fix link

fix emoji and formatting
improved explanation
2026-07-30 21:19:40 +00:00 · 2026-07-29 13:21:16 +02:00 · 2026-07-29 13:11:36 +02:00 · 2026-07-29 13:09:20 +02:00 · 2026-07-29 09:43:25 +02:00 · 2026-07-28 17:16:34 +02:00
182 changed files with 635 additions and 1279 deletions
@@ -61,20 +61,16 @@ Full details in [`docs/source/so101.mdx`](./docs/source/so101.mdx) and [`docs/so
 **4.1 Install**

 ```bash
-# uv (recommended — see AGENTS.md and CLAUDE.md)
-uv sync --locked --extra feetech          # SO-100/SO-101 motor stack
-# uv sync --locked --extra all            # everything
-# uv sync --locked --extra smolvla        # add SmolVLA deps
-
-# pip (alternative, e.g. when not working from source)
-# pip install 'lerobot[feetech]'
-# pip install 'lerobot[all]'
-# pip install 'lerobot[smolvla]'
-
+pip install 'lerobot[feetech]'              # SO-100/SO-101 motor stack
+# pip install 'lerobot[all]'                # everything
+# pip install 'lerobot[aloha,pusht]'        # specific features
+# pip install 'lerobot[smolvla]'            # add SmolVLA deps
 git lfs install && git lfs pull
-hf auth login                             # required to push datasets/policies
+hf auth login                               # required to push datasets/policies
 ```

+Contributors can alternatively use `uv sync --locked --extra feetech` (see `AGENTS.md`).
+
 **4.2 Find USB ports** — run once per arm, unplug when prompted.

 ```bash
@@ -68,16 +68,17 @@ ENV HOME=/home/user_lerobot \
 # issues with MuJoCo and OpenGL drivers.
 RUN uv venv --python python${PYTHON_VERSION}

-# Install third-party dependencies separately for layer caching
+# Install Python dependencies for caching
 COPY --chown=user_lerobot:user_lerobot setup.py pyproject.toml uv.lock README.md MANIFEST.in ./
-RUN uv sync --locked --extra all --no-install-project --no-cache
+COPY --chown=user_lerobot:user_lerobot src/ src/
+
+RUN uv sync --locked --extra all --no-cache

 RUN chmod +x /lerobot/.venv/lib/python${PYTHON_VERSION}/site-packages/triton/backends/nvidia/bin/ptxas

-# Copy the application source code and install the local project
+# Copy the rest of the application source code
 # Make sure to have the git-LFS files for testing
 COPY --chown=user_lerobot:user_lerobot . .
-RUN uv sync --locked --extra all --no-cache

 # Set the default command
 CMD ["/bin/bash"]
@@ -60,14 +60,15 @@ ENV HOME=/home/user_lerobot \
 # run other Python projects in the same container without dependency conflicts.
 RUN uv venv

-# Install third-party dependencies separately for layer caching
+# Install Python dependencies for caching
 COPY --chown=user_lerobot:user_lerobot setup.py pyproject.toml uv.lock README.md MANIFEST.in ./
-RUN uv sync --locked --extra all --no-install-project --no-cache
+COPY --chown=user_lerobot:user_lerobot src/ src/

-# Copy the application code and install the local project
+RUN uv sync --locked --extra all --no-cache
+
+# Copy the rest of the application code
 # Make sure to have the git-LFS files for testing
 COPY --chown=user_lerobot:user_lerobot . .
-RUN uv sync --locked --extra all --no-cache

 # Set the default command
 CMD ["/bin/bash"]
@@ -58,7 +58,7 @@ final_action = postprocessor(action)

 ## Hardware API redesign

-PR [#777](https://github.com/huggingface/lerobot/pull/777) improves the LeRobot calibration but is **not backward-compatible**. Below is an overview of what changed and how you can continue to work with datasets created before this pull request.
+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?

@@ -129,8 +129,8 @@ python examples/backward_compatibility/replay.py \

 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 first replaying an episode of the dataset your policy was trained on using the section above.
-Then, add these same transformations to your inference script (shown here in the `record.py` script):
+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(
@@ -164,8 +164,8 @@ includes the range reported by the sensor. Requesting an unsupported control als
 Omitted controls leave the sensor's existing automatic or manual setting unchanged. These options
 require `use_rgb=True`.

-Manual color controls require a dedicated RGB module. Cameras without one, such as the RealSense
-D405, do not support them and raise an error at connection time.
+On the RealSense D405, the color stream is provided by the Stereo Module, so changing manual
+exposure or gain also affects the depth stream.

 </hfoption>
 </hfoptions>
@@ -88,6 +88,20 @@ policy_preprocessor = NormalizerProcessorStep(stats=dataset_stats)

 The same policy can work with different environment processors, and the same environment processor can work with different policies:

+````python
+# Use SmolVLA policy with LIBERO environment
+# Use SmolVLA policy with LIBERO environment
+libero_preprocessor, libero_postprocessor = make_env_pre_post_processors(
+    env_cfg=libero_cfg,
+    policy_cfg=smolvla_cfg,
+)
+smolvla_preprocessor, smolvla_postprocessor = make_pre_post_processors(smolvla_cfg)
+# Or use ACT policy with the same LIBERO environment
+libero_preprocessor, libero_postprocessor = make_env_pre_post_processors(
+    env_cfg=libero_cfg,
+    policy_cfg=act_cfg,
+)
+act_preprocessor, act_postprocessor = make_pre_post_processors(act_cfg)
 ```python
 # Use SmolVLA policy with LIBERO environment
 libero_preprocessor, libero_postprocessor = make_env_pre_post_processors(
@@ -102,7 +116,6 @@ libero_preprocessor, libero_postprocessor = make_env_pre_post_processors(
    policy_cfg=act_cfg,
 )
 act_preprocessor, act_postprocessor = make_pre_post_processors(act_cfg)
-```

 ### 3. **Easier Experimentation**

@@ -132,7 +145,7 @@ class LiberoVelocityProcessorStep(ObservationProcessorStep):
        state = torch.cat([eef_pos, eef_axisangle, eef_vel,
                          gripper_pos, gripper_vel], dim=-1)  # 14D
        return state
-```
+````

 ### 4. **Cleaner Environment Code**

@@ -40,10 +40,10 @@ This tutorial guides you through updating the firmware of Feetech motors using t
 For each motor you want to update:

 1. **Select the motor** from the list by clicking on it
-2. **Click the Upgrade tab**:
-3. **Click the Online button**:
-   - If a potential firmware update is found, it will be displayed in the box
-4. **Click the Upgrade button**:
+2. **Click on Upgrade tab**:
+3. **Click on Online button**:
+   - If an potential firmware update is found, it will be displayed in the box
+4. **Click on Upgrade button**:
   - The update progress will be displayed

 ## Step 6: Verify Update
@@ -59,7 +59,6 @@ The `lerobot-rollout --strategy.type=dagger` mode requires **teleoperators with

 - `bi_openarm_mini` - Bimanual OpenArm Mini
 - `so_leader` - SO100 / SO101 leader arm
- `bi_so_leader` - Bimanual SO100 / SO101 leader arms

 > [!IMPORTANT]
 > The provided commands default to `bi_openarm_follower` + `bi_openarm_mini`.
@@ -211,7 +211,7 @@ Record, Replay and Train with Hope-JR is still experimental.

 ### Record

-This step records the dataset, which can be seen as an example [here](https://huggingface.co/datasets/nepyope/hand_record_test_with_video_data).
+This step records the dataset, which can be seen as an example [here](https://huggingface.co/datasets/nepyope/hand_record_test_with_video_data/settings).

 ```bash
 lerobot-record \
@@ -1,23 +1,177 @@
+# LeRobot
+
 <div class="flex justify-center">
  <a target="_blank" href="https://huggingface.co/lerobot">
    <img
-      alt="HuggingFace Expert Acceleration Program"
+      alt="LeRobot, Hugging Face Robotics Library"
      src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/lerobot-logo-thumbnail.png"
-      style="width: 100%"
    ></img>
  </a>
 </div>

-# LeRobot
-
 **State-of-the-art machine learning for real-world robotics**

-🤗 LeRobot aims to provide models, datasets, and tools for real-world robotics in PyTorch. The goal is to lower the barrier for entry to robotics so that everyone can contribute and benefit from sharing datasets and pretrained models.
+🤗 LeRobot provides a hardware-agnostic, Python-native interface for controlling real robots - from affordable arms like the SO-ARM101 to full humanoids. Plus the tools to record, store, and share the datasets they generate. Every dataset uses the standardized **LeRobotDataset** format (synchronized video + action/state data) and can be streamed directly from the [Hugging Face Hub](https://huggingface.co/lerobot).

-🤗 LeRobot contains state-of-the-art approaches that have been shown to transfer to the real-world with a focus on imitation learning and reinforcement learning.
+🤗 On top of that data, LeRobot implements state-of-the-art policies - from lightweight imitation-learning models like ACT to large vision-language-action models like π₀ and SmolVLA - all trainable, shareable, and deployable with the same handful of CLI commands.

-🤗 LeRobot already provides a set of pretrained models, datasets with human collected demonstrations, and simulated environments so that everyone can get started.
+The goal: lower the barrier to entry for robotics, so that everyone can contribute to, and benefit from, shared datasets and pretrained models.

-🤗 LeRobot hosts pretrained models and datasets on the LeRobot HuggingFace page.
+<div align="center" style="display: flex; justify-content: center; gap: 8px; flex-wrap: wrap; margin: 20px 0;">
+  <a href="https://discord.gg/s3KuuzsPFb" target="_blank">
+    <img alt="Discord" src="https://img.shields.io/badge/Discord-Join_the_Community-5865F2?style=flat&logo=discord&logoColor=white">
+  </a>
+  <a href="https://x.com/LeRobotHF" target="_blank">
+    <img alt="X (Twitter)" src="https://img.shields.io/badge/X-Follow_%40LeRobotHF-black?style=flat&logo=x&logoColor=white">
+  </a>
+  <a href="https://huggingface.co/lerobot" target="_blank">
+    <img alt="Hugging Face Hub" src="https://img.shields.io/badge/HF_Hub-Models_%26_Datasets-FFD21E?style=flat">
+  </a>
+</div>

-Join the LeRobot community on [Discord](https://discord.gg/s3KuuzsPFb)
+<div align="center">
+  <img src="../../media/readme/robots_control_video.webp" width="640px" alt="Reachy 2 Demo">
+</div>
+
+## How It Works
+
+**Teleoperate → Record → Train → Deploy**
+
+1. **Teleoperate** - control the robot yourself (with a leader arm, keyboard, or phone) so it can learn from your movements.
+2. **Record** - each demonstration is saved as a dataset: synchronized camera video plus the actions you took.
+3. **Train** - a policy (the neural network that will control the robot) learns to imitate your demonstrations.
+4. **Deploy** - run the trained policy on the robot and watch it complete the task on its own.
+
+## Get Started
+
+New here? [Install LeRobot](./installation), then pick your path:
+
+<div class="grid grid-cols-1 md:grid-cols-3 gap-4 my-6">
+  <div class="border dark:border-gray-700 rounded-lg p-4 shadow">
+    <div class="text-lg font-semibold mb-2">🔧 I have a robot</div>
+    <p class="text-gray-700 dark:text-gray-300 text-sm">
+      LeRobot supports a wide range of arms and mobile robots. Popular picks:
+    </p>
+    <ul class="text-gray-700 dark:text-gray-300 text-sm list-disc pl-5 mb-2">
+      <li>
+        <a href="./so101">SO-101</a> - our flagship, low-cost arm
+      </li>
+      <li>
+        <a href="./lekiwi">LeKiwi</a> - a mobile base with an arm on top
+      </li>
+      <li>
+        <a href="./koch">Koch v1.1</a> - a long-time community favorite
+      </li>
+      <li>
+        or find yours under <strong>Robots</strong> in the sidebar
+      </li>
+    </ul>
+    <p class="text-gray-700 dark:text-gray-300 text-sm">
+      Once it's assembled and calibrated, record a dataset and train your first
+      policy with the <a href="./il_robots">imitation learning tutorial</a> - or
+      skip the CLI entirely with <a href="./lelab">LeLab</a>, a browser GUI for
+      the same workflow.
+    </p>
+  </div>
+  <div class="border dark:border-gray-700 rounded-lg p-4 shadow">
+    <div class="text-lg font-semibold mb-2">💻 No hardware yet</div>
+    <p class="text-gray-700 dark:text-gray-300 text-sm">
+      You can still train and evaluate policies without owning a robot:
+    </p>
+    <ul class="text-gray-700 dark:text-gray-300 text-sm list-disc pl-5 mb-2">
+      <li>
+        train on an existing
+        <a href="https://huggingface.co/datasets?other=LeRobot">
+          LeRobot dataset
+        </a>
+        from the Hub
+      </li>
+      <li>
+        evaluate in <a href="./envhub">simulation</a>, against benchmarks like
+        LIBERO or Meta-World
+      </li>
+      <li>
+        try the free <a href="./notebooks">Colab notebooks</a> - nothing to
+        install
+      </li>
+    </ul>
+  </div>
+  <div class="border dark:border-gray-700 rounded-lg p-4 shadow">
+    <div class="text-lg font-semibold mb-2">🤝 I want to contribute</div>
+    <p class="text-gray-700 dark:text-gray-300 text-sm">
+      Start with the <a href="./contributing">Contributing guide</a>, then
+      <a href="./bring_your_own_policies">add a new policy</a> or
+      <a href="./integrate_hardware">bring your own hardware</a>.
+    </p>
+  </div>
+</div>
+
+## Explore the Docs
+
+<div class="grid grid-cols-1 md:grid-cols-3 gap-4 my-6">
+  <a
+    class="!no-underline border dark:border-gray-700 rounded-lg p-4 shadow hover:shadow-lg"
+    href="./cheat-sheet"
+  >
+    <div class="font-semibold mb-1">📋 Cheat Sheet</div>
+    <p class="text-gray-700 dark:text-gray-300 text-sm">
+      Every LeRobot CLI command, copy-paste ready.
+    </p>
+  </a>
+  <a
+    class="!no-underline border dark:border-gray-700 rounded-lg p-4 shadow hover:shadow-lg"
+    href="./hardware_guide"
+  >
+    <div class="font-semibold mb-1">🖥️ Compute & Hardware Guide</div>
+    <p class="text-gray-700 dark:text-gray-300 text-sm">
+      Which policy fits your GPU, and how long training takes.
+    </p>
+  </a>
+  <a
+    class="!no-underline border dark:border-gray-700 rounded-lg p-4 shadow hover:shadow-lg"
+    href="./lerobot-dataset-v3"
+  >
+    <div class="font-semibold mb-1">🗂️ LeRobotDataset</div>
+    <p class="text-gray-700 dark:text-gray-300 text-sm">
+      Load, stream, and visualize robot datasets from the Hub.
+    </p>
+  </a>
+  <a
+    class="!no-underline border dark:border-gray-700 rounded-lg p-4 shadow hover:shadow-lg"
+    href="./lelab"
+  >
+    <div class="font-semibold mb-1">🖼 LeLab</div>
+    <p class="text-gray-700 dark:text-gray-300 text-sm">
+      A browser GUI for calibrating, recording, and training - no CLI required.
+    </p>
+  </a>
+  <a
+    class="!no-underline border dark:border-gray-700 rounded-lg p-4 shadow hover:shadow-lg"
+    href="./act"
+  >
+    <div class="font-semibold mb-1">🧠 Policies</div>
+    <p class="text-gray-700 dark:text-gray-300 text-sm">
+      Start with ACT, our recommended first policy - or browse SmolVLA, π₀, and
+      more in the sidebar.
+    </p>
+  </a>
+  <a
+    class="!no-underline border dark:border-gray-700 rounded-lg p-4 shadow hover:shadow-lg"
+    href="./envhub"
+  >
+    <div class="font-semibold mb-1">🎮 Simulation & Benchmarks</div>
+    <p class="text-gray-700 dark:text-gray-300 text-sm">
+      Train and evaluate in simulated environments before touching real
+      hardware.
+    </p>
+  </a>
+</div>
+
+## Common Problems
+
+Running into issues? A few of the most frequent ones:
+
+- **Blurry or unusable camera footage** - lighting matters more than resolution. See the [Cameras](./cameras) guide.
+- **Build or install errors** (`cmake`, `ffmpeg`, CUDA) - see the Troubleshooting section of the [Installation guide](./installation#troubleshooting).
+- **Not sure which policy fits your GPU** - check the [Compute & Hardware Guide](./hardware_guide).
+- **Still stuck?** Ask on [Discord](https://discord.gg/s3KuuzsPFb) - the community (and the LeRobot team) is there to help.
@@ -18,7 +18,7 @@ If you're using Feetech or Dynamixel motors, LeRobot provides built-in bus inter
 - [`DynamixelMotorsBus`](https://github.com/huggingface/lerobot/blob/main/src/lerobot/motors/dynamixel/dynamixel.py) – for controlling Dynamixel servos

 Please refer to the [`MotorsBus`](https://github.com/huggingface/lerobot/blob/main/src/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/src/lerobot/robots/so_follower/so_follower.py)
+For a good example of how it can be used, you can have a look at our own [SO101 follower implementation](https://github.com/huggingface/lerobot/blob/main/src/lerobot/robots/so_follower/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):

@@ -51,7 +51,7 @@ In addition to these instructions, you need to install the Feetech SDK & ZeroMQ
 pip install -e ".[lekiwi]"
 ```

-Great 🤗! You are now done installing LeRobot, and we can begin assembling the SO100/SO101 arms and the mobile base 🤖.
+Great :hugs:! You are now done installing LeRobot, and we can begin assembling the SO100/SO101 arms and the mobile base :robot:.
 Every time you now want to use LeRobot, you can go to the `~/lerobot` folder where we installed LeRobot and run one of the commands.

 # Step-by-Step Assembly Instructions
@@ -174,7 +174,7 @@ The model takes images, text instructions, and robot state as input, and outputs

 ## Reproducing π₀Fast results

-We reproduce the results of π₀Fast on the LIBERO benchmark using the LeRobot implementation. We take the LeRobot PiFast base model [lerobot/pi0fast-base](https://huggingface.co/lerobot/pi0fast-base) and finetune for an additional 40k steps in bfloat16, with batch size of 256 on 8 H100 GPUs using the [HuggingFace LIBERO dataset](https://huggingface.co/datasets/HuggingFaceVLA/libero).
+We reproduce the results of π₀Fast on the LIBERO benchmark using the LeRobot implementation. We take the LeRobot PiFast base model [lerobot/pi0fast-base](https://huggingface.co/lerobot/pi0fast-base) and finetune for an additional 40kk steps in bfloat16, with batch size of 256 on 8 H100 GPUs using the [HuggingFace LIBERO dataset](https://huggingface.co/datasets/HuggingFaceVLA/libero).

 The finetuned model can be found here:

@@ -22,7 +22,7 @@ With processors, you choose the learning features you want to use for your polic
 ## Three pipelines

 We often compose three pipelines. Depending on your setup, some can be empty if action and observation spaces already match.
-Each of these pipelines handles different conversions between different action and observation spaces. Below is a quick explanation of each pipeline.
+Each of these pipelines handle different conversions between different action and observation spaces. Below is a quick explanation of each pipeline.

 1. Pipeline 1: Teleop action space → dataset action space (phone pose → EE targets)
 2. Pipeline 2: Dataset action space → robot command space (EE targets → joints)
@@ -74,15 +74,15 @@ In the phone to SO-100 follower examples we use the following adapters:
 - `robot_action_to_transition`: transforms the teleop action dict to a pipeline transition.
 - `transition_to_robot_action`: transforms the pipeline transition to a robot action dict.
 - `observation_to_transition`: transforms the robot observation dict to a pipeline transition.
- `transition_to_observation`: transforms the pipeline transition to an observation dict.
+- `transition_to_observation`: transforms the pipeline transition to a observation dict.

-Check out [src/lerobot/processor/converters.py](https://github.com/huggingface/lerobot/blob/main/src/lerobot/processor/converters.py) for more details.
+Checkout [src/lerobot/processor/converters.py](https://github.com/huggingface/lerobot/blob/main/src/lerobot/processor/converters.py) for more details.

 ## Dataset feature contracts

 Dataset features are determined by the keys saved in the dataset. Each step can declare what features it modifies in a contract called `transform_features(...)`. Once you build a processor, the processor can then aggregate all of these features with `aggregate_pipeline_dataset_features()` and merge multiple feature dicts with `combine_feature_dicts(...)`.

-Below is an example of how we declare features with the `transform_features` method in the phone to SO-100 follower examples:
+Below is and example of how we declare features with the `transform_features` method in the phone to SO-100 follower examples:

 ```python
    def transform_features(
@@ -82,8 +82,6 @@ By default the env samples objects only from the `lightwheel` registry (what `--

 All eval snippets below mirror the CI command (see `.github/workflows/benchmark_tests.yml`). The `--rename_map` argument maps RoboCasa's native camera keys (`robot0_agentview_left` / `robot0_eye_in_hand` / `robot0_agentview_right`) onto the three-camera (`camera1` / `camera2` / `camera3`) input layout the released `smolvla_robocasa` policy was trained on.

-By default, each task uses the rollout horizon registered by RoboCasa. Set `--env.episode_length=<steps>` to apply the same explicit horizon to every selected task.
-
 ### Single-task evaluation (recommended for quick iteration)

 ```bash
@@ -57,7 +57,7 @@ policy_cfg.rtc_config = RTCConfig(
 policy = PI0Policy.from_pretrained("lerobot/pi0_base", policy_cfg=policy_cfg, device="cuda")

 # Now use predict_action_chunk with RTC parameters
-inference_delay = 4  # How many steps of inference latency, this value should be calculated based on the inference latency of the policy
+inference_delay = 4  # How many steps of inference latency, this values should be calculated based on the inference latency of the policy

 # Initialize the action queue
 action_queue = ActionQueue(policy_cfg.rtc_config)
@@ -100,7 +100,7 @@ Typical values: 8-12 steps
 RTCConfig(execution_horizon=10)
 ```

-**`max_guidance_weight`**: How strongly to enforce consistency with the previous chunk. This is a hyperparameter that can be tuned to balance the smoothness of the transitions and the reactivity of the policy. For 10 steps flow matching (SmolVLA, Pi0, Pi0.5), a value of 10.0 is an optimal value.
+**`max_guidance_weight`**: How strongly to enforce consistency with the previous chunk. This is a hyperparameter that can be tuned to balance the smoothness of the transitions and the reactivity of the policy. For 10 steps flow matching (SmolVLA, Pi0, Pi0.5), a value of 10.0 is a optimal value.

 **`prefix_attention_schedule`**: How to weight consistency across the overlap region.

@@ -93,7 +93,7 @@ lerobot-train --help

 ## 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](./il_robots#record-a-dataset).
+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](./il_robots).
 Once you are logged in, you can run inference in your setup by doing:

 ```bash
@@ -338,7 +338,7 @@ It is advisable to install one 3-pin cable in the motor after placing them befor
 <hfoption id="Leader">

 - Mount the leader holder onto the wrist and secure it with 4 M3x6mm screws.
- Attach the handle to the leader holder using 1 M2x6mm screw.
+- Attach the handle to motor 5 using 1 M2x6mm screw.
 - Insert the gripper motor, secure it with 2 M2x6mm screws on each side, attach a motor horn using a M3x6mm horn screw.
 - Attach the follower trigger with 4 M3x6mm screws.

@@ -50,11 +50,11 @@ lerobot-edit-dataset \
 Divide a dataset into multiple subsets.

 ```bash
-# Split by fractions (e.g. 60% train, 20% val, 20% test)
+# Split by fractions (e.g. 80% train, 20% test, 20% val)
 lerobot-edit-dataset \
    --repo_id lerobot/pusht \
    --operation.type split \
-    --operation.splits '{"train": 0.6, "val": 0.2, "test": 0.2}'
+    --operation.splits '{"train": 0.8, "test": 0.2, "val": 0.2}'

 # Split by specific episode indices
 lerobot-edit-dataset \
@@ -44,7 +44,6 @@ from typing import Protocol

 import numpy as np

-from lerobot.lerobot_types import RobotAction, RobotObservation
 from lerobot.model.kinematics import RobotKinematics
 from lerobot.processor import (
    RobotProcessorPipeline,
@@ -57,6 +56,7 @@ from lerobot.robots.so_follower.robot_kinematic_processor import (
    EEBoundsAndSafety,
    InverseKinematicsEEToJoints,
 )
+from lerobot.types import RobotAction, RobotObservation
 from lerobot.utils.constants import HF_LEROBOT_CALIBRATION, HF_LEROBOT_HOME, TELEOPERATORS
 from lerobot.utils.robot_utils import precise_sleep

@@ -38,7 +38,7 @@ from typing import TYPE_CHECKING

 import numpy as np

-from lerobot.lerobot_types import RobotAction
+from lerobot.types import RobotAction

 from .base import _GRIPPER_MOTOR_SCALE, IsaacTeleopTeleoperator, _isaacteleop_available
 from .config_isaac_teleop import SO101LeaderArmConfig
@@ -32,7 +32,7 @@ from typing import TYPE_CHECKING, Any

 import numpy as np

-from lerobot.lerobot_types import RobotAction
+from lerobot.types import RobotAction

 from .base import IsaacTeleopTeleoperator, _isaacteleop_available
 from .config_isaac_teleop import XRControllerConfig
@@ -26,8 +26,8 @@ from __future__ import annotations
 from dataclasses import dataclass

 from lerobot.configs.types import FeatureType, PipelineFeatureType, PolicyFeature
-from lerobot.lerobot_types import RobotAction
 from lerobot.processor import ProcessorStepRegistry, RobotActionProcessorStep
+from lerobot.types import RobotAction
 from lerobot.utils.rotation import Rotation

 from .base import _GRIPPER_MOTOR_SCALE
@@ -21,7 +21,6 @@ from lerobot.cameras.opencv import OpenCVCameraConfig
 from lerobot.common.control_utils import predict_action
 from lerobot.configs import FeatureType, PolicyFeature
 from lerobot.datasets import LeRobotDataset, aggregate_pipeline_dataset_features, create_initial_features
-from lerobot.lerobot_types import RobotAction, RobotObservation
 from lerobot.model.kinematics import RobotKinematics
 from lerobot.policies import make_pre_post_processors
 from lerobot.policies.act import ACTPolicy
@@ -39,6 +38,7 @@ from lerobot.robots.so_follower.robot_kinematic_processor import (
    ForwardKinematicsJointsToEE,
    InverseKinematicsEEToJoints,
 )
+from lerobot.types import RobotAction, RobotObservation
 from lerobot.utils.constants import ACTION, OBS_STR
 from lerobot.utils.feature_utils import build_dataset_frame, combine_feature_dicts
 from lerobot.utils.keyboard_input import init_keyboard_listener
@@ -16,7 +16,6 @@

 from lerobot.cameras.opencv import OpenCVCameraConfig
 from lerobot.datasets import LeRobotDataset, aggregate_pipeline_dataset_features, create_initial_features
-from lerobot.lerobot_types import RobotAction, RobotObservation
 from lerobot.model.kinematics import RobotKinematics
 from lerobot.processor import (
    RobotProcessorPipeline,
@@ -37,6 +36,7 @@ from lerobot.scripts.lerobot_record import record_loop
 from lerobot.teleoperators.phone import Phone, PhoneConfig
 from lerobot.teleoperators.phone.config_phone import PhoneOS
 from lerobot.teleoperators.phone.phone_processor import MapPhoneActionToRobotAction
+from lerobot.types import RobotAction, RobotObservation
 from lerobot.utils.feature_utils import combine_feature_dicts
 from lerobot.utils.keyboard_input import init_keyboard_listener
 from lerobot.utils.utils import log_say
@@ -17,7 +17,6 @@
 import time

 from lerobot.datasets import LeRobotDataset
-from lerobot.lerobot_types import RobotAction, RobotObservation
 from lerobot.model.kinematics import RobotKinematics
 from lerobot.processor import (
    RobotProcessorPipeline,
@@ -28,6 +27,7 @@ from lerobot.robots.so_follower import SO100Follower, SO100FollowerConfig
 from lerobot.robots.so_follower.robot_kinematic_processor import (
    InverseKinematicsEEToJoints,
 )
+from lerobot.types import RobotAction, RobotObservation
 from lerobot.utils.constants import ACTION
 from lerobot.utils.robot_utils import precise_sleep
 from lerobot.utils.utils import log_say
@@ -27,7 +27,6 @@ Highlight, or DAgger via ``lerobot-rollout --strategy.type=...``.

 from lerobot.cameras.opencv import OpenCVCameraConfig
 from lerobot.configs import PreTrainedConfig
-from lerobot.lerobot_types import RobotAction, RobotObservation
 from lerobot.model.kinematics import RobotKinematics
 from lerobot.processor import (
    RobotProcessorPipeline,
@@ -44,6 +43,7 @@ from lerobot.robots.so_follower.robot_kinematic_processor import (
 from lerobot.rollout import BaseStrategyConfig, RolloutConfig, build_rollout_context
 from lerobot.rollout.inference import SyncInferenceConfig
 from lerobot.rollout.strategies import BaseStrategy
+from lerobot.types import RobotAction, RobotObservation
 from lerobot.utils.process import ProcessSignalHandler
 from lerobot.utils.utils import init_logging

@@ -15,7 +15,6 @@

 import time

-from lerobot.lerobot_types import RobotAction, RobotObservation
 from lerobot.model.kinematics import RobotKinematics
 from lerobot.processor import (
    RobotProcessorPipeline,
@@ -32,6 +31,7 @@ from lerobot.robots.so_follower.robot_kinematic_processor import (
 from lerobot.teleoperators.phone import Phone, PhoneConfig
 from lerobot.teleoperators.phone.config_phone import PhoneOS
 from lerobot.teleoperators.phone.phone_processor import MapPhoneActionToRobotAction
+from lerobot.types import RobotAction, RobotObservation
 from lerobot.utils.robot_utils import precise_sleep
 from lerobot.utils.visualization_utils import init_rerun, log_rerun_data

@@ -21,7 +21,6 @@ from lerobot.cameras.opencv import OpenCVCameraConfig
 from lerobot.common.control_utils import predict_action
 from lerobot.configs import FeatureType, PolicyFeature
 from lerobot.datasets import LeRobotDataset, aggregate_pipeline_dataset_features, create_initial_features
-from lerobot.lerobot_types import RobotAction, RobotObservation
 from lerobot.model.kinematics import RobotKinematics
 from lerobot.policies import make_pre_post_processors
 from lerobot.policies.act import ACTPolicy
@@ -39,6 +38,7 @@ from lerobot.robots.so_follower.robot_kinematic_processor import (
    ForwardKinematicsJointsToEE,
    InverseKinematicsEEToJoints,
 )
+from lerobot.types import RobotAction, RobotObservation
 from lerobot.utils.constants import ACTION, OBS_STR
 from lerobot.utils.feature_utils import build_dataset_frame, combine_feature_dicts
 from lerobot.utils.keyboard_input import init_keyboard_listener
@@ -17,7 +17,6 @@

 from lerobot.cameras.opencv import OpenCVCameraConfig
 from lerobot.datasets import LeRobotDataset, aggregate_pipeline_dataset_features, create_initial_features
-from lerobot.lerobot_types import RobotAction, RobotObservation
 from lerobot.model.kinematics import RobotKinematics
 from lerobot.processor import (
    RobotProcessorPipeline,
@@ -34,6 +33,7 @@ from lerobot.robots.so_follower.robot_kinematic_processor import (
 )
 from lerobot.scripts.lerobot_record import record_loop
 from lerobot.teleoperators.so_leader import SO100Leader, SO100LeaderConfig
+from lerobot.types import RobotAction, RobotObservation
 from lerobot.utils.feature_utils import combine_feature_dicts
 from lerobot.utils.keyboard_input import init_keyboard_listener
 from lerobot.utils.utils import log_say
@@ -18,7 +18,6 @@
 import time

 from lerobot.datasets import LeRobotDataset
-from lerobot.lerobot_types import RobotAction, RobotObservation
 from lerobot.model.kinematics import RobotKinematics
 from lerobot.processor import (
    RobotProcessorPipeline,
@@ -29,6 +28,7 @@ from lerobot.robots.so_follower import SO100Follower, SO100FollowerConfig
 from lerobot.robots.so_follower.robot_kinematic_processor import (
    InverseKinematicsEEToJoints,
 )
+from lerobot.types import RobotAction, RobotObservation
 from lerobot.utils.constants import ACTION
 from lerobot.utils.robot_utils import precise_sleep
 from lerobot.utils.utils import log_say
@@ -25,7 +25,6 @@ forward/inverse kinematics.

 from lerobot.cameras.opencv import OpenCVCameraConfig
 from lerobot.configs import PreTrainedConfig
-from lerobot.lerobot_types import RobotAction, RobotObservation
 from lerobot.model.kinematics import RobotKinematics
 from lerobot.processor import (
    RobotProcessorPipeline,
@@ -42,6 +41,7 @@ from lerobot.robots.so_follower.robot_kinematic_processor import (
 from lerobot.rollout import BaseStrategyConfig, RolloutConfig, build_rollout_context
 from lerobot.rollout.inference import SyncInferenceConfig
 from lerobot.rollout.strategies import BaseStrategy
+from lerobot.types import RobotAction, RobotObservation
 from lerobot.utils.process import ProcessSignalHandler
 from lerobot.utils.utils import init_logging

@@ -16,7 +16,6 @@

 import time

-from lerobot.lerobot_types import RobotAction, RobotObservation
 from lerobot.model.kinematics import RobotKinematics
 from lerobot.processor import (
    RobotProcessorPipeline,
@@ -31,6 +30,7 @@ from lerobot.robots.so_follower.robot_kinematic_processor import (
    InverseKinematicsEEToJoints,
 )
 from lerobot.teleoperators.so_leader import SO100Leader, SO100LeaderConfig
+from lerobot.types import RobotAction, RobotObservation
 from lerobot.utils.robot_utils import precise_sleep
 from lerobot.utils.visualization_utils import init_rerun, log_rerun_data

@@ -67,7 +67,7 @@ dependencies = [
    "einops>=0.8.0,<0.9.0",

    # Config & Hub
-    "draccus>=0.11.6,<0.12.0",
+    "draccus==0.10.0", # TODO: Relax version constraint
    "huggingface-hub>=1.0.0,<2.0.0",
    "requests>=2.32.0,<3.0.0",

@@ -494,19 +494,6 @@ ignore_errors = true
 module = "lerobot.envs.*"
 ignore_errors = false

-[[tool.mypy.overrides]]
-module = "lerobot.annotations.*"
-ignore_errors = false
-disallow_untyped_defs = true
-disallow_incomplete_defs = true
-check_untyped_defs = true
-
-[[tool.mypy.overrides]]
-module = "lerobot.transforms.*"
-ignore_errors = false
-disallow_untyped_defs = true
-disallow_incomplete_defs = true
-check_untyped_defs = true

 # [[tool.mypy.overrides]]
 # module = "lerobot.utils.*"
@@ -38,7 +38,6 @@ import draccus
 import grpc
 import torch

-from lerobot.lerobot_types import PolicyAction
 from lerobot.policies import get_policy_class, make_pre_post_processors
 from lerobot.processor import PolicyProcessorPipeline
 from lerobot.transport import (
@@ -46,6 +45,7 @@ from lerobot.transport import (
    services_pb2_grpc,  # type: ignore
 )
 from lerobot.transport.utils import receive_bytes_in_chunks
+from lerobot.types import PolicyAction

 from .configs import PolicyServerConfig
 from .constants import SUPPORTED_POLICIES
@@ -365,12 +365,11 @@ class RealSenseCamera(Camera):
        return self._async_read(timeout_ms=10000, read_depth=read_depth)

    def _get_color_sensor(self) -> "rs.sensor":
-        """Returns the dedicated "RGB Camera" sensor that controls the color stream.
+        """Returns the sensor that controls the color stream.

-        Manual color controls are only applied to a dedicated RGB module. Cameras
-        without one (e.g. the D405, whose color stream comes from the shared
-        "Stereo Module") are unsupported, so we never fall back to another sensor
-        to avoid altering the depth stream.
+        Most RealSense cameras expose "RGB Camera" for color. The D405 has no
+        separate RGB module — its color stream comes from "Stereo Module".
+        We try RGB Camera first, then fall back to Stereo Module.
        """
        if self.rs_profile is None:
            raise RuntimeError(f"{self}: rs_profile must be initialized before use.")
@@ -378,14 +377,12 @@ class RealSenseCamera(Camera):
        device = self.rs_profile.get_device()
        sensors = {s.get_info(rs.camera_info.name): s for s in device.query_sensors()}

-        if "RGB Camera" in sensors:
-            return sensors["RGB Camera"]
+        for name in ("RGB Camera", "Stereo Module"):
+            if name in sensors:
+                return sensors[name]

        available = list(sensors.keys())
-        raise RuntimeError(
-            f"{self}: manual color controls require a dedicated 'RGB Camera' module, which this camera does not have. ",
-            f"Available sensors: {available}.",
-        )
+        raise RuntimeError(f"{self}: no color sensor found. Available sensors: {available}")

    def _set_sensor_option(self, sensor: "rs.sensor", option: "rs.option", value: float, label: str) -> None:
        """Sets a sensor option, re-raising range errors with actionable diagnostics."""
@@ -35,9 +35,9 @@ else:

 if TYPE_CHECKING:
    from lerobot.datasets import LeRobotDataset
-from lerobot.lerobot_types import PolicyAction
 from lerobot.processor import PolicyProcessorPipeline
 from lerobot.robots import Robot
+from lerobot.types import PolicyAction


 def predict_action(
@@ -163,10 +163,8 @@ class PreTrainedConfig(draccus.ChoiceRegistry, HubMixin, abc.ABC):  # type: igno
        return None

    def _save_pretrained(self, save_directory: Path) -> None:
-        # Encode against the base class so draccus includes the choice "type" key,
-        # which `from_pretrained` needs to resolve the concrete subclass.
-        with open(save_directory / CONFIG_NAME, "w") as f:
-            json.dump(draccus.encode(self, PreTrainedConfig), f, indent=4)
+        with open(save_directory / CONFIG_NAME, "w") as f, draccus.config_type("json"):
+            draccus.dump(self, f, indent=4)

    @classmethod
    def from_pretrained(
@@ -103,10 +103,8 @@ class RewardModelConfig(draccus.ChoiceRegistry, HubMixin, abc.ABC):
        pass

    def _save_pretrained(self, save_directory: Path) -> None:
-        # Encode against the base class so draccus includes the choice "type" key,
-        # which `from_pretrained` needs to resolve the concrete subclass.
-        with open(save_directory / CONFIG_NAME, "w") as f:
-            json.dump(draccus.encode(self, RewardModelConfig), f, indent=4)
+        with open(save_directory / CONFIG_NAME, "w") as f, draccus.config_type("json"):
+            draccus.dump(self, f, indent=4)

    @classmethod
    def from_pretrained(
@@ -194,11 +194,7 @@ class TrainPipelineConfig(HubMixin):
            )

        if Path(config_path).resolve().exists():
-            # `config_path` may point at the checkpoint's train_config.json or at its
-            # pretrained_model/ directory (both documented above) — resolve either to
-            # the pretrained_model/ directory.
-            config_path_obj = Path(config_path)
-            policy_dir = config_path_obj.parent if config_path_obj.is_file() else config_path_obj
+            policy_dir = Path(config_path).parent
            self.checkpoint_path = policy_dir.parent
        elif self.job.is_remote:
            return
@@ -19,7 +19,6 @@ import copy
 import logging
 import shutil
 from pathlib import Path
-from typing import Any, NotRequired, TypedDict

 import datasets
 import pandas as pd
@@ -50,32 +49,8 @@ from .utils import (
 )
 from .video_utils import concatenate_video_files, get_video_duration_in_s

-logger = logging.getLogger(__name__)

-type FeatureDict = dict[str, dict[str, Any]]
-type ChunkFile = tuple[int, int]
-
-
-class IndexState(TypedDict):
-    chunk: int
-    file: int
-    src_to_dst: NotRequired[dict[ChunkFile, ChunkFile]]
-
-
-class VideoIndex(TypedDict):
-    chunk: int
-    file: int
-    latest_duration: float
-    episode_duration: float
-    src_to_offset: NotRequired[dict[ChunkFile, float]]
-    src_to_dst: NotRequired[dict[ChunkFile, ChunkFile]]
-    dst_file_durations: NotRequired[dict[ChunkFile, float]]
-
-
-type VideoIndexState = dict[str, VideoIndex]
-
-
-def merge_video_feature_info_for_aggregate(all_metadata: list[LeRobotDatasetMetadata]) -> FeatureDict:
+def merge_video_feature_info_for_aggregate(all_metadata: list[LeRobotDatasetMetadata]) -> dict[str, dict]:
    """Create a merged video feature info dictionary for aggregation. The video encoder info is merged field-by-field: each key is kept only when every source agrees; otherwise that key is set to ``null`` (or ``{}`` for ``video.extra_options``) and a warning is logged.

    Args:
@@ -84,14 +59,14 @@ def merge_video_feature_info_for_aggregate(all_metadata: list[LeRobotDatasetMeta
    Returns:
        dict: A dictionary of merged video feature info.
    """
-    merged_info: FeatureDict = copy.deepcopy(all_metadata[0].features)
+    merged_info = copy.deepcopy(all_metadata[0].features)
    video_keys = [k for k in merged_info if merged_info[k].get("dtype") == "video"]

    for vk in video_keys:
        video_infos = [m.features.get(vk, {}).get("info") or {} for m in all_metadata]
        base_video_info = video_infos[0]

-        merged_encoder_info: dict[str, Any] = {}
+        merged_encoder_info: dict = {}
        fallback_keys: list[str] = []
        for info_key in VIDEO_ENCODER_INFO_KEYS:
            values = [info.get(info_key, None) for info in video_infos]
@@ -105,7 +80,7 @@ def merge_video_feature_info_for_aggregate(all_metadata: list[LeRobotDatasetMeta
                merged_encoder_info[info_key] = {} if info_key == "video.extra_options" else None

        if fallback_keys:
-            logger.warning(
+            logging.warning(
                f"Merging heterogeneous or incomplete video encoder metadata for feature {vk}. "
                f"Setting these keys to null: {fallback_keys}.",
            )
@@ -117,7 +92,7 @@ def merge_video_feature_info_for_aggregate(all_metadata: list[LeRobotDatasetMeta
    return merged_info


-def validate_all_metadata(all_metadata: list[LeRobotDatasetMetadata]) -> tuple[int, str | None, FeatureDict]:
+def validate_all_metadata(all_metadata: list[LeRobotDatasetMetadata]):
    """Validates that all dataset metadata have consistent properties.

    Ensures all datasets have the same fps, robot_type, and features to guarantee
@@ -154,9 +129,7 @@ def validate_all_metadata(all_metadata: list[LeRobotDatasetMetadata]) -> tuple[i
    return fps, robot_type, features


-def update_data_df(
-    df: pd.DataFrame, src_meta: LeRobotDatasetMetadata, dst_meta: LeRobotDatasetMetadata
-) -> pd.DataFrame:
+def update_data_df(df, src_meta, dst_meta):
    """Updates a data DataFrame with new indices and task mappings for aggregation.

    Adjusts episode indices, frame indices, and task indices to account for
@@ -181,12 +154,12 @@ def update_data_df(


 def update_meta_data(
-    df: pd.DataFrame,
-    dst_meta: LeRobotDatasetMetadata,
-    meta_idx: IndexState,
-    data_idx: IndexState,
-    videos_idx: VideoIndexState,
-) -> pd.DataFrame:
+    df,
+    dst_meta,
+    meta_idx,
+    data_idx,
+    videos_idx,
+):
    """Updates metadata DataFrame with new chunk, file, and timestamp indices.

    Adjusts all indices and timestamps to account for previously aggregated
@@ -316,7 +289,7 @@ def aggregate_datasets(
    chunk_size: int | None = None,
    concatenate_videos: bool = True,
    concatenate_data: bool = True,
-) -> None:
+):
    """Aggregates multiple LeRobot datasets into a single unified dataset.

    This is the main function that orchestrates the aggregation process by:
@@ -336,7 +309,7 @@ def aggregate_datasets(
        concatenate_videos: When False, keep one mp4 per source file instead of packing into shards.
        concatenate_data: When False, keep one parquet per source file instead of packing into shards.
    """
-    logger.info("Start aggregate_datasets")
+    logging.info("Start aggregate_datasets")

    if data_files_size_in_mb is None:
        data_files_size_in_mb = DEFAULT_DATA_FILE_SIZE_IN_MB
@@ -368,15 +341,15 @@ def aggregate_datasets(
        video_files_size_in_mb=video_files_size_in_mb,
    )

-    logger.info("Find all tasks")
+    logging.info("Find all tasks")
    unique_tasks = pd.concat([m.tasks for m in all_metadata]).index.unique()
    dst_meta.tasks = pd.DataFrame(
        {"task_index": range(len(unique_tasks))}, index=pd.Index(unique_tasks, name="task")
    )

-    meta_idx: IndexState = {"chunk": 0, "file": 0}
-    data_idx: IndexState = {"chunk": 0, "file": 0}
-    videos_idx: VideoIndexState = {
+    meta_idx = {"chunk": 0, "file": 0}
+    data_idx = {"chunk": 0, "file": 0}
+    videos_idx = {
        key: {"chunk": 0, "file": 0, "latest_duration": 0, "episode_duration": 0} for key in video_keys
    }

@@ -400,17 +373,12 @@ def aggregate_datasets(
        dst_meta.info.total_frames += src_meta.total_frames

    finalize_aggregation(dst_meta, all_metadata)
-    logger.info("Aggregation complete.")
+    logging.info("Aggregation complete.")


 def aggregate_videos(
-    src_meta: LeRobotDatasetMetadata,
-    dst_meta: LeRobotDatasetMetadata,
-    videos_idx: VideoIndexState,
-    video_files_size_in_mb: float,
-    chunk_size: int,
-    concatenate_videos: bool = True,
-) -> VideoIndexState:
+    src_meta, dst_meta, videos_idx, video_files_size_in_mb, chunk_size, concatenate_videos=True
+):
    """Aggregates video chunks from a source dataset into the destination dataset.

    Handles video file concatenation and rotation based on file size limits.
@@ -438,16 +406,15 @@ def aggregate_videos(
            videos_idx[key]["dst_file_durations"] = {}

    for key, video_idx in videos_idx.items():
-        unique_chunk_file_pairs: list[ChunkFile] = sorted(
-            {
-                (chunk, file)
-                for chunk, file in zip(
-                    src_meta.episodes[f"videos/{key}/chunk_index"],
-                    src_meta.episodes[f"videos/{key}/file_index"],
-                    strict=False,
-                )
-            }
-        )
+        unique_chunk_file_pairs = {
+            (chunk, file)
+            for chunk, file in zip(
+                src_meta.episodes[f"videos/{key}/chunk_index"],
+                src_meta.episodes[f"videos/{key}/file_index"],
+                strict=False,
+            )
+        }
+        unique_chunk_file_pairs = sorted(unique_chunk_file_pairs)

        chunk_idx = video_idx["chunk"]
        file_idx = video_idx["file"]
@@ -522,14 +489,7 @@ def aggregate_videos(
    return videos_idx


-def aggregate_data(
-    src_meta: LeRobotDatasetMetadata,
-    dst_meta: LeRobotDatasetMetadata,
-    data_idx: IndexState,
-    data_files_size_in_mb: float,
-    chunk_size: int,
-    concatenate_data: bool = True,
-) -> IndexState:
+def aggregate_data(src_meta, dst_meta, data_idx, data_files_size_in_mb, chunk_size, concatenate_data=True):
    """Aggregates data chunks from a source dataset into the destination dataset.

    Reads source data files, updates indices to match the aggregated dataset,
@@ -550,16 +510,14 @@ def aggregate_data(
    Returns:
        dict: Updated data_idx with current chunk and file indices.
    """
-    unique_chunk_file_ids: list[ChunkFile] = sorted(
-        {
-            (c, f)
-            for c, f in zip(
-                src_meta.episodes["data/chunk_index"],
-                src_meta.episodes["data/file_index"],
-                strict=False,
-            )
-        }
-    )
+    unique_chunk_file_ids = {
+        (c, f)
+        for c, f in zip(
+            src_meta.episodes["data/chunk_index"], src_meta.episodes["data/file_index"], strict=False
+        )
+    }
+
+    unique_chunk_file_ids = sorted(unique_chunk_file_ids)
    contains_images = len(dst_meta.image_keys) > 0

    # retrieve features schema for proper image typing in parquet
@@ -567,7 +525,7 @@ def aggregate_data(

    # Track source to destination file mapping for metadata update
    # This is critical for handling datasets that are already results of a merge
-    src_to_dst: dict[ChunkFile, ChunkFile] = {}
+    src_to_dst: dict[tuple[int, int], tuple[int, int]] = {}

    for src_chunk_idx, src_file_idx in unique_chunk_file_ids:
        src_path = src_meta.root / DEFAULT_DATA_PATH.format(
@@ -606,13 +564,7 @@ def aggregate_data(
    return data_idx


-def aggregate_metadata(
-    src_meta: LeRobotDatasetMetadata,
-    dst_meta: LeRobotDatasetMetadata,
-    meta_idx: IndexState,
-    data_idx: IndexState,
-    videos_idx: VideoIndexState,
-) -> IndexState:
+def aggregate_metadata(src_meta, dst_meta, meta_idx, data_idx, videos_idx):
    """Aggregates metadata from a source dataset into the destination dataset.

    Reads source metadata files, updates all indices and timestamps,
@@ -628,16 +580,16 @@ def aggregate_metadata(
    Returns:
        dict: Updated meta_idx with current chunk and file indices.
    """
-    chunk_file_ids: list[ChunkFile] = sorted(
-        {
-            (c, f)
-            for c, f in zip(
-                src_meta.episodes["meta/episodes/chunk_index"],
-                src_meta.episodes["meta/episodes/file_index"],
-                strict=False,
-            )
-        }
-    )
+    chunk_file_ids = {
+        (c, f)
+        for c, f in zip(
+            src_meta.episodes["meta/episodes/chunk_index"],
+            src_meta.episodes["meta/episodes/file_index"],
+            strict=False,
+        )
+    }
+
+    chunk_file_ids = sorted(chunk_file_ids)
    for chunk_idx, file_idx in chunk_file_ids:
        src_path = src_meta.root / DEFAULT_EPISODES_PATH.format(chunk_index=chunk_idx, file_index=file_idx)
        df = pd.read_parquet(src_path)
@@ -670,16 +622,16 @@ def aggregate_metadata(
 def append_or_create_parquet_file(
    df: pd.DataFrame,
    src_path: Path,
-    idx: IndexState,
+    idx: dict[str, int],
    max_mb: float,
    chunk_size: int,
    default_path: str,
    contains_images: bool = False,
-    aggr_root: Path | None = None,
+    aggr_root: Path = None,
    hf_features: datasets.Features | None = None,
    concatenate: bool = True,
    one_row_group_per_episode: bool = False,
-) -> tuple[IndexState, ChunkFile]:
+) -> tuple[dict[str, int], tuple[int, int]]:
    """Appends data to an existing parquet file or creates a new one based on size constraints.

    Manages file rotation when size limits are exceeded to prevent individual files
@@ -702,13 +654,7 @@ def append_or_create_parquet_file(
    Returns:
        tuple: (updated_idx, (dst_chunk, dst_file)) where updated_idx is the index dict
               and (dst_chunk, dst_file) is the actual destination file the data was written to.
-
-    Raises:
-        ValueError: If aggr_root is not provided.
    """
-    if aggr_root is None:
-        raise ValueError("aggr_root must be provided.")
-
    dst_chunk, dst_file = idx["chunk"], idx["file"]
    dst_path = aggr_root / default_path.format(chunk_index=dst_chunk, file_index=dst_file)

@@ -752,9 +698,7 @@ def append_or_create_parquet_file(
    return idx, (dst_chunk, dst_file)


-def finalize_aggregation(
-    aggr_meta: LeRobotDatasetMetadata, all_metadata: list[LeRobotDatasetMetadata]
-) -> None:
+def finalize_aggregation(aggr_meta, all_metadata):
    """Finalizes the dataset aggregation by writing summary files and statistics.

    Writes the tasks file, info file with total counts and splits, and
@@ -764,16 +708,16 @@ def finalize_aggregation(
        aggr_meta: Aggregated dataset metadata.
        all_metadata: List of all source dataset metadata objects.
    """
-    logger.info("write tasks")
+    logging.info("write tasks")
    write_tasks(aggr_meta.tasks, aggr_meta.root)

-    logger.info("write info")
+    logging.info("write info")
    aggr_meta.info.total_tasks = len(aggr_meta.tasks)
    aggr_meta.info.total_episodes = sum(m.total_episodes for m in all_metadata)
    aggr_meta.info.total_frames = sum(m.total_frames for m in all_metadata)
    aggr_meta.info.splits = {"train": f"0:{sum(m.total_episodes for m in all_metadata)}"}
    write_info(aggr_meta.info, aggr_meta.root)

-    logger.info("write stats")
+    logging.info("write stats")
    aggr_meta.stats = aggregate_stats([m.stats for m in all_metadata])
    write_stats(aggr_meta.stats, aggr_meta.root)
@@ -188,8 +188,8 @@ class LeRobotDatasetMetadata:
    def _load_metadata(self):
        self.info = load_info(self.root)
        check_version_compatibility(self.repo_id, self._version, CODEBASE_VERSION)
-        self.tasks = load_tasks(self.root) if self.total_tasks > 0 else None
-        self.episodes = load_episodes(self.root) if self.total_episodes > 0 else None
+        self.tasks = load_tasks(self.root)
+        self.episodes = load_episodes(self.root)
        self.stats = load_stats(self.root)

    def ensure_readable(self) -> None:
@@ -1045,12 +1045,10 @@ def _copy_data_with_feature_changes(
                    df[feature_name] = feature_values
                else:
                    feature_slice = values[frame_idx:end_idx]
-                    if feature_slice.ndim == 1:
-                        df[feature_name] = feature_slice
-                    elif feature_slice.ndim == 2 and feature_slice.shape[1] == 1:
+                    if len(feature_slice.shape) > 1 and feature_slice.shape[1] == 1:
                        df[feature_name] = feature_slice.flatten()
                    else:
-                        df[feature_name] = list(feature_slice)
+                        df[feature_name] = feature_slice
            frame_idx = end_idx

        # Write using the same chunk/file structure as source
@@ -17,8 +17,8 @@ from collections.abc import Sequence
 from typing import Any

 from lerobot.configs import PipelineFeatureType
-from lerobot.lerobot_types import RobotAction, RobotObservation
 from lerobot.processor import DataProcessorPipeline
+from lerobot.types import RobotAction, RobotObservation
 from lerobot.utils.constants import ACTION, OBS_IMAGES, OBS_STATE, OBS_STR
 from lerobot.utils.feature_utils import hw_to_dataset_features

@@ -58,10 +58,6 @@ class LookAheadError(Exception):
    pass


-class _ShardExhaustedError(Exception):
-    """Raised when a streaming dataset shard has no more items."""
-
-
 class Backtrackable[T]:
    """
    Wrap any iterator/iterable so you can step back up to `history` items
@@ -182,7 +178,7 @@ class Backtrackable[T]:
        """
        Check if we can go back `steps` items without raising an IndexError.
        """
-        return steps < len(self._back_buf) + self._cursor
+        return steps <= len(self._back_buf) + self._cursor

    def can_peek_ahead(self, steps: int = 1) -> bool:
        """
@@ -426,7 +422,10 @@ class StreamingLeRobotDataset(torch.utils.data.IterableDataset):
                    else:
                        frames_buffer.append(frame)
                    break  # random shard sampled, switch shard
-            except _ShardExhaustedError:
+            except (
+                RuntimeError,
+                StopIteration,
+            ):  # NOTE: StopIteration inside a generator throws a RuntimeError since python 3.7
                del idx_to_backtrack_dataset[shard_key]  # Remove exhausted shard, onto another shard

        # Once shards are all exhausted, shuffle the buffer and yield the remaining frames
@@ -504,11 +503,7 @@ class StreamingLeRobotDataset(torch.utils.data.IterableDataset):

    def make_frame(self, dataset_iterator: Backtrackable) -> Generator:
        """Makes a frame starting from a dataset iterator"""
-        try:
-            item = next(dataset_iterator)
-        except StopIteration as e:
-            # Translate exhaustion here, before PEP 479 turns it into an indistinguishable RuntimeError.
-            raise _ShardExhaustedError from e
+        item = next(dataset_iterator)
        item = item_to_torch(item)

        updates = []  # list of "updates" to apply to the item retrieved from hf_dataset (w/o camera features)
@@ -507,7 +507,7 @@ class MetaworldEnv(EnvConfig):
 class RoboCasaEnv(EnvConfig):
    task: str = "CloseFridge"
    fps: int = 20
-    episode_length: int | None = None
+    episode_length: int = 1000
    obs_type: str = "pixels_agent_pos"
    render_mode: str = "rgb_array"
    camera_name: str = "robot0_agentview_left,robot0_eye_in_hand,robot0_agentview_right"
@@ -30,7 +30,7 @@ from gymnasium import spaces
 from libero.libero import benchmark, get_libero_path
 from libero.libero.envs import OffScreenRenderEnv

-from lerobot.lerobot_types import RobotObservation
+from lerobot.types import RobotObservation

 from .utils import _LazyAsyncVectorEnv, parse_camera_names

@@ -384,12 +384,7 @@ class LiberoEnv(gym.Env):

    def close(self):
        if self._env is not None:
-            try:
-                self._env.close()
-            finally:
-                # LIBERO deletes its inner env on close, so this wrapper must
-                # be recreated before the next reset.
-                self._env = None
+            self._env.close()


 def _make_env_fns(
@@ -25,7 +25,7 @@ import metaworld.policies as policies
 import numpy as np
 from gymnasium import spaces

-from lerobot.lerobot_types import RobotObservation
+from lerobot.types import RobotObservation

 from .utils import _LazyAsyncVectorEnv

@@ -25,7 +25,7 @@ import gymnasium as gym
 import numpy as np
 from gymnasium import spaces

-from lerobot.lerobot_types import RobotObservation
+from lerobot.types import RobotObservation

 from .utils import _LazyAsyncVectorEnv, parse_camera_names

@@ -98,19 +98,6 @@ def _resolve_tasks(task: str) -> tuple[list[str], str | None]:
    return names, None


-def _get_task_horizon(task: str) -> int:
-    """Return the rollout horizon registered by RoboCasa for a task."""
-    from robocasa.utils.dataset_registry_utils import get_task_horizon
-
-    try:
-        return int(get_task_horizon(task))
-    except ValueError as exc:
-        raise ValueError(
-            f"No RoboCasa horizon is registered for task '{task}'. "
-            "Set `--env.episode_length=<steps>` explicitly."
-        ) from exc
-
-
 def convert_action(flat_action: np.ndarray) -> dict[str, Any]:
    """Split a flat (12,) action vector into a RoboCasa action dict.

@@ -167,7 +154,7 @@ class RoboCasaEnv(gym.Env):

        self.camera_name = parse_camera_names(camera_name)

-        self._max_episode_steps = episode_length if episode_length is not None else _get_task_horizon(task)
+        self._max_episode_steps = episode_length if episode_length is not None else 1000

        # Deferred — created on first reset() inside the worker subprocess
        # to avoid inheriting stale GPU/EGL contexts across fork().
@@ -28,7 +28,7 @@ import numpy as np
 import torch
 from gymnasium import spaces

-from lerobot.lerobot_types import RobotObservation
+from lerobot.types import RobotObservation
 from lerobot.utils.import_utils import _scipy_available

 from .utils import _LazyAsyncVectorEnv
@@ -384,9 +384,7 @@ class RoboTwinEnv(gym.Env):

        self._env: Any | None = None  # deferred — created on first reset() inside worker
        self._step_count: int = 0
-        self._black_frame: np.ndarray = np.zeros(
-            (self.observation_height, self.observation_width, 3), dtype=np.uint8
-        )
+        self._black_frame = np.zeros((self.observation_height, self.observation_width, 3), dtype=np.uint8)

        image_spaces = {
            cam: spaces.Box(
@@ -37,7 +37,7 @@ import numpy as np
 from gymnasium import spaces
 from scipy.spatial.transform import Rotation

-from lerobot.lerobot_types import RobotObservation
+from lerobot.types import RobotObservation

 from .utils import _LazyAsyncVectorEnv

@@ -373,7 +373,7 @@ class VLABenchEnv(gym.Env):

        if action.shape[0] != 7:
            # Unknown layout — fall back to zero-pad so the sim doesn't crash.
-            padded: np.ndarray = np.zeros(ctrl_dim, dtype=np.float64)
+            padded = np.zeros(ctrl_dim, dtype=np.float64)
            padded[: min(action.shape[0], ctrl_dim)] = action[:ctrl_dim]
            return padded

@@ -22,7 +22,6 @@ from typing import TYPE_CHECKING, Any
 import torch

 from lerobot.configs.types import FeatureType, PipelineFeatureType, PolicyFeature
-from lerobot.lerobot_types import TransitionKey
 from lerobot.processor import (
    ComplementaryDataProcessorStep,
    PolicyAction,
@@ -32,6 +31,7 @@ from lerobot.processor import (
    make_default_policy_processor_steps,
    make_policy_processor_pipelines,
 )
+from lerobot.types import TransitionKey
 from lerobot.utils.constants import OBS_STATE
 from lerobot.utils.import_utils import _transformers_available, require_package

@@ -42,9 +42,6 @@ class Evo1Policy(PreTrainedPolicy):
    config_class = Evo1Config
    name = "evo1"

-    def supports_rtc(self) -> bool:
-        return True
-
    def __init__(self, config: Evo1Config, *, vlm_hub_kwargs: dict | None = None, **kwargs):
        super().__init__(config)
        config.validate_features()
@@ -21,7 +21,6 @@ from typing import Any
 import torch

 from lerobot.configs import FeatureType, PipelineFeatureType, PolicyFeature
-from lerobot.lerobot_types import EnvTransition, TransitionKey
 from lerobot.processor import (
    AddBatchDimensionProcessorStep,
    DeviceProcessorStep,
@@ -41,6 +40,7 @@ from lerobot.processor.converters import (
    policy_action_to_transition,
    transition_to_policy_action,
 )
+from lerobot.types import EnvTransition, TransitionKey
 from lerobot.utils.constants import (
    ACTION,
    DONE,
@@ -302,33 +302,6 @@ def _pad_evo1_stats(
    return padded_stats


-def _refresh_evo1_normalization_steps(
-    config: Evo1Config,
-    preprocessor: PolicyProcessorPipeline,
-    postprocessor: PolicyProcessorPipeline,
-) -> None:
-    """Re-pad checkpoint-loaded (un)normalizer stats/features to EVO1's fixed widths.
-
-    Loading a checkpoint injects the raw dataset stats (unpadded to max_state_dim/max_action_dim)
-    into the (un)normalizer via the generic override path in make_pre_post_processors. Those stats
-    and their declared features must be re-padded/reshaped to EVO1's fixed widths, otherwise
-    normalization fails against the padded state/action tensors (e.g. state padded to 24 vs. 8-dim
-    LIBERO stats). Padding is a no-op when stats are already at the target width.
-    """
-    normalization_features = _evo1_normalization_features(config)
-    action_features = _evo1_action_features(config)
-    for step in preprocessor.steps:
-        if isinstance(step, NormalizerProcessorStep):
-            step.features = normalization_features
-            step.stats = _pad_evo1_stats(config, step.stats)
-            step.to(device=step.device, dtype=step.dtype)
-    for step in postprocessor.steps:
-        if isinstance(step, UnnormalizerProcessorStep):
-            step.features = action_features
-            step.stats = _pad_evo1_stats(config, step.stats)
-            step.to(device=step.device, dtype=step.dtype)
-
-
 def reconcile_evo1_processors(
    config: Evo1Config,
    preprocessor: PolicyProcessorPipeline,
@@ -336,19 +309,16 @@ def reconcile_evo1_processors(
 ) -> tuple[PolicyProcessorPipeline, PolicyProcessorPipeline]:
    """Reconcile checkpoint-loaded pipelines with the current EVO1 config.

-    Three things cannot be restored from a serialized pipeline alone: the EVO1 batch converter
-    (converters are plain functions and are never serialized), eval-time CLI overrides of the
-    action postprocessing flags (`postprocess_action_dim`, `binarize_gripper`, `gripper_*`), and the
-    (un)normalizer stats/features when the generic override path injects raw, unpadded dataset
-    stats. This restores the converter, re-pads the normalization stats to EVO1's fixed widths, and
-    rebuilds the action step from the current config so those overrides take effect.
+    Two things cannot be restored from a serialized pipeline alone: the EVO1 batch converter
+    (converters are plain functions and are never serialized), and eval-time CLI overrides of the
+    action postprocessing flags (`postprocess_action_dim`, `binarize_gripper`, `gripper_*`). This
+    restores the converter and rebuilds the action step from the current config so those overrides
+    take effect.
    """
    # Pipelines reloaded from a checkpoint come back with the default batch converter, which drops
    # non-observation extras (embodiment_id, state_mask, custom task fields) needed by EVO1.
    preprocessor.to_transition = evo1_batch_to_transition

-    _refresh_evo1_normalization_steps(config, preprocessor, postprocessor)
-
    action_step = Evo1ActionProcessorStep(
        action_dim=_evo1_action_dim(config),
        binarize_gripper=config.binarize_gripper,
@@ -28,7 +28,6 @@ if TYPE_CHECKING:

 from lerobot.configs import FeatureType, PreTrainedConfig
 from lerobot.envs import EnvConfig, env_to_policy_features
-from lerobot.lerobot_types import PolicyAction
 from lerobot.processor import (
    AbsoluteActionsProcessorStep,
    PolicyProcessorPipeline,
@@ -38,6 +37,7 @@ from lerobot.processor import (
    transition_to_batch,
    transition_to_policy_action,
 )
+from lerobot.types import PolicyAction
 from lerobot.utils.constants import (
    ACTION,
    POLICY_POSTPROCESSOR_DEFAULT_NAME,
@@ -68,9 +68,6 @@ class GrootPolicy(PreTrainedPolicy):
    name = "groot"
    config_class = GrootConfig

-    def supports_rtc(self) -> bool:
-        return True
-
    def __init__(self, config: GrootConfig, **kwargs):
        """Initialize Groot policy wrapper."""
        require_package("transformers", extra="groot")
@@ -50,7 +50,6 @@ if TYPE_CHECKING or _datasets_available:
 else:
    LeRobotDataset = None

-from lerobot.lerobot_types import EnvTransition, TransitionKey
 from lerobot.processor import (
    AbsoluteActionsProcessorStep,
    AddBatchDimensionProcessorStep,
@@ -67,6 +66,7 @@ from lerobot.processor import (
    transition_to_batch,
    transition_to_policy_action,
 )
+from lerobot.types import EnvTransition, TransitionKey
 from lerobot.utils.constants import (
    ACTION,
    OBS_IMAGE,
@@ -520,9 +520,6 @@ class MolmoAct2Policy(PreTrainedPolicy):
    config_class = MolmoAct2Config
    name = "molmoact2"

-    def supports_rtc(self) -> bool:
-        return self.config.inference_action_mode == "continuous"
-
    def __init__(
        self,
        config: MolmoAct2Config,
@@ -36,7 +36,6 @@ import torch
 from torch import Tensor

 from lerobot.configs import FeatureType, PipelineFeatureType, PolicyFeature
-from lerobot.lerobot_types import EnvTransition, TransitionKey
 from lerobot.processor import (
    AddBatchDimensionProcessorStep,
    DeviceProcessorStep,
@@ -50,6 +49,7 @@ from lerobot.processor import (
    policy_action_to_transition,
    transition_to_policy_action,
 )
+from lerobot.types import EnvTransition, TransitionKey
 from lerobot.utils.constants import (
    ACTION,
    OBS_IMAGES,
@@ -749,9 +749,6 @@ class PI0Policy(PreTrainedPolicy):
    config_class = PI0Config
    name = "pi0"

-    def supports_rtc(self) -> bool:
-        return True
-
    def __init__(
        self,
        config: PI0Config,
@@ -714,9 +714,6 @@ class PI05Policy(PreTrainedPolicy):
    config_class = PI05Config
    name = "pi05"

-    def supports_rtc(self) -> bool:
-        return True
-
    def __init__(
        self,
        config: PI05Config,
@@ -22,7 +22,6 @@ import numpy as np
 import torch

 from lerobot.configs import PipelineFeatureType, PolicyFeature
-from lerobot.lerobot_types import EnvTransition, TransitionKey
 from lerobot.processor import (
    AbsoluteActionsProcessorStep,
    PolicyAction,
@@ -34,6 +33,7 @@ from lerobot.processor import (
    make_default_policy_processor_steps,
    make_policy_processor_pipelines,
 )
+from lerobot.types import EnvTransition, TransitionKey
 from lerobot.utils.constants import OBS_STATE

 from .configuration_pi05 import PI05Config
@@ -22,7 +22,6 @@ import numpy as np
 import torch

 from lerobot.configs import PipelineFeatureType, PolicyFeature
-from lerobot.lerobot_types import EnvTransition, TransitionKey
 from lerobot.processor import (
    AbsoluteActionsProcessorStep,
    ActionTokenizerProcessorStep,
@@ -35,6 +34,7 @@ from lerobot.processor import (
    make_default_policy_processor_steps,
    make_policy_processor_pipelines,
 )
+from lerobot.types import EnvTransition, TransitionKey
 from lerobot.utils.constants import OBS_STATE

 from .configuration_pi0_fast import PI0FastConfig
@@ -249,10 +249,6 @@ class PreTrainedPolicy(nn.Module, HubMixin, abc.ABC):
        """
        raise NotImplementedError

-    def supports_rtc(self) -> bool:
-        """Whether this policy implements Real-Time Chunking inference semantics."""
-        return False
-
    # TODO(aliberts, rcadene): split into 'forward' and 'compute_loss'?
    @abc.abstractmethod
    def forward(self, batch: dict[str, Tensor]) -> tuple[Tensor, dict | None]:
@@ -145,9 +145,6 @@ class SmolVLAPolicy(PreTrainedPolicy):
    config_class = SmolVLAConfig
    name = "smolvla"

-    def supports_rtc(self) -> bool:
-        return True
-
    def __init__(
        self,
        config: SmolVLAConfig,
@@ -168,23 +168,14 @@ class SmolVLMWithExpertModel(nn.Module):
                last_layers.append(self.num_vlm_layers - 2)
            frozen_layers = [
                "lm_head",
-                "text_model.norm.weight",
+                "text_model.model.norm.weight",
            ]
            for layer in last_layers:
-                frozen_layers.append(f"text_model.layers.{layer}.")
+                frozen_layers.append(f"text_model.model.layers.{layer}.")

-            unmatched_patterns = set(frozen_layers)
            for name, params in self.vlm.named_parameters():
-                matched_patterns = [k for k in frozen_layers if k in name]
-                if matched_patterns:
+                if any(k in name for k in frozen_layers):
                    params.requires_grad = False
-                    unmatched_patterns.difference_update(matched_patterns)
-            if unmatched_patterns:
-                raise RuntimeError(
-                    "Some frozen layer patterns matched no VLM parameters, so the corresponding layers "
-                    "would silently remain trainable (parameter naming may have changed in transformers): "
-                    f"{sorted(unmatched_patterns)}"
-                )
        # To avoid unused params issue with distributed training
        for name, params in self.lm_expert.named_parameters():
            if "lm_head" in name:
@@ -22,7 +22,7 @@ import torch
 from torch import nn

 from lerobot.configs import FeatureType, PolicyFeature, PreTrainedConfig
-from lerobot.lerobot_types import PolicyAction, RobotAction, RobotObservation
+from lerobot.types import PolicyAction, RobotAction, RobotObservation
 from lerobot.utils.constants import ACTION, OBS_STR
 from lerobot.utils.feature_utils import build_dataset_frame

@@ -150,7 +150,7 @@ class XVLAModel(nn.Module):
        # Freeze or unfreeze policy transformer
        if not self.config.train_policy_transformer:
            for name, param in self.transformer.named_parameters():
-                if "soft_prompt" not in name:
+                if "soft_prompts" not in name:
                    param.requires_grad = False

        # Freeze or unfreeze soft prompts
@@ -21,7 +21,6 @@ import numpy as np
 import torch

 from lerobot.configs import PipelineFeatureType, PolicyFeature
-from lerobot.lerobot_types import EnvTransition, TransitionKey
 from lerobot.processor import (
    ObservationProcessorStep,
    PolicyAction,
@@ -32,6 +31,7 @@ from lerobot.processor import (
    make_default_policy_processor_steps,
    make_policy_processor_pipelines,
 )
+from lerobot.types import EnvTransition, TransitionKey
 from lerobot.utils.constants import (
    IMAGENET_STATS,
    OBS_IMAGES,
@@ -14,7 +14,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.

-from lerobot.lerobot_types import (
+from lerobot.types import (
    EnvAction,
    EnvTransition,
    PolicyAction,
@@ -25,7 +25,7 @@ from dataclasses import dataclass, field
 from torch import Tensor

 from lerobot.configs import PipelineFeatureType, PolicyFeature
-from lerobot.lerobot_types import EnvTransition, PolicyAction
+from lerobot.types import EnvTransition, PolicyAction
 from lerobot.utils.constants import OBS_ENV_STATE, OBS_IMAGE, OBS_IMAGES, OBS_STATE

 from .pipeline import (
@@ -23,7 +23,7 @@ from typing import Any
 import numpy as np
 import torch

-from lerobot.lerobot_types import EnvTransition, PolicyAction, RobotAction, RobotObservation, TransitionKey
+from lerobot.types import EnvTransition, PolicyAction, RobotAction, RobotObservation, TransitionKey
 from lerobot.utils.constants import ACTION, DONE, INFO, OBS_PREFIX, REWARD, TRUNCATED


@@ -17,7 +17,7 @@
 from dataclasses import dataclass

 from lerobot.configs import FeatureType, PipelineFeatureType, PolicyFeature
-from lerobot.lerobot_types import PolicyAction, RobotAction
+from lerobot.types import PolicyAction, RobotAction

 from .pipeline import ActionProcessorStep, ProcessorStepRegistry, RobotActionProcessorStep

@@ -25,7 +25,7 @@ from typing import Any
 import torch

 from lerobot.configs import PipelineFeatureType, PolicyFeature
-from lerobot.lerobot_types import EnvTransition, PolicyAction, TransitionKey
+from lerobot.types import EnvTransition, PolicyAction, TransitionKey
 from lerobot.utils.device_utils import get_safe_torch_device

 from .pipeline import ProcessorStep, ProcessorStepRegistry
@@ -20,7 +20,7 @@ from typing import Any
 import torch

 from lerobot.configs.policies import PreTrainedConfig
-from lerobot.lerobot_types import PolicyAction, RobotAction, RobotObservation
+from lerobot.types import PolicyAction, RobotAction, RobotObservation
 from lerobot.utils.constants import POLICY_POSTPROCESSOR_DEFAULT_NAME, POLICY_PREPROCESSOR_DEFAULT_NAME

 from .batch_processor import AddBatchDimensionProcessorStep
@@ -17,7 +17,7 @@
 from dataclasses import dataclass

 from lerobot.configs import PipelineFeatureType, PolicyFeature
-from lerobot.lerobot_types import EnvAction, EnvTransition, PolicyAction, TransitionKey
+from lerobot.types import EnvAction, EnvTransition, PolicyAction, TransitionKey

 from .converters import to_tensor
 from .hil_processor import TELEOP_ACTION_KEY
@@ -29,7 +29,7 @@ from lerobot.teleoperators.utils import TeleopEvents
 if TYPE_CHECKING:
    from lerobot.teleoperators.teleoperator import Teleoperator

-from lerobot.lerobot_types import EnvTransition, PolicyAction, TransitionKey
+from lerobot.types import EnvTransition, PolicyAction, TransitionKey

 from .pipeline import (
    ComplementaryDataProcessorStep,
@@ -25,7 +25,7 @@ import torch
 from torch import Tensor

 from lerobot.configs import FeatureType, NormalizationMode, PipelineFeatureType, PolicyFeature
-from lerobot.lerobot_types import EnvTransition, PolicyAction, TransitionKey
+from lerobot.types import EnvTransition, PolicyAction, TransitionKey

 if TYPE_CHECKING:
    from lerobot.datasets import LeRobotDataset
@@ -45,14 +45,7 @@ from huggingface_hub import hf_hub_download
 from safetensors.torch import load_file, save_file

 from lerobot.configs import PipelineFeatureType, PolicyFeature
-from lerobot.lerobot_types import (
-    EnvAction,
-    EnvTransition,
-    PolicyAction,
-    RobotAction,
-    RobotObservation,
-    TransitionKey,
-)
+from lerobot.types import EnvAction, EnvTransition, PolicyAction, RobotAction, RobotObservation, TransitionKey
 from lerobot.utils.constants import HF_LEROBOT_HOME
 from lerobot.utils.hub import HubMixin

@@ -20,7 +20,7 @@ from typing import Any
 import torch

 from lerobot.configs import FeatureType, PipelineFeatureType, PolicyFeature
-from lerobot.lerobot_types import PolicyAction, RobotAction
+from lerobot.types import PolicyAction, RobotAction
 from lerobot.utils.constants import ACTION

 from .pipeline import ActionProcessorStep, ProcessorStepRegistry
@@ -20,7 +20,7 @@ import torch
 from torch import Tensor

 from lerobot.configs import PipelineFeatureType, PolicyFeature
-from lerobot.lerobot_types import EnvTransition, TransitionKey
+from lerobot.types import EnvTransition, TransitionKey
 from lerobot.utils.constants import OBS_STATE

 from .delta_action_processor import MapDeltaActionToRobotActionStep, MapTensorToDeltaActionDictStep
@@ -23,7 +23,7 @@ from lerobot.configs import PipelineFeatureType, PolicyFeature
 from lerobot.configs.recipe import TrainingRecipe
 from lerobot.datasets.language import LANGUAGE_EVENTS, LANGUAGE_PERSISTENT
 from lerobot.datasets.language_render import render_sample
-from lerobot.lerobot_types import EnvTransition, TransitionKey
+from lerobot.types import EnvTransition, TransitionKey
 from lerobot.utils.utils import unwrap_scalar

 from .pipeline import ProcessorStep, ProcessorStepRegistry
@@ -30,7 +30,7 @@ from typing import TYPE_CHECKING, Any
 import torch

 from lerobot.configs import FeatureType, PipelineFeatureType, PolicyFeature
-from lerobot.lerobot_types import EnvTransition, RobotObservation, TransitionKey
+from lerobot.types import EnvTransition, RobotObservation, TransitionKey
 from lerobot.utils.constants import (
    ACTION_TOKEN_MASK,
    ACTION_TOKENS,
@@ -57,10 +57,10 @@ import torch
 from tqdm import tqdm

 from lerobot.datasets import LeRobotDataset
-from lerobot.lerobot_types import TransitionKey
 from lerobot.rewards.robometer.configuration_robometer import RobometerConfig
 from lerobot.rewards.robometer.modeling_robometer import RobometerRewardModel
 from lerobot.rewards.robometer.processor_robometer import RobometerEncoderProcessorStep
+from lerobot.types import TransitionKey

 DEFAULT_OUTPUT_FILENAME = "robometer_progress.parquet"

@@ -25,7 +25,6 @@ from PIL import Image
 from torch import Tensor

 from lerobot.configs import PipelineFeatureType, PolicyFeature
-from lerobot.lerobot_types import EnvTransition, TransitionKey
 from lerobot.processor import (
    AddBatchDimensionProcessorStep,
    DeviceProcessorStep,
@@ -40,6 +39,7 @@ from lerobot.rewards.robometer.configuration_robometer import (
    RobometerConfig,
 )
 from lerobot.rewards.robometer.modeling_robometer import ROBOMETER_FEATURE_PREFIX
+from lerobot.types import EnvTransition, TransitionKey
 from lerobot.utils.constants import (
    OBS_IMAGES,
    POLICY_POSTPROCESSOR_DEFAULT_NAME,
@@ -47,7 +47,6 @@ else:
    Faker = None  # type: ignore[assignment, misc]

 from lerobot.configs import FeatureType, PipelineFeatureType, PolicyFeature
-from lerobot.lerobot_types import EnvTransition, PolicyAction, TransitionKey
 from lerobot.processor import (
    AddBatchDimensionProcessorStep,
    DeviceProcessorStep,
@@ -59,6 +58,7 @@ from lerobot.processor import (
    policy_action_to_transition,
    transition_to_policy_action,
 )
+from lerobot.types import EnvTransition, PolicyAction, TransitionKey
 from lerobot.utils.constants import POLICY_POSTPROCESSOR_DEFAULT_NAME, POLICY_PREPROCESSOR_DEFAULT_NAME

 from .configuration_sarm import SARMConfig
@@ -48,10 +48,10 @@ import torch
 from tqdm import tqdm

 from lerobot.datasets import LeRobotDataset
-from lerobot.lerobot_types import TransitionKey
 from lerobot.rewards.topreward.configuration_topreward import TOPRewardConfig
 from lerobot.rewards.topreward.modeling_topreward import TOPRewardModel
 from lerobot.rewards.topreward.processor_topreward import TOPRewardEncoderProcessorStep
+from lerobot.types import TransitionKey

 DEFAULT_OUTPUT_FILENAME = "topreward_progress.parquet"

@@ -23,7 +23,6 @@ import torch
 from torch import Tensor

 from lerobot.configs import PipelineFeatureType, PolicyFeature
-from lerobot.lerobot_types import EnvTransition, TransitionKey
 from lerobot.processor import (
    AddBatchDimensionProcessorStep,
    DeviceProcessorStep,
@@ -38,6 +37,7 @@ from lerobot.rewards.topreward.configuration_topreward import (
    DEFAULT_PROMPT_SUFFIX_TEMPLATE,
    TOPRewardConfig,
 )
+from lerobot.types import EnvTransition, TransitionKey
 from lerobot.utils.constants import (
    OBS_IMAGES,
    OBS_PREFIX,
@@ -28,7 +28,7 @@ from huggingface_hub.errors import HfHubHTTPError
 from safetensors.torch import load_file as load_safetensors, save_file as save_safetensors
 from torch.optim import Optimizer

-from lerobot.lerobot_types import BatchType
+from lerobot.types import BatchType
 from lerobot.utils.hub import HubMixin

 from .configs import RLAlgorithmConfig, TrainingStats
@@ -16,7 +16,6 @@ from __future__ import annotations

 import abc
 import builtins
-import json
 import logging
 import os
 from dataclasses import dataclass, field
@@ -79,10 +78,8 @@ class RLAlgorithmConfig(draccus.ChoiceRegistry, HubMixin, abc.ABC):

    def _save_pretrained(self, save_directory: Path) -> None:
        """Serialize this config as ``config.json`` inside ``save_directory``."""
-        # Encode against the base class so draccus includes the choice "type" key,
-        # which `from_pretrained` needs to resolve the concrete subclass.
-        with open(save_directory / CONFIG_NAME, "w") as f:
-            json.dump(draccus.encode(self, RLAlgorithmConfig), f, indent=4)
+        with open(save_directory / CONFIG_NAME, "w") as f, draccus.config_type("json"):
+            draccus.dump(self, f, indent=4)

    @classmethod
    def from_pretrained(
@@ -26,7 +26,6 @@ import torch.nn.functional as F  # noqa: N812
 from torch import Tensor
 from torch.optim import Optimizer

-from lerobot.lerobot_types import BatchType
 from lerobot.policies.gaussian_actor.modeling_gaussian_actor import (
    DISCRETE_DIMENSION_INDEX,
    MLP,
@@ -36,6 +35,7 @@ from lerobot.policies.gaussian_actor.modeling_gaussian_actor import (
    orthogonal_init,
 )
 from lerobot.policies.utils import get_device_from_parameters
+from lerobot.types import BatchType
 from lerobot.utils.constants import ACTION
 from lerobot.utils.transition import move_state_dict_to_device

@@ -18,7 +18,7 @@ import functools
 import threading
 from collections.abc import Callable, Sequence
 from contextlib import suppress
-from typing import NotRequired, TypedDict
+from typing import TypedDict

 import torch
 import torch.nn.functional as F  # noqa: N812
@@ -36,7 +36,7 @@ class BatchTransition(TypedDict):
    next_state: dict[str, torch.Tensor]
    done: torch.Tensor
    truncated: torch.Tensor
-    complementary_info: NotRequired[dict[str, torch.Tensor | float | int] | None]
+    complementary_info: dict[str, torch.Tensor | float | int] | None = None


 def random_crop_vectorized(images: torch.Tensor, output_size: tuple) -> torch.Tensor:
@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.

-from lerobot.lerobot_types import BatchType
+from lerobot.types import BatchType

 from .data_mixer import DataMixer, OnlineOfflineMixer

@@ -16,7 +16,7 @@ from __future__ import annotations

 import abc

-from lerobot.lerobot_types import BatchType
+from lerobot.types import BatchType

 from ..buffer import ReplayBuffer, concatenate_batch_transitions

@@ -17,7 +17,7 @@ from __future__ import annotations
 from collections.abc import Iterator
 from typing import Any

-from lerobot.lerobot_types import BatchType
+from lerobot.types import BatchType

 from .algorithms.base import RLAlgorithm
 from .algorithms.configs import TrainingStats
@@ -17,7 +17,7 @@
 import logging
 from functools import cached_property

-from lerobot.lerobot_types import RobotAction, RobotObservation
+from lerobot.types import RobotAction, RobotObservation
 from lerobot.utils.bimanual import BimanualMixin
 from lerobot.utils.decorators import check_if_not_connected

@@ -17,7 +17,7 @@
 import logging
 from functools import cached_property

-from lerobot.lerobot_types import RobotAction, RobotObservation
+from lerobot.types import RobotAction, RobotObservation
 from lerobot.utils.bimanual import BimanualMixin
 from lerobot.utils.decorators import check_if_not_connected

@@ -17,7 +17,7 @@
 import logging
 from functools import cached_property

-from lerobot.lerobot_types import RobotAction, RobotObservation
+from lerobot.types import RobotAction, RobotObservation
 from lerobot.utils.bimanual import BimanualMixin
 from lerobot.utils.decorators import check_if_not_connected

@@ -62,7 +62,6 @@ class BiSOFollower(BimanualMixin, Robot):
            position_i_coefficient=config.left_arm_config.position_i_coefficient,
            position_d_coefficient=config.left_arm_config.position_d_coefficient,
            use_degrees=config.left_arm_config.use_degrees,
-            num_read_retries=config.left_arm_config.num_read_retries,
            cameras=left_arm_cameras,
        )

@@ -76,7 +75,6 @@ class BiSOFollower(BimanualMixin, Robot):
            position_i_coefficient=config.right_arm_config.position_i_coefficient,
            position_d_coefficient=config.right_arm_config.position_d_coefficient,
            use_degrees=config.right_arm_config.use_degrees,
-            num_read_retries=config.right_arm_config.num_read_retries,
            cameras=config.right_arm_config.cameras,
        )

@@ -23,7 +23,7 @@ import cv2
 import numpy as np
 import requests

-from lerobot.lerobot_types import RobotAction, RobotObservation
+from lerobot.types import RobotAction, RobotObservation
 from lerobot.utils.decorators import check_if_already_connected, check_if_not_connected
 from lerobot.utils.errors import DeviceNotConnectedError

@@ -19,12 +19,12 @@ import time
 from functools import cached_property

 from lerobot.cameras import make_cameras_from_configs
-from lerobot.lerobot_types import RobotAction, RobotObservation
 from lerobot.motors import Motor, MotorNormMode
 from lerobot.motors.calibration_gui import RangeFinderGUI
 from lerobot.motors.feetech import (
    FeetechMotorsBus,
 )
+from lerobot.types import RobotAction, RobotObservation
 from lerobot.utils.decorators import check_if_already_connected, check_if_not_connected

 from ..robot import Robot
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Nikodem Bartnik	ee3e796270	fix link	2026-07-29 13:21:16 +02:00
Nikodem Bartnik	e548375e4c	fix emoji and formatting	2026-07-29 13:11:36 +02:00
Nikodem Bartnik	17d5675738	improved explanation	2026-07-29 13:09:20 +02:00
Nikodem Bartnik	ffd0e26fbe	text fix	2026-07-29 09:43:25 +02:00
Nikodem Bartnik	9cade7db18	fix logo size	2026-07-28 17:16:34 +02:00
Nikodem Bartnik	775bbc19f4	fix formatting	2026-07-28 17:13:44 +02:00
Nikodem Bartnik	2b78c7e582	bring community section up + bigger logo	2026-07-28 16:28:38 +02:00
Nikodem Bartnik	b101ff0e56	extend index.mdx	2026-07-28 16:10:15 +02:00