            @ screw_axis_to_transform(self.S_BF, theta_elbow_flex)
+            @ screw_axis_to_transform(self.S_BR, theta_wrist_flex)
+            @ screw_axis_to_transform(self.S_BG, theta_wrist_roll)
+            @ self.X_GoGt
+            @ self.X_BoGo
+            @ self.gripper_X0
+        )
+
+    def compute_jacobian(self, robot_pos_deg, fk_func=None):
+        """Finite differences to compute the Jacobian.
+        J(i, j) represents how the ith component of the end-effector's velocity changes wrt a small change
+        in the jth joint's velocity.
+
+        Args:
+            robot_pos_deg: Current joint positions in degrees
+            fk_func: Forward kinematics function to use (defaults to fk_gripper)
+        """
+        if fk_func is None:
+            fk_func = self.fk_gripper
+
+        eps = 1e-8
+        jac = np.zeros(shape=(6, 5))
+        delta = np.zeros(len(robot_pos_deg[:-1]), dtype=np.float64)
+        for el_ix in range(len(robot_pos_deg[:-1])):
+            delta *= 0
+            delta[el_ix] = eps / 2
+            Sdot = (
+                pose_difference_se3(
+                    fk_func(robot_pos_deg[:-1] + delta),
+                    fk_func(robot_pos_deg[:-1] - delta),
+                )
+                / eps
+            )
+            jac[:, el_ix] = Sdot
+        return jac
+
+    def compute_positional_jacobian(self, robot_pos_deg, fk_func=None):
+        """Finite differences to compute the positional Jacobian.
+        J(i, j) represents how the ith component of the end-effector's position changes wrt a small change
+        in the jth joint's velocity.
+
+        Args:
+            robot_pos_deg: Current joint positions in degrees
+            fk_func: Forward kinematics function to use (defaults to fk_gripper)
+        """
+        if fk_func is None:
+            fk_func = self.fk_gripper
+
+        eps = 1e-8
+        jac = np.zeros(shape=(3, 5))
+        delta = np.zeros(len(robot_pos_deg[:-1]), dtype=np.float64)
+        for el_ix in range(len(robot_pos_deg[:-1])):
+            delta *= 0
+            delta[el_ix] = eps / 2
+            Sdot = (
+                fk_func(robot_pos_deg[:-1] + delta)[:3, 3] - fk_func(robot_pos_deg[:-1] - delta)[:3, 3]
+            ) / eps
+            jac[:, el_ix] = Sdot
+        return jac
+
+    def ik(self, current_joint_pos, desired_ee_pose, position_only=True, fk_func=None):
+        """Inverse kinematics using gradient descent.
+
+        Args:
+            current_joint_state: Initial joint positions in degrees
+            desired_ee_pose: Target end-effector pose as a 4x4 transformation matrix
+            position_only: If True, only match end-effector position, not orientation
+            fk_func: Forward kinematics function to use (defaults to fk_gripper)
+
+        Returns:
+            Joint positions in degrees that achieve the desired end-effector pose
+        """
+        if fk_func is None:
+            fk_func = self.fk_gripper
+
+        # Do gradient descent.
+        current_joint_state = current_joint_pos.copy()
+        max_iterations = 5
+        learning_rate = 1
+        for _ in range(max_iterations):
+            current_ee_pose = fk_func(current_joint_state)
+            if not position_only:
+                error = se3_error(desired_ee_pose, current_ee_pose)
+                jac = self.compute_jacobian(current_joint_state, fk_func)
+            else:
+                error = desired_ee_pose[:3, 3] - current_ee_pose[:3, 3]
+                jac = self.compute_positional_jacobian(current_joint_state, fk_func)
+            delta_angles = np.linalg.pinv(jac) @ error
+            current_joint_state[:-1] += learning_rate * delta_angles
+
+            if np.linalg.norm(error) < 5e-3:
+                return current_joint_state
+        return current_joint_state
+
+
+if __name__ == "__main__":
+    import time
+
+    def run_test(robot_type):
+        """Run test suite for a specific robot type."""
+        print(f"\n--- Testing {robot_type.upper()} Robot ---")
+
+        # Initialize kinematics for this robot
+        robot = RobotKinematics(robot_type)
+
+        # Test 1: Forward kinematics consistency
+        print("Test 1: Forward kinematics consistency")
+        test_angles = np.array([30, 45, -30, 20, 10, 0])  # Example joint angles in degrees
+
+        # Calculate FK for different joints
+        shoulder_pose = robot.fk_shoulder(test_angles)
+        humerus_pose = robot.fk_humerus(test_angles)
+        forearm_pose = robot.fk_forearm(test_angles)
+        wrist_pose = robot.fk_wrist(test_angles)
+        gripper_pose = robot.fk_gripper(test_angles)
+        gripper_tip_pose = robot.fk_gripper_tip(test_angles)
+
+        # Check that poses form a consistent kinematic chain (positions should be progressively further from origin)
+        distances = [
+            np.linalg.norm(shoulder_pose[:3, 3]),
+            np.linalg.norm(humerus_pose[:3, 3]),
+            np.linalg.norm(forearm_pose[:3, 3]),
+            np.linalg.norm(wrist_pose[:3, 3]),
+            np.linalg.norm(gripper_pose[:3, 3]),
+            np.linalg.norm(gripper_tip_pose[:3, 3]),
+        ]
+
+        # Check if distances generally increase along the chain
+        is_consistent = all(distances[i] <= distances[i + 1] for i in range(len(distances) - 1))
+        print(f"  Pose distances from origin: {[round(d, 3) for d in distances]}")
+        print(f"  Kinematic chain consistency: {'PASSED' if is_consistent else 'FAILED'}")
+
+        # Test 2: Jacobian computation
+        print("Test 2: Jacobian computation")
+        jacobian = robot.compute_jacobian(test_angles)
+        positional_jacobian = robot.compute_positional_jacobian(test_angles)
+
+        # Check shapes
+        jacobian_shape_ok = jacobian.shape == (6, 5)
+        pos_jacobian_shape_ok = positional_jacobian.shape == (3, 5)
+
+        print(f"  Jacobian shape: {'PASSED' if jacobian_shape_ok else 'FAILED'}")
+        print(f"  Positional Jacobian shape: {'PASSED' if pos_jacobian_shape_ok else 'FAILED'}")
+
+        # Test 3: Inverse kinematics
+        print("Test 3: Inverse kinematics (position only)")
+
+        # Generate target pose from known joint angles
+        original_angles = np.array([10, 20, 30, -10, 5, 0])
+        target_pose = robot.fk_gripper(original_angles)
+
+        # Start IK from a different position
+        initial_guess = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0])
+
+        # Measure IK performance
+        start_time = time.time()
+        computed_angles = robot.ik(initial_guess.copy(), target_pose)
+        ik_time = time.time() - start_time
+
+        # Compute resulting pose from IK solution
+        result_pose = robot.fk_gripper(computed_angles)
+
+        # Calculate position error
+        pos_error = np.linalg.norm(target_pose[:3, 3] - result_pose[:3, 3])
+        passed = pos_error < 0.01  # Accept errors less than 1cm
+
+        print(f"  IK computation time: {ik_time:.4f} seconds")
+        print(f"  Position error: {pos_error:.4f}")
+        print(f"  IK position accuracy: {'PASSED' if passed else 'FAILED'}")
+
+        return is_consistent and jacobian_shape_ok and pos_jacobian_shape_ok and passed
+
+    # Run tests for all robot types
+    results = {}
+    for robot_type in ["koch", "so100", "moss", "so101"]:
+        results[robot_type] = run_test(robot_type)
+
+    # Print overall summary
+    print("\n=== Test Summary ===")
+    all_passed = all(results.values())
+    for robot_type, passed in results.items():
+        print(f"{robot_type.upper()}: {'PASSED' if passed else 'FAILED'}")
+    print(f"\nOverall: {'ALL TESTS PASSED' if all_passed else 'SOME TESTS FAILED'}")

lerobot/common/motors/configs.py

@@ -0,0 +1,41 @@
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import abc
+from dataclasses import dataclass
+
+import draccus
+
+
+@dataclass
+class MotorsBusConfig(draccus.ChoiceRegistry, abc.ABC):
+    @property
+    def type(self) -> str:
+        return self.get_choice_name(self.__class__)
+
+
+@MotorsBusConfig.register_subclass("dynamixel")
+@dataclass
+class DynamixelMotorsBusConfig(MotorsBusConfig):
+    port: str
+    motors: dict[str, tuple[int, str]]
+    mock: bool = False
+
+
+@MotorsBusConfig.register_subclass("feetech")
+@dataclass
+class FeetechMotorsBusConfig(MotorsBusConfig):
+    port: str
+    motors: dict[str, tuple[int, str]]
+    mock: bool = False

lerobot/common/motors/dynamixel/dynamixel.py

@@ -22,7 +22,7 @@ import logging
 from copy import deepcopy
 from enum import Enum
 
-from lerobot.utils.encoding_utils import decode_twos_complement, encode_twos_complement
+from lerobot.common.utils.encoding_utils import decode_twos_complement, encode_twos_complement
 
 from ..motors_bus import Motor, MotorCalibration, MotorsBus, NameOrID, Value, get_address
 from .tables import (
@@ -39,6 +39,7 @@ DEFAULT_BAUDRATE = 1_000_000
 DEFAULT_TIMEOUT_MS = 1000
 
 NORMALIZED_DATA = ["Goal_Position", "Present_Position"]
+CONVERT_UINT32_TO_INT32_REQUIRED = ["Goal_Position", "Present_Position"]
 
 logger = logging.getLogger(__name__)
 

lerobot/common/motors/feetech/feetech.py

@@ -17,7 +17,7 @@ from copy import deepcopy
 from enum import Enum
 from pprint import pformat
 
-from lerobot.utils.encoding_utils import decode_sign_magnitude, encode_sign_magnitude
+from lerobot.common.utils.encoding_utils import decode_sign_magnitude, encode_sign_magnitude
 
 from ..motors_bus import Motor, MotorCalibration, MotorsBus, NameOrID, Value, get_address
 from .tables import (
@@ -154,7 +154,7 @@ class FeetechMotorsBus(MotorsBus):
             )
 
     def _assert_same_firmware(self) -> None:
-        firmware_versions = self._read_firmware_version(self.ids, raise_on_error=True)
+        firmware_versions = self._read_firmware_version(self.ids)
         if len(set(firmware_versions.values())) != 1:
             raise RuntimeError(
                 "Some Motors use different firmware versions:"
@@ -251,6 +251,7 @@ class FeetechMotorsBus(MotorsBus):
 
     def read_calibration(self) -> dict[str, MotorCalibration]:
         offsets, mins, maxes = {}, {}, {}
+        drive_modes = dict.fromkeys(self.motors, 0)
         for motor in self.motors:
             mins[motor] = self.read("Min_Position_Limit", motor, normalize=False)
             maxes[motor] = self.read("Max_Position_Limit", motor, normalize=False)
@@ -262,7 +263,7 @@ class FeetechMotorsBus(MotorsBus):
         for motor, m in self.motors.items():
             calibration[motor] = MotorCalibration(
                 id=m.id,
-                drive_mode=0,
+                drive_mode=drive_modes[motor],
                 homing_offset=offsets[motor],
                 range_min=mins[motor],
                 range_max=maxes[motor],
@@ -358,10 +359,13 @@ class FeetechMotorsBus(MotorsBus):
         self.port_handler.setPacketTimeoutMillis((wait_length * tx_time_per_byte) + (3.0 * scs.MAX_ID) + 16.0)
 
         rxpacket = []
-        while not self.port_handler.isPacketTimeout() and rx_length < wait_length:
+        while True:
             rxpacket += self.port_handler.readPort(wait_length - rx_length)
             rx_length = len(rxpacket)
 
+            if self.port_handler.isPacketTimeout():  # or rx_length >= wait_length
+                break
+
         self.port_handler.is_using = False
 
         if rx_length == 0:
@@ -430,13 +434,13 @@ class FeetechMotorsBus(MotorsBus):
                 *FIRMWARE_MAJOR_VERSION, id_, raise_on_error=raise_on_error
             )
             if not self._is_comm_success(comm) or self._is_error(error):
-                continue
+                return
 
             firm_ver_minor, comm, error = self._read(
                 *FIRMWARE_MINOR_VERSION, id_, raise_on_error=raise_on_error
             )
             if not self._is_comm_success(comm) or self._is_error(error):
-                continue
+                return
 
             firmware_versions[id_] = f"{firm_ver_major}.{firm_ver_minor}"
 
@@ -447,7 +451,7 @@ class FeetechMotorsBus(MotorsBus):
         for id_ in motor_ids:
             model_nb, comm, error = self._read(*MODEL_NUMBER, id_, raise_on_error=raise_on_error)
             if not self._is_comm_success(comm) or self._is_error(error):
-                continue
+                return
 
             model_numbers[id_] = model_nb
 

lerobot/common/motors/motors_bus.py

@@ -32,12 +32,14 @@ import serial
 from deepdiff import DeepDiff
 from tqdm import tqdm
 
-from lerobot.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError
-from lerobot.utils.utils import enter_pressed, move_cursor_up
+from lerobot.common.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError
+from lerobot.common.utils.utils import enter_pressed, move_cursor_up
 
 NameOrID: TypeAlias = str | int
 Value: TypeAlias = int | float
 
+MAX_ID_RANGE = 252
+
 logger = logging.getLogger(__name__)
 
 
@@ -77,10 +79,11 @@ def assert_same_address(model_ctrl_table: dict[str, dict], motor_models: list[st
         )
 
 
-class MotorNormMode(str, Enum):
-    RANGE_0_100 = "range_0_100"
-    RANGE_M100_100 = "range_m100_100"
-    DEGREES = "degrees"
+class MotorNormMode(Enum):
+    DEGREE = 0
+    RANGE_0_100 = 1
+    RANGE_M100_100 = 2
+    VELOCITY = 3
 
 
 @dataclass
@@ -238,11 +241,11 @@ class MotorsBus(abc.ABC):
     )
     bus.connect()
 
-    position = bus.read("Present_Position", "my_motor", normalize=False)
+    position = bus.read("Present_Position", normalize=False)
 
     # Move from a few motor steps as an example
     few_steps = 30
-    bus.write("Goal_Position", "my_motor", position + few_steps, normalize=False)
+    bus.write("Goal_Position", position + few_steps, normalize=False)
 
     # When done, properly disconnect the port using
     bus.disconnect()
@@ -446,7 +449,7 @@ 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 -m lerobot.find_port`\n"
+                "\nTry running `python lerobot/scripts/find_motors_bus_port.py`\n"
             ) from e
 
     @abc.abstractmethod
@@ -496,7 +499,6 @@ class MotorsBus(abc.ABC):
                 tqdm.write(f"Motors found for {baudrate=}: {pformat(ids_models, indent=4)}")
                 baudrate_ids[baudrate] = list(ids_models)
 
-        bus.port_handler.closePort()
         return baudrate_ids
 
     def setup_motor(
@@ -580,8 +582,8 @@ class MotorsBus(abc.ABC):
 
         Args:
             motor (int): Same semantics as :pymeth:`disable_torque`. Defaults to `None`.
-            num_retry (int, optional): Number of additional retry attempts on communication failure.
-                Defaults to 0.
+            model (str): _description_
+            num_retry (int, optional): _description_. Defaults to 0.
         """
         pass
 
@@ -746,9 +748,7 @@ class MotorsBus(abc.ABC):
         start_positions = self.sync_read("Present_Position", motors, normalize=False)
         mins = start_positions.copy()
         maxes = start_positions.copy()
-
-        user_pressed_enter = False
-        while not user_pressed_enter:
+        while True:
             positions = self.sync_read("Present_Position", motors, normalize=False)
             mins = {motor: min(positions[motor], min_) for motor, min_ in mins.items()}
             maxes = {motor: max(positions[motor], max_) for motor, max_ in maxes.items()}
@@ -760,9 +760,9 @@ class MotorsBus(abc.ABC):
                     print(f"{motor:<15} | {mins[motor]:>6} | {positions[motor]:>6} | {maxes[motor]:>6}")
 
             if enter_pressed():
-                user_pressed_enter = True
+                break
 
-            if display_values and not user_pressed_enter:
+            if display_values:
                 # Move cursor up to overwrite the previous output
                 move_cursor_up(len(motors) + 3)
 
@@ -786,17 +786,23 @@ class MotorsBus(abc.ABC):
                 raise ValueError(f"Invalid calibration for motor '{motor}': min and max are equal.")
 
             bounded_val = min(max_, max(min_, val))
+            # TODO(Steven): normalization can go boom if max_ == min_, we should add a check probably in record_ranges_of_motions
+            # (which probably indicates the user forgot to move a motor, most likely a gripper-like one)
             if self.motors[motor].norm_mode is MotorNormMode.RANGE_M100_100:
                 norm = (((bounded_val - min_) / (max_ - min_)) * 200) - 100
                 normalized_values[id_] = -norm if drive_mode else norm
             elif self.motors[motor].norm_mode is MotorNormMode.RANGE_0_100:
                 norm = ((bounded_val - min_) / (max_ - min_)) * 100
                 normalized_values[id_] = 100 - norm if drive_mode else norm
-            elif self.motors[motor].norm_mode is MotorNormMode.DEGREES:
-                mid = (min_ + max_) / 2
-                max_res = self.model_resolution_table[self._id_to_model(id_)] - 1
-                normalized_values[id_] = (val - mid) * 360 / max_res
+            elif self.motors[motor].norm_mode is MotorNormMode.DEGREE:
+                resolution = self.model_resolution_table[self.motors[motor].model]
+                if drive_mode:
+                    val *= -1
+                # middle_pos = homing_offset + (resolution - 1) // 2
+                middle_pos = int((max_ + min_) / 2)
+                normalized_values[id_] = ((val - middle_pos) / (resolution // 2)) * 180
             else:
+                # TODO(alibers): velocity and degree modes
                 raise NotImplementedError
 
         return normalized_values
@@ -822,11 +828,17 @@ class MotorsBus(abc.ABC):
                 val = 100 - val if drive_mode else val
                 bounded_val = min(100.0, max(0.0, val))
                 unnormalized_values[id_] = int((bounded_val / 100) * (max_ - min_) + min_)
-            elif self.motors[motor].norm_mode is MotorNormMode.DEGREES:
-                mid = (min_ + max_) / 2
-                max_res = self.model_resolution_table[self._id_to_model(id_)] - 1
-                unnormalized_values[id_] = int((val * max_res / 360) + mid)
+            elif self.motors[motor].norm_mode is MotorNormMode.DEGREE:
+                resolution = self.model_resolution_table[self.motors[motor].model]
+                middle_pos = int((max_ + min_) / 2)
+                unnormalized_values[id_] = int((val / 180) * resolution // 2) + middle_pos
+                if drive_mode:
+                    unnormalized_values[id_] *= -1
+
+                # if unnormalized_values[id_] < 0:
+                #     breakpoint()
             else:
+                # TODO(aliberts): degree mode
                 raise NotImplementedError
 
         return unnormalized_values

lerobot/common/motors/utils.py

@@ -0,0 +1,56 @@
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from .configs import MotorsBusConfig
+from .motors_bus import MotorsBus
+
+
+def make_motors_buses_from_configs(motors_bus_configs: dict[str, MotorsBusConfig]) -> list[MotorsBus]:
+    motors_buses = {}
+
+    for key, cfg in motors_bus_configs.items():
+        if cfg.type == "dynamixel":
+            from .dynamixel import DynamixelMotorsBus
+
+            motors_buses[key] = DynamixelMotorsBus(cfg)
+
+        elif cfg.type == "feetech":
+            from lerobot.common.motors.feetech.feetech import FeetechMotorsBus
+
+            motors_buses[key] = FeetechMotorsBus(cfg)
+
+        else:
+            raise ValueError(f"The motor type '{cfg.type}' is not valid.")
+
+    return motors_buses
+
+
+def make_motors_bus(motor_type: str, **kwargs) -> MotorsBus:
+    if motor_type == "dynamixel":
+        from .configs import DynamixelMotorsBusConfig
+        from .dynamixel import DynamixelMotorsBus
+
+        config = DynamixelMotorsBusConfig(**kwargs)
+        return DynamixelMotorsBus(config)
+
+    elif motor_type == "feetech":
+        from feetech import FeetechMotorsBus
+
+        from .configs import FeetechMotorsBusConfig
+
+        config = FeetechMotorsBusConfig(**kwargs)
+        return FeetechMotorsBus(config)
+
+    else:
+        raise ValueError(f"The motor type '{motor_type}' is not valid.")

lerobot/common/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(

lerobot/common/optim/optimizers.py

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

lerobot/common/optim/schedulers.py

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

lerobot/common/policies/__init__.py

@@ -15,7 +15,5 @@
 from .act.configuration_act import ACTConfig as ACTConfig
 from .diffusion.configuration_diffusion import DiffusionConfig as DiffusionConfig
 from .pi0.configuration_pi0 import PI0Config as PI0Config
-from .smolvla.configuration_smolvla import SmolVLAConfig as SmolVLAConfig
-from .smolvla2.configuration_smolvla2 import SmolVLA2Config as SmolVLA2Config
 from .tdmpc.configuration_tdmpc import TDMPCConfig as TDMPCConfig
 from .vqbet.configuration_vqbet import VQBeTConfig as VQBeTConfig

lerobot/common/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")

lerobot/common/policies/act/modeling_act.py

@@ -15,7 +15,7 @@
 # limitations under the License.
 """Action Chunking Transformer Policy
 
-As per Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware (https://huggingface.co/papers/2304.13705).
+As per Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware (https://arxiv.org/abs/2304.13705).
 The majority of changes here involve removing unused code, unifying naming, and adding helpful comments.
 """
 
@@ -33,16 +33,15 @@ from torch import Tensor, nn
 from torchvision.models._utils import IntermediateLayerGetter
 from torchvision.ops.misc import FrozenBatchNorm2d
 
-from lerobot.constants import ACTION, OBS_IMAGES
-from lerobot.policies.act.configuration_act import ACTConfig
-from lerobot.policies.normalize import Normalize, Unnormalize
-from lerobot.policies.pretrained import PreTrainedPolicy
+from lerobot.common.policies.act.configuration_act import ACTConfig
+from lerobot.common.policies.normalize import Normalize, Unnormalize
+from lerobot.common.policies.pretrained import PreTrainedPolicy
 
 
 class ACTPolicy(PreTrainedPolicy):
     """
     Action Chunking Transformer Policy as per Learning Fine-Grained Bimanual Manipulation with Low-Cost
-    Hardware (paper: https://huggingface.co/papers/2304.13705, code: https://github.com/tonyzhaozh/act)
+    Hardware (paper: https://arxiv.org/abs/2304.13705, code: https://github.com/tonyzhaozh/act)
     """
 
     config_class = ACTConfig
@@ -115,49 +114,46 @@ class ACTPolicy(PreTrainedPolicy):
         environment. It works by managing the actions in a queue and only calling `select_actions` when the
         queue is empty.
         """
-        self.eval()  # keeping the policy in eval mode as it could be set to train mode while queue is consumed
+        self.eval()
 
+        batch = self.normalize_inputs(batch)
+        if self.config.image_features:
+            batch = dict(batch)  # shallow copy so that adding a key doesn't modify the original
+            batch["observation.images"] = [batch[key] for key in self.config.image_features]
+
+        # If we are doing temporal ensembling, do online updates where we keep track of the number of actions
+        # we are ensembling over.
         if self.config.temporal_ensemble_coeff is not None:
-            actions = self.predict_action_chunk(batch)
+            actions = self.model(batch)[0]  # (batch_size, chunk_size, action_dim)
+            actions = self.unnormalize_outputs({"action": actions})["action"]
             action = self.temporal_ensembler.update(actions)
             return action
 
         # Action queue logic for n_action_steps > 1. When the action_queue is depleted, populate it by
         # querying the policy.
         if len(self._action_queue) == 0:
-            actions = self.predict_action_chunk(batch)[:, : self.config.n_action_steps]
+            actions = self.model(batch)[0][:, : self.config.n_action_steps]
+
+            # TODO(rcadene): make _forward return output dictionary?
+            actions = self.unnormalize_outputs({"action": actions})["action"]
 
             # `self.model.forward` returns a (batch_size, n_action_steps, action_dim) tensor, but the queue
             # effectively has shape (n_action_steps, batch_size, *), hence the transpose.
             self._action_queue.extend(actions.transpose(0, 1))
         return self._action_queue.popleft()
 
-    @torch.no_grad
-    def predict_action_chunk(self, batch: dict[str, Tensor]) -> Tensor:
-        """Predict a chunk of actions given environment observations."""
-        self.eval()
-
-        batch = self.normalize_inputs(batch)
-        if self.config.image_features:
-            batch = dict(batch)  # shallow copy so that adding a key doesn't modify the original
-            batch[OBS_IMAGES] = [batch[key] for key in self.config.image_features]
-
-        actions = self.model(batch)[0]
-        actions = self.unnormalize_outputs({ACTION: actions})[ACTION]
-        return actions
-
     def forward(self, batch: dict[str, Tensor]) -> tuple[Tensor, dict]:
         """Run the batch through the model and compute the loss for training or validation."""
         batch = self.normalize_inputs(batch)
         if self.config.image_features:
             batch = dict(batch)  # shallow copy so that adding a key doesn't modify the original
-            batch[OBS_IMAGES] = [batch[key] for key in self.config.image_features]
+            batch["observation.images"] = [batch[key] for key in self.config.image_features]
 
         batch = self.normalize_targets(batch)
         actions_hat, (mu_hat, log_sigma_x2_hat) = self.model(batch)
 
         l1_loss = (
-            F.l1_loss(batch[ACTION], actions_hat, reduction="none") * ~batch["action_is_pad"].unsqueeze(-1)
+            F.l1_loss(batch["action"], actions_hat, reduction="none") * ~batch["action_is_pad"].unsqueeze(-1)
         ).mean()
 
         loss_dict = {"l1_loss": l1_loss.item()}
@@ -165,7 +161,7 @@ class ACTPolicy(PreTrainedPolicy):
             # Calculate Dₖₗ(latent_pdf || standard_normal). Note: After computing the KL-divergence for
             # each dimension independently, we sum over the latent dimension to get the total
             # KL-divergence per batch element, then take the mean over the batch.
-            # (See App. B of https://huggingface.co/papers/1312.6114 for more details).
+            # (See App. B of https://arxiv.org/abs/1312.6114 for more details).
             mean_kld = (
                 (-0.5 * (1 + log_sigma_x2_hat - mu_hat.pow(2) - (log_sigma_x2_hat).exp())).sum(-1).mean()
             )
@@ -179,7 +175,7 @@ class ACTPolicy(PreTrainedPolicy):
 
 class ACTTemporalEnsembler:
     def __init__(self, temporal_ensemble_coeff: float, chunk_size: int) -> None:
-        """Temporal ensembling as described in Algorithm 2 of https://huggingface.co/papers/2304.13705.
+        """Temporal ensembling as described in Algorithm 2 of https://arxiv.org/abs/2304.13705.
 
         The weights are calculated as wᵢ = exp(-temporal_ensemble_coeff * i) where w₀ is the oldest action.
         They are then normalized to sum to 1 by dividing by Σwᵢ. Here's some intuition around how the

lerobot/common/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")
@@ -81,7 +81,7 @@ class DiffusionConfig(PreTrainedConfig):
         n_groups: Number of groups used in the group norm of the Unet's convolutional blocks.
         diffusion_step_embed_dim: The Unet is conditioned on the diffusion timestep via a small non-linear
             network. This is the output dimension of that network, i.e., the embedding dimension.
-        use_film_scale_modulation: FiLM (https://huggingface.co/papers/1709.07871) is used for the Unet conditioning.
+        use_film_scale_modulation: FiLM (https://arxiv.org/abs/1709.07871) is used for the Unet conditioning.
             Bias modulation is used be default, while this parameter indicates whether to also use scale
             modulation.
         noise_scheduler_type: Name of the noise scheduler to use. Supported options: ["DDPM", "DDIM"].

lerobot/common/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.constants import ACTION, OBS_ENV_STATE, OBS_IMAGES, OBS_STATE
-from lerobot.policies.diffusion.configuration_diffusion import DiffusionConfig
-from lerobot.policies.normalize import Normalize, Unnormalize
-from lerobot.policies.pretrained import PreTrainedPolicy
-from lerobot.policies.utils import (
+from lerobot.common.constants import OBS_ENV_STATE, OBS_STATE
+from lerobot.common.policies.diffusion.configuration_diffusion import DiffusionConfig
+from lerobot.common.policies.normalize import Normalize, Unnormalize
+from lerobot.common.policies.pretrained import PreTrainedPolicy
+from lerobot.common.policies.utils import (
     get_device_from_parameters,
     get_dtype_from_parameters,
     get_output_shape,
@@ -48,7 +48,7 @@ from lerobot.policies.utils import (
 class DiffusionPolicy(PreTrainedPolicy):
     """
     Diffusion Policy as per "Diffusion Policy: Visuomotor Policy Learning via Action Diffusion"
-    (paper: https://huggingface.co/papers/2303.04137, code: https://github.com/real-stanford/diffusion_policy).
+    (paper: https://arxiv.org/abs/2303.04137, code: https://github.com/real-stanford/diffusion_policy).
     """
 
     config_class = DiffusionConfig
@@ -99,18 +99,6 @@ class DiffusionPolicy(PreTrainedPolicy):
         if self.config.env_state_feature:
             self._queues["observation.environment_state"] = deque(maxlen=self.config.n_obs_steps)
 
-    @torch.no_grad
-    def predict_action_chunk(self, batch: dict[str, Tensor]) -> Tensor:
-        """Predict a chunk of actions given environment observations."""
-        # stack n latest observations from the queue
-        batch = {k: torch.stack(list(self._queues[k]), dim=1) for k in batch if k in self._queues}
-        actions = self.diffusion.generate_actions(batch)
-
-        # TODO(rcadene): make above methods return output dictionary?
-        actions = self.unnormalize_outputs({ACTION: actions})[ACTION]
-
-        return actions
-
     @torch.no_grad
     def select_action(self, batch: dict[str, Tensor]) -> Tensor:
         """Select a single action given environment observations.
@@ -136,15 +124,23 @@ class DiffusionPolicy(PreTrainedPolicy):
         batch = self.normalize_inputs(batch)
         if self.config.image_features:
             batch = dict(batch)  # shallow copy so that adding a key doesn't modify the original
-            batch[OBS_IMAGES] = torch.stack([batch[key] for key in self.config.image_features], dim=-4)
+            batch["observation.images"] = torch.stack(
+                [batch[key] for key in self.config.image_features], dim=-4
+            )
         # Note: It's important that this happens after stacking the images into a single key.
         self._queues = populate_queues(self._queues, batch)
 
-        if len(self._queues[ACTION]) == 0:
-            actions = self.predict_action_chunk(batch)
-            self._queues[ACTION].extend(actions.transpose(0, 1))
+        if len(self._queues["action"]) == 0:
+            # stack n latest observations from the queue
+            batch = {k: torch.stack(list(self._queues[k]), dim=1) for k in batch if k in self._queues}
+            actions = self.diffusion.generate_actions(batch)
 
-        action = self._queues[ACTION].popleft()
+            # TODO(rcadene): make above methods return output dictionary?
+            actions = self.unnormalize_outputs({"action": actions})["action"]
+
+            self._queues["action"].extend(actions.transpose(0, 1))
+
+        action = self._queues["action"].popleft()
         return action
 
     def forward(self, batch: dict[str, Tensor]) -> tuple[Tensor, None]:
@@ -152,7 +148,9 @@ class DiffusionPolicy(PreTrainedPolicy):
         batch = self.normalize_inputs(batch)
         if self.config.image_features:
             batch = dict(batch)  # shallow copy so that adding a key doesn't modify the original
-            batch[OBS_IMAGES] = torch.stack([batch[key] for key in self.config.image_features], dim=-4)
+            batch["observation.images"] = torch.stack(
+                [batch[key] for key in self.config.image_features], dim=-4
+            )
         batch = self.normalize_targets(batch)
         loss = self.diffusion.compute_loss(batch)
         # no output_dict so returning None
@@ -372,7 +370,7 @@ class DiffusionModel(nn.Module):
 class SpatialSoftmax(nn.Module):
     """
     Spatial Soft Argmax operation described in "Deep Spatial Autoencoders for Visuomotor Learning" by Finn et al.
-    (https://huggingface.co/papers/1509.06113). A minimal port of the robomimic implementation.
+    (https://arxiv.org/pdf/1509.06113). A minimal port of the robomimic implementation.
 
     At a high level, this takes 2D feature maps (from a convnet/ViT) and returns the "center of mass"
     of activations of each channel, i.e., keypoints in the image space for the policy to focus on.
@@ -730,7 +728,7 @@ class DiffusionConditionalResidualBlock1d(nn.Module):
 
         self.conv1 = DiffusionConv1dBlock(in_channels, out_channels, kernel_size, n_groups=n_groups)
 
-        # FiLM modulation (https://huggingface.co/papers/1709.07871) outputs per-channel bias and (maybe) scale.
+        # FiLM modulation (https://arxiv.org/abs/1709.07871) outputs per-channel bias and (maybe) scale.
         cond_channels = out_channels * 2 if use_film_scale_modulation else out_channels
         self.cond_encoder = nn.Sequential(nn.Mish(), nn.Linear(cond_dim, cond_channels))
 

lerobot/common/policies/factory.py

@@ -18,67 +18,56 @@ 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.reward_model.configuration_classifier import RewardClassifierConfig
+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.smolvla2.configuration_smolvla2 import SmolVLA2Config
-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.policies.tdmpc.modeling_tdmpc import TDMPCPolicy
+        from lerobot.common.policies.tdmpc.modeling_tdmpc import TDMPCPolicy
 
         return TDMPCPolicy
     elif name == "diffusion":
-        from lerobot.policies.diffusion.modeling_diffusion import DiffusionPolicy
+        from lerobot.common.policies.diffusion.modeling_diffusion import DiffusionPolicy
 
         return DiffusionPolicy
     elif name == "act":
-        from lerobot.policies.act.modeling_act import ACTPolicy
+        from lerobot.common.policies.act.modeling_act import ACTPolicy
 
         return ACTPolicy
     elif name == "vqbet":
-        from lerobot.policies.vqbet.modeling_vqbet import VQBeTPolicy
+        from lerobot.common.policies.vqbet.modeling_vqbet import VQBeTPolicy
 
         return VQBeTPolicy
     elif name == "pi0":
-        from lerobot.policies.pi0.modeling_pi0 import PI0Policy
+        from lerobot.common.policies.pi0.modeling_pi0 import PI0Policy
 
         return PI0Policy
     elif name == "pi0fast":
-        from lerobot.policies.pi0fast.modeling_pi0fast import PI0FASTPolicy
+        from lerobot.common.policies.pi0fast.modeling_pi0fast import PI0FASTPolicy
 
         return PI0FASTPolicy
     elif name == "sac":
-        from lerobot.policies.sac.modeling_sac import SACPolicy
+        from lerobot.common.policies.sac.modeling_sac import SACPolicy
 
         return SACPolicy
     elif name == "reward_classifier":
-        from lerobot.policies.sac.reward_model.modeling_classifier import Classifier
+        from lerobot.common.policies.reward_model.modeling_classifier import Classifier
 
         return Classifier
-    elif name == "smolvla":
-        from lerobot.policies.smolvla.modeling_smolvla import SmolVLAPolicy
-
-        return SmolVLAPolicy
-    elif name == "smolvla2":
-        from lerobot.policies.smolvla2.modeling_smolvla2 import SmolVLA2Policy
-
-        return SmolVLA2Policy
     else:
         raise NotImplementedError(f"Policy with name {name} is not implemented.")
 
@@ -96,12 +85,6 @@ def make_policy_config(policy_type: str, **kwargs) -> PreTrainedConfig:
         return PI0Config(**kwargs)
     elif policy_type == "pi0fast":
         return PI0FASTConfig(**kwargs)
-    elif policy_type == "sac":
-        return SACConfig(**kwargs)
-    elif policy_type == "smolvla":
-        return SmolVLAConfig(**kwargs)
-    elif policy_type == "smolvla2":
-        return SmolVLA2Config(**kwargs)
     elif policy_type == "reward_classifier":
         return RewardClassifierConfig(**kwargs)
     else:
@@ -154,18 +137,7 @@ def make_policy(
     kwargs = {}
     if ds_meta is not None:
         features = dataset_to_policy_features(ds_meta.features)
-        # Handle robot-type grouped stats - flatten to feature-level stats  
-        if ds_meta.stats and len(ds_meta.stats) == 1:
-            # Single robot type - use its stats directly
-            robot_type = list(ds_meta.stats.keys())[0]
-            kwargs["dataset_stats"] = ds_meta.stats[robot_type]
-        elif ds_meta.stats and len(ds_meta.stats) > 1:
-            # Multiple robot types - need to aggregate across all robot types
-            # For now, use the first robot type (TODO: proper multi-robot handling)
-            robot_type = list(ds_meta.stats.keys())[0]
-            kwargs["dataset_stats"] = ds_meta.stats[robot_type]
-        else:
-            kwargs["dataset_stats"] = ds_meta.stats
+        kwargs["dataset_stats"] = ds_meta.stats
     else:
         if not cfg.pretrained_path:
             logging.warning(

lerobot/common/policies/normalize.py

@@ -0,0 +1,230 @@
+#!/usr/bin/env python
+
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import numpy as np
+import torch
+from torch import Tensor, nn
+
+from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature
+
+
+def _no_stats_error_str(name: str) -> str:
+    return (
+        f"`{name}` is infinity. You should either initialize with `stats` as an argument, or use a "
+        "pretrained model."
+    )
+
+
+def _initialize_stats_buffers(
+    module: nn.Module,
+    features: dict[str, PolicyFeature],
+    norm_map: dict[str, NormalizationMode],
+    stats: dict[str, dict[str, Tensor]] | None = None,
+) -> None:
+    """Register statistics buffers (mean/std or min/max) on the given *module*.
+
+    The logic matches the previous constructors of `NormalizeBuffer` and `UnnormalizeBuffer`,
+    but is factored out so it can be reused by both classes and stay in sync.
+    """
+    for key, ft in features.items():
+        norm_mode = norm_map.get(ft.type, NormalizationMode.IDENTITY)
+        if norm_mode is NormalizationMode.IDENTITY:
+            continue
+
+        shape: tuple[int, ...] = tuple(ft.shape)
+        if ft.type is FeatureType.VISUAL:
+            # reduce spatial dimensions, keep channel dimension only
+            c, *_ = shape
+            shape = (c, 1, 1)
+
+        prefix = key.replace(".", "_")
+
+        if norm_mode is NormalizationMode.MEAN_STD:
+            mean = torch.full(shape, torch.inf, dtype=torch.float32)
+            std = torch.full(shape, torch.inf, dtype=torch.float32)
+
+            if stats and key in stats and "mean" in stats[key] and "std" in stats[key]:
+                mean_data = stats[key]["mean"]
+                std_data = stats[key]["std"]
+                if isinstance(mean_data, torch.Tensor):
+                    # Note: The clone is needed to make sure that the logic in save_pretrained doesn't see duplicated
+                    # tensors anywhere (for example, when we use the same stats for normalization and
+                    # unnormalization). See the logic here
+                    # https://github.com/huggingface/safetensors/blob/079781fd0dc455ba0fe851e2b4507c33d0c0d407/bindings/python/py_src/safetensors/torch.py#L97.
+                    mean = mean_data.clone().to(dtype=torch.float32)
+                    std = std_data.clone().to(dtype=torch.float32)
+                elif isinstance(mean_data, np.ndarray):
+                    mean = torch.from_numpy(mean_data).to(dtype=torch.float32)
+                    std = torch.from_numpy(std_data).to(dtype=torch.float32)
+                else:
+                    raise ValueError(f"Unsupported stats type for key '{key}' (expected ndarray or Tensor).")
+
+            module.register_buffer(f"{prefix}_mean", mean)
+            module.register_buffer(f"{prefix}_std", std)
+            continue
+
+        if norm_mode is NormalizationMode.MIN_MAX:
+            min_val = torch.full(shape, torch.inf, dtype=torch.float32)
+            max_val = torch.full(shape, torch.inf, dtype=torch.float32)
+
+            if stats and key in stats and "min" in stats[key] and "max" in stats[key]:
+                min_data = stats[key]["min"]
+                max_data = stats[key]["max"]
+                if isinstance(min_data, torch.Tensor):
+                    min_val = min_data.clone().to(dtype=torch.float32)
+                    max_val = max_data.clone().to(dtype=torch.float32)
+                elif isinstance(min_data, np.ndarray):
+                    min_val = torch.from_numpy(min_data).to(dtype=torch.float32)
+                    max_val = torch.from_numpy(max_data).to(dtype=torch.float32)
+                else:
+                    raise ValueError(f"Unsupported stats type for key '{key}' (expected ndarray or Tensor).")
+
+            module.register_buffer(f"{prefix}_min", min_val)
+            module.register_buffer(f"{prefix}_max", max_val)
+            continue
+
+        raise ValueError(norm_mode)
+
+
+class Normalize(nn.Module):
+    """Normalizes data (e.g. "observation.image") for more stable and faster convergence during training."""
+
+    def __init__(
+        self,
+        features: dict[str, PolicyFeature],
+        norm_map: dict[str, NormalizationMode],
+        stats: dict[str, dict[str, Tensor]] | None = None,
+    ):
+        super().__init__()
+        self.features = features
+        self.norm_map = norm_map
+
+        _initialize_stats_buffers(self, features, norm_map, stats)
+
+    def forward(self, batch: dict[str, Tensor]) -> dict[str, Tensor]:
+        batch = dict(batch)
+        for key, ft in self.features.items():
+            if key not in batch:
+                continue
+
+            norm_mode = self.norm_map.get(ft.type, NormalizationMode.IDENTITY)
+            if norm_mode is NormalizationMode.IDENTITY:
+                continue
+
+            prefix = key.replace(".", "_")
+
+            if norm_mode is NormalizationMode.MEAN_STD:
+                mean = getattr(self, f"{prefix}_mean")
+                std = getattr(self, f"{prefix}_std")
+                assert not torch.isinf(mean).any(), _no_stats_error_str("mean")
+                assert not torch.isinf(std).any(), _no_stats_error_str("std")
+                batch[key] = (batch[key] - mean) / (std + 1e-8)
+                continue
+
+            if norm_mode is NormalizationMode.MIN_MAX:
+                min_val = getattr(self, f"{prefix}_min")
+                max_val = getattr(self, f"{prefix}_max")
+                assert not torch.isinf(min_val).any(), _no_stats_error_str("min")
+                assert not torch.isinf(max_val).any(), _no_stats_error_str("max")
+                batch[key] = (batch[key] - min_val) / (max_val - min_val + 1e-8)
+                batch[key] = batch[key] * 2 - 1
+                continue
+
+            raise ValueError(norm_mode)
+
+        return batch
+
+
+class Unnormalize(nn.Module):
+    """Inverse operation of `Normalize`. Uses registered buffers for statistics."""
+
+    def __init__(
+        self,
+        features: dict[str, PolicyFeature],
+        norm_map: dict[str, NormalizationMode],
+        stats: dict[str, dict[str, Tensor]] | None = None,
+    ):
+        super().__init__()
+        self.features = features
+        self.norm_map = norm_map
+
+        _initialize_stats_buffers(self, features, norm_map, stats)
+
+    def forward(self, batch: dict[str, Tensor]) -> dict[str, Tensor]:
+        # batch = dict(batch)
+        for key, ft in self.features.items():
+            if key not in batch:
+                continue
+
+            norm_mode = self.norm_map.get(ft.type, NormalizationMode.IDENTITY)
+            if norm_mode is NormalizationMode.IDENTITY:
+                continue
+
+            prefix = key.replace(".", "_")
+
+            if norm_mode is NormalizationMode.MEAN_STD:
+                mean = getattr(self, f"{prefix}_mean")
+                std = getattr(self, f"{prefix}_std")
+                assert not torch.isinf(mean).any(), _no_stats_error_str("mean")
+                assert not torch.isinf(std).any(), _no_stats_error_str("std")
+                batch[key] = batch[key] * std + mean
+                continue
+
+            if norm_mode is NormalizationMode.MIN_MAX:
+                min_val = getattr(self, f"{prefix}_min")
+                max_val = getattr(self, f"{prefix}_max")
+                assert not torch.isinf(min_val).any(), _no_stats_error_str("min")
+                assert not torch.isinf(max_val).any(), _no_stats_error_str("max")
+                batch[key] = (batch[key] + 1) / 2
+                batch[key] = batch[key] * (max_val - min_val) + min_val
+                continue
+
+            raise ValueError(norm_mode)
+
+        return batch
+
+
+def convert_normalize_to_buffer_state_dict(state_dict: dict[str, torch.Tensor]) -> dict[str, torch.Tensor]:
+    """
+    Convert state dict from Normalize/Unnormalize classes to NormalizeBuffer/UnnormalizeBuffer format.
+
+    Args:
+        state_dict: State dict from a model using Normalize/Unnormalize classes
+
+    Returns:
+        Converted state dict compatible with NormalizeBuffer/UnnormalizeBuffer classes
+
+    Example:
+        # Old format (Normalize): "buffer_observation_image.mean"
+        # New format (NormalizeBuffer): "observation_image_mean"
+    """
+    converted_state_dict = {}
+
+    for key, value in state_dict.items():
+        # Check if this is a normalization buffer parameter
+        if key.startswith("buffer_") and ("." in key):
+            # Extract the prefix and stat type
+            # e.g. "buffer_observation_image.mean" -> "observation_image", "mean"
+            buffer_part = key[7:]  # Remove "buffer_" prefix
+            prefix, stat_type = buffer_part.rsplit(".", 1)
+
+            # Convert to new format: "observation_image_mean"
+            new_key = f"{prefix}_{stat_type}"
+            converted_state_dict[new_key] = value
+        else:
+            # Keep non-normalization keys unchanged
+            converted_state_dict[key] = value
+
+    return converted_state_dict

lerobot/common/policies/pi0/configuration_pi0.py

@@ -14,12 +14,12 @@
 
 from dataclasses import dataclass, field
 
-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 (
+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
 
 
 @PreTrainedConfig.register_subclass("pi0")