style: remove decorative comment separator in transforms.py

Made-with: Cursor

.github/workflows/fast_tests.yml

@@ -44,7 +44,7 @@ permissions:
 # Sets up the environment variables
 env:
   UV_VERSION: "0.8.0"
-  PYTHON_VERSION: "3.10"
+  PYTHON_VERSION: "3.12"
 
 # Ensures that only the latest commit for a PR or branch is built, canceling older runs.
 concurrency:
@@ -61,6 +61,7 @@ jobs:
       MUJOCO_GL: egl
       HF_HOME: /mnt/cache/.cache/huggingface
       HF_LEROBOT_HOME: /mnt/cache/.cache/huggingface/lerobot
+      HF_USER_TOKEN: ${{ secrets.LEROBOT_HF_USER }}
     steps:
       - uses: actions/checkout@v6
         with:
@@ -89,5 +90,11 @@ jobs:
       - name: Install lerobot with test extras
         run: uv sync --extra "test"
 
+      - name: Login to Hugging Face
+        if: env.HF_USER_TOKEN != ''
+        run: |
+          uv run hf auth login --token "$HF_USER_TOKEN" --add-to-git-credential
+          uv run hf auth whoami
+
       - name: Run pytest
         run: uv run pytest tests -vv --maxfail=10

.github/workflows/full_tests.yml

@@ -37,7 +37,7 @@ permissions:
 # Sets up the environment variables
 env:
   UV_VERSION: "0.8.0"
-  PYTHON_VERSION: "3.10"
+  PYTHON_VERSION: "3.12"
   DOCKER_IMAGE_NAME: huggingface/lerobot-gpu
 
 # Ensures that only the latest action is built, canceling older runs.
@@ -60,6 +60,7 @@ jobs:
       MUJOCO_GL: egl
       HF_HOME: /mnt/cache/.cache/huggingface
       HF_LEROBOT_HOME: /mnt/cache/.cache/huggingface/lerobot
+      HF_USER_TOKEN: ${{ secrets.LEROBOT_HF_USER }}
     steps:
       - uses: actions/checkout@v6
         with:
@@ -87,6 +88,12 @@ jobs:
       - name: Install lerobot with all extras
         run: uv sync --extra all # TODO(Steven): Make flash-attn optional
 
+      - name: Login to Hugging Face
+        if: env.HF_USER_TOKEN != ''
+        run: |
+          uv run hf auth login --token "$HF_USER_TOKEN" --add-to-git-credential
+          uv run hf auth whoami
+
       - name: Run pytest (all extras)
         run: uv run pytest tests -vv --maxfail=10
 
@@ -162,6 +169,7 @@ jobs:
       HF_LEROBOT_HOME: /home/user_lerobot/.cache/huggingface/lerobot
       TORCH_HOME: /home/user_lerobot/.cache/torch
       TRITON_CACHE_DIR: /home/user_lerobot/.cache/triton
+      HF_USER_TOKEN: ${{ secrets.LEROBOT_HF_USER }}
     container:
       image: ${{ needs.build-and-push-docker.outputs.image_tag }} # zizmor: ignore[unpinned-images]
       options: --gpus all --shm-size "16gb"
@@ -173,8 +181,13 @@ jobs:
         shell: bash
         working-directory: /lerobot
     steps:
+      - name: Login to Hugging Face
+        if: env.HF_USER_TOKEN != ''
+        run: |
+          hf auth login --token "$HF_USER_TOKEN" --add-to-git-credential
+          hf auth whoami
       - name: Fix ptxas permissions
-        run: chmod +x /lerobot/.venv/lib/python3.10/site-packages/triton/backends/nvidia/bin/ptxas
+        run: chmod +x /lerobot/.venv/lib/python3.12/site-packages/triton/backends/nvidia/bin/ptxas
       - name: Run pytest on GPU
         run: pytest tests -vv --maxfail=10
       - name: Run end-to-end tests

.github/workflows/nightly.yml

@@ -28,7 +28,7 @@ on:
 # Sets up the environment variables
 env:
   UV_VERSION: "0.8.0"
-  PYTHON_VERSION: "3.10"
+  PYTHON_VERSION: "3.12"
   DOCKER_IMAGE_NAME_CPU: huggingface/lerobot-cpu:latest
   DOCKER_IMAGE_NAME_GPU: huggingface/lerobot-gpu:latest
 
@@ -119,6 +119,7 @@ jobs:
       HF_LEROBOT_HOME: /home/user_lerobot/.cache/huggingface/lerobot
       TORCH_HOME: /home/user_lerobot/.cache/torch
       TRITON_CACHE_DIR: /home/user_lerobot/.cache/triton
+      HF_USER_TOKEN: ${{ secrets.LEROBOT_HF_USER }}
     container:
       image: ${{ needs.build-docker-cpu-nightly.outputs.image_tag }} # zizmor: ignore[unpinned-images]
       options: --shm-size "16gb"
@@ -130,6 +131,11 @@ jobs:
         shell: bash
         working-directory: /lerobot
     steps:
+      - name: Login to Hugging Face
+        if: env.HF_USER_TOKEN != ''
+        run: |
+          hf auth login --token "$HF_USER_TOKEN" --add-to-git-credential
+          hf auth whoami
       - name: Run pytest on CPU
         run: pytest tests -vv --maxfail=10
       - name: Run end-to-end tests
@@ -146,6 +152,7 @@ jobs:
       HF_LEROBOT_HOME: /home/user_lerobot/.cache/huggingface/lerobot
       TORCH_HOME: /home/user_lerobot/.cache/torch
       TRITON_CACHE_DIR: /home/user_lerobot/.cache/triton
+      HF_USER_TOKEN: ${{ secrets.LEROBOT_HF_USER }}
     container:
       image: ${{ needs.build-docker-gpu-nightly.outputs.image_tag }} # zizmor: ignore[unpinned-images]
       options: --gpus all --shm-size "16gb"
@@ -157,6 +164,11 @@ jobs:
         shell: bash
         working-directory: /lerobot
     steps:
+      - name: Login to Hugging Face
+        if: env.HF_USER_TOKEN != ''
+        run: |
+          hf auth login --token "$HF_USER_TOKEN" --add-to-git-credential
+          hf auth whoami
       - name: Run pytest on GPU
         run: pytest tests -vv --maxfail=10
       - name: Run end-to-end tests
@@ -174,6 +186,7 @@ jobs:
       TORCH_HOME: /home/user_lerobot/.cache/torch
       TRITON_CACHE_DIR: /home/user_lerobot/.cache/triton
       CUDA_VISIBLE_DEVICES: "0,1,2,3"
+      HF_USER_TOKEN: ${{ secrets.LEROBOT_HF_USER }}
     container:
       image: ${{ needs.build-docker-gpu-nightly.outputs.image_tag }} # zizmor: ignore[unpinned-images]
       options: --gpus all --shm-size "16gb"
@@ -185,12 +198,15 @@ jobs:
         shell: bash
         working-directory: /lerobot
     steps:
+      - name: Login to Hugging Face
+        if: env.HF_USER_TOKEN != ''
+        run: |
+          hf auth login --token "$HF_USER_TOKEN" --add-to-git-credential
+          hf auth whoami
       - name: Verify GPU availability
         run: |
           nvidia-smi
           python -c "import torch; print(f'PyTorch CUDA available: {torch.cuda.is_available()}'); print(f'Number of GPUs: {torch.cuda.device_count()}')"
 
       - name: Run multi-GPU training tests
-      # TODO(Steven): Investigate why motors tests are failing in multi-GPU setup
+        run: pytest -vv tests/training/
-        run: pytest tests -vv --maxfail=10 --ignore=tests/motors/
-        timeout-minutes: 10

.github/workflows/quality.yml

@@ -50,7 +50,7 @@ jobs:
       - name: Set up Python
         uses: actions/setup-python@v6
         with:
-          python-version: '3.10'
+          python-version: '3.12'
 
       - name: Run pre-commit hooks
         uses: pre-commit/action@v3.0.1 # zizmor: ignore[unpinned-uses]

.github/workflows/release.yml

@@ -22,7 +22,7 @@ on:
 # Sets up the environment variables
 env:
   UV_VERSION: "0.8.0"
-  PYTHON_VERSION: "3.10"
+  PYTHON_VERSION: "3.12"
 
 jobs:
   # This job builds the Python package and publishes it to PyPI
@@ -45,7 +45,7 @@ jobs:
       - name: Set up Python
         uses: actions/setup-python@v6
         with:
-          python-version: '3.10'
+          python-version: '3.12'
 
       - name: Extract Version
         id: extract_info
@@ -83,14 +83,6 @@ jobs:
             exit 1
           fi
 
-      - name: Remove Tags with Git dependencies
-        # TODO(Steven): Temporary patch to remove pi from PyPi 0.4.0 release due to its reliance on git dependencies.
-        run: |
-          echo "::info:: Checking for Git dependencies to remove from pyproject.toml..."
-          grep -E '@ git\+https|lerobot\[pi\]' pyproject.toml | sed 's/^/::warning:: Removing line: /' || true
-          sed -E -i '/@ git\+https|lerobot\[pi\]/d' pyproject.toml
-          echo "::info:: Git dependencies removed. Proceeding with build."
-
       - name: Install build dependencies
         run: python -m pip install build
 

.github/workflows/unbound_deps_tests.yml

@@ -29,7 +29,7 @@ permissions:
 # Sets up the environment variables
 env:
   UV_VERSION: "0.8.0"
-  PYTHON_VERSION: "3.10"
+  PYTHON_VERSION: "3.12"
   DOCKER_IMAGE_NAME: huggingface/lerobot-gpu:unbound
 
 # Ensures that only the latest action is built, canceling older runs.
@@ -48,6 +48,7 @@ jobs:
       MUJOCO_GL: egl
       HF_HOME: /mnt/cache/.cache/huggingface
       HF_LEROBOT_HOME: /mnt/cache/.cache/huggingface/lerobot
+      HF_USER_TOKEN: ${{ secrets.LEROBOT_HF_USER }}
     steps:
       - uses: actions/checkout@v6
         with:
@@ -79,7 +80,11 @@ jobs:
 
       - name: Install lerobot with all extras
         run: uv sync --extra all # TODO(Steven): Make flash-attn optional
-
+      - name: Login to Hugging Face
+        if: env.HF_USER_TOKEN != ''
+        run: |
+          uv run hf auth login --token "$HF_USER_TOKEN" --add-to-git-credential
+          uv run hf auth whoami
       - name: Run pytest (all extras)
         run: uv run pytest tests -vv
 
@@ -137,6 +142,7 @@ jobs:
       HF_LEROBOT_HOME: /home/user_lerobot/.cache/huggingface/lerobot
       TORCH_HOME: /home/user_lerobot/.cache/torch
       TRITON_CACHE_DIR: /home/user_lerobot/.cache/triton
+      HF_USER_TOKEN: ${{ secrets.LEROBOT_HF_USER }}
     container:
       image: ${{ needs.build-and-push-docker.outputs.image_tag }} # zizmor: ignore[unpinned-images]
       options: --gpus all --shm-size "16gb"
@@ -148,6 +154,11 @@ jobs:
         shell: bash
         working-directory: /lerobot
     steps:
+      - name: Login to Hugging Face
+        if: env.HF_USER_TOKEN != ''
+        run: |
+          hf auth login --token "$HF_USER_TOKEN" --add-to-git-credential
+          hf auth whoami
       - name: Run pytest on GPU
         run: pytest tests -vv
       - name: Run end-to-end tests

.pre-commit-config.yaml

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 default_language_version:
-    python: python3.10
+    python: python3.12
 
 exclude: "tests/artifacts/.*\\.safetensors$"
 
@@ -55,7 +55,7 @@ repos:
     rev: v3.21.0
     hooks:
     -   id: pyupgrade
-        args: [--py310-plus]
+        args: [--py312-plus]
 
   ##### Markdown Quality #####
   - repo: https://github.com/rbubley/mirrors-prettier

AI_POLICY.md

@@ -0,0 +1,25 @@
+# AI Usage Policy
+
+The LeRobot project welcomes contributions from everyone, and we have a few guidelines regarding AI usage to ensure high code quality, clear communication, and a healthy open-source ecosystem:
+
+- **Please disclose significant AI assistance.** If you used AI tools (e.g., Copilot, Claude, Cursor, ChatGPT) to generate a substantial portion of your code or text, let us know in your PR description. Transparency helps us review your changes more effectively.
+- **Own your code (The Human-in-the-Loop).** You must fully understand all the changes you are proposing. If you cannot explain what your AI-assisted code does or how it interacts with LeRobot's broader architecture, please take the time to learn and test it before submitting.
+- **Keep issues and discussions focused.** You are welcome to use AI to help draft issues or PR descriptions, but please review and edit them carefully before posting. AI can often be overly verbose; trimming the noise and getting straight to the point helps our maintainers address your needs faster.
+
+Our core maintainers also use AI tools to aid their workflows, but they do so while bringing deep contextual knowledge of the LeRobot codebase to validate the output. We ask all contributors to apply that same level of rigor.
+
+## Remember the Human Maintainers
+
+Please remember that LeRobot is maintained by a dedicated team of humans.
+
+Every discussion, issue, and pull request is read and reviewed by real people. While AI tools can generate thousands of lines of code in seconds, reviewing that code still takes human time and energy. Submitting unverified or low-effort AI output puts an unfair burden on our maintainers.
+
+Today, the quality of the AI output still heavily depends on the developer driving the tool. We ask that you respect our maintainers' time by thoroughly vetting, testing, and refining your submissions.
+
+## AI is Welcome Here
+
+LeRobot operates at the cutting edge of AI and robotics, and many of our maintainers actively embrace AI coding assistants as valuable productivity tools. We are a pro-AI project!
+
+Our reason for having an AI policy is not an anti-AI stance. Rather, it exists to ensure that AI is used to enhance human contributions, not replace them with unverified noise. It's about how the tools are used, not the tools themselves.
+
+We value the unique human insight you bring to the LeRobot community. Let AI empower your workflow, but always let your own judgment take the wheel.

CONTRIBUTING.md

@@ -2,7 +2,7 @@
 
 Everyone is welcome to contribute, and we value everybody's contribution. Code is not the only way to help the community. Answering questions, helping others, reaching out, and improving the documentation are immensely valuable.
 
-Whichever way you choose to contribute, please be mindful to respect our [code of conduct](./CODE_OF_CONDUCT.md).
+Whichever way you choose to contribute, please be mindful to respect our [code of conduct](./CODE_OF_CONDUCT.md) and our [AI policy](./AI_POLICY.md).
 
 ## Ways to Contribute
 

README.md

@@ -135,7 +135,7 @@ Learn how to implement your own simulation environment or benchmark and distribu
 
 ## Citation
 
-If you use LeRobot in your research, please cite:
+If you use LeRobot in your project, please cite the GitHub repository to acknowledge the ongoing development and contributors:
 
 ```bibtex
 @misc{cadene2024lerobot,
@@ -146,6 +146,23 @@ If you use LeRobot in your research, please cite:
 }
 ```
 
+If you are referencing our research or the academic paper, please also cite our ICLR publication:
+
+<details>
+<summary><b>ICLR 2026 Paper</b></summary>
+
+```bibtex
+@inproceedings{cadenelerobot,
+  title={LeRobot: An Open-Source Library for End-to-End Robot Learning},
+  author={Cadene, Remi and Alibert, Simon and Capuano, Francesco and Aractingi, Michel and Zouitine, Adil and Kooijmans, Pepijn and Choghari, Jade and Russi, Martino and Pascal, Caroline and Palma, Steven and Shukor, Mustafa and Moss, Jess and Soare, Alexander and Aubakirova, Dana and Lhoest, Quentin and Gallou\'edec, Quentin and Wolf, Thomas},
+  booktitle={The Fourteenth International Conference on Learning Representations},
+  year={2026},
+  url={https://arxiv.org/abs/2602.22818}
+}
+```
+
+</details>
+
 ## Contribute
 
 We welcome contributions from everyone in the community! To get started, please read our [CONTRIBUTING.md](./CONTRIBUTING.md) guide. Whether you're adding a new feature, improving documentation, or fixing a bug, your help and feedback are invaluable. We're incredibly excited about the future of open-source robotics and can't wait to work with you on what's next—thank you for your support!

docker/Dockerfile.internal

@@ -24,7 +24,7 @@ ARG OS_VERSION=22.04
 FROM nvidia/cuda:${CUDA_VERSION}-base-ubuntu${OS_VERSION}
 
 # Define Python version argument
-ARG PYTHON_VERSION=3.10
+ARG PYTHON_VERSION=3.12
 
 # Configure environment variables
 ENV DEBIAN_FRONTEND=noninteractive \

docker/Dockerfile.user

@@ -19,7 +19,7 @@
 # docker run -it --rm lerobot-user
 
 # Configure the base image
-ARG PYTHON_VERSION=3.10
+ARG PYTHON_VERSION=3.12
 FROM python:${PYTHON_VERSION}-slim
 
 # Configure environment variables

docs/source/_toctree.yml

@@ -31,6 +31,8 @@
     title: Using Subtasks in the Dataset
   - local: streaming_video_encoding
     title: Streaming Video Encoding
+  - local: multi_dataset_training
+    title: Multi-Dataset Training
   title: "Datasets"
 - sections:
   - local: act

docs/source/async.mdx

@@ -48,7 +48,7 @@ python -m lerobot.async_inference.robot_client \
     --task="dummy" \ # POLICY: The task to run the policy on (`Fold my t-shirt`). Not necessarily defined for all policies, such as `act`
     --policy_type=your_policy_type \ # POLICY: the type of policy to run (smolvla, act, etc)
     --pretrained_name_or_path=user/model \ # POLICY: the model name/path on server to the checkpoint to run (e.g., lerobot/smolvla_base)
-    --policy_device=mps \ # POLICY: the device to run the policy on, on the server
+    --policy_device=mps \ # POLICY: the device to run the policy on, on the server (cuda, mps, xpu, cpu)
     --actions_per_chunk=50 \ # POLICY: the number of actions to output at once
     --chunk_size_threshold=0.5 \ # CLIENT: the threshold for the chunk size before sending a new observation to the server
     --aggregate_fn_name=weighted_average \ # CLIENT: the function to aggregate actions on overlapping portions

docs/source/bring_your_own_policies.mdx

@@ -32,7 +32,7 @@ version = "0.1.0"
 dependencies = [
     # your policy-specific dependencies
 ]
-requires-python = ">= 3.11"
+requires-python = ">= 3.12"
 
 [build-system]
 build-backend = # your-build-backend
@@ -82,7 +82,7 @@ Create your policy implementation by inheriting from LeRobot's base `PreTrainedP
 # modeling_my_custom_policy.py
 import torch
 import torch.nn as nn
-from typing import Dict, Any
+from typing import Any
 
 from lerobot.policies.pretrained import PreTrainedPolicy
 from .configuration_my_custom_policy import MyCustomPolicyConfig
@@ -91,7 +91,7 @@ class MyCustomPolicy(PreTrainedPolicy):
     config_class = MyCustomPolicyConfig
     name = "my_custom_policy"
 
-    def __init__(self, config: MyCustomPolicyConfig, dataset_stats: Dict[str, Any] = None):
+    def __init__(self, config: MyCustomPolicyConfig, dataset_stats: dict[str, Any] = None):
         super().__init__(config, dataset_stats)
         ...
 ```
@@ -102,7 +102,7 @@ Create processor functions:
 
 ```python
 # processor_my_custom_policy.py
-from typing import Dict, Any
+from typing import Any
 import torch
 
 

docs/source/earthrover_mini_plus.mdx

@@ -13,7 +13,7 @@ The EarthRover Mini Plus is a fully open source mobile robot that connects throu
 ### Hardware
 
 - EarthRover Mini robot
-- Computer with Python 3.10 or newer
+- Computer with Python 3.12 or newer
 - Internet connection
 
 ### Setting Up the Frodobots SDK
@@ -170,13 +170,13 @@ Once you can drive the robot well, you can start recording data to train AI mode
 We use Hugging Face to store your data online. First, log in with your token from [Hugging Face settings](https://huggingface.co/settings/tokens):
 
 ```bash
-huggingface-cli login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential
+hf auth login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential
 ```
 
 Store your Hugging Face username:
 
 ```bash
-HF_USER=$(huggingface-cli whoami | head -n 1)
+HF_USER=$(hf auth whoami | awk -F': *' 'NR==1 {print $2}')
 echo $HF_USER
 ```
 

docs/source/envhub.mdx

@@ -155,10 +155,10 @@ Upload your repository to Hugging Face:
 pip install huggingface_hub
 
 # Login to Hugging Face
-huggingface-cli login
+hf auth login
 
 # Create a new repository
-huggingface-cli repo create my-custom-env --type space --org my-org
+hf repo create my-org/my-custom-env
 
 # Initialize git and push
 git init

docs/source/il_robots.mdx

@@ -159,7 +159,7 @@ We use the Hugging Face hub features for uploading your dataset. If you haven't
 Add your token to the CLI by running this command:
 
 ```bash
-huggingface-cli login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential
+hf auth login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential
 ```
 
 Then store your Hugging Face repository name in a variable:
@@ -327,7 +327,7 @@ You can look for other LeRobot datasets on the hub by searching for `LeRobot` [t
 You can also push your local dataset to the Hub manually, running:
 
 ```bash
-huggingface-cli upload ${HF_USER}/record-test ~/.cache/huggingface/lerobot/{repo-id} --repo-type dataset
+hf upload ${HF_USER}/record-test ~/.cache/huggingface/lerobot/{repo-id} --repo-type dataset
 ```
 
 #### Record function
@@ -491,7 +491,7 @@ If your local computer doesn't have a powerful GPU you could utilize Google Cola
 Once training is done, upload the latest checkpoint with:
 
 ```bash
-huggingface-cli upload ${HF_USER}/act_so101_test \
+hf upload ${HF_USER}/act_so101_test \
   outputs/train/act_so101_test/checkpoints/last/pretrained_model
 ```
 
@@ -499,7 +499,7 @@ You can also upload intermediate checkpoints with:
 
 ```bash
 CKPT=010000
-huggingface-cli upload ${HF_USER}/act_so101_test${CKPT} \
+hf upload ${HF_USER}/act_so101_test${CKPT} \
   outputs/train/act_so101_test/checkpoints/${CKPT}/pretrained_model
 ```
 

docs/source/installation.mdx

@@ -1,8 +1,8 @@
 # Installation
 
-This guide uses conda (via miniforge) to manage environments. If you prefer another environment manager (e.g. `uv`, `venv`), ensure you have Python >=3.10 and ffmpeg installed with the `libsvtav1` encoder, then skip ahead to [Install LeRobot](#step-3-install-lerobot-).
+This guide uses `conda` (via miniforge) to manage environments (recommended). If you prefer another environment manager (e.g. `uv`, `venv`), ensure you have Python >=3.12 and `ffmpeg` installed with the `libsvtav1` encoder, then skip ahead to [Environment Setup](#step-2-environment-setup).
 
-## Step 1: Install [`miniforge`](https://conda-forge.org/download/)
+## Step 1 (`conda` only): Install [`miniforge`](https://conda-forge.org/download/)
 
 ```bash
 wget "https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-$(uname -m).sh"
@@ -11,22 +11,47 @@ bash Miniforge3-$(uname)-$(uname -m).sh
 
 ## Step 2: Environment Setup
 
-Create a virtual environment with Python 3.10, using conda:
+Create a virtual environment with Python 3.12:
 
+<!-- prettier-ignore-start -->
+<hfoptions id="create_venv">
+<hfoption id="conda">
 ```bash
-conda create -y -n lerobot python=3.10
+conda create -y -n lerobot python=3.12
 ```
-
+</hfoption>
-Then activate your conda environment, you have to do this each time you open a shell to use lerobot:
+<hfoption id="uv">
-
 ```bash
+uv python install 3.12
+uv venv --python 3.12
+```
+</hfoption>
+</hfoptions>
+<!-- prettier-ignore-end -->
+
+Then activate your virtual environment, you have to do this each time you open a shell to use lerobot:
+
+<!-- prettier-ignore-start -->
+<hfoptions id="activate_venv">
+<hfoption id="conda">```bash
 conda activate lerobot
+```</hfoption>
+<hfoption id="uv">
+```bash
+# Linux/macOSsource
+source .venv/bin/activate
+# Windows PowerShell
+source .venv\Scripts\Activate.ps1
 ```
+</hfoption>
+</hfoptions>
+<!-- prettier-ignore-end -->
 
 When using `conda`, install `ffmpeg` in your environment:
 
 ```bash
 conda install ffmpeg -c conda-forge
+ffmpeg -version  # ffmpeg 8.X is not yet supported !
 ```
 
 > [!TIP]
@@ -47,6 +72,9 @@ conda install ffmpeg -c conda-forge
 > conda install evdev -c conda-forge
 > ```
 
+> [!IMPORTANT]
+> If you are using `uv` you will have to install `ffmpeg` system-wide (outside of the virtual environment). You rely on `uv` and `torchcodec` ability to dynamically link to the system `ffmpeg`.
+
 ## Step 3: Install LeRobot 🤗
 
 ### From Source
@@ -60,23 +88,45 @@ cd lerobot
 
 Then, install the library in editable mode. This is useful if you plan to contribute to the code.
 
+<!-- prettier-ignore-start -->
+<hfoptions id="install_lerobot_src">
+<hfoption id="conda">
 ```bash
 pip install -e .
 ```
+</hfoption>
+<hfoption id="uv">
+```bash
+uv pip install -e .
+```
+</hfoption>
+</hfoptions>
+<!-- prettier-ignore-end -->
 
 ### Installation from PyPI
 
 **Core Library:**
 Install the base package with:
 
+<!-- prettier-ignore-start -->
+<hfoptions id="install_lerobot_pypi">
+<hfoption id="conda">
 ```bash
 pip install lerobot
 ```
+</hfoption>
+<hfoption id="uv">
+```bash
+uv pip install lerobot
+```
+</hfoption>
+</hfoptions>
+<!-- prettier-ignore-end -->
 
 _This installs only the default dependencies._
 
 **Extra Features:**
-To install additional functionality, use one of the following:
+To install additional functionality, use one of the following (If you are using `uv`, replace `pip install` with `uv pip install` in the commands below.):
 
 ```bash
 pip install 'lerobot[all]'          # All available features
@@ -90,13 +140,10 @@ _Replace `[...]` with your desired features._
 For a full list of optional dependencies, see:
 https://pypi.org/project/lerobot/
 
-> [!NOTE]
-> For lerobot 0.4.0, if you want to install pi, you will have to do: `pip install "lerobot[pi]@git+https://github.com/huggingface/lerobot.git"`
-
 ### Troubleshooting
 
 If you encounter build errors, you may need to install additional dependencies: `cmake`, `build-essential`, and `ffmpeg libs`.
-To install these for linux run:
+To install these for Linux run:
 
 ```bash
 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
@@ -106,7 +153,7 @@ For other systems, see: [Compiling PyAV](https://pyav.org/docs/develop/overview/
 
 ## Optional dependencies
 
-LeRobot provides optional extras for specific functionalities. Multiple extras can be combined (e.g., `.[aloha,feetech]`). For all available extras, refer to `pyproject.toml`.
+LeRobot provides optional extras for specific functionalities. Multiple extras can be combined (e.g., `.[aloha,feetech]`). For all available extras, refer to `pyproject.toml`. If you are using `uv`, replace `pip install` with `uv pip install` in the commands below.
 
 ### Simulations
 

docs/source/lekiwi.mdx

@@ -279,13 +279,13 @@ We use the Hugging Face hub features for uploading your dataset. If you haven't
 Add your token to the CLI by running this command:
 
 ```bash
-huggingface-cli login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential
+hf auth login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential
 ```
 
 Then store your Hugging Face repository name in a variable:
 
 ```bash
-HF_USER=$(huggingface-cli whoami | head -n 1)
+HF_USER=$(hf auth whoami | awk -F': *' 'NR==1 {print $2}')
 echo $HF_USER
 ```
 

docs/source/multi_dataset_training.mdx

@@ -0,0 +1,232 @@
+# Multi-Dataset Training
+
+This guide covers how to train a single policy on multiple heterogeneous datasets using `MultiLeRobotDataset`.
+
+## Overview
+
+Real-world robot learning datasets come from different environments, robots, and camera setups. A RoboCasa dataset might have three cameras named `robot0_agentview_left`, `robot0_agentview_right`, and `robot0_eye_in_hand`, while a LIBERO dataset uses `observation.images.front` and `observation.images.wrist`, and a RoboMME dataset uses bare `image` and `wrist_image` keys. State and action dimensions also differ.
+
+`MultiLeRobotDataset` lets you train on all of them jointly by:
+
+- **Mapping** each dataset's feature keys into a shared namespace
+- **Padding** features that a dataset doesn't have with zeros
+- **Weighting** how often each dataset is sampled
+- **Transforming** samples per-dataset (e.g. padding actions to a common dimension)
+- **Aggregating** statistics across all sub-datasets for normalization
+
+## Configuration
+
+Multi-dataset training is configured via `MultiDatasetConfig` in a YAML config file. Instead of a single `dataset.repo_id`, you provide a `datasets` list where each entry is a `SubDatasetConfig`.
+
+### SubDatasetConfig fields
+
+| Field | Type | Default | Description |
+|-------|------|---------|-------------|
+| `repo_id` | `str` | required | HuggingFace repo ID or local dataset name |
+| `root` | `str \| None` | `None` | Local root directory for the dataset |
+| `episodes` | `list[int] \| None` | `None` | Subset of episode indices to use |
+| `revision` | `str \| None` | `None` | Dataset version / revision |
+| `video_backend` | `str` | auto | Video decoding backend (`pyav`, `torchcodec`, etc.) |
+| `weight` | `float` | `1.0` | Relative sampling weight for this dataset |
+| `feature_map` | `dict[str, str]` | `{}` | Maps dataset keys to unified policy keys |
+| `transforms` | `list` | `None` | Per-dataset transform steps (applied per sample) |
+
+### Example: Three-dataset config
+
+```yaml
+dataset:
+  type: multi
+  use_imagenet_stats: true
+  datasets:
+    # RoboCasa: 3 cameras, state(16), action(12)
+    - repo_id: pepijn223/robocasa_PrepareCoffee
+      root: /data/robocasa_PrepareCoffee
+      weight: 1.0
+      feature_map:
+        observation.images.robot0_agentview_left: observation.images.front_left
+        observation.images.robot0_agentview_right: observation.images.front_right
+        observation.images.robot0_eye_in_hand: observation.images.wrist
+
+    # LIBERO-plus: 2 cameras, state(8), action(7)
+    - repo_id: pepijn223/libero_plus_lerobot
+      root: /data/libero_plus_lerobot
+      weight: 0.5
+      feature_map:
+        observation.images.front: observation.images.front_left
+        observation.images.wrist: observation.images.wrist
+      transforms:
+        - type: pad_action
+          kwargs: {target_dim: 12}
+        - type: pad_state
+          kwargs: {target_dim: 16}
+
+    # RoboMME: 2 cameras (non-standard keys), state(8), action(8)
+    - repo_id: pepijn223/robomme_data_lerobot
+      root: /data/robomme_data_lerobot
+      weight: 0.3
+      feature_map:
+        image: observation.images.front_left
+        wrist_image: observation.images.wrist
+        state: observation.state
+        actions: action
+      transforms:
+        - type: pad_action
+          kwargs: {target_dim: 12}
+        - type: pad_state
+          kwargs: {target_dim: 16}
+```
+
+## Feature Mapping
+
+The `feature_map` dictionary renames dataset-local keys into a shared namespace. Keys not listed pass through unchanged. In the example above, all three datasets end up with the same camera key names (`observation.images.front_left`, `observation.images.wrist`) even though they use different conventions internally.
+
+After mapping, the **union** of all features across datasets defines the unified schema. If a feature exists in some datasets but not others, it is automatically zero-padded for datasets that lack it, and a boolean `{key}_is_pad` flag is added to the sample so the policy can optionally mask padded features.
+
+## Automatic Padding
+
+When a sub-dataset doesn't have a feature that exists in the unified schema:
+
+- **Images/videos**: padded with a black frame (zeros) matching the expected resolution
+- **Float tensors** (state, action): padded with zeros
+- **Integer/bool tensors**: padded with zeros / False
+
+A companion `{key}_is_pad = True` tensor is added so the model can distinguish real data from padding.
+
+## Per-Dataset Transforms
+
+Each sub-dataset can have its own `transforms` pipeline that runs after feature renaming but before cross-dataset padding. This is useful for making shapes compatible before PyTorch's collate function stacks the batch.
+
+### Built-in transforms
+
+| Name | Description | Parameters |
+|------|-------------|------------|
+| `pad_action` | Zero-pad `action` to a target dimension | `target_dim: int` |
+| `pad_state` | Zero-pad `observation.state` to a target dimension | `target_dim: int` |
+| `resize_images` | Resize all `observation.images.*` tensors | `height: int`, `width: int` |
+
+### Custom transforms
+
+You can register your own transforms in `lerobot/datasets/transforms.py`:
+
+```python
+from lerobot.datasets.transforms import DatasetTransformStep, register_dataset_transform
+
+@register_dataset_transform("my_transform")
+class MyTransform(DatasetTransformStep):
+    def __init__(self, some_param: int):
+        self.some_param = some_param
+
+    def __call__(self, sample: dict) -> dict:
+        # Modify sample in-place or return a new dict
+        sample["action"] = sample["action"] * self.some_param
+        return sample
+```
+
+Then reference it in the config:
+
+```yaml
+transforms:
+  - type: my_transform
+    kwargs: {some_param: 2}
+```
+
+## Weighted Sampling
+
+The `weight` field on each sub-dataset controls how often it is sampled during training. Weights are relative and automatically normalized to probabilities. For example, with weights `[1.0, 0.5, 0.3]`, the first dataset is sampled roughly 56% of the time, the second 28%, and the third 16%.
+
+This uses `WeightedEpisodeAwareSampler`, which respects episode boundaries (so `drop_n_last_frames` and similar policy settings work correctly) while sampling across datasets proportionally.
+
+## Stats Aggregation
+
+Normalization statistics (mean, std, min, max, quantiles) are automatically aggregated across all sub-datasets using the mapped feature keys. The aggregation uses a weighted parallel variance algorithm so that datasets with more frames contribute proportionally to the global statistics.
+
+The aggregated stats are used by the standard LeRobot preprocessor for normalization during training.
+
+## Training
+
+Launch training the same way as single-dataset training. The factory and training script automatically detect `MultiDatasetConfig` and set up the weighted sampler:
+
+```bash
+python -m lerobot.scripts.lerobot_train \
+    --config_path path/to/multi_dataset_config.yaml
+```
+
+## Architecture
+
+The data flow during training with `MultiLeRobotDataset`:
+
+```
+┌─────────────────────────────────────────────────────────┐
+│  MultiLeRobotDataset.__getitem__(global_idx)            │
+│                                                         │
+│  1. Map global_idx → (dataset_idx, local_idx)           │
+│  2. Fetch sample from sub-dataset                       │
+│  3. Rename keys via feature_map                         │
+│  4. Apply per-dataset transforms (pad_action, etc.)     │
+│  5. Zero-pad missing features + add _is_pad flags       │
+│  6. Add dataset_index tag                               │
+└─────────────────────┬───────────────────────────────────┘
+                      │
+         ┌────────────▼────────────┐
+         │  PyTorch DataLoader     │
+         │  (collates into batch)  │
+         └────────────┬────────────┘
+                      │
+         ┌────────────▼────────────┐
+         │  LeRobot Preprocessor   │
+         │  (normalize, tokenize)  │
+         └────────────┬────────────┘
+                      │
+         ┌────────────▼────────────┐
+         │  Policy forward + loss  │
+         └─────────────────────────┘
+```
+
+## API Reference
+
+### `NewMultiLeRobotDataset`
+
+```python
+from lerobot.datasets.multi_dataset import NewMultiLeRobotDataset
+
+dataset = NewMultiLeRobotDataset(
+    configs=[...],           # list[SubDatasetConfig]
+    image_transforms=None,   # optional image augmentation
+    delta_timestamps=None,   # optional temporal neighbors
+    tolerance_s=1e-4,        # timestamp tolerance
+)
+
+dataset.num_frames      # total frames across all sub-datasets
+dataset.num_episodes    # total episodes
+dataset.meta            # MultiDatasetMeta (stats, features, episodes)
+dataset.dataset_weights # list of per-dataset weights
+dataset.features        # unified feature dict (union of all mapped features)
+dataset.camera_keys     # unified camera key list
+```
+
+### `WeightedEpisodeAwareSampler`
+
+```python
+from lerobot.datasets.sampler import WeightedEpisodeAwareSampler
+
+sampler = WeightedEpisodeAwareSampler(
+    dataset_from_indices=dataset.meta.episodes["dataset_from_index"],
+    dataset_to_indices=dataset.meta.episodes["dataset_to_index"],
+    dataset_membership=dataset.meta.episodes["dataset_source"],
+    dataset_weights=dataset.dataset_weights,
+    shuffle=True,
+)
+```
+
+### `DatasetTransformPipeline`
+
+```python
+from lerobot.datasets.transforms import DatasetTransformPipeline, DatasetTransformStepConfig
+
+pipeline = DatasetTransformPipeline([
+    DatasetTransformStepConfig(type="pad_action", kwargs={"target_dim": 12}),
+    DatasetTransformStepConfig(type="pad_state", kwargs={"target_dim": 16}),
+])
+
+sample = pipeline(sample)  # modifies the sample dict
+```

docs/source/pi0.mdx

@@ -34,11 +34,6 @@ As described by Physical Intelligence, while AI has achieved remarkable success
    pip install -e ".[pi]"
    ```
 
-   > [!NOTE]
-   > For lerobot 0.4.0, if you want to install pi tag, you will have to do: `pip install "lerobot[pi]@git+https://github.com/huggingface/lerobot.git"`.
-   >
-   > This will be solved in the next patch release
-
 ## Training Data and Capabilities
 
 π₀ is trained on the largest robot interaction dataset to date, combining three key data sources:

docs/source/pi05.mdx

@@ -36,11 +36,6 @@ This diverse training mixture creates a "curriculum" that enables generalization
    pip install -e ".[pi]"
    ```
 
-   > [!NOTE]
-   > For lerobot 0.4.0, if you want to install pi tag, you will have to do: `pip install "lerobot[pi]@git+https://github.com/huggingface/lerobot.git"`.
-   >
-   > This will be solved in the next patch release
-
 ## Usage
 
 To use π₀.₅ in your LeRobot configuration, specify the policy type as:

docs/source/pi0fast.mdx

@@ -43,16 +43,11 @@ This approach can transform **any existing VLM** into a VLA by training it to pr
    pip install -e ".[pi]"
    ```
 
-   > [!NOTE]
-   > For lerobot 0.4.0, if you want to install the pi tag, you will have to do: `pip install "lerobot[pi]@git+https://github.com/huggingface/lerobot.git"`.
-   >
-   > This will be solved in the next patch release
-
 ## Training a Custom FAST Tokenizer
 
 You have two options for the FAST tokenizer:
 
-1. **Use the pre-trained tokenizer**: The `physical-intelligence/fast` tokenizer was trained on 1M+ real robot action sequences and works as a general-purpose tokenizer.
+1. **Use the pre-trained tokenizer**: The `lerobot/fast-action-tokenizer` tokenizer was trained on 1M+ real robot action sequences and works as a general-purpose tokenizer.
 
 2. **Train your own tokenizer**: For maximum performance on your specific dataset, you can finetune the tokenizer on your own data.
 
@@ -115,13 +110,13 @@ lerobot-train \
 ### Key Training Parameters
 
 | Parameter                              | Description                                        | Default                         |
-| -------------------------------------- | -------------------------------------------------- | ---------------------------- |
+| -------------------------------------- | -------------------------------------------------- | ------------------------------- |
 | `--policy.gradient_checkpointing=true` | Reduces memory usage significantly during training | `false`                         |
 | `--policy.dtype=bfloat16`              | Use mixed precision training for efficiency        | `float32`                       |
 | `--policy.chunk_size`                  | Number of action steps to predict (action horizon) | `50`                            |
 | `--policy.n_action_steps`              | Number of action steps to execute                  | `50`                            |
 | `--policy.max_action_tokens`           | Maximum number of FAST tokens per action chunk     | `256`                           |
-| `--policy.action_tokenizer_name`       | FAST tokenizer to use                              | `physical-intelligence/fast` |
+| `--policy.action_tokenizer_name`       | FAST tokenizer to use                              | `lerobot/fast-action-tokenizer` |
 | `--policy.compile_model=true`          | Enable torch.compile for faster training           | `false`                         |
 
 ## Inference

docs/source/unitree_g1.mdx

@@ -1,23 +1,49 @@
 # Unitree G1
 
-This guide covers the complete setup process for the Unitree G1 humanoid, from initial connection to running gr00t_wbc locomotion.
+<img
+  src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/unitree_thumbnail.jpg"
+  alt="Unitree G1 locomanipulation demo"
+  style={{ width: "100%" }}
+/>
 
-## About
+The Unitree G1 humanoid is now supported in LeRobot! You can teleoperate, train locomanipulation policies, test in sim, and more. Both 29 and 23 DoF variants are supported.
-
-We support both 29 and 23 DOF G1 EDU version. We introduce:
-
-- **`unitree g1` robot class, handling low level read/write from/to the humanoid**
-- **ZMQ socket bridge** for remote communication and camera streaming, allowing for remote policy deployment over wlan, eth or directly on the robot
-- **Locomotion policies** from NVIDIA gr00t and Amazon FAR Holosoma
-- **Simulation mode** for testing policies without the physical robot in mujoco
 
 ---
 
-## Connection guide
+## Part 1: Getting Started
 
-### Step 1: Configure Ethernet Interface
+### Install LeRobot on Your Machine
 
-Set a static IP on the same subnet as the robot:
+```bash
+conda create -y -n lerobot python=3.12
+conda activate lerobot
+git clone https://github.com/unitreerobotics/unitree_sdk2_python.git
+cd unitree_sdk2_python && pip install -e .
+git clone https://github.com/huggingface/lerobot.git
+cd lerobot
+pip install -e '.[unitree_g1]'
+```
+
+### Test the Installation (Simulation)
+
+```bash
+lerobot-teleoperate \
+  --robot.type=unitree_g1 \
+  --robot.is_simulation=true \
+  --teleop.type=unitree_g1 \
+  --teleop.id=wbc_unitree \
+  --robot.cameras='{"global_view": {"type": "zmq", "server_address": "localhost", "port": 5555, "camera_name": "head_camera", "width": 640, "height": 480, "fps": 30}}' \
+  --display_data=true
+```
+
+This will launch a [MuJoCo sim instance](https://huggingface.co/lerobot/unitree-g1-mujoco/tree/main) for the G1.
+
+- Press `9` to release the robot
+- Press `7` / `8` to increase / decrease waist height
+
+### Connect to the Robot
+
+The G1's Ethernet IP is fixed at `192.168.123.164`. Your machine must have a static IP on the same subnet: `192.168.123.x` where `x ≠ 164`.
 
 ```bash
 # Replace 'enp131s0' with your ethernet interface name (check with `ip a`)
@@ -26,171 +52,121 @@ sudo ip addr add 192.168.123.200/24 dev enp131s0
 sudo ip link set enp131s0 up
 ```
 
-**Note**: The G1's Ethernet IP is fixed at `192.168.123.164`. Your computer must use `192.168.123.x` with x ≠ 164.
+### SSH into the Robot
-
-### Step 2: SSH into the Robot
 
 ```bash
 ssh unitree@192.168.123.164
 # Password: 123
 ```
 
-You should now be connected to the G1's Orin.
+### Install LeRobot on the G1
+
+From the robot:
+
+```bash
+conda create -y -n lerobot python=3.12
+conda activate lerobot
+git clone https://github.com/unitreerobotics/unitree_sdk2_python.git
+cd unitree_sdk2_python && pip install -e .
+git clone https://github.com/huggingface/lerobot.git
+cd lerobot
+pip install -e '.[unitree_g1]'
+```
+
+> **Note:** The Unitree SDK requires CycloneDDS v0.10.2. See the [Unitree SDK docs](https://github.com/unitreerobotics/unitree_sdk2_python) for details.
 
 ---
 
 ## Part 2: Enable WiFi on the Robot
 
-Wlan0 is disabled by default on the G1. To enable it:
+Wi-Fi connectivity is blocked by default on the G1. To activate:
-
-### Step 1: Enable WiFi Hardware
 
 ```bash
-sudo rfkill unblock wifi
 sudo rfkill unblock all
-
-# Bring up wlan0
 sudo ip link set wlan0 up
-
-# Enable NetworkManager control of wlan0
 sudo nmcli radio wifi on
 sudo nmcli device set wlan0 managed yes
 sudo systemctl restart NetworkManager
 ```
 
-### Step 2: Enable Internet Forwarding
+**On your laptop** (share internet via Ethernet):
-
-**On your laptop:**
 
 ```bash
-# Enable IP forwarding
 sudo sysctl -w net.ipv4.ip_forward=1
 
-# Set up NAT (replace wlp132s0f0 with your WiFi interface)
+# Replace wlp132s0f0 with your WiFi interface name
 sudo iptables -t nat -A POSTROUTING -o wlp132s0f0 -s 192.168.123.0/24 -j MASQUERADE
 sudo iptables -A FORWARD -i wlp132s0f0 -o enp131s0 -m state --state RELATED,ESTABLISHED -j ACCEPT
 sudo iptables -A FORWARD -i enp131s0 -o wlp132s0f0 -j ACCEPT
 ```
 
-**On the G1:**
+**On the G1** (set default route through your laptop):
 
 ```bash
-# Add laptop as default gateway
 sudo ip route del default 2>/dev/null || true
 sudo ip route add default via 192.168.123.200 dev eth0
 echo "nameserver 8.8.8.8" | sudo tee /etc/resolv.conf
 
-# Test connection
+# Verify
 ping -c 3 8.8.8.8
 ```
 
-### Step 3: Connect to WiFi Network
+**Connect to a WiFi network:**
 
 ```bash
-# List available networks
 nmcli device wifi list
 
-# Connect to your WiFi (example)
 sudo nmcli connection add type wifi ifname wlan0 con-name "YourNetwork" ssid "YourNetwork"
 sudo nmcli connection modify "YourNetwork" wifi-sec.key-mgmt wpa-psk
 sudo nmcli connection modify "YourNetwork" wifi-sec.psk "YourPassword"
 sudo nmcli connection modify "YourNetwork" connection.autoconnect yes
 sudo nmcli connection up "YourNetwork"
 
-# Check WiFi IP address
 ip a show wlan0
 ```
 
-### Step 4: SSH Over WiFi
+You can now SSH over WiFi:
-
-Once connected to WiFi, note the robot's IP address and disconnect the Ethernet cable. You can now SSH over WiFi:
 
 ```bash
-ssh unitree@<YOUR_ROBOT_IP>
+ssh unitree@<ROBOT_WIFI_IP>
 # Password: 123
 ```
 
-Replace `<YOUR_ROBOT_IP>` with your robot's actual WiFi IP address.
-
 ---
 
-## Part 3: Robot Server Setup
+## Part 3: Teleoperation & Locomotion
 
-### Step 1: Install LeRobot on the Orin
+### Run the Robot Server
-
-SSH into the robot and install LeRobot:
-
-```bash
-ssh unitree@<YOUR_ROBOT_IP>
-
-conda create -y -n lerobot python=3.10
-conda activate lerobot
-git clone https://github.com/huggingface/lerobot.git
-cd lerobot
-pip install -e '.[unitree_g1]'
-git clone https://github.com/unitreerobotics/unitree_sdk2_python.git
-cd unitree_sdk2_python  && pip install -e .
-```
-
-**Note**: The Unitree SDK requires CycloneDDS v0.10.2 to be installed. See the [Unitree SDK documentation](https://github.com/unitreerobotics/unitree_sdk2_python) for details.
-
-### Step 2: Run the Robot Server
 
 On the robot:
 
 ```bash
-python src/lerobot/robots/unitree_g1/run_g1_server.py
+python src/lerobot/robots/unitree_g1/run_g1_server.py --camera
 ```
 
-**Important**: Keep this terminal running. The server must be active for remote control.
+### Run the Locomotion Policy
+
+```bash
+lerobot-teleoperate \
+  --robot.type=unitree_g1 \
+  --robot.is_simulation=false \
+  --robot.robot_ip=<ROBOT_IP> \
+  --teleop.type=unitree_g1 \
+  --teleop.id=wbc_unitree \
+  --robot.cameras='{"global_view": {"type": "zmq", "server_address": "<ROBOT_IP>", "port": 5555, "camera_name": "head_camera", "width": 640, "height": 480, "fps": 30}}' \
+  --display_data=true \
+  --robot.controller=HolosomaLocomotionController
+```
+
+We support both [HolosomaLocomotionController](https://github.com/amazon-far/holosoma) and [GrootLocomotionController](https://github.com/NVlabs/GR00T-WholeBodyControl).
 
 ---
 
-## Part 4: Controlling the robot
+## Part 4: Loco-Manipulation with the Homunculus Exoskeleton
 
-With the robot server running, you can now control the robot remotely. Let's launch a locomotion policy
+We provide a loco-manipulation solution via the Homunculus Exoskeleton — an open-source 7 DoF exoskeleton for whole-body control. Assembly instructions [here](https://github.com/nepyope/hmc_exo).
 
-### Step 1: Install LeRobot on your machine
+### Calibrate
-
-```bash
-conda create -y -n lerobot python=3.10
-conda activate lerobot
-git clone https://github.com/huggingface/lerobot.git
-cd lerobot
-pip install -e '.[unitree_g1]'
-git clone https://github.com/unitreerobotics/unitree_sdk2_python.git
-cd unitree_sdk2_python  && pip install -e .
-```
-
-### Step 2: Update Robot IP in Config
-
-Edit the config file to match your robot's WiFi IP:
-
-```python
-# In src/lerobot/robots/unitree_g1/config_unitree_g1.py
-robot_ip: str = "<YOUR_ROBOT_IP>"  # Replace with your robot's WiFi IP.
-```
-
-### Step 3: Run the Locomotion Policy
-
-```bash
-# Run GR00T locomotion controller
-python examples/unitree_g1/gr00t_locomotion.py --repo-id "nepyope/GR00T-WholeBodyControl_g1"
-
-# Run Holosoma locomotion controller
-python examples/unitree_g1/holosoma_locomotion.py
-
-```
-
-Press `Ctrl+C` to stop the policy.
-
----
-
-## Running in Simulation Mode (MuJoCo)
-
-You can test policies before deploying on the physical robot using MuJoCo simulation. Set `is_simulation=True` in config or pass `--robot.is_simulation=true` via CLI.
-
-### Calibrate Exoskeleton Teleoperator
 
 ```bash
 lerobot-calibrate \
@@ -200,20 +176,9 @@ lerobot-calibrate \
   --teleop.id=exo
 ```
 
-### Teleoperate in Simulation
+During calibration move each joint through its entire range. After fitting, move the joint in a neutral position and press `n` to advance.
 
-```bash
+### Record a Dataset
-lerobot-teleoperate \
-    --robot.type=unitree_g1 \
-    --robot.is_simulation=true \
-    --teleop.type=unitree_g1 \
-    --teleop.left_arm_config.port=/dev/ttyACM1 \
-    --teleop.right_arm_config.port=/dev/ttyACM0 \
-    --teleop.id=exo \
-    --fps=100
-```
-
-### Record Dataset in Simulation
 
 ```bash
 lerobot-record \
@@ -231,66 +196,55 @@ lerobot-record \
   --dataset.reset_time_s=5 \
   --dataset.push_to_hub=true \
   --dataset.streaming_encoding=true \
-    # --dataset.vcodec=auto \
   --dataset.encoder_threads=2
 ```
 
-Example simulation dataset: [nepyope/teleop_test_sim](https://huggingface.co/datasets/nepyope/teleop_test_sim)
+> **Note:** Omit `--teleop.left_arm_config.port` and `--teleop.right_arm_config.port` if you're only using the joystick.
+
+Example dataset: [nepyope/unitree_box_move_blue_full](https://huggingface.co/datasets/nepyope/unitree_box_move_blue_full)
 
 ---
 
-## Running on Real Robot
+## Part 5: Training & Inference
 
-Once the robot server is running on the G1 (see Part 3), you can teleoperate and record on the real robot.
+### Train
-
-### Start the Camera Server
-
-On the robot, start the ZMQ image server:
 
 ```bash
-python src/lerobot/cameras/zmq/image_server.py
+python src/lerobot/scripts/lerobot_train.py \
-```
-
-Keep this running in a separate terminal for camera streaming during recording.
-
-### Teleoperate Real Robot
-
-```bash
-lerobot-teleoperate \
-    --robot.type=unitree_g1 \
-    --robot.is_simulation=false \
-    --teleop.type=unitree_g1 \
-    --teleop.left_arm_config.port=/dev/ttyACM1 \
-    --teleop.right_arm_config.port=/dev/ttyACM0 \
-    --teleop.id=exo \
-    --fps=100
-```
-
-### Record Dataset on Real Robot
-
-```bash
-lerobot-record \
-    --robot.type=unitree_g1 \
-    --robot.is_simulation=false \
-    --robot.cameras='{"global_view": {"type": "zmq", "server_address": "172.18.129.215", "port": 5555, "camera_name": "head_camera", "width": 640, "height": 480, "fps": 30}}' \
-    --teleop.type=unitree_g1 \
-    --teleop.left_arm_config.port=/dev/ttyACM1 \
-    --teleop.right_arm_config.port=/dev/ttyACM0 \
-    --teleop.id=exo \
   --dataset.repo_id=your-username/dataset-name  \
-    --dataset.single_task="Test" \
+  --policy.type=pi05 \
-    --dataset.num_episodes=2 \
+  --output_dir=./outputs/pi05_training \
-    --dataset.episode_time_s=5 \
+  --job_name=pi05_training \
-    --dataset.reset_time_s=5 \
+  --policy.repo_id=your-username/your-repo-id \
-    --dataset.push_to_hub=true \
+  --policy.pretrained_path=lerobot/pi05_base \
-    --dataset.streaming_encoding=true \
+  --policy.compile_model=true \
-    # --dataset.vcodec=auto \
+  --policy.gradient_checkpointing=true \
-    --dataset.encoder_threads=2
+  --wandb.enable=true \
+  --policy.dtype=bfloat16 \
+  --policy.freeze_vision_encoder=false \
+  --policy.train_expert_only=false \
+  --steps=3000 \
+  --policy.device=cuda \
+  --batch_size=32
 ```
 
-**Note**: Update `server_address` to match your robot's camera server IP.
+### Inference with RTC
 
-Example real robot dataset: [nepyope/teleop_test_real](https://huggingface.co/datasets/nepyope/teleop_test_real)
+Once trained, we recommend deploying policies using inference-time RTC:
+
+```bash
+python examples/rtc/eval_with_real_robot.py \
+  --policy.path=your-username/your-repo-id \
+  --policy.device=cuda \
+  --robot.type=unitree_g1 \
+  --robot.is_simulation=false \
+  --robot.controller=HolosomaLocomotionController \
+  --robot.cameras='{"global_view": {"type": "zmq", "server_address": "<ROBOT_IP>", "port": 5555, "camera_name": "head_camera", "width": 640, "height": 480, "fps": 30}}' \
+  --task="task_description" \
+  --duration=1000 \
+  --fps=30 \
+  --rtc.enabled=true
+```
 
 ---
 
@@ -300,8 +254,8 @@ Example real robot dataset: [nepyope/teleop_test_real](https://huggingface.co/da
 - [GR00T-WholeBodyControl](https://github.com/NVlabs/GR00T-WholeBodyControl)
 - [Holosoma](https://github.com/amazon-far/holosoma)
 - [LeRobot Documentation](https://github.com/huggingface/lerobot)
-- [Unitree_IL_Lerobot](https://github.com/unitreerobotics/unitree_IL_lerobot)
+- [Unitree IL LeRobot](https://github.com/unitreerobotics/unitree_IL_lerobot)
 
 ---
 
-_Last updated: December 2025_
+_Last updated: March 2026_

examples/backward_compatibility/replay.py

@@ -57,7 +57,7 @@ class DatasetReplayConfig:
     repo_id: str
     # Episode to replay.
     episode: int
-    # Root directory where the dataset will be stored (e.g. 'dataset/path').
+    # Root directory where the dataset will be stored (e.g. 'dataset/path'). If None, defaults to $HF_LEROBOT_HOME/repo_id.
     root: str | Path | None = None
     # Limit the frames per second. By default, uses the policy fps.
     fps: int = 30

examples/dataset/slurm_compute_rabc.py

@@ -0,0 +1,490 @@
+#!/usr/bin/env python
+
+# Copyright 2025 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.
+
+"""
+SLURM-distributed SARM RA-BC annotation pipeline.
+
+Computes SARM progress values for all frames in a dataset, distributed across
+SLURM workers, then merges the shards into a single sarm_progress.parquet.
+
+Two subcommands, each a separate SLURM submission:
+
+  compute    – N workers, each computes progress for a subset of episodes
+  aggregate  – 1 worker, merges N shards into sarm_progress.parquet, pushes to hub
+
+Usage:
+    python slurm_compute_rabc.py compute \\
+        --repo-id user/dataset --reward-model-path user/sarm_model \\
+        --stride 10 --device cpu --workers 50 --partition cpu
+
+    python slurm_compute_rabc.py aggregate \\
+        --repo-id user/dataset --reward-model-path user/sarm_model \\
+        --partition cpu --push-to-hub
+"""
+
+import argparse
+from pathlib import Path
+
+from datatrove.executor import LocalPipelineExecutor
+from datatrove.executor.slurm import SlurmPipelineExecutor
+from datatrove.pipeline.base import PipelineStep
+
+
+class ComputeProgressShards(PipelineStep):
+    """Each worker computes SARM progress for its assigned episodes."""
+
+    def __init__(
+        self, repo_id, reward_model_path, stride=1, head_mode="sparse", device="cpu", shard_dir="rabc_shards"
+    ):
+        super().__init__()
+        if stride < 1:
+            raise ValueError(f"stride must be >= 1, got {stride}")
+        self.repo_id = repo_id
+        self.reward_model_path = reward_model_path
+        self.stride = stride
+        self.head_mode = head_mode
+        self.device = device
+        self.shard_dir = shard_dir
+
+    def run(self, data=None, rank: int = 0, world_size: int = 1):
+        import logging
+        from pathlib import Path
+
+        import numpy as np
+        import pyarrow as pa
+        import pyarrow.parquet as pq
+        import torch
+        from tqdm import tqdm
+
+        from lerobot.policies.sarm.compute_rabc_weights import (
+            generate_all_frame_indices,
+            interpolate_progress,
+            load_sarm_resources,
+        )
+        from lerobot.utils.utils import init_logging
+
+        init_logging()
+
+        dataset, reward_model, preprocess = load_sarm_resources(
+            self.repo_id,
+            self.reward_model_path,
+            self.device,
+        )
+
+        if hasattr(preprocess, "eval"):
+            preprocess.eval()
+        for step in preprocess.steps:
+            if hasattr(step, "eval"):
+                step.eval()
+
+        image_key = reward_model.config.image_key
+        state_key = reward_model.config.state_key
+        frame_gap = reward_model.config.frame_gap
+        center_idx = reward_model.config.n_obs_steps // 2
+
+        dual_mode = reward_model.config.uses_dual_heads
+        compute_sparse = self.head_mode in ("sparse", "both") or not dual_mode
+        compute_dense = self.head_mode in ("dense", "both") and dual_mode
+
+        my_episodes = list(range(dataset.num_episodes))[rank::world_size]
+        if not my_episodes:
+            logging.info(f"Rank {rank}: no episodes assigned")
+            return
+        logging.info(f"Rank {rank}: {len(my_episodes)} / {dataset.num_episodes} episodes")
+
+        all_rows = []
+
+        for ep_idx in tqdm(my_episodes, desc=f"Rank {rank}"):
+            ep = dataset.meta.episodes[ep_idx]
+            ep_start, ep_end = ep["dataset_from_index"], ep["dataset_to_index"]
+            task = dataset[ep_start].get("task", "perform the task")
+
+            all_ep_indices = generate_all_frame_indices(ep_start, ep_end, frame_gap)
+            if self.stride > 1:
+                compute_indices = [i for i in all_ep_indices if (i - ep_start) % self.stride == 0]
+                if (ep_end - 1) not in compute_indices:
+                    compute_indices.append(ep_end - 1)
+                compute_indices = sorted(set(compute_indices))
+            else:
+                compute_indices = all_ep_indices
+
+            frame_results = {}
+            for qi in tqdm(compute_indices, desc=f"  Ep {ep_idx}", leave=False):
+                try:
+                    sample = dataset[qi]
+                    batch = {
+                        image_key: sample[image_key],
+                        "task": task,
+                        "index": qi,
+                        "episode_index": ep_idx,
+                    }
+                    if state_key in sample:
+                        batch[state_key] = sample[state_key]
+
+                    with torch.no_grad():
+                        processed = preprocess(batch)
+                        vf = processed["video_features"].to(self.device)
+                        tf = processed["text_features"].to(self.device)
+                        sf = processed.get("state_features")
+                        if sf is not None:
+                            sf = sf.to(self.device)
+                        lengths = processed.get("lengths")
+
+                        sparse_val = dense_val = np.nan
+                        if compute_sparse:
+                            r = reward_model.calculate_rewards(
+                                text_embeddings=tf,
+                                video_embeddings=vf,
+                                state_features=sf,
+                                lengths=lengths,
+                                return_all_frames=True,
+                                head_mode="sparse",
+                            )
+                            sparse_val = float(r[0, center_idx] if r.ndim == 2 else r[center_idx])
+                        if compute_dense:
+                            r = reward_model.calculate_rewards(
+                                text_embeddings=tf,
+                                video_embeddings=vf,
+                                state_features=sf,
+                                lengths=lengths,
+                                return_all_frames=True,
+                                head_mode="dense",
+                            )
+                            dense_val = float(r[0, center_idx] if r.ndim == 2 else r[center_idx])
+
+                        frame_results[qi] = (sparse_val, dense_val)
+                except Exception as e:
+                    logging.warning(f"Failed frame {qi}: {e}")
+
+            if not frame_results:
+                logging.warning(f"Episode {ep_idx}: all frames failed, skipping")
+                continue
+
+            # Interpolate to all frames in this episode
+            computed_idx = np.array(sorted(frame_results.keys()))
+            all_frame_arr = np.arange(ep_start, ep_end)
+
+            sparse_vals = np.array([frame_results[i][0] for i in computed_idx]) if compute_sparse else None
+            dense_vals = np.array([frame_results[i][1] for i in computed_idx]) if compute_dense else None
+
+            if self.stride > 1 and len(computed_idx) > 1:
+                if compute_sparse:
+                    sparse_vals = interpolate_progress(computed_idx, sparse_vals, all_frame_arr)
+                if compute_dense:
+                    dense_vals = interpolate_progress(computed_idx, dense_vals, all_frame_arr)
+                output_frames = all_frame_arr
+            else:
+                # Use only successfully computed frames to avoid indexing mismatch on failures
+                output_frames = computed_idx
+
+            for i, fi in enumerate(output_frames):
+                row = {"index": int(fi), "episode_index": ep_idx, "frame_index": int(fi - ep_start)}
+                if compute_sparse:
+                    row["progress_sparse"] = float(sparse_vals[i])
+                if compute_dense:
+                    row["progress_dense"] = float(dense_vals[i])
+                all_rows.append(row)
+
+        if all_rows:
+            import pandas as pd
+
+            df = pd.DataFrame(all_rows).sort_values("index").reset_index(drop=True)
+            table = pa.Table.from_pandas(df, preserve_index=False)
+            table = table.replace_schema_metadata({b"reward_model_path": self.reward_model_path.encode()})
+            shard_dir = Path(self.shard_dir)
+            shard_dir.mkdir(parents=True, exist_ok=True)
+            out = shard_dir / f"shard_{rank:05d}.parquet"
+            pq.write_table(table, out)
+            logging.info(f"Rank {rank}: saved {len(df)} rows to {out}")
+
+
+class AggregateProgress(PipelineStep):
+    """Merge all shard parquets into final sarm_progress.parquet."""
+
+    def __init__(self, repo_id, reward_model_path, shard_dir="rabc_shards", push_to_hub=False):
+        super().__init__()
+        self.repo_id = repo_id
+        self.reward_model_path = reward_model_path
+        self.shard_dir = shard_dir
+        self.push_to_hub = push_to_hub
+
+    def run(self, data=None, rank: int = 0, world_size: int = 1):
+        import datetime
+        import logging
+        import os
+        from pathlib import Path
+
+        import pandas as pd
+        import pyarrow as pa
+        import pyarrow.parquet as pq
+
+        from lerobot.datasets.lerobot_dataset import LeRobotDataset
+        from lerobot.utils.utils import init_logging
+
+        init_logging()
+        if rank != 0:
+            return
+
+        shard_dir = Path(self.shard_dir)
+        shards = sorted(shard_dir.glob("shard_*.parquet"))
+        if not shards:
+            raise FileNotFoundError(f"No shards found in {shard_dir}")
+
+        # Log shard modification time range to help detect stale files
+        mtimes = [os.path.getmtime(s) for s in shards]
+        oldest = datetime.datetime.fromtimestamp(min(mtimes)).isoformat(timespec="seconds")
+        newest = datetime.datetime.fromtimestamp(max(mtimes)).isoformat(timespec="seconds")
+        logging.info(f"Aggregating {len(shards)} shards (oldest: {oldest}, newest: {newest})")
+
+        df = pd.concat([pd.read_parquet(s) for s in shards], ignore_index=True)
+        df = df.sort_values("index").reset_index(drop=True)
+
+        table = pa.Table.from_pandas(df, preserve_index=False)
+        table = table.replace_schema_metadata({b"reward_model_path": self.reward_model_path.encode()})
+
+        temp_ds = LeRobotDataset(self.repo_id, download_videos=False)
+        out_path = Path(temp_ds.root) / "sarm_progress.parquet"
+        out_path.parent.mkdir(parents=True, exist_ok=True)
+        pq.write_table(table, out_path)
+        logging.info(f"Saved {len(df)} rows to {out_path}")
+
+        for col in ["progress_sparse", "progress_dense"]:
+            if col in df.columns:
+                v = df[col].dropna()
+                logging.info(
+                    f"{col}: mean={v.mean():.4f} std={v.std():.4f} min={v.min():.4f} max={v.max():.4f}"
+                )
+
+        if self.push_to_hub:
+            from huggingface_hub import HfApi
+
+            api = HfApi()
+            hub_path = "sarm_progress.parquet"
+            logging.info(f"Uploading to {self.repo_id}/{hub_path}")
+            api.upload_file(
+                path_or_fileobj=str(out_path),
+                path_in_repo=hub_path,
+                repo_id=self.repo_id,
+                repo_type="dataset",
+            )
+            logging.info(f"Uploaded: https://huggingface.co/datasets/{self.repo_id}/blob/main/{hub_path}")
+
+
+def make_compute_executor(
+    repo_id,
+    reward_model_path,
+    stride,
+    head_mode,
+    device,
+    shard_dir,
+    logs_dir,
+    job_name,
+    slurm,
+    workers,
+    partition,
+    cpus_per_task,
+    mem_per_cpu,
+):
+    kwargs = {
+        "pipeline": [
+            ComputeProgressShards(repo_id, reward_model_path, stride, head_mode, device, str(shard_dir)),
+        ],
+        "logging_dir": str(logs_dir / job_name),
+    }
+
+    if slurm:
+        kwargs.update(
+            {
+                "job_name": job_name,
+                "tasks": workers,
+                "workers": workers,
+                "time": "24:00:00",
+                "partition": partition,
+                "cpus_per_task": cpus_per_task,
+                "sbatch_args": {"mem-per-cpu": mem_per_cpu},
+            }
+        )
+        return SlurmPipelineExecutor(**kwargs)
+
+    kwargs.update({"tasks": workers, "workers": 1})
+    return LocalPipelineExecutor(**kwargs)
+
+
+def make_aggregate_executor(
+    repo_id,
+    reward_model_path,
+    shard_dir,
+    logs_dir,
+    job_name,
+    slurm,
+    partition,
+    cpus_per_task,
+    mem_per_cpu,
+    push_to_hub,
+):
+    kwargs = {
+        "pipeline": [
+            AggregateProgress(repo_id, reward_model_path, str(shard_dir), push_to_hub),
+        ],
+        "logging_dir": str(logs_dir / job_name),
+    }
+
+    if slurm:
+        kwargs.update(
+            {
+                "job_name": job_name,
+                "tasks": 1,
+                "workers": 1,
+                "time": "02:00:00",
+                "partition": partition,
+                "cpus_per_task": cpus_per_task,
+                "sbatch_args": {"mem-per-cpu": mem_per_cpu},
+            }
+        )
+        return SlurmPipelineExecutor(**kwargs)
+
+    kwargs.update({"tasks": 1, "workers": 1})
+    return LocalPipelineExecutor(**kwargs)
+
+
+def _add_shared_args(p):
+    p.add_argument(
+        "--repo-id",
+        type=str,
+        required=True,
+        help="Hugging Face repository identifier, e.g. 'user/dataset'.",
+    )
+    p.add_argument(
+        "--shard-dir",
+        type=Path,
+        default=Path("rabc_shards"),
+        help="Directory to read/write per-rank parquet shards.",
+    )
+    p.add_argument(
+        "--logs-dir",
+        type=Path,
+        default=Path("logs"),
+        help="Directory for datatrove logs.",
+    )
+    p.add_argument(
+        "--job-name",
+        type=str,
+        default=None,
+        help="SLURM job name (defaults to rabc_<subcommand>).",
+    )
+    p.add_argument(
+        "--slurm",
+        type=int,
+        default=1,
+        help="1 = submit via SLURM; 0 = run locally (useful for debugging).",
+    )
+    p.add_argument(
+        "--partition",
+        type=str,
+        default=None,
+        help="SLURM partition to submit to.",
+    )
+    p.add_argument(
+        "--cpus-per-task",
+        type=int,
+        default=4,
+        help="Number of CPUs per SLURM task.",
+    )
+    p.add_argument(
+        "--mem-per-cpu",
+        type=str,
+        default="4G",
+        help="Memory per CPU, e.g. '4G' or '1950M'.",
+    )
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="SLURM-distributed SARM RA-BC annotation pipeline",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+    )
+    sub = parser.add_subparsers(dest="command", required=True)
+
+    # compute subcommand
+    cp = sub.add_parser(
+        "compute",
+        help="Distribute progress computation across SLURM workers.",
+    )
+    _add_shared_args(cp)
+    cp.add_argument(
+        "--reward-model-path",
+        type=str,
+        required=True,
+        help="Path or HF repo id of the SARM reward model.",
+    )
+    cp.add_argument(
+        "--stride",
+        type=int,
+        default=1,
+        help="Compute every Nth frame; intermediate frames are interpolated (must be >= 1).",
+    )
+    cp.add_argument(
+        "--head-mode",
+        type=str,
+        default="sparse",
+        choices=["sparse", "dense", "both"],
+        help="Which reward head(s) to compute.",
+    )
+    cp.add_argument(
+        "--device",
+        type=str,
+        default="cpu",
+        help="Device for reward model inference, e.g. 'cpu' or 'cuda'.",
+    )
+    cp.add_argument(
+        "--workers",
+        type=int,
+        default=50,
+        help="Number of parallel SLURM tasks (one shard per worker).",
+    )
+
+    # aggregate subcommand
+    ap = sub.add_parser(
+        "aggregate",
+        help="Merge per-rank shards into a single sarm_progress.parquet.",
+    )
+    _add_shared_args(ap)
+    ap.add_argument(
+        "--reward-model-path",
+        type=str,
+        required=True,
+        help="Path or HF repo id of the SARM reward model (stored in parquet metadata).",
+    )
+    ap.add_argument(
+        "--push-to-hub",
+        action="store_true",
+        help="Upload sarm_progress.parquet to the Hugging Face Hub after aggregation.",
+    )
+
+    args = parser.parse_args()
+    job_name = args.job_name or f"rabc_{args.command}"
+    kwargs = vars(args)
+    kwargs["slurm"] = kwargs.pop("slurm") == 1
+    kwargs["job_name"] = job_name
+    command = kwargs.pop("command")
+
+    executor = make_compute_executor(**kwargs) if command == "compute" else make_aggregate_executor(**kwargs)
+
+    executor.run()
+
+
+if __name__ == "__main__":
+    main()

examples/rtc/eval_with_real_robot.py

@@ -78,6 +78,7 @@ from torch import Tensor
 
 from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig  # noqa: F401
 from lerobot.cameras.realsense.configuration_realsense import RealSenseCameraConfig  # noqa: F401
+from lerobot.cameras.zmq.configuration_zmq import ZMQCameraConfig  # noqa: F401
 from lerobot.configs import parser
 from lerobot.configs.policies import PreTrainedConfig
 from lerobot.configs.types import RTCAttentionSchedule
@@ -97,6 +98,7 @@ from lerobot.robots import (  # noqa: F401
     bi_so_follower,
     koch_follower,
     so_follower,
+    unitree_g1,
 )
 from lerobot.robots.utils import make_robot_from_config
 from lerobot.utils.constants import OBS_IMAGES

pyproject.toml

@@ -25,11 +25,11 @@ discord = "https://discord.gg/s3KuuzsPFb"
 
 [project]
 name = "lerobot"
-version = "0.4.4"
+version = "0.5.1"
 description = "🤗 LeRobot: State-of-the-art Machine Learning for Real-World Robotics in Pytorch"
 dynamic = ["readme"]
 license = { text = "Apache-2.0" }
-requires-python = ">=3.10"
+requires-python = ">=3.12"
 authors = [
     { name = "Rémi Cadène", email = "re.cadene@gmail.com" },
     { name = "Simon Alibert", email = "alibert.sim@gmail.com" },
@@ -50,7 +50,8 @@ classifiers = [
     "Intended Audience :: Education",
     "Intended Audience :: Science/Research",
     "License :: OSI Approved :: Apache Software License",
-    "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.12",
+    "Programming Language :: Python :: 3.13",
     "Topic :: Software Development :: Build Tools",
     "Topic :: Scientific/Engineering :: Artificial Intelligence",
 ]
@@ -61,26 +62,28 @@ dependencies = [
     # Hugging Face dependencies
     "datasets>=4.0.0,<5.0.0",
     "diffusers>=0.27.2,<0.36.0",
-    "huggingface-hub[hf-transfer,cli]>=0.34.2,<0.36.0",
+    "huggingface-hub>=1.0.0,<2.0.0",
     "accelerate>=1.10.0,<2.0.0",
 
     # Core dependencies
+    "numpy>=2.0.0,<2.3.0", # NOTE: Explicitly listing numpy helps the resolver converge faster. Upper bound imposed by opencv-python-headless.
     "setuptools>=71.0.0,<81.0.0",
     "cmake>=3.29.0.1,<4.2.0",
+    "packaging>=24.2,<26.0",
+
+    "torch>=2.2.1,<2.11.0",
+    "torchcodec>=0.2.1,<0.11.0; sys_platform != 'win32' and (sys_platform != 'linux' or (platform_machine != 'aarch64' and platform_machine != 'arm64' and platform_machine != 'armv7l')) and (sys_platform != 'darwin' or platform_machine != 'x86_64')",
+    "torchvision>=0.21.0,<0.26.0",
+
     "einops>=0.8.0,<0.9.0",
     "opencv-python-headless>=4.9.0,<4.13.0",
     "av>=15.0.0,<16.0.0",
     "jsonlines>=4.0.0,<5.0.0",
-    "packaging>=24.2,<26.0",
+    "pynput>=1.7.8,<1.9.0",
-    "pynput>=1.7.7,<1.9.0",
     "pyserial>=3.5,<4.0",
+
     "wandb>=0.24.0,<0.25.0",
-
+    "draccus==0.10.0", # TODO: Relax version constraint
-    "torch>=2.2.1,<2.11.0", # TODO: Bump dependency
-    "torchcodec>=0.2.1,<0.11.0; sys_platform != 'win32' and (sys_platform != 'linux' or (platform_machine != 'aarch64' and platform_machine != 'arm64' and platform_machine != 'armv7l')) and (sys_platform != 'darwin' or platform_machine != 'x86_64')", # TODO: Bump dependency
-    "torchvision>=0.21.0,<0.26.0", # TODO: Bump dependency
-
-    "draccus==0.10.0", # TODO: Remove ==
     "gymnasium>=1.1.1,<2.0.0",
     "rerun-sdk>=0.24.0,<0.27.0",
 
@@ -95,10 +98,14 @@ dependencies = [
 
 # Common
 pygame-dep = ["pygame>=2.5.1,<2.7.0"]
-placo-dep = ["placo>=0.9.6,<0.10.0"]
+placo-dep = ["placo>=0.9.6,<0.9.17"]
-transformers-dep = ["transformers>=4.57.1,<5.0.0"]
+transformers-dep = ["transformers>=5.3.0,<6.0.0"]
 grpcio-dep = ["grpcio==1.73.1", "protobuf>=6.31.1,<6.32.0"]
 can-dep = ["python-can>=4.2.0,<5.0.0"]
+peft-dep = ["peft>=0.18.0,<1.0.0"]
+scipy-dep = ["scipy>=1.14.0,<2.0.0"]
+qwen-vl-utils-dep = ["qwen-vl-utils>=0.0.11,<0.1.0"]
+matplotlib-dep = ["matplotlib>=3.10.3,<4.0.0", "contourpy>=1.3.0,<2.0.0"] # NOTE: Explicitly listing contourpy helps the resolver converge faster.
 
 # Motors
 feetech = ["feetech-servo-sdk>=1.0.0,<2.0.0"]
@@ -112,34 +119,36 @@ gamepad = ["lerobot[pygame-dep]", "hidapi>=0.14.0,<0.15.0"]
 hopejr = ["lerobot[feetech]", "lerobot[pygame-dep]"]
 lekiwi = ["lerobot[feetech]", "pyzmq>=26.2.1,<28.0.0"]
 unitree_g1 = [
+    "unitree-sdk2==1.0.1",
     "pyzmq>=26.2.1,<28.0.0",
     "onnxruntime>=1.16.0,<2.0.0",
     "pin>=3.0.0,<4.0.0",
     "meshcat>=0.3.0,<0.4.0",
-    "matplotlib>=3.9.0,<4.0.0",
+    "lerobot[matplotlib-dep]",
+    "lerobot[pygame-dep]",
     "casadi>=3.6.0,<4.0.0",
 ]
 reachy2 = ["reachy2_sdk>=1.0.15,<1.1.0"]
 kinematics = ["lerobot[placo-dep]"]
 intelrealsense = [
     "pyrealsense2>=2.55.1.6486,<2.57.0 ; sys_platform != 'darwin'",
-    "pyrealsense2-macosx>=2.54,<2.55.0 ; sys_platform == 'darwin'",
+    "pyrealsense2-macosx>=2.54,<2.57.0 ; sys_platform == 'darwin'",
 ]
-phone = ["hebi-py>=2.8.0,<2.12.0", "teleop>=0.1.0,<0.2.0", "fastapi<1.0"]
+phone = ["hebi-py>=2.8.0,<2.12.0", "teleop>=0.1.0,<0.2.0", "fastapi<1.0", "lerobot[scipy-dep]"]
 
 # Policies
 wallx = [
-    "transformers==4.49.0",
+    "lerobot[transformers-dep]",
-    "peft==0.17.1",
+    "lerobot[peft]",
-    "scipy==1.15.3",
+    "lerobot[scipy-dep]",
-    "torchdiffeq==0.2.5",
+    "torchdiffeq>=0.2.4,<0.3.0",
-    "qwen_vl_utils==0.0.11"
+    "lerobot[qwen-vl-utils-dep]",
 ]
-pi = ["transformers @ git+https://github.com/huggingface/transformers.git@fix/lerobot_openpi", "scipy>=1.10.1,<1.15"]
+pi = ["lerobot[transformers-dep]", "lerobot[scipy-dep]"]
 smolvla = ["lerobot[transformers-dep]", "num2words>=0.5.14,<0.6.0", "accelerate>=1.7.0,<2.0.0", "safetensors>=0.4.3,<1.0.0"]
 groot = [
     "lerobot[transformers-dep]",
-    "peft>=0.13.0,<1.0.0",
+    "lerobot[peft]",
     "dm-tree>=0.1.8,<1.0.0",
     "timm>=1.0.0,<1.1.0",
     "safetensors>=0.4.3,<1.0.0",
@@ -148,13 +157,13 @@ groot = [
     "ninja>=1.11.1,<2.0.0",
     "flash-attn>=2.5.9,<3.0.0 ; sys_platform != 'darwin'"
 ]
-sarm = ["lerobot[transformers-dep]", "faker>=33.0.0,<35.0.0", "matplotlib>=3.10.3,<4.0.0", "qwen-vl-utils>=0.0.14,<0.1.0"]
+sarm = ["lerobot[transformers-dep]", "faker>=33.0.0,<35.0.0", "lerobot[matplotlib-dep]", "lerobot[qwen-vl-utils-dep]"]
 xvla = ["lerobot[transformers-dep]"]
 hilserl = ["lerobot[transformers-dep]", "gym-hil>=0.1.13,<0.2.0", "lerobot[grpcio-dep]", "lerobot[placo-dep]"]
 
 # Features
-async = ["lerobot[grpcio-dep]", "matplotlib>=3.10.3,<4.0.0"]
+async = ["lerobot[grpcio-dep]", "lerobot[matplotlib-dep]"]
-peft = ["lerobot[transformers-dep]", "peft>=0.18.0,<1.0.0"]
+peft = ["lerobot[transformers-dep]", "lerobot[peft-dep]"]
 
 # Development
 dev = ["pre-commit>=3.7.0,<5.0.0", "debugpy>=1.8.1,<1.9.0", "lerobot[grpcio-dep]", "grpcio-tools==1.73.1", "mypy>=1.19.1"]
@@ -162,13 +171,27 @@ test = ["pytest>=8.1.0,<9.0.0", "pytest-timeout>=2.4.0,<3.0.0", "pytest-cov>=5.0
 video_benchmark = ["scikit-image>=0.23.2,<0.26.0", "pandas>=2.2.2,<2.4.0"]
 
 # Simulation
-aloha = ["gym-aloha>=0.1.2,<0.2.0"]
+# NOTE: Explicitly listing scipy helps flatten the dependecy tree.
+aloha = ["gym-aloha>=0.1.2,<0.2.0", "lerobot[scipy-dep]"]
 pusht = ["gym-pusht>=0.1.5,<0.2.0", "pymunk>=6.6.0,<7.0.0"] # TODO: Fix pymunk version in gym-pusht instead
-libero = ["lerobot[transformers-dep]", "hf-libero>=0.1.3,<0.2.0"]
+libero = ["lerobot[transformers-dep]", "hf-libero>=0.1.3,<0.2.0; sys_platform == 'linux'", "lerobot[scipy-dep]"]
-metaworld = ["metaworld==3.0.0"]
+libero_plus = [
+    "lerobot[transformers-dep]",
+    "libero @ git+https://github.com/sylvestf/LIBERO-plus.git@main ; sys_platform == 'linux'",
+    "lerobot[scipy-dep]",
+]
+robomme = [
+    "robomme @ git+https://github.com/RoboMME/robomme_benchmark.git@main ; sys_platform == 'linux'",
+]
+metaworld = ["metaworld==3.0.0", "lerobot[scipy-dep]"]
 
 # All
 all = [
+    # NOTE(resolver hint): scipy is pulled in transitively via lerobot[scipy-dep] through
+    # multiple extras (aloha, metaworld, pi, wallx, phone). Listing it explicitly
+    # helps pip's resolver converge by constraining scipy early, before it encounters
+    # the loose scipy requirements from transitive deps like dm-control and metaworld.
+    "scipy>=1.14.0,<2.0.0",
     "lerobot[dynamixel]",
     "lerobot[gamepad]",
     "lerobot[hopejr]",
@@ -176,8 +199,8 @@ all = [
     "lerobot[reachy2]",
     "lerobot[kinematics]",
     "lerobot[intelrealsense]",
-    # "lerobot[wallx]",
+    "lerobot[wallx]",
-    # "lerobot[pi]", TODO(Pepijn): Update pi to transformers v5
+    "lerobot[pi]",
     "lerobot[smolvla]",
     # "lerobot[groot]", TODO(Steven): Gr00t requires specific installation instructions for flash-attn
     "lerobot[xvla]",
@@ -189,10 +212,11 @@ all = [
     "lerobot[aloha]",
     "lerobot[pusht]",
     "lerobot[phone]",
-    "lerobot[libero]",
+    "lerobot[libero]; sys_platform == 'linux'",
     "lerobot[metaworld]",
     "lerobot[sarm]",
     "lerobot[peft]",
+    # "lerobot[unitree_g1]", TODO: Unitree requires specific installation instructions for unitree_sdk2
 ]
 
 [project.scripts]
@@ -221,7 +245,7 @@ lerobot = ["envs/*.json"]
 where = ["src"]
 
 [tool.ruff]
-target-version = "py310"
+target-version = "py312"
 line-length = 110
 exclude = ["tests/artifacts/**/*.safetensors", "*_pb2.py", "*_pb2_grpc.py"]
 
@@ -313,7 +337,7 @@ default.extend-ignore-identifiers-re = [
 # Uncomment [tool.mypy] first, then uncomment individual module overrides as they get proper type annotations
 
 [tool.mypy]
-python_version = "3.10"
+python_version = "3.12"
 ignore_missing_imports = true
 follow_imports = "skip"
 # warn_return_any = true
@@ -397,85 +421,3 @@ ignore_errors = false
 # [[tool.mypy.overrides]]
 # module = "lerobot.scripts.*"
 # ignore_errors = false
-
-[tool.uv]
-# wallx requires transformers==4.49.0 which conflicts with other extras that need >=4.53.0
-conflicts = [
-    [
-        { extra = "wallx" },
-        { extra = "transformers-dep" },
-    ],
-    [
-        { extra = "wallx" },
-        { extra = "pi" },
-    ],
-    [
-        { extra = "wallx" },
-        { extra = "smolvla" },
-    ],
-    [
-        { extra = "wallx" },
-        { extra = "groot" },
-    ],
-    [
-        { extra = "wallx" },
-        { extra = "xvla" },
-    ],
-    [
-        { extra = "wallx" },
-        { extra = "sarm" },
-    ],
-    [
-        { extra = "wallx" },
-        { extra = "hilserl" },
-    ],
-    [
-        { extra = "wallx" },
-        { extra = "libero" },
-    ],
-    [
-        { extra = "wallx" },
-        { extra = "peft" },
-    ],
-    [
-        { extra = "wallx" },
-        { extra = "all" },
-    ],
-    # pi uses custom branch which conflicts with transformers-dep
-    [
-        { extra = "pi" },
-        { extra = "transformers-dep" },
-    ],
-    [
-        { extra = "pi" },
-        { extra = "smolvla" },
-    ],
-    [
-        { extra = "pi" },
-        { extra = "groot" },
-    ],
-    [
-        { extra = "pi" },
-        { extra = "xvla" },
-    ],
-    [
-        { extra = "pi" },
-        { extra = "sarm" },
-    ],
-    [
-        { extra = "pi" },
-        { extra = "hilserl" },
-    ],
-    [
-        { extra = "pi" },
-        { extra = "libero" },
-    ],
-    [
-        { extra = "pi" },
-        { extra = "peft" },
-    ],
-    [
-        { extra = "pi" },
-        { extra = "all" },
-    ],
-]

requirements-macos.txt

@@ -1,76 +1,73 @@
 #
-# This file is autogenerated by pip-compile with Python 3.10
+# This file is autogenerated by pip-compile with Python 3.12
 # by the following command:
 #
 #    pip-compile --output-file=requirements-macos.txt requirements.in
 #
 -e .[all]
     # via -[all]
-absl-py==2.3.1
+absl-py==2.4.0
     # via
     #   dm-control
     #   dm-env
     #   dm-tree
     #   labmaze
     #   mujoco
-    #   tensorboard
+accelerate==1.13.0
-accelerate==1.11.0
     # via
     #   lerobot
     #   peft
 aiohappyeyeballs==2.6.1
     # via aiohttp
-aiohttp==3.13.1
+aiohttp==3.13.3
     # via fsspec
 aiosignal==1.4.0
     # via aiohttp
+annotated-doc==0.0.4
+    # via
+    #   fastapi
+    #   typer
 annotated-types==0.7.0
     # via pydantic
-antlr4-python3-runtime==4.9.3
+anyio==4.12.1
-    # via
-    #   hydra-core
-    #   omegaconf
-anyio==4.11.0
     # via
+    #   httpx
     #   starlette
     #   watchfiles
-asttokens==3.0.0
+asttokens==3.0.1
     # via stack-data
-async-timeout==5.0.1
-    # via aiohttp
 attrs==25.4.0
     # via
     #   aiohttp
     #   dm-tree
     #   jsonlines
-    #   jsonschema
-    #   referencing
     #   rerun-sdk
 av==15.1.0
-    # via lerobot
-bddl==1.0.1
-    # via libero
-certifi==2025.10.5
     # via
+    #   lerobot
+    #   qwen-vl-utils
+certifi==2026.2.25
+    # via
+    #   httpcore
+    #   httpx
     #   requests
     #   sentry-sdk
 cffi==2.0.0
     # via pymunk
-cfgv==3.4.0
+cfgv==3.5.0
     # via pre-commit
-charset-normalizer==3.4.4
+charset-normalizer==3.4.5
     # via requests
-click==8.3.0
+click==8.3.1
     # via
+    #   typer
     #   uvicorn
     #   wandb
-cloudpickle==3.1.1
+cloudpickle==3.1.2
-    # via
+    # via gymnasium
-    #   gymnasium
+cmake==4.1.3
-    #   libero
-cmake==4.1.0
     # via lerobot
-cmeel==0.57.3
+cmeel==0.59.0
     # via
     #   cmeel-assimp
     #   cmeel-boost
@@ -108,15 +105,17 @@ cmeel-zlib==1.3.1
     # via cmeel-assimp
 coal-library==3.0.1
     # via pin
-contourpy==1.3.2
+contourpy==1.3.3
-    # via matplotlib
+    # via
-coverage[toml]==7.11.0
+    #   lerobot
+    #   matplotlib
+coverage[toml]==7.13.4
     # via pytest-cov
 cycler==0.12.1
     # via matplotlib
-datasets==4.1.1
+datasets==4.6.1
     # via lerobot
-debugpy==1.8.17
+debugpy==1.8.20
     # via lerobot
 decorator==5.2.1
     # via ipython
@@ -130,7 +129,7 @@ dill==0.4.0
     #   multiprocess
 distlib==0.4.0
     # via virtualenv
-dm-control==1.0.34
+dm-control==1.0.37
     # via gym-aloha
 dm-env==1.6
     # via dm-control
@@ -138,69 +137,55 @@ dm-tree==0.1.9
     # via
     #   dm-control
     #   dm-env
-    #   lerobot
 docopt==0.6.2
     # via num2words
 draccus==0.10.0
     # via lerobot
 dynamixel-sdk==3.8.4
     # via lerobot
-easydict==1.13
-    # via libero
-egl-probe @ git+https://github.com/huggingface/egl_probe.git
-    # via
-    #   libero
-    #   robomimic
 eigenpy==3.10.3
     # via coal-library
-einops==0.8.1
+einops==0.8.2
-    # via
+    # via lerobot
-    #   lerobot
-    #   libero
 eiquadprog==1.2.9
     # via placo
-etils[epath,epy]==1.13.0
+etils[epath,epy]==1.14.0
     # via mujoco
-exceptiongroup==1.3.0
-    # via
-    #   anyio
-    #   ipython
-    #   pytest
 executing==2.2.1
     # via stack-data
+faker==34.0.2
+    # via lerobot
 farama-notifications==0.0.4
     # via gymnasium
-fastapi==0.119.1
+fastapi==0.135.1
-    # via teleop
+    # via
-fastjsonschema==2.21.2
+    #   lerobot
-    # via nbformat
+    #   teleop
 feetech-servo-sdk==1.0.0
     # via lerobot
-filelock==3.20.0
+filelock==3.25.0
     # via
     #   datasets
     #   diffusers
     #   huggingface-hub
+    #   python-discovery
     #   torch
-    #   transformers
     #   virtualenv
-fonttools==4.60.1
+fonttools==4.61.1
     # via matplotlib
 frozenlist==1.8.0
     # via
     #   aiohttp
     #   aiosignal
-fsspec[http]==2025.9.0
+fsspec[http]==2026.2.0
     # via
     #   datasets
     #   etils
     #   huggingface-hub
     #   torch
-future==1.0.0
-    # via libero
 gitdb==4.0.12
     # via gitpython
-gitpython==3.1.45
+gitpython==3.1.46
     # via wandb
 glfw==2.10.0
     # via
@@ -212,7 +197,6 @@ grpcio==1.73.1
     #   lerobot
     #   reachy2-sdk
     #   reachy2-sdk-api
-    #   tensorboard
 grpcio-tools==1.73.1
     # via
     #   lerobot
@@ -223,71 +207,67 @@ gym-hil==0.1.13
     # via lerobot
 gym-pusht==0.1.6
     # via lerobot
-gymnasium==1.2.1
+gymnasium==1.2.3
     # via
     #   gym-aloha
     #   gym-hil
     #   gym-pusht
     #   lerobot
-    #   libero
     #   metaworld
 h11==0.16.0
-    # via uvicorn
+    # via
-h5py==3.15.1
+    #   httpcore
-    # via robomimic
+    #   uvicorn
 hebi-py==2.11.0
     # via lerobot
-hf-transfer==0.1.9
+hf-xet==1.3.2
-    # via huggingface-hub
-hf-xet==1.1.10
     # via huggingface-hub
 hidapi==0.14.0.post4
     # via
     #   gym-hil
     #   lerobot
+httpcore==1.0.9
+    # via httpx
 httptools==0.7.1
     # via uvicorn
-huggingface-hub[cli,hf-transfer]==0.35.3
+httpx==0.28.1
+    # via
+    #   datasets
+    #   huggingface-hub
+huggingface-hub==1.6.0
     # via
     #   accelerate
     #   datasets
     #   diffusers
     #   lerobot
     #   peft
-    #   timm
     #   tokenizers
     #   transformers
-hydra-core==1.3.2
+identify==2.6.17
-    # via libero
-identify==2.6.15
     # via pre-commit
 idna==3.11
     # via
     #   anyio
+    #   httpx
     #   requests
     #   yarl
-imageio[ffmpeg]==2.37.0
+imageio[ffmpeg]==2.37.2
     # via
     #   gym-aloha
     #   gym-hil
     #   lerobot
     #   metaworld
-    #   robomimic
     #   scikit-image
 imageio-ffmpeg==0.6.0
-    # via
+    # via imageio
-    #   imageio
+importlib-metadata==8.7.1
-    #   robomimic
-importlib-metadata==8.7.0
     # via diffusers
-importlib-resources==6.5.2
-    # via etils
 iniconfig==2.3.0
     # via pytest
-inquirerpy==0.3.4
+ipython==9.11.0
-    # via huggingface-hub
-ipython==8.37.0
     # via meshcat
+ipython-pygments-lexers==1.1.1
+    # via ipython
 ischedule==1.2.7
     # via placo
 jedi==0.19.2
@@ -296,44 +276,24 @@ jinja2==3.1.6
     # via torch
 jsonlines==4.0.0
     # via lerobot
-jsonschema==4.25.1
-    # via nbformat
-jsonschema-specifications==2025.9.1
-    # via jsonschema
-jupyter-core==5.9.1
-    # via nbformat
-jupytext==1.18.1
-    # via bddl
 kiwisolver==1.4.9
     # via matplotlib
 labmaze==1.0.6
     # via dm-control
-lazy-loader==0.4
+lazy-loader==0.5
     # via scikit-image
-libero @ git+https://github.com/huggingface/lerobot-libero.git@main
+librt==0.8.1
-    # via lerobot
+    # via mypy
-llvmlite==0.45.1
-    # via numba
 lxml==6.0.2
     # via dm-control
-markdown==3.9
-    # via tensorboard
 markdown-it-py==4.0.0
-    # via
+    # via rich
-    #   jupytext
-    #   mdit-py-plugins
 markupsafe==3.0.3
-    # via
+    # via jinja2
-    #   jinja2
+matplotlib==3.10.8
-    #   werkzeug
+    # via lerobot
-matplotlib==3.10.7
-    # via
-    #   lerobot
-    #   libero
 matplotlib-inline==0.2.1
     # via ipython
-mdit-py-plugins==0.5.0
-    # via jupytext
 mdurl==0.1.2
     # via markdown-it-py
 mergedeep==1.3.4
@@ -346,41 +306,35 @@ mock-serial==0.0.1
     # via lerobot
 mpmath==1.3.0
     # via sympy
-mujoco==3.3.7
+mujoco==3.5.0
     # via
     #   dm-control
     #   gym-aloha
     #   gym-hil
-    #   libero
     #   metaworld
-    #   robosuite
+multidict==6.7.1
-multidict==6.7.0
     # via
     #   aiohttp
     #   yarl
-multiprocess==0.70.16
+multiprocess==0.70.18
     # via datasets
+mypy==1.19.1
+    # via lerobot
 mypy-extensions==1.1.0
-    # via typing-inspect
-nbformat==5.10.4
-    # via jupytext
-networkx==3.4.2
     # via
-    #   bddl
+    #   mypy
+    #   typing-inspect
+networkx==3.6.1
+    # via
     #   scikit-image
     #   torch
-ninja==1.13.0
+nodeenv==1.10.0
-    # via lerobot
-nodeenv==1.9.1
     # via pre-commit
 num2words==0.5.14
     # via lerobot
-numba==0.62.1
-    # via robosuite
 numpy==2.2.6
     # via
     #   accelerate
-    #   bddl
     #   cmeel-boost
     #   contourpy
     #   datasets
@@ -389,16 +343,14 @@ numpy==2.2.6
     #   dm-env
     #   dm-tree
     #   gymnasium
-    #   h5py
     #   hebi-py
     #   imageio
     #   labmaze
-    #   libero
+    #   lerobot
     #   matplotlib
     #   meshcat
     #   metaworld
     #   mujoco
-    #   numba
     #   opencv-python
     #   opencv-python-headless
     #   pandas
@@ -406,26 +358,18 @@ numpy==2.2.6
     #   pyquaternion
     #   reachy2-sdk
     #   rerun-sdk
-    #   robomimic
-    #   robosuite
     #   scikit-image
     #   scipy
     #   shapely
     #   teleop
-    #   tensorboard
-    #   tensorboardx
     #   tifffile
     #   torchvision
     #   transformers
     #   transforms3d
-omegaconf==2.3.0
+opencv-python==4.13.0.92
-    # via hydra-core
-opencv-python==4.12.0.88
     # via
     #   gym-pusht
-    #   libero
     #   reachy2-sdk
-    #   robosuite
 opencv-python-headless==4.12.0.88
     # via lerobot
 orderly-set==5.5.0
@@ -435,97 +379,87 @@ packaging==25.0
     #   accelerate
     #   datasets
     #   huggingface-hub
-    #   hydra-core
-    #   jupytext
     #   lazy-loader
     #   lerobot
     #   matplotlib
     #   peft
     #   pytest
+    #   qwen-vl-utils
     #   reachy2-sdk
     #   scikit-image
-    #   tensorboard
-    #   tensorboardx
     #   transformers
     #   wandb
 pandas==2.3.3
     # via
     #   datasets
     #   lerobot
-parso==0.8.5
+parso==0.8.6
     # via jedi
-peft==0.17.1
+pathspec==1.0.4
+    # via mypy
+peft==0.18.1
     # via lerobot
 pexpect==4.9.0
     # via ipython
-pfzy==0.3.4
+pillow==12.1.1
-    # via inquirerpy
-pillow==12.0.0
     # via
     #   diffusers
     #   imageio
-    #   lerobot
     #   matplotlib
     #   meshcat
+    #   qwen-vl-utils
     #   rerun-sdk
-    #   robosuite
     #   scikit-image
-    #   tensorboard
     #   torchvision
 pin==3.4.0
     # via placo
-placo==0.9.14
+placo==0.9.16
     # via lerobot
-platformdirs==4.5.0
+platformdirs==4.9.4
     # via
-    #   jupyter-core
+    #   python-discovery
     #   virtualenv
     #   wandb
 pluggy==1.6.0
     # via
     #   pytest
     #   pytest-cov
-pre-commit==4.3.0
+pre-commit==4.5.1
     # via lerobot
 prompt-toolkit==3.0.52
-    # via
+    # via ipython
-    #   inquirerpy
-    #   ipython
 propcache==0.4.1
     # via
     #   aiohttp
     #   yarl
-protobuf==6.31.0
+protobuf==6.31.1
     # via
     #   dm-control
     #   grpcio-tools
     #   lerobot
     #   reachy2-sdk
     #   reachy2-sdk-api
-    #   tensorboard
-    #   tensorboardx
     #   wandb
-psutil==7.1.1
+psutil==7.2.2
     # via
     #   accelerate
     #   imageio
     #   peft
-    #   robomimic
 ptyprocess==0.7.0
     # via pexpect
 pure-eval==0.2.3
     # via stack-data
-pyarrow==21.0.0
+pyarrow==23.0.1
     # via
     #   datasets
     #   rerun-sdk
-pycparser==2.23
+pycparser==3.0
     # via cffi
-pydantic==2.12.3
+pydantic==2.12.5
     # via
     #   fastapi
     #   wandb
-pydantic-core==2.41.4
+pydantic-core==2.41.5
     # via pydantic
 pygame==2.6.1
     # via
@@ -535,33 +469,35 @@ pygame==2.6.1
 pygments==2.19.2
     # via
     #   ipython
+    #   ipython-pygments-lexers
     #   pytest
+    #   rich
 pymunk==6.11.1
     # via
     #   gym-pusht
     #   lerobot
-pyngrok==7.4.1
+pyngrok==7.5.1
     # via meshcat
 pynput==1.8.1
     # via
     #   gym-hil
     #   lerobot
-pyobjc-core==12.0
+pyobjc-core==12.1
     # via
     #   pyobjc-framework-applicationservices
     #   pyobjc-framework-cocoa
     #   pyobjc-framework-coretext
     #   pyobjc-framework-quartz
-pyobjc-framework-applicationservices==12.0
+pyobjc-framework-applicationservices==12.1
     # via pynput
-pyobjc-framework-cocoa==12.0
+pyobjc-framework-cocoa==12.1
     # via
     #   pyobjc-framework-applicationservices
     #   pyobjc-framework-coretext
     #   pyobjc-framework-quartz
-pyobjc-framework-coretext==12.0
+pyobjc-framework-coretext==12.1
     # via pyobjc-framework-applicationservices
-pyobjc-framework-quartz==12.0
+pyobjc-framework-quartz==12.1
     # via
     #   pynput
     #   pyobjc-framework-applicationservices
@@ -570,13 +506,13 @@ pyopengl==3.1.10
     # via
     #   dm-control
     #   mujoco
-pyparsing==3.2.5
+pyparsing==3.3.2
     # via
     #   dm-control
     #   matplotlib
 pyquaternion==0.9.9
     # via reachy2-sdk
-pyrealsense2-macosx==2.54.2
+pyrealsense2-macosx==2.56.5
     # via lerobot
 pyserial==3.5
     # via
@@ -585,7 +521,6 @@ pyserial==3.5
     #   lerobot
 pytest==8.4.2
     # via
-    #   bddl
     #   lerobot
     #   pytest-cov
     #   pytest-timeout
@@ -596,11 +531,14 @@ pytest-timeout==2.4.0
     # via lerobot
 python-dateutil==2.9.0.post0
     # via
+    #   faker
     #   matplotlib
     #   pandas
-python-dotenv==1.1.1
+python-discovery==1.1.1
+    # via virtualenv
+python-dotenv==1.2.2
     # via uvicorn
-pytz==2025.2
+pytz==2026.1.post1
     # via pandas
 pyyaml==6.0.3
     # via
@@ -609,13 +547,10 @@ pyyaml==6.0.3
     #   draccus
     #   hebi-py
     #   huggingface-hub
-    #   jupytext
-    #   omegaconf
     #   peft
     #   pre-commit
     #   pyngrok
     #   pyyaml-include
-    #   timm
     #   transformers
     #   uvicorn
     #   wandb
@@ -625,15 +560,13 @@ pyzmq==27.1.0
     # via
     #   lerobot
     #   meshcat
-reachy2-sdk==1.0.14
+qwen-vl-utils==0.0.14
+    # via lerobot
+reachy2-sdk==1.0.15
     # via lerobot
 reachy2-sdk-api==1.0.21
     # via reachy2-sdk
-referencing==0.37.0
+regex==2026.2.28
-    # via
-    #   jsonschema
-    #   jsonschema-specifications
-regex==2025.10.23
     # via
     #   diffusers
     #   transformers
@@ -642,184 +575,150 @@ requests==2.32.5
     #   datasets
     #   diffusers
     #   dm-control
-    #   huggingface-hub
+    #   qwen-vl-utils
     #   teleop
-    #   transformers
     #   wandb
-rerun-sdk==0.26.1
+rerun-sdk==0.26.2
     # via lerobot
 rhoban-cmeel-jsoncpp==1.9.4.9
     # via placo
-robomimic==0.2.0
+rich==14.3.3
-    # via libero
+    # via typer
-robosuite==1.4.0
+safetensors==0.7.0
-    # via libero
-rpds-py==0.28.0
-    # via
-    #   jsonschema
-    #   referencing
-safetensors==0.6.2
     # via
     #   accelerate
     #   diffusers
     #   lerobot
     #   peft
-    #   timm
     #   transformers
 scikit-image==0.25.2
     # via
     #   gym-pusht
     #   lerobot
-scipy==1.15.3
+scipy==1.17.1
     # via
     #   dm-control
+    #   lerobot
     #   metaworld
-    #   robosuite
     #   scikit-image
-sentry-sdk==2.42.1
+    #   torchdiffeq
+sentry-sdk==2.54.0
     # via wandb
 shapely==2.1.2
     # via gym-pusht
+shellingham==1.5.4
+    # via typer
 six==1.17.0
     # via
     #   pynput
     #   python-dateutil
-smmap==5.0.2
+smmap==5.0.3
     # via gitdb
-sniffio==1.3.1
-    # via anyio
 stack-data==0.6.3
     # via ipython
-starlette==0.48.0
+starlette==0.52.1
     # via fastapi
 sympy==1.14.0
     # via torch
-teleop==0.1.2
+teleop==0.1.4
     # via lerobot
-tensorboard==2.20.0
+termcolor==3.3.0
-    # via robomimic
+    # via lerobot
-tensorboard-data-server==0.7.2
+tifffile==2026.3.3
-    # via tensorboard
-tensorboardx==2.6.4
-    # via robomimic
-termcolor==3.1.0
-    # via
-    #   lerobot
-    #   robomimic
-thop==0.1.1.post2209072238
-    # via libero
-tifffile==2025.5.10
     # via scikit-image
-timm==1.0.20
+tokenizers==0.22.2
-    # via lerobot
-tokenizers==0.22.1
     # via transformers
 toml==0.10.2
     # via draccus
-tomli==2.3.0
+torch==2.10.0
-    # via
-    #   cmeel
-    #   coverage
-    #   jupytext
-    #   pytest
-torch==2.7.1
     # via
     #   accelerate
     #   lerobot
     #   peft
-    #   robomimic
+    #   torchdiffeq
-    #   thop
-    #   timm
     #   torchvision
-torchcodec==0.5
+torchcodec==0.10.0
     # via lerobot
-torchvision==0.22.1
+torchdiffeq==0.2.5
-    # via
+    # via lerobot
-    #   lerobot
+torchvision==0.25.0
-    #   robomimic
+    # via lerobot
-    #   timm
+tornado==6.5.4
-tornado==6.5.2
     # via meshcat
-tqdm==4.67.1
+tqdm==4.67.3
     # via
     #   datasets
     #   dm-control
     #   huggingface-hub
     #   peft
-    #   robomimic
     #   transformers
 traitlets==5.14.3
     # via
     #   ipython
-    #   jupyter-core
     #   matplotlib-inline
-    #   nbformat
+transformers==5.3.0
-transformers==4.57.1
     # via
     #   lerobot
-    #   libero
     #   peft
 transforms3d==0.4.2
     # via teleop
+typer==0.24.1
+    # via
+    #   huggingface-hub
+    #   transformers
 typing-extensions==4.15.0
     # via
     #   aiosignal
     #   anyio
     #   etils
-    #   exceptiongroup
+    #   faker
     #   fastapi
     #   gymnasium
     #   huggingface-hub
-    #   ipython
+    #   mypy
-    #   multidict
     #   pydantic
     #   pydantic-core
-    #   referencing
     #   rerun-sdk
     #   starlette
     #   torch
     #   typing-inspect
     #   typing-inspection
-    #   uvicorn
-    #   virtualenv
     #   wandb
 typing-inspect==0.9.0
     # via draccus
 typing-inspection==0.4.2
-    # via pydantic
+    # via
-tzdata==2025.2
+    #   fastapi
+    #   pydantic
+tzdata==2025.3
     # via pandas
 u-msgpack-python==2.8.0
     # via meshcat
-urllib3==2.5.0
+urllib3==2.6.3
     # via
     #   requests
     #   sentry-sdk
-uvicorn[standard]==0.38.0
+uvicorn[standard]==0.41.0
     # via teleop
 uvloop==0.22.1
     # via uvicorn
-virtualenv==20.35.3
+virtualenv==21.1.0
     # via pre-commit
-wandb==0.21.4
+wandb==0.24.2
-    # via
+    # via lerobot
-    #   lerobot
-    #   libero
 watchfiles==1.1.1
     # via uvicorn
-wcwidth==0.2.14
+wcwidth==0.6.0
     # via prompt-toolkit
 websocket-client==1.9.0
     # via teleop
-websockets==15.0.1
+websockets==16.0
     # via uvicorn
-werkzeug==3.1.3
+wrapt==2.1.2
-    # via tensorboard
-wrapt==2.0.0
     # via dm-tree
 xxhash==3.6.0
     # via datasets
-yarl==1.22.0
+yarl==1.23.0
     # via aiohttp
 zipp==3.23.0
     # via

requirements-ubuntu.txt

@@ -1,12 +1,12 @@
 #
-# This file is autogenerated by pip-compile with Python 3.10
+# This file is autogenerated by pip-compile with Python 3.12
 # by the following command:
 #
 #    pip-compile --output-file=requirements-ubuntu.txt requirements.in
 #
 -e .[all]
     # via -[all]
-absl-py==2.3.1
+absl-py==2.4.0
     # via
     #   dm-control
     #   dm-env
@@ -14,30 +14,33 @@ absl-py==2.3.1
     #   labmaze
     #   mujoco
     #   tensorboard
-accelerate==1.11.0
+accelerate==1.13.0
     # via
     #   lerobot
     #   peft
 aiohappyeyeballs==2.6.1
     # via aiohttp
-aiohttp==3.13.1
+aiohttp==3.13.3
     # via fsspec
 aiosignal==1.4.0
     # via aiohttp
+annotated-doc==0.0.4
+    # via
+    #   fastapi
+    #   typer
 annotated-types==0.7.0
     # via pydantic
 antlr4-python3-runtime==4.9.3
     # via
     #   hydra-core
     #   omegaconf
-anyio==4.11.0
+anyio==4.12.1
     # via
+    #   httpx
     #   starlette
     #   watchfiles
-asttokens==3.0.0
+asttokens==3.0.1
     # via stack-data
-async-timeout==5.0.1
-    # via aiohttp
 attrs==25.4.0
     # via
     #   aiohttp
@@ -47,30 +50,35 @@ attrs==25.4.0
     #   referencing
     #   rerun-sdk
 av==15.1.0
-    # via lerobot
-bddl==1.0.1
-    # via libero
-certifi==2025.10.5
     # via
+    #   lerobot
+    #   qwen-vl-utils
+bddl==1.0.1
+    # via hf-libero
+certifi==2026.2.25
+    # via
+    #   httpcore
+    #   httpx
     #   requests
     #   sentry-sdk
 cffi==2.0.0
     # via pymunk
-cfgv==3.4.0
+cfgv==3.5.0
     # via pre-commit
-charset-normalizer==3.4.4
+charset-normalizer==3.4.5
     # via requests
-click==8.3.0
+click==8.3.1
     # via
+    #   typer
     #   uvicorn
     #   wandb
-cloudpickle==3.1.1
+cloudpickle==3.1.2
     # via
     #   gymnasium
-    #   libero
+    #   hf-libero
-cmake==4.1.0
+cmake==4.1.3
     # via lerobot
-cmeel==0.57.3
+cmeel==0.59.0
     # via
     #   cmeel-assimp
     #   cmeel-boost
@@ -108,20 +116,24 @@ cmeel-zlib==1.3.1
     # via cmeel-assimp
 coal-library==3.0.1
     # via pin
-contourpy==1.3.2
+contourpy==1.3.3
-    # via matplotlib
+    # via
-coverage[toml]==7.11.0
+    #   lerobot
+    #   matplotlib
+coverage[toml]==7.13.4
     # via pytest-cov
+cuda-bindings==12.9.4
+    # via torch
+cuda-pathfinder==1.4.1
+    # via cuda-bindings
 cycler==0.12.1
     # via matplotlib
-datasets==4.1.1
+datasets==4.6.1
     # via lerobot
-debugpy==1.8.17
+debugpy==1.8.20
     # via lerobot
 decorator==5.2.1
     # via ipython
-decord==0.6.0
-    # via lerobot
 deepdiff==8.6.1
     # via lerobot
 diffusers==0.35.2
@@ -132,7 +144,7 @@ dill==0.4.0
     #   multiprocess
 distlib==0.4.0
     # via virtualenv
-dm-control==1.0.34
+dm-control==1.0.37
     # via gym-aloha
 dm-env==1.6
     # via dm-control
@@ -140,7 +152,6 @@ dm-tree==0.1.9
     # via
     #   dm-control
     #   dm-env
-    #   lerobot
 docopt==0.6.2
     # via num2words
 draccus==0.10.0
@@ -148,66 +159,60 @@ draccus==0.10.0
 dynamixel-sdk==3.8.4
     # via lerobot
 easydict==1.13
-    # via libero
+    # via hf-libero
-egl-probe @ git+https://github.com/huggingface/egl_probe.git
+egl-probe==1.0.2
-    # via
+    # via robomimic
-    #   libero
-    #   robomimic
 eigenpy==3.10.3
     # via coal-library
-einops==0.8.1
+einops==0.8.2
     # via
-    #   flash-attn
+    #   hf-libero
     #   lerobot
-    #   libero
 eiquadprog==1.2.9
     # via placo
-etils[epath,epy]==1.13.0
+etils[epath,epy]==1.14.0
     # via mujoco
-evdev==1.9.2
+evdev==1.9.3
     # via pynput
-exceptiongroup==1.3.0
-    # via
-    #   anyio
-    #   ipython
-    #   pytest
 executing==2.2.1
     # via stack-data
+faker==34.0.2
+    # via lerobot
 farama-notifications==0.0.4
     # via gymnasium
-fastapi==0.119.1
+fastapi==0.135.1
-    # via teleop
+    # via
+    #   lerobot
+    #   teleop
 fastjsonschema==2.21.2
     # via nbformat
 feetech-servo-sdk==1.0.0
     # via lerobot
-filelock==3.20.0
+filelock==3.25.0
     # via
     #   datasets
     #   diffusers
     #   huggingface-hub
+    #   python-discovery
     #   torch
-    #   transformers
     #   virtualenv
-flash-attn==2.8.3
+fonttools==4.61.1
-    # via lerobot
-fonttools==4.60.1
     # via matplotlib
 frozenlist==1.8.0
     # via
     #   aiohttp
     #   aiosignal
-fsspec[http]==2025.9.0
+fsspec[http]==2026.2.0
     # via
     #   datasets
     #   etils
     #   huggingface-hub
     #   torch
 future==1.0.0
-    # via libero
+    # via hf-libero
 gitdb==4.0.12
     # via gitpython
-gitpython==3.1.45
+gitpython==3.1.46
     # via wandb
 glfw==2.10.0
     # via
@@ -230,50 +235,60 @@ gym-hil==0.1.13
     # via lerobot
 gym-pusht==0.1.6
     # via lerobot
-gymnasium==1.2.1
+gymnasium==1.2.3
     # via
     #   gym-aloha
     #   gym-hil
     #   gym-pusht
+    #   hf-libero
     #   lerobot
-    #   libero
     #   metaworld
 h11==0.16.0
-    # via uvicorn
+    # via
-h5py==3.15.1
+    #   httpcore
+    #   uvicorn
+h5py==3.16.0
     # via robomimic
 hebi-py==2.11.0
     # via lerobot
-hf-transfer==0.1.9
+hf-egl-probe==1.0.2
-    # via huggingface-hub
+    # via hf-libero
-hf-xet==1.1.10
+hf-libero==0.1.3
+    # via lerobot
+hf-xet==1.3.2
     # via huggingface-hub
 hidapi==0.14.0.post4
     # via
     #   gym-hil
     #   lerobot
+httpcore==1.0.9
+    # via httpx
 httptools==0.7.1
     # via uvicorn
-huggingface-hub[cli,hf-transfer]==0.35.3
+httpx==0.28.1
+    # via
+    #   datasets
+    #   huggingface-hub
+huggingface-hub==1.6.0
     # via
     #   accelerate
     #   datasets
     #   diffusers
     #   lerobot
     #   peft
-    #   timm
     #   tokenizers
     #   transformers
 hydra-core==1.3.2
-    # via libero
+    # via hf-libero
-identify==2.6.15
+identify==2.6.17
     # via pre-commit
 idna==3.11
     # via
     #   anyio
+    #   httpx
     #   requests
     #   yarl
-imageio[ffmpeg]==2.37.0
+imageio[ffmpeg]==2.37.2
     # via
     #   gym-aloha
     #   gym-hil
@@ -285,16 +300,14 @@ imageio-ffmpeg==0.6.0
     # via
     #   imageio
     #   robomimic
-importlib-metadata==8.7.0
+importlib-metadata==8.7.1
     # via diffusers
-importlib-resources==6.5.2
-    # via etils
 iniconfig==2.3.0
     # via pytest
-inquirerpy==0.3.4
+ipython==9.11.0
-    # via huggingface-hub
-ipython==8.37.0
     # via meshcat
+ipython-pygments-lexers==1.1.1
+    # via ipython
 ischedule==1.2.7
     # via placo
 jedi==0.19.2
@@ -303,40 +316,41 @@ jinja2==3.1.6
     # via torch
 jsonlines==4.0.0
     # via lerobot
-jsonschema==4.25.1
+jsonschema==4.26.0
     # via nbformat
 jsonschema-specifications==2025.9.1
     # via jsonschema
 jupyter-core==5.9.1
     # via nbformat
-jupytext==1.18.1
+jupytext==1.19.1
     # via bddl
 kiwisolver==1.4.9
     # via matplotlib
 labmaze==1.0.6
     # via dm-control
-lazy-loader==0.4
+lazy-loader==0.5
     # via scikit-image
-libero @ git+https://github.com/huggingface/lerobot-libero.git@main
+librt==0.8.1
-    # via lerobot
+    # via mypy
-llvmlite==0.45.1
+llvmlite==0.46.0
     # via numba
 lxml==6.0.2
     # via dm-control
-markdown==3.9
+markdown==3.10.2
     # via tensorboard
 markdown-it-py==4.0.0
     # via
     #   jupytext
     #   mdit-py-plugins
+    #   rich
 markupsafe==3.0.3
     # via
     #   jinja2
     #   werkzeug
-matplotlib==3.10.7
+matplotlib==3.10.8
     # via
+    #   hf-libero
     #   lerobot
-    #   libero
 matplotlib-inline==0.2.1
     # via ipython
 mdit-py-plugins==0.5.0
@@ -353,36 +367,38 @@ mock-serial==0.0.1
     # via lerobot
 mpmath==1.3.0
     # via sympy
-mujoco==3.3.7
+mujoco==3.5.0
     # via
     #   dm-control
     #   gym-aloha
     #   gym-hil
-    #   libero
+    #   hf-libero
     #   metaworld
     #   robosuite
-multidict==6.7.0
+multidict==6.7.1
     # via
     #   aiohttp
     #   yarl
-multiprocess==0.70.16
+multiprocess==0.70.18
     # via datasets
+mypy==1.19.1
+    # via lerobot
 mypy-extensions==1.1.0
-    # via typing-inspect
+    # via
+    #   mypy
+    #   typing-inspect
 nbformat==5.10.4
     # via jupytext
-networkx==3.4.2
+networkx==3.6.1
     # via
     #   bddl
     #   scikit-image
     #   torch
-ninja==1.13.0
+nodeenv==1.10.0
-    # via lerobot
-nodeenv==1.9.1
     # via pre-commit
 num2words==0.5.14
     # via lerobot
-numba==0.62.1
+numba==0.64.0
     # via robosuite
 numpy==2.2.6
     # via
@@ -391,7 +407,6 @@ numpy==2.2.6
     #   cmeel-boost
     #   contourpy
     #   datasets
-    #   decord
     #   diffusers
     #   dm-control
     #   dm-env
@@ -399,9 +414,10 @@ numpy==2.2.6
     #   gymnasium
     #   h5py
     #   hebi-py
+    #   hf-libero
     #   imageio
     #   labmaze
-    #   libero
+    #   lerobot
     #   matplotlib
     #   meshcat
     #   metaworld
@@ -426,49 +442,51 @@ numpy==2.2.6
     #   torchvision
     #   transformers
     #   transforms3d
-nvidia-cublas-cu12==12.6.4.1
+nvidia-cublas-cu12==12.8.4.1
     # via
     #   nvidia-cudnn-cu12
     #   nvidia-cusolver-cu12
     #   torch
-nvidia-cuda-cupti-cu12==12.6.80
+nvidia-cuda-cupti-cu12==12.8.90
     # via torch
-nvidia-cuda-nvrtc-cu12==12.6.77
+nvidia-cuda-nvrtc-cu12==12.8.93
     # via torch
-nvidia-cuda-runtime-cu12==12.6.77
+nvidia-cuda-runtime-cu12==12.8.90
     # via torch
-nvidia-cudnn-cu12==9.5.1.17
+nvidia-cudnn-cu12==9.10.2.21
     # via torch
-nvidia-cufft-cu12==11.3.0.4
+nvidia-cufft-cu12==11.3.3.83
     # via torch
-nvidia-cufile-cu12==1.11.1.6
+nvidia-cufile-cu12==1.13.1.3
     # via torch
-nvidia-curand-cu12==10.3.7.77
+nvidia-curand-cu12==10.3.9.90
     # via torch
-nvidia-cusolver-cu12==11.7.1.2
+nvidia-cusolver-cu12==11.7.3.90
     # via torch
-nvidia-cusparse-cu12==12.5.4.2
+nvidia-cusparse-cu12==12.5.8.93
     # via
     #   nvidia-cusolver-cu12
     #   torch
-nvidia-cusparselt-cu12==0.6.3
+nvidia-cusparselt-cu12==0.7.1
     # via torch
-nvidia-nccl-cu12==2.26.2
+nvidia-nccl-cu12==2.27.5
     # via torch
-nvidia-nvjitlink-cu12==12.6.85
+nvidia-nvjitlink-cu12==12.8.93
     # via
     #   nvidia-cufft-cu12
     #   nvidia-cusolver-cu12
     #   nvidia-cusparse-cu12
     #   torch
-nvidia-nvtx-cu12==12.6.77
+nvidia-nvshmem-cu12==3.4.5
+    # via torch
+nvidia-nvtx-cu12==12.8.90
     # via torch
 omegaconf==2.3.0
     # via hydra-core
-opencv-python==4.12.0.88
+opencv-python==4.13.0.92
     # via
     #   gym-pusht
-    #   libero
+    #   hf-libero
     #   reachy2-sdk
     #   robosuite
 opencv-python-headless==4.12.0.88
@@ -487,6 +505,7 @@ packaging==25.0
     #   matplotlib
     #   peft
     #   pytest
+    #   qwen-vl-utils
     #   reachy2-sdk
     #   scikit-image
     #   tensorboard
@@ -497,21 +516,21 @@ pandas==2.3.3
     # via
     #   datasets
     #   lerobot
-parso==0.8.5
+parso==0.8.6
     # via jedi
-peft==0.17.1
+pathspec==1.0.4
+    # via mypy
+peft==0.18.1
     # via lerobot
 pexpect==4.9.0
     # via ipython
-pfzy==0.3.4
+pillow==12.1.1
-    # via inquirerpy
-pillow==12.0.0
     # via
     #   diffusers
     #   imageio
-    #   lerobot
     #   matplotlib
     #   meshcat
+    #   qwen-vl-utils
     #   rerun-sdk
     #   robosuite
     #   scikit-image
@@ -519,28 +538,27 @@ pillow==12.0.0
     #   torchvision
 pin==3.4.0
     # via placo
-placo==0.9.14
+placo==0.9.16
     # via lerobot
-platformdirs==4.5.0
+platformdirs==4.9.4
     # via
     #   jupyter-core
+    #   python-discovery
     #   virtualenv
     #   wandb
 pluggy==1.6.0
     # via
     #   pytest
     #   pytest-cov
-pre-commit==4.3.0
+pre-commit==4.5.1
     # via lerobot
 prompt-toolkit==3.0.52
-    # via
+    # via ipython
-    #   inquirerpy
-    #   ipython
 propcache==0.4.1
     # via
     #   aiohttp
     #   yarl
-protobuf==6.31.0
+protobuf==6.31.1
     # via
     #   dm-control
     #   grpcio-tools
@@ -550,7 +568,7 @@ protobuf==6.31.0
     #   tensorboard
     #   tensorboardx
     #   wandb
-psutil==7.1.1
+psutil==7.2.2
     # via
     #   accelerate
     #   imageio
@@ -560,17 +578,17 @@ ptyprocess==0.7.0
     # via pexpect
 pure-eval==0.2.3
     # via stack-data
-pyarrow==21.0.0
+pyarrow==23.0.1
     # via
     #   datasets
     #   rerun-sdk
-pycparser==2.23
+pycparser==3.0
     # via cffi
-pydantic==2.12.3
+pydantic==2.12.5
     # via
     #   fastapi
     #   wandb
-pydantic-core==2.41.4
+pydantic-core==2.41.5
     # via pydantic
 pygame==2.6.1
     # via
@@ -580,12 +598,14 @@ pygame==2.6.1
 pygments==2.19.2
     # via
     #   ipython
+    #   ipython-pygments-lexers
     #   pytest
+    #   rich
 pymunk==6.11.1
     # via
     #   gym-pusht
     #   lerobot
-pyngrok==7.4.1
+pyngrok==7.5.1
     # via meshcat
 pynput==1.8.1
     # via
@@ -595,7 +615,7 @@ pyopengl==3.1.10
     # via
     #   dm-control
     #   mujoco
-pyparsing==3.2.5
+pyparsing==3.3.2
     # via
     #   dm-control
     #   matplotlib
@@ -621,13 +641,16 @@ pytest-timeout==2.4.0
     # via lerobot
 python-dateutil==2.9.0.post0
     # via
+    #   faker
     #   matplotlib
     #   pandas
-python-dotenv==1.1.1
+python-discovery==1.1.1
+    # via virtualenv
+python-dotenv==1.2.2
     # via uvicorn
 python-xlib==0.33
     # via pynput
-pytz==2025.2
+pytz==2026.1.post1
     # via pandas
 pyyaml==6.0.3
     # via
@@ -642,7 +665,6 @@ pyyaml==6.0.3
     #   pre-commit
     #   pyngrok
     #   pyyaml-include
-    #   timm
     #   transformers
     #   uvicorn
     #   wandb
@@ -652,7 +674,9 @@ pyzmq==27.1.0
     # via
     #   lerobot
     #   meshcat
-reachy2-sdk==1.0.14
+qwen-vl-utils==0.0.14
+    # via lerobot
+reachy2-sdk==1.0.15
     # via lerobot
 reachy2-sdk-api==1.0.21
     # via reachy2-sdk
@@ -660,7 +684,7 @@ referencing==0.37.0
     # via
     #   jsonschema
     #   jsonschema-specifications
-regex==2025.10.23
+regex==2026.2.28
     # via
     #   diffusers
     #   transformers
@@ -669,60 +693,62 @@ requests==2.32.5
     #   datasets
     #   diffusers
     #   dm-control
-    #   huggingface-hub
+    #   qwen-vl-utils
     #   teleop
-    #   transformers
     #   wandb
-rerun-sdk==0.26.1
+rerun-sdk==0.26.2
     # via lerobot
 rhoban-cmeel-jsoncpp==1.9.4.9
     # via placo
+rich==14.3.3
+    # via typer
 robomimic==0.2.0
-    # via libero
+    # via hf-libero
 robosuite==1.4.0
-    # via libero
+    # via hf-libero
-rpds-py==0.28.0
+rpds-py==0.30.0
     # via
     #   jsonschema
     #   referencing
-safetensors==0.6.2
+safetensors==0.7.0
     # via
     #   accelerate
     #   diffusers
     #   lerobot
     #   peft
-    #   timm
     #   transformers
 scikit-image==0.25.2
     # via
     #   gym-pusht
     #   lerobot
-scipy==1.15.3
+scipy==1.17.1
     # via
     #   dm-control
+    #   lerobot
     #   metaworld
     #   robosuite
     #   scikit-image
-sentry-sdk==2.42.1
+    #   torchdiffeq
+sentry-sdk==2.54.0
     # via wandb
 shapely==2.1.2
     # via gym-pusht
+shellingham==1.5.4
+    # via typer
 six==1.17.0
     # via
     #   pynput
     #   python-dateutil
     #   python-xlib
-smmap==5.0.2
+smmap==5.0.3
     # via gitdb
-sniffio==1.3.1
-    # via anyio
 stack-data==0.6.3
     # via ipython
-starlette==0.48.0
+starlette==0.52.1
     # via fastapi
 sympy==1.14.0
     # via torch
-teleop==0.1.2
+teleop==0.1.4
     # via lerobot
 tensorboard==2.20.0
     # via robomimic
@@ -730,46 +756,38 @@ tensorboard-data-server==0.7.2
     # via tensorboard
 tensorboardx==2.6.4
     # via robomimic
-termcolor==3.1.0
+termcolor==3.3.0
     # via
     #   lerobot
     #   robomimic
 thop==0.1.1.post2209072238
-    # via libero
+    # via hf-libero
-tifffile==2025.5.10
+tifffile==2026.3.3
     # via scikit-image
-timm==1.0.20
+tokenizers==0.22.2
-    # via lerobot
-tokenizers==0.22.1
     # via transformers
 toml==0.10.2
     # via draccus
-tomli==2.3.0
+torch==2.10.0
-    # via
-    #   cmeel
-    #   coverage
-    #   jupytext
-    #   pytest
-torch==2.7.1
     # via
     #   accelerate
-    #   flash-attn
     #   lerobot
     #   peft
     #   robomimic
     #   thop
-    #   timm
+    #   torchdiffeq
     #   torchvision
-torchcodec==0.5
+torchcodec==0.10.0
     # via lerobot
-torchvision==0.22.1
+torchdiffeq==0.2.5
+    # via lerobot
+torchvision==0.25.0
     # via
     #   lerobot
     #   robomimic
-    #   timm
+tornado==6.5.4
-tornado==6.5.2
     # via meshcat
-tqdm==4.67.1
+tqdm==4.67.3
     # via
     #   datasets
     #   dm-control
@@ -783,26 +801,29 @@ traitlets==5.14.3
     #   jupyter-core
     #   matplotlib-inline
     #   nbformat
-transformers==4.57.1
+transformers==5.3.0
     # via
+    #   hf-libero
     #   lerobot
-    #   libero
     #   peft
 transforms3d==0.4.2
     # via teleop
-triton==3.3.1
+triton==3.6.0
     # via torch
+typer==0.24.1
+    # via
+    #   huggingface-hub
+    #   transformers
 typing-extensions==4.15.0
     # via
     #   aiosignal
     #   anyio
     #   etils
-    #   exceptiongroup
+    #   faker
     #   fastapi
     #   gymnasium
     #   huggingface-hub
-    #   ipython
+    #   mypy
-    #   multidict
     #   pydantic
     #   pydantic-core
     #   referencing
@@ -811,46 +832,46 @@ typing-extensions==4.15.0
     #   torch
     #   typing-inspect
     #   typing-inspection
-    #   uvicorn
-    #   virtualenv
     #   wandb
 typing-inspect==0.9.0
     # via draccus
 typing-inspection==0.4.2
-    # via pydantic
+    # via
-tzdata==2025.2
+    #   fastapi
+    #   pydantic
+tzdata==2025.3
     # via pandas
 u-msgpack-python==2.8.0
     # via meshcat
-urllib3==2.5.0
+urllib3==2.6.3
     # via
     #   requests
     #   sentry-sdk
-uvicorn[standard]==0.38.0
+uvicorn[standard]==0.41.0
     # via teleop
 uvloop==0.22.1
     # via uvicorn
-virtualenv==20.35.3
+virtualenv==21.1.0
     # via pre-commit
-wandb==0.21.4
+wandb==0.24.2
     # via
+    #   hf-libero
     #   lerobot
-    #   libero
 watchfiles==1.1.1
     # via uvicorn
-wcwidth==0.2.14
+wcwidth==0.6.0
     # via prompt-toolkit
 websocket-client==1.9.0
     # via teleop
-websockets==15.0.1
+websockets==16.0
     # via uvicorn
-werkzeug==3.1.3
+werkzeug==3.1.6
     # via tensorboard
-wrapt==2.0.0
+wrapt==2.1.2
     # via dm-tree
 xxhash==3.6.0
     # via datasets
-yarl==1.22.0
+yarl==1.23.0
     # via aiohttp
 zipp==3.23.0
     # via

requirements.in

@@ -1,9 +1,9 @@
 # requirements.in
 
-# requirements-macos.txt was generated on macOS and is platform-specific (macOS 26.0.1 25A362 arm64).
+# requirements-macos.txt was generated on macOS and is platform-specific (macOS 26.3.1 25D2128 arm64).
-# Darwin MacBook-Pro.local 25.0.0 Darwin Kernel Version 25.0.0: Wed Sep 17 21:42:08 PDT 2025; root:xnu-12377.1.9~141/RELEASE_ARM64_T8132 arm64
+# Darwin MacBook-Pro.local 25.3.0 Darwin Kernel Version 25.3.0: Wed Jan 28 20:54:55 PST 2026; root:xnu-12377.91.3~2/RELEASE_ARM64_T8132 arm64
 
-# requirements-ubuntu.txt was generated on Linux and is platform-specific (Ubuntu 24.04.3 LTS x86_64).
+# requirements-ubuntu.txt was generated on Linux and is platform-specific (Ubuntu 24.04.4 LTS x86_64).
-# Linux mlerobot-linux 6.14.0-33-generic #33~24.04.1-Ubuntu SMP PREEMPT_DYNAMIC Fri Sep 19 17:02:30 UTC 2 x86_64 x86_64 x86_64 GNU/Linux
+# Linux lerobot-linux 6.17.0-14-generic #14~24.04.1-Ubuntu SMP PREEMPT_DYNAMIC Thu Jan 15 15:52:10 UTC 2 x86_64 x86_64 x86_64 GNU/Linux
 
 -e .[all]

src/lerobot/async_inference/robot_client.py

@@ -49,9 +49,14 @@ import torch
 
 from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig  # noqa: F401
 from lerobot.cameras.realsense.configuration_realsense import RealSenseCameraConfig  # noqa: F401
-from lerobot.robots import (
+from lerobot.robots import (  # noqa: F401
-    RobotConfig,  # noqa: F401
+    Robot,
+    RobotConfig,
+    bi_so_follower,
+    koch_follower,
     make_robot_from_config,
+    omx_follower,
+    so_follower,
 )
 from lerobot.transport import (
     services_pb2,  # type: ignore

src/lerobot/cameras/zmq/camera_zmq.py

@@ -181,7 +181,7 @@ class ZMQCamera(Camera):
         try:
             message = self.socket.recv_string()
         except Exception as e:
-            # Check for ZMQ timeout (EAGAIN/Again) without requiring global zmq import
+            # zmq is lazy-imported in connect(), so check by name to avoid a top-level import
             if type(e).__name__ == "Again":
                 raise TimeoutError(f"{self} timeout after {self.timeout_ms}ms") from e
             raise

src/lerobot/cameras/zmq/image_server.py

@@ -23,6 +23,7 @@ import base64
 import contextlib
 import json
 import logging
+import threading
 import time
 from collections import deque
 
@@ -42,10 +43,57 @@ def encode_image(image: np.ndarray, quality: int = 80) -> str:
     return base64.b64encode(buffer).decode("utf-8")
 
 
+class CameraCaptureThread:
+    """Background thread that continuously captures and encodes frames from a camera."""
+
+    def __init__(self, camera: OpenCVCamera, name: str):
+        self.camera = camera
+        self.name = name
+        self.latest_encoded: str | None = None  # Pre-encoded JPEG as base64
+        self.latest_timestamp: float = 0.0
+        self.frame_lock = threading.Lock()
+        self.running = False
+        self.thread: threading.Thread | None = None
+
+    def start(self):
+        """Start the capture thread."""
+        self.running = True
+        self.thread = threading.Thread(target=self._capture_loop, daemon=True)
+        self.thread.start()
+
+    def stop(self):
+        """Stop the capture thread."""
+        self.running = False
+        if self.thread:
+            self.thread.join(timeout=1.0)
+
+    def _capture_loop(self):
+        """Continuously capture and encode frames at the camera's native rate."""
+        while self.running:
+            try:
+                frame = self.camera.read()  # Blocks at camera's native rate
+                timestamp = time.time()
+                # Encode immediately in capture thread (this is the slow part)
+                encoded = encode_image(frame)
+                with self.frame_lock:
+                    self.latest_encoded = encoded
+                    self.latest_timestamp = timestamp
+            except Exception as e:
+                logger.warning(f"Camera {self.name} capture error: {e}")
+                time.sleep(0.01)
+
+    def get_latest(self) -> tuple[str | None, float]:
+        """Get the latest encoded frame and its timestamp."""
+        with self.frame_lock:
+            return self.latest_encoded, self.latest_timestamp
+
+
 class ImageServer:
     def __init__(self, config: dict, port: int = 5555):
+        # fps controls the publish loop rate (how often frames are sent over ZMQ), not the camera capture rate
         self.fps = config.get("fps", 30)
         self.cameras: dict[str, OpenCVCamera] = {}
+        self.capture_threads: dict[str, CameraCaptureThread] = {}
 
         for name, cfg in config.get("cameras", {}).items():
             shape = cfg.get("shape", [480, 640])
@@ -61,6 +109,10 @@ class ImageServer:
             self.cameras[name] = camera
             logger.info(f"Camera {name}: {shape[1]}x{shape[0]}")
 
+            # Create capture thread for this camera
+            capture_thread = CameraCaptureThread(camera, name)
+            self.capture_threads[name] = capture_thread
+
         # ZMQ PUB socket
         self.context = zmq.Context()
         self.socket = self.context.socket(zmq.PUB)
@@ -73,6 +125,18 @@ class ImageServer:
     def run(self):
         frame_count = 0
         frame_times = deque(maxlen=60)
+        last_published_ts: dict[str, float] = {}
+
+        # Start all capture threads
+        for capture_thread in self.capture_threads.values():
+            capture_thread.start()
+
+        # Wait for first frames to be captured and encoded
+        logger.info("Waiting for cameras to start capturing...")
+        for name, capture_thread in self.capture_threads.items():
+            while capture_thread.get_latest()[0] is None:
+                time.sleep(0.01)
+            logger.info(f"Camera {name} ready (capture + encode in background)")
 
         try:
             while True:
@@ -80,10 +144,12 @@ class ImageServer:
 
                 # Build message
                 message = {"timestamps": {}, "images": {}}
-                for name, cam in self.cameras.items():
+                for name, capture_thread in self.capture_threads.items():
-                    frame = cam.read()  # Returns RGB
+                    encoded, timestamp = capture_thread.get_latest()
-                    message["timestamps"][name] = time.time()
+                    if encoded is not None and timestamp > last_published_ts.get(name, 0.0):
-                    message["images"][name] = encode_image(frame)
+                        message["timestamps"][name] = timestamp
+                        message["images"][name] = encoded
+                        last_published_ts[name] = timestamp
 
                 # Send as JSON string (suppress if buffer full)
                 with contextlib.suppress(zmq.Again):
@@ -102,6 +168,8 @@ class ImageServer:
         except KeyboardInterrupt:
             pass
         finally:
+            for capture_thread in self.capture_threads.values():
+                capture_thread.stop()
             for cam in self.cameras.values():
                 cam.disconnect()
             self.socket.close()

src/lerobot/configs/default.py

@@ -16,18 +16,13 @@
 
 from dataclasses import dataclass, field
 
-from lerobot.datasets.transforms import ImageTransformsConfig
+from lerobot.datasets.transforms import DatasetTransformStepConfig, ImageTransformsConfig
 from lerobot.datasets.video_utils import get_safe_default_codec
 
 
 @dataclass
 class DatasetConfig:
-    # You may provide a list of datasets here. `train.py` creates them all and concatenates them. Note: only data
-    # keys common between the datasets are kept. Each dataset gets and additional transform that inserts the
-    # "dataset_index" into the returned item. The index mapping is made according to the order in which the
-    # datasets are provided.
     repo_id: str
-    # Root directory where the dataset will be stored (e.g. 'dataset/path').
     root: str | None = None
     episodes: list[int] | None = None
     image_transforms: ImageTransformsConfig = field(default_factory=ImageTransformsConfig)
@@ -37,6 +32,32 @@ class DatasetConfig:
     streaming: bool = False
 
 
+@dataclass
+class SubDatasetConfig:
+    """Configuration for a single dataset within a MultiDatasetConfig."""
+
+    repo_id: str
+    root: str | None = None
+    episodes: list[int] | None = None
+    revision: str | None = None
+    video_backend: str = field(default_factory=get_safe_default_codec)
+    weight: float = 1.0
+    # Maps dataset-local feature keys to unified policy keys.
+    # Keys not listed pass through unchanged.
+    feature_map: dict[str, str] = field(default_factory=dict)
+    # Per-dataset transforms applied after feature renaming, before cross-dataset padding.
+    transforms: list[DatasetTransformStepConfig] | None = None
+
+
+@dataclass
+class MultiDatasetConfig:
+    """Configuration for training on multiple datasets jointly."""
+
+    datasets: list[SubDatasetConfig] = field(default_factory=list)
+    image_transforms: ImageTransformsConfig = field(default_factory=ImageTransformsConfig)
+    use_imagenet_stats: bool = True
+
+
 @dataclass
 class WandBConfig:
     enable: bool = False

src/lerobot/configs/train.py

@@ -24,7 +24,7 @@ from huggingface_hub.errors import HfHubHTTPError
 
 from lerobot import envs
 from lerobot.configs import parser
-from lerobot.configs.default import DatasetConfig, EvalConfig, PeftConfig, WandBConfig
+from lerobot.configs.default import DatasetConfig, EvalConfig, MultiDatasetConfig, PeftConfig, WandBConfig
 from lerobot.configs.policies import PreTrainedConfig
 from lerobot.optim import OptimizerConfig
 from lerobot.optim.schedulers import LRSchedulerConfig
@@ -35,7 +35,7 @@ TRAIN_CONFIG_NAME = "train_config.json"
 
 @dataclass
 class TrainPipelineConfig(HubMixin):
-    dataset: DatasetConfig
+    dataset: DatasetConfig | MultiDatasetConfig
     env: envs.EnvConfig | None = None
     policy: PreTrainedConfig | None = None
     # Set `dir` to where you would like to save all of the run outputs. If you run another training session
@@ -50,6 +50,9 @@ class TrainPipelineConfig(HubMixin):
     # `seed` is used for training (eg: model initialization, dataset shuffling)
     # AND for the evaluation environments.
     seed: int | None = 1000
+    # Set to True to use deterministic cuDNN algorithms for reproducibility.
+    # This disables cudnn.benchmark and may reduce training speed by ~10-20%.
+    cudnn_deterministic: bool = False
     # Number of workers for the dataloader.
     num_workers: int = 4
     batch_size: int = 8
@@ -126,8 +129,9 @@ class TrainPipelineConfig(HubMixin):
             train_dir = f"{now:%Y-%m-%d}/{now:%H-%M-%S}_{self.job_name}"
             self.output_dir = Path("outputs/train") / train_dir
 
-        if isinstance(self.dataset.repo_id, list):
+        if isinstance(self.dataset, MultiDatasetConfig):
-            raise NotImplementedError("LeRobotMultiDataset is not currently implemented.")
+            if len(self.dataset.datasets) < 1:
+                raise ValueError("MultiDatasetConfig.datasets must contain at least one sub-dataset.")
 
         if not self.use_policy_training_preset and (self.optimizer is None or self.scheduler is None):
             raise ValueError("Optimizer and Scheduler must be set when the policy presets are not used.")
@@ -140,8 +144,7 @@ class TrainPipelineConfig(HubMixin):
                 "'policy.repo_id' argument missing. Please specify it to push the model to the hub."
             )
 
-        if self.use_rabc and not self.rabc_progress_path:
+        if self.use_rabc and not self.rabc_progress_path and isinstance(self.dataset, DatasetConfig):
-            # Auto-detect from dataset path
             repo_id = self.dataset.repo_id
             if self.dataset.root:
                 self.rabc_progress_path = str(Path(self.dataset.root) / "sarm_progress.parquet")

src/lerobot/datasets/aggregate.py

@@ -289,7 +289,9 @@ def aggregate_datasets(
 
     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=unique_tasks)
+    dst_meta.tasks = pd.DataFrame(
+        {"task_index": range(len(unique_tasks))}, index=pd.Index(unique_tasks, name="task")
+    )
 
     meta_idx = {"chunk": 0, "file": 0}
     data_idx = {"chunk": 0, "file": 0}

src/lerobot/datasets/dataset_tools.py

@@ -89,8 +89,8 @@ def delete_episodes(
     Args:
         dataset: The source LeRobotDataset.
         episode_indices: List of episode indices to delete.
-        output_dir: Directory to save the new dataset. If None, uses default location.
+        output_dir: Root directory where the edited dataset will be stored. If not specified, defaults to $HF_LEROBOT_HOME/repo_id. Equivalent to new_root in EditDatasetConfig.
-        repo_id: Repository ID for the new dataset. If None, appends "_modified" to original.
+        repo_id: Edited dataset identifier. Equivalent to new_repo_id in EditDatasetConfig.
     """
     if not episode_indices:
         raise ValueError("No episodes to delete")
@@ -152,7 +152,7 @@ def split_dataset(
         dataset: The source LeRobotDataset to split.
         splits: Either a dict mapping split names to episode indices, or a dict mapping
                 split names to fractions (must sum to <= 1.0).
-        output_dir: Base directory for output datasets. If None, uses default location.
+        output_dir: Root directory where the split datasets will be stored. If not specified, defaults to $HF_LEROBOT_HOME/repo_id.
 
     Examples:
       Split by specific episodes
@@ -243,8 +243,8 @@ def merge_datasets(
 
     Args:
         datasets: List of LeRobotDatasets to merge.
-        output_repo_id: Repository ID for the merged dataset.
+        output_repo_id: Merged dataset identifier.
-        output_dir: Directory to save the merged dataset. If None, uses default location.
+        output_dir: Root directory where the merged dataset will be stored. If not specified, defaults to $HF_LEROBOT_HOME/output_repo_id.
     """
     if not datasets:
         raise ValueError("No datasets to merge")
@@ -288,8 +288,8 @@ def modify_features(
         dataset: The source LeRobotDataset.
         add_features: Optional dict mapping feature names to (feature_values, feature_info) tuples.
         remove_features: Optional feature name(s) to remove. Can be a single string or list.
-        output_dir: Directory to save the new dataset. If None, uses default location.
+        output_dir: Root directory where the edited dataset will be stored. If not specified, defaults to $HF_LEROBOT_HOME/repo_id. Equivalent to new_root in EditDatasetConfig.
-        repo_id: Repository ID for the new dataset. If None, appends "_modified" to original.
+        repo_id: Edited dataset identifier. Equivalent to new_repo_id in EditDatasetConfig.
 
     Returns:
         New dataset with features modified.
@@ -390,8 +390,8 @@ def add_features(
     Args:
         dataset: The source LeRobotDataset.
         features: Dictionary mapping feature names to (feature_values, feature_info) tuples.
-        output_dir: Directory to save the new dataset. If None, uses default location.
+        output_dir: Root directory where the edited dataset will be stored. If not specified, defaults to $HF_LEROBOT_HOME/repo_id. Equivalent to new_root in EditDatasetConfig.
-        repo_id: Repository ID for the new dataset. If None, appends "_modified" to original.
+        repo_id: Edited dataset identifier. Equivalent to new_repo_id in EditDatasetConfig.
 
     Returns:
         New dataset with all features added.
@@ -427,8 +427,8 @@ def remove_feature(
     Args:
         dataset: The source LeRobotDataset.
         feature_names: Name(s) of features to remove. Can be a single string or list.
-        output_dir: Directory to save the new dataset. If None, uses default location.
+        output_dir: Root directory where the edited dataset will be stored. If not specified, defaults to $HF_LEROBOT_HOME/repo_id. Equivalent to new_root in EditDatasetConfig.
-        repo_id: Repository ID for the new dataset. If None, appends "_modified" to original.
+        repo_id: Edited dataset identifier. Equivalent to new_repo_id in EditDatasetConfig.
 
     Returns:
         New dataset with features removed.
@@ -1475,7 +1475,9 @@ def modify_tasks(
 
     # Collect all unique tasks and create new task mapping
     unique_tasks = sorted(set(episode_to_task.values()))
-    new_task_df = pd.DataFrame({"task_index": list(range(len(unique_tasks)))}, index=unique_tasks)
+    new_task_df = pd.DataFrame(
+        {"task_index": list(range(len(unique_tasks)))}, index=pd.Index(unique_tasks, name="task")
+    )
     task_to_index = {task: idx for idx, task in enumerate(unique_tasks)}
 
     logging.info(f"Modifying tasks in {dataset.repo_id}")
@@ -1529,7 +1531,7 @@ def modify_tasks(
 
 def convert_image_to_video_dataset(
     dataset: LeRobotDataset,
-    output_dir: Path,
+    output_dir: Path | None = None,
     repo_id: str | None = None,
     vcodec: str = "libsvtav1",
     pix_fmt: str = "yuv420p",
@@ -1548,8 +1550,8 @@ def convert_image_to_video_dataset(
 
     Args:
         dataset: The source LeRobot dataset with images
-        output_dir: Directory to save the new video dataset
+        output_dir: Root directory where the edited dataset will be stored. If not specified, defaults to $HF_LEROBOT_HOME/repo_id. Equivalent to new_root in EditDatasetConfig.
-        repo_id: Repository ID for the new dataset (default: original_id + "_video")
+        repo_id: Edited dataset identifier. Equivalent to new_repo_id in EditDatasetConfig.
         vcodec: Video codec (default: libsvtav1)
         pix_fmt: Pixel format (default: yuv420p)
         g: Group of pictures size (default: 2)
@@ -1600,6 +1602,7 @@ def convert_image_to_video_dataset(
             # Video info will be updated after episodes are encoded
 
     # Create new metadata for video dataset
+    output_dir = Path(output_dir) if output_dir is not None else HF_LEROBOT_HOME / repo_id
     new_meta = LeRobotDatasetMetadata.create(
         repo_id=repo_id,
         fps=dataset.meta.fps,

src/lerobot/datasets/factory.py

@@ -18,13 +18,14 @@ from pprint import pformat
 
 import torch
 
+from lerobot.configs.default import DatasetConfig, MultiDatasetConfig
 from lerobot.configs.policies import PreTrainedConfig
 from lerobot.configs.train import TrainPipelineConfig
 from lerobot.datasets.lerobot_dataset import (
     LeRobotDataset,
     LeRobotDatasetMetadata,
-    MultiLeRobotDataset,
 )
+from lerobot.datasets.multi_dataset import NewMultiLeRobotDataset
 from lerobot.datasets.streaming_dataset import StreamingLeRobotDataset
 from lerobot.datasets.transforms import ImageTransforms
 from lerobot.utils.constants import ACTION, OBS_PREFIX, REWARD
@@ -68,66 +69,81 @@ def resolve_delta_timestamps(
     return delta_timestamps
 
 
-def make_dataset(cfg: TrainPipelineConfig) -> LeRobotDataset | MultiLeRobotDataset:
+def make_dataset(cfg: TrainPipelineConfig) -> LeRobotDataset | NewMultiLeRobotDataset:
-    """Handles the logic of setting up delta timestamps and image transforms before creating a dataset.
+    """Create a single or multi-dataset depending on the config type.
-
-    Args:
-        cfg (TrainPipelineConfig): A TrainPipelineConfig config which contains a DatasetConfig and a PreTrainedConfig.
-
-    Raises:
-        NotImplementedError: The MultiLeRobotDataset is currently deactivated.
 
     Returns:
-        LeRobotDataset | MultiLeRobotDataset
+        LeRobotDataset | NewMultiLeRobotDataset
     """
-    image_transforms = (
+    if isinstance(cfg.dataset, MultiDatasetConfig):
-        ImageTransforms(cfg.dataset.image_transforms) if cfg.dataset.image_transforms.enable else None
+        return _make_multi_dataset(cfg)
-    )
 
-    if isinstance(cfg.dataset.repo_id, str):
+    return _make_single_dataset(cfg)
-        ds_meta = LeRobotDatasetMetadata(
+
-            cfg.dataset.repo_id, root=cfg.dataset.root, revision=cfg.dataset.revision
+
+def _make_single_dataset(cfg: TrainPipelineConfig) -> LeRobotDataset:
+    ds_cfg: DatasetConfig = cfg.dataset  # type: ignore[assignment]
+    image_transforms = (
+        ImageTransforms(ds_cfg.image_transforms) if ds_cfg.image_transforms.enable else None
     )
+    ds_meta = LeRobotDatasetMetadata(ds_cfg.repo_id, root=ds_cfg.root, revision=ds_cfg.revision)
     delta_timestamps = resolve_delta_timestamps(cfg.policy, ds_meta)
-        if not cfg.dataset.streaming:
+
+    if not ds_cfg.streaming:
         dataset = LeRobotDataset(
-                cfg.dataset.repo_id,
+            ds_cfg.repo_id,
-                root=cfg.dataset.root,
+            root=ds_cfg.root,
-                episodes=cfg.dataset.episodes,
+            episodes=ds_cfg.episodes,
             delta_timestamps=delta_timestamps,
             image_transforms=image_transforms,
-                revision=cfg.dataset.revision,
+            revision=ds_cfg.revision,
-                video_backend=cfg.dataset.video_backend,
+            video_backend=ds_cfg.video_backend,
             tolerance_s=cfg.tolerance_s,
         )
     else:
         dataset = StreamingLeRobotDataset(
-                cfg.dataset.repo_id,
+            ds_cfg.repo_id,
-                root=cfg.dataset.root,
+            root=ds_cfg.root,
-                episodes=cfg.dataset.episodes,
+            episodes=ds_cfg.episodes,
             delta_timestamps=delta_timestamps,
             image_transforms=image_transforms,
-                revision=cfg.dataset.revision,
+            revision=ds_cfg.revision,
             max_num_shards=cfg.num_workers,
             tolerance_s=cfg.tolerance_s,
         )
-    else:
-        raise NotImplementedError("The MultiLeRobotDataset isn't supported for now.")
-        dataset = MultiLeRobotDataset(
-            cfg.dataset.repo_id,
-            # TODO(aliberts): add proper support for multi dataset
-            # delta_timestamps=delta_timestamps,
-            image_transforms=image_transforms,
-            video_backend=cfg.dataset.video_backend,
-        )
-        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:
+    if ds_cfg.use_imagenet_stats:
         for key in dataset.meta.camera_keys:
-            for stats_type, stats in IMAGENET_STATS.items():
+            for stats_type, stats_val in IMAGENET_STATS.items():
-                dataset.meta.stats[key][stats_type] = torch.tensor(stats, dtype=torch.float32)
+                dataset.meta.stats[key][stats_type] = torch.tensor(stats_val, dtype=torch.float32)
+
+    return dataset
+
+
+def _make_multi_dataset(cfg: TrainPipelineConfig) -> NewMultiLeRobotDataset:
+    multi_cfg: MultiDatasetConfig = cfg.dataset  # type: ignore[assignment]
+    image_transforms = (
+        ImageTransforms(multi_cfg.image_transforms) if multi_cfg.image_transforms.enable else None
+    )
+
+    dataset = NewMultiLeRobotDataset(
+        configs=multi_cfg.datasets,
+        image_transforms=image_transforms,
+        tolerance_s=cfg.tolerance_s,
+    )
+
+    logging.info(
+        "MultiLeRobotDataset created with %d sub-datasets:\n%s",
+        len(multi_cfg.datasets),
+        pformat(
+            {i: c.repo_id for i, c in enumerate(multi_cfg.datasets)},
+            indent=2,
+        ),
+    )
+
+    if multi_cfg.use_imagenet_stats:
+        for key in dataset.meta.camera_keys:
+            for stats_type, stats_val in IMAGENET_STATS.items():
+                dataset.meta.stats[key][stats_type] = torch.tensor(stats_val, dtype=torch.float32)
 
     return dataset

src/lerobot/datasets/lerobot_dataset.py

@@ -314,7 +314,7 @@ class LeRobotDatasetMetadata:
         if self.tasks is None:
             new_tasks = tasks
             task_indices = range(len(tasks))
-            self.tasks = pd.DataFrame({"task_index": task_indices}, index=tasks)
+            self.tasks = pd.DataFrame({"task_index": task_indices}, index=pd.Index(tasks, name="task"))
         else:
             new_tasks = [task for task in tasks if task not in self.tasks.index]
             new_task_indices = range(len(self.tasks), len(self.tasks) + len(new_tasks))
@@ -664,11 +664,11 @@ class LeRobotDataset(torch.utils.data.Dataset):
         for the README).
 
         Args:
-            repo_id (str): This is the repo id that will be used to fetch the dataset. Locally, the dataset
+            repo_id (str): This is the repo id that will be used to fetch the dataset.
-                will be stored under root/repo_id.
+            root (Path | None, optional): Local directory where the dataset will be downloaded and
-            root (Path | None, optional): Local directory to use for downloading/writing files. You can also
+                stored. If set, all dataset files will be stored directly under this path. If not set, the
-                set the HF_LEROBOT_HOME environment variable to point to a different location. Defaults to
+                dataset files will be stored under $HF_LEROBOT_HOME/repo_id (configurable via the
-                '~/.cache/huggingface/lerobot'.
+                HF_LEROBOT_HOME environment variable).
             episodes (list[int] | None, optional): If specified, this will only load episodes specified by
                 their episode_index in this list. Defaults to None.
             image_transforms (Callable | None, optional): You can pass standard v2 image transforms from

src/lerobot/datasets/multi_dataset.py

@@ -0,0 +1,364 @@
+"""MultiLeRobotDataset: joint training over heterogeneous LeRobot datasets.
+
+Supports:
+- Per-dataset feature mapping (rename keys to a unified namespace)
+- Automatic zero-padding for features missing in some datasets
+- Per-dataset transform pipelines
+- Weighted sampling via dataset weights
+- Aggregated stats across all sub-datasets
+- A ``meta`` shim compatible with EpisodeAwareSampler and make_policy
+"""
+
+from __future__ import annotations
+
+import logging
+from collections.abc import Callable
+
+import numpy as np
+import torch
+import torch.utils.data
+
+from lerobot.configs.default import SubDatasetConfig
+from lerobot.datasets.compute_stats import aggregate_stats
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.datasets.transforms import DatasetTransformPipeline
+
+
+class MultiDatasetMeta:
+    """Lightweight metadata shim that exposes the same interface as ``LeRobotDatasetMetadata``.
+
+    Built by aggregating the metadata of multiple sub-datasets after their
+    feature keys have been mapped to a unified namespace.
+    """
+
+    def __init__(
+        self,
+        datasets: list[LeRobotDataset],
+        feature_maps: list[dict[str, str]],
+    ):
+        self._datasets = datasets
+        self._feature_maps = feature_maps
+
+        self._unified_features = self._build_unified_features()
+        self._episodes = self._build_episodes()
+        self._stats = self._build_stats()
+
+    # ------------------------------------------------------------------
+    # Feature union
+    # ------------------------------------------------------------------
+
+    def _build_unified_features(self) -> dict[str, dict]:
+        """Build feature dict as the *union* of all mapped feature keys."""
+        unified: dict[str, dict] = {}
+        for ds, fmap in zip(self._datasets, self._feature_maps):
+            for original_key, feat_info in ds.meta.features.items():
+                mapped_key = fmap.get(original_key, original_key)
+                if mapped_key not in unified:
+                    unified[mapped_key] = dict(feat_info)
+                else:
+                    existing_shape = tuple(unified[mapped_key]["shape"])
+                    new_shape = tuple(feat_info["shape"])
+                    if existing_shape != new_shape and unified[mapped_key]["dtype"] == feat_info["dtype"]:
+                        logging.warning(
+                            "Feature '%s' has shape %s in one dataset but %s in another. "
+                            "The larger shape will be used (padding applied automatically).",
+                            mapped_key,
+                            existing_shape,
+                            new_shape,
+                        )
+                        if np.prod(new_shape) > np.prod(existing_shape):
+                            unified[mapped_key] = dict(feat_info)
+        return unified
+
+    # ------------------------------------------------------------------
+    # Episode metadata (global flat indexing)
+    # ------------------------------------------------------------------
+
+    def _build_episodes(self) -> dict[str, list]:
+        """Concatenate episode boundaries across sub-datasets with frame offsets.
+
+        Produces the same column structure as ``load_episodes()`` so that
+        ``EpisodeAwareSampler`` and ``WeightedEpisodeAwareSampler`` can consume it.
+        """
+        from_indices: list[int] = []
+        to_indices: list[int] = []
+        dataset_source: list[int] = []
+
+        frame_offset = 0
+        for ds_idx, ds in enumerate(self._datasets):
+            eps = ds.meta.episodes
+            for ep in eps:
+                from_indices.append(ep["dataset_from_index"] + frame_offset)
+                to_indices.append(ep["dataset_to_index"] + frame_offset)
+                dataset_source.append(ds_idx)
+            frame_offset += ds.num_frames
+
+        return {
+            "dataset_from_index": from_indices,
+            "dataset_to_index": to_indices,
+            "dataset_source": dataset_source,
+        }
+
+    # ------------------------------------------------------------------
+    # Stats aggregation
+    # ------------------------------------------------------------------
+
+    def _build_stats(self) -> dict[str, dict[str, np.ndarray]]:
+        """Aggregate stats across sub-datasets using mapped feature keys."""
+        mapped_stats_list: list[dict[str, dict]] = []
+        for ds, fmap in zip(self._datasets, self._feature_maps):
+            reverse_map = {v: k for k, v in fmap.items()}
+            mapped: dict[str, dict] = {}
+            for unified_key in self._unified_features:
+                original_key = reverse_map.get(unified_key, unified_key)
+                if original_key in ds.meta.stats:
+                    mapped[unified_key] = ds.meta.stats[original_key]
+            mapped_stats_list.append(mapped)
+
+        return aggregate_stats(mapped_stats_list)
+
+    # ------------------------------------------------------------------
+    # Properties matching LeRobotDatasetMetadata API
+    # ------------------------------------------------------------------
+
+    @property
+    def features(self) -> dict[str, dict]:
+        return self._unified_features
+
+    @property
+    def image_keys(self) -> list[str]:
+        return [k for k, f in self._unified_features.items() if f["dtype"] == "image"]
+
+    @property
+    def video_keys(self) -> list[str]:
+        return [k for k, f in self._unified_features.items() if f["dtype"] == "video"]
+
+    @property
+    def camera_keys(self) -> list[str]:
+        return [k for k, f in self._unified_features.items() if f["dtype"] in ("video", "image")]
+
+    @property
+    def names(self) -> dict[str, list | dict]:
+        return {k: f["names"] for k, f in self._unified_features.items()}
+
+    @property
+    def shapes(self) -> dict[str, tuple]:
+        return {k: tuple(f["shape"]) for k, f in self._unified_features.items()}
+
+    @property
+    def fps(self) -> int:
+        fps_values = {ds.meta.fps for ds in self._datasets}
+        if len(fps_values) > 1:
+            logging.warning("Sub-datasets have different FPS values: %s. Using the first.", fps_values)
+        return self._datasets[0].meta.fps
+
+    @property
+    def stats(self) -> dict[str, dict[str, np.ndarray]]:
+        return self._stats
+
+    @stats.setter
+    def stats(self, value: dict):
+        self._stats = value
+
+    @property
+    def episodes(self) -> dict[str, list]:
+        return self._episodes
+
+    @property
+    def total_episodes(self) -> int:
+        return sum(ds.meta.total_episodes for ds in self._datasets)
+
+    @property
+    def total_frames(self) -> int:
+        return sum(ds.meta.total_frames for ds in self._datasets)
+
+    @property
+    def total_tasks(self) -> int:
+        return sum(ds.meta.total_tasks for ds in self._datasets)
+
+    @property
+    def info(self) -> dict:
+        return {
+            "fps": self.fps,
+            "features": self._unified_features,
+            "total_episodes": self.total_episodes,
+            "total_frames": self.total_frames,
+            "total_tasks": self.total_tasks,
+            "codebase_version": "v3.0",
+        }
+
+
+class NewMultiLeRobotDataset(torch.utils.data.Dataset):
+    """Dataset that wraps multiple ``LeRobotDataset`` instances with feature mapping and padding.
+
+    Each sub-dataset can have different feature names and shapes.  A per-dataset
+    ``feature_map`` renames keys into a shared namespace.  Features that a given
+    sub-dataset does not provide are zero-padded so every ``__getitem__`` returns
+    the full unified feature set.
+    """
+
+    def __init__(
+        self,
+        configs: list[SubDatasetConfig],
+        image_transforms: Callable | None = None,
+        delta_timestamps: dict[str, list[float]] | None = None,
+        tolerance_s: float = 1e-4,
+    ):
+        super().__init__()
+        self._configs = configs
+        self.image_transforms = image_transforms
+
+        self._datasets: list[LeRobotDataset] = []
+        self._feature_maps: list[dict[str, str]] = []
+        self._transform_pipelines: list[DatasetTransformPipeline | None] = []
+        self._weights: list[float] = []
+
+        for cfg in configs:
+            ds = LeRobotDataset(
+                repo_id=cfg.repo_id,
+                root=cfg.root,
+                episodes=cfg.episodes,
+                image_transforms=image_transforms,
+                delta_timestamps=delta_timestamps,
+                tolerance_s=tolerance_s,
+                revision=cfg.revision,
+                video_backend=cfg.video_backend,
+            )
+            self._datasets.append(ds)
+            self._feature_maps.append(cfg.feature_map or {})
+            self._transform_pipelines.append(
+                DatasetTransformPipeline(cfg.transforms) if cfg.transforms else None
+            )
+            self._weights.append(cfg.weight)
+
+        self._meta = MultiDatasetMeta(self._datasets, self._feature_maps)
+
+        # Pre-compute cumulative frame counts for fast index mapping.
+        self._cumulative_frames: list[int] = []
+        total = 0
+        for ds in self._datasets:
+            total += ds.num_frames
+            self._cumulative_frames.append(total)
+
+        # Build reverse maps (unified_key -> original_key) per dataset for padding.
+        self._reverse_maps: list[dict[str, str]] = []
+        for fmap in self._feature_maps:
+            self._reverse_maps.append({v: k for k, v in fmap.items()})
+
+        logging.info(
+            "MultiLeRobotDataset: %d sub-datasets, %d total frames, %d total episodes, "
+            "%d unified features",
+            len(self._datasets),
+            self.num_frames,
+            self.num_episodes,
+            len(self._meta.features),
+        )
+
+    # ------------------------------------------------------------------
+    # Public interface
+    # ------------------------------------------------------------------
+
+    @property
+    def meta(self) -> MultiDatasetMeta:
+        return self._meta
+
+    @property
+    def dataset_weights(self) -> list[float]:
+        return self._weights
+
+    @property
+    def num_frames(self) -> int:
+        return self._cumulative_frames[-1] if self._cumulative_frames else 0
+
+    @property
+    def num_episodes(self) -> int:
+        return sum(ds.num_episodes for ds in self._datasets)
+
+    @property
+    def episodes(self) -> list[int] | None:
+        return None
+
+    @property
+    def fps(self) -> int:
+        return self._meta.fps
+
+    @property
+    def features(self) -> dict[str, dict]:
+        return self._meta.features
+
+    @property
+    def camera_keys(self) -> list[str]:
+        return self._meta.camera_keys
+
+    # ------------------------------------------------------------------
+    # Indexing
+    # ------------------------------------------------------------------
+
+    def _locate(self, idx: int) -> tuple[int, int]:
+        """Map a global frame index to (dataset_index, local_index)."""
+        for ds_idx, cum in enumerate(self._cumulative_frames):
+            if idx < cum:
+                local = idx - (self._cumulative_frames[ds_idx - 1] if ds_idx > 0 else 0)
+                return ds_idx, local
+        raise IndexError(f"Index {idx} out of range (total {self.num_frames})")
+
+    def __len__(self) -> int:
+        return self.num_frames
+
+    def __getitem__(self, idx: int) -> dict[str, torch.Tensor]:
+        ds_idx, local_idx = self._locate(idx)
+        item = self._datasets[ds_idx][local_idx]
+
+        # 1. Rename keys according to feature_map.
+        fmap = self._feature_maps[ds_idx]
+        if fmap:
+            renamed: dict[str, torch.Tensor] = {}
+            for key, value in item.items():
+                renamed[fmap.get(key, key)] = value
+            item = renamed
+
+        # 2. Apply per-dataset transform pipeline.
+        pipeline = self._transform_pipelines[ds_idx]
+        if pipeline is not None:
+            item = pipeline(item)
+
+        # 3. Pad missing features with zeros.
+        reverse_map = self._reverse_maps[ds_idx]
+        ds_features = self._datasets[ds_idx].meta.features
+        for unified_key, feat_info in self._meta.features.items():
+            if unified_key in item:
+                continue
+            original_key = reverse_map.get(unified_key, unified_key)
+            if original_key in ds_features:
+                continue
+            shape = tuple(feat_info["shape"])
+            dtype = feat_info["dtype"]
+            if dtype in ("video", "image"):
+                # Camera tensors are (C, H, W) after transforms.
+                c, h, w = (shape[2], shape[0], shape[1]) if len(shape) == 3 else (3, shape[0], shape[1])
+                item[unified_key] = torch.zeros(c, h, w, dtype=torch.float32)
+            elif dtype in ("float32", "float64"):
+                item[unified_key] = torch.zeros(shape, dtype=torch.float32)
+            elif dtype in ("int32", "int64"):
+                item[unified_key] = torch.zeros(shape, dtype=torch.int64)
+            elif dtype == "bool":
+                item[unified_key] = torch.zeros(shape, dtype=torch.bool)
+            else:
+                item[unified_key] = torch.zeros(shape, dtype=torch.float32)
+            item[f"{unified_key}_is_pad"] = torch.tensor(True)
+
+        # 4. Tag which dataset this sample came from.
+        item["dataset_index"] = torch.tensor(ds_idx)
+        return item
+
+    def __repr__(self) -> str:
+        repo_ids = [c.repo_id for c in self._configs]
+        return (
+            f"NewMultiLeRobotDataset(\n"
+            f"  repo_ids={repo_ids},\n"
+            f"  num_frames={self.num_frames},\n"
+            f"  num_episodes={self.num_episodes},\n"
+            f"  unified_features={list(self._meta.features.keys())},\n"
+            f"  weights={self._weights},\n"
+            f")"
+        )

src/lerobot/datasets/sampler.py

@@ -59,3 +59,80 @@ class EpisodeAwareSampler:
 
     def __len__(self) -> int:
         return len(self.indices)
+
+
+class WeightedEpisodeAwareSampler:
+    """Sampler that draws frames from multiple datasets according to per-dataset weights.
+
+    Each iteration first selects a sub-dataset proportionally to its weight, then
+    uniformly samples a frame from that sub-dataset's valid index set.  Episode
+    boundary information is respected so that dropped frames are excluded.
+
+    Args:
+        dataset_from_indices: Start index for each episode (global, flat).
+        dataset_to_indices: End index (exclusive) for each episode (global, flat).
+        dataset_membership: Which sub-dataset each episode belongs to (integer id).
+        dataset_weights: Relative sampling weight per sub-dataset.
+        episode_indices_to_use: If given, only episodes in this set are used.
+        drop_n_first_frames: Frames to skip at the start of each episode.
+        drop_n_last_frames: Frames to skip at the end of each episode.
+        shuffle: Whether to shuffle within each epoch.
+        num_samples: How many samples per epoch. Defaults to total valid frames.
+        generator: Optional torch.Generator for reproducibility.
+    """
+
+    def __init__(
+        self,
+        dataset_from_indices: list[int],
+        dataset_to_indices: list[int],
+        dataset_membership: list[int],
+        dataset_weights: list[float],
+        episode_indices_to_use: list | None = None,
+        drop_n_first_frames: int = 0,
+        drop_n_last_frames: int = 0,
+        shuffle: bool = False,
+        num_samples: int | None = None,
+        generator: torch.Generator | None = None,
+    ):
+        n_datasets = max(dataset_membership) + 1 if dataset_membership else 0
+        self._per_dataset_indices: list[list[int]] = [[] for _ in range(n_datasets)]
+
+        episodes_to_use = set(episode_indices_to_use) if episode_indices_to_use is not None else None
+
+        for ep_idx, (start, end, ds_id) in enumerate(
+            zip(dataset_from_indices, dataset_to_indices, dataset_membership, strict=True)
+        ):
+            if episodes_to_use is not None and ep_idx not in episodes_to_use:
+                continue
+            frame_range = range(start + drop_n_first_frames, end - drop_n_last_frames)
+            self._per_dataset_indices[ds_id].extend(frame_range)
+
+        # Normalise weights (only over datasets that actually have frames).
+        raw_weights = list(dataset_weights[:n_datasets])
+        self._weights = torch.zeros(n_datasets)
+        for i, w in enumerate(raw_weights):
+            if len(self._per_dataset_indices[i]) > 0:
+                self._weights[i] = w
+        total_w = self._weights.sum()
+        if total_w > 0:
+            self._weights /= total_w
+
+        self._total_frames = sum(len(idx) for idx in self._per_dataset_indices)
+        self._num_samples = num_samples if num_samples is not None else self._total_frames
+        self.shuffle = shuffle
+        self._generator = generator
+
+    def __iter__(self) -> Iterator[int]:
+        if not self.shuffle:
+            for ds_indices in self._per_dataset_indices:
+                yield from ds_indices
+            return
+
+        for _ in range(self._num_samples):
+            ds_id = int(torch.multinomial(self._weights, 1, generator=self._generator).item())
+            indices = self._per_dataset_indices[ds_id]
+            local_idx = int(torch.randint(len(indices), (1,), generator=self._generator).item())
+            yield indices[local_idx]
+
+    def __len__(self) -> int:
+        return self._num_samples

src/lerobot/datasets/transforms.py

@@ -14,11 +14,13 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import collections
+import logging
 from collections.abc import Callable, Sequence
 from dataclasses import dataclass, field
 from typing import Any
 
 import torch
+import torch.nn.functional as F_nn
 from torchvision.transforms import v2
 from torchvision.transforms.v2 import (
     Transform,
@@ -258,3 +260,114 @@ class ImageTransforms(Transform):
 
     def forward(self, *inputs: Any) -> Any:
         return self.tf(*inputs)
+
+
+
+# Per-dataset transform pipeline (used by MultiLeRobotDataset)
+
+@dataclass
+class DatasetTransformStepConfig:
+    """Config for a single per-dataset transform step."""
+
+    type: str
+    kwargs: dict[str, Any] = field(default_factory=dict)
+
+
+_DATASET_TRANSFORM_REGISTRY: dict[str, type["DatasetTransformStep"]] = {}
+
+
+def register_dataset_transform(name: str):
+    """Decorator to register a DatasetTransformStep by name."""
+
+    def decorator(cls: type["DatasetTransformStep"]) -> type["DatasetTransformStep"]:
+        _DATASET_TRANSFORM_REGISTRY[name] = cls
+        return cls
+
+    return decorator
+
+
+class DatasetTransformStep:
+    """Base class for a single per-dataset transform applied to a sample dict."""
+
+    def __call__(self, sample: dict) -> dict:
+        raise NotImplementedError
+
+
+@register_dataset_transform("pad_action")
+class PadAction(DatasetTransformStep):
+    """Zero-pad the ``action`` tensor to *target_dim* along the last axis."""
+
+    def __init__(self, target_dim: int):
+        self.target_dim = target_dim
+
+    def __call__(self, sample: dict) -> dict:
+        action = sample.get("action")
+        if action is None:
+            return sample
+        current = action.shape[-1]
+        if current < self.target_dim:
+            sample["action"] = F_nn.pad(action, (0, self.target_dim - current))
+        return sample
+
+
+@register_dataset_transform("pad_state")
+class PadState(DatasetTransformStep):
+    """Zero-pad ``observation.state`` to *target_dim* along the last axis."""
+
+    def __init__(self, target_dim: int):
+        self.target_dim = target_dim
+
+    def __call__(self, sample: dict) -> dict:
+        state = sample.get("observation.state")
+        if state is None:
+            return sample
+        current = state.shape[-1]
+        if current < self.target_dim:
+            sample["observation.state"] = F_nn.pad(state, (0, self.target_dim - current))
+        return sample
+
+
+@register_dataset_transform("resize_images")
+class ResizeImages(DatasetTransformStep):
+    """Resize all image/video camera tensors to (height, width)."""
+
+    def __init__(self, height: int, width: int):
+        self.size = (height, width)
+
+    def __call__(self, sample: dict) -> dict:
+        for key in list(sample.keys()):
+            if not key.startswith("observation.images."):
+                continue
+            img = sample[key]
+            if not isinstance(img, torch.Tensor) or img.ndim < 3:
+                continue
+            sample[key] = F.resize(img, self.size, antialias=True)
+        return sample
+
+
+class DatasetTransformPipeline:
+    """Sequential pipeline of DatasetTransformStep instances."""
+
+    def __init__(self, configs: list[DatasetTransformStepConfig] | None = None):
+        self.steps: list[DatasetTransformStep] = []
+        if configs:
+            for cfg in configs:
+                self.steps.append(self._build(cfg))
+
+    @staticmethod
+    def _build(cfg: DatasetTransformStepConfig) -> DatasetTransformStep:
+        cls = _DATASET_TRANSFORM_REGISTRY.get(cfg.type)
+        if cls is None:
+            raise ValueError(
+                f"Unknown dataset transform '{cfg.type}'. "
+                f"Available: {list(_DATASET_TRANSFORM_REGISTRY)}"
+            )
+        return cls(**cfg.kwargs)
+
+    def __call__(self, sample: dict) -> dict:
+        for step in self.steps:
+            sample = step(sample)
+        return sample
+
+    def __repr__(self) -> str:
+        return f"DatasetTransformPipeline(steps={self.steps})"

src/lerobot/datasets/utils.py

@@ -21,7 +21,7 @@ from collections import deque
 from collections.abc import Iterable, Iterator
 from pathlib import Path
 from pprint import pformat
-from typing import Any, Generic, TypeVar
+from typing import Any
 
 import datasets
 import numpy as np
@@ -78,8 +78,6 @@ DEFAULT_FEATURES = {
     "task_index": {"dtype": "int64", "shape": (1,), "names": None},
 }
 
-T = TypeVar("T")
-
 
 def get_parquet_file_size_in_mb(parquet_path: str | Path) -> float:
     metadata = pq.read_metadata(parquet_path)
@@ -341,6 +339,7 @@ def write_tasks(tasks: pandas.DataFrame, local_dir: Path) -> None:
 
 def load_tasks(local_dir: Path) -> pandas.DataFrame:
     tasks = pd.read_parquet(local_dir / DEFAULT_TASKS_PATH)
+    tasks.index.name = "task"
     return tasks
 
 
@@ -1233,7 +1232,7 @@ class LookAheadError(Exception):
     pass
 
 
-class Backtrackable(Generic[T]):
+class Backtrackable[T]:
     """
     Wrap any iterator/iterable so you can step back up to `history` items
     and look ahead up to `lookahead` items.

src/lerobot/datasets/v30/convert_dataset_v21_to_v30.py

@@ -36,8 +36,11 @@ Convert a local dataset (works in place):
 ```bash
 python src/lerobot/datasets/v30/convert_dataset_v21_to_v30.py \
     --repo-id=lerobot/pusht \
-    --root=/path/to/local/dataset/directory
+    --root=/path/to/local/dataset/directory \
     --push-to-hub=false
+
+N.B. Path semantics (v2): --root is the exact dataset folder containing
+meta/, data/, videos/. When omitted, defaults to $HF_LEROBOT_HOME/{repo_id}.
 ```
 
 """
@@ -105,7 +108,7 @@ episodes.jsonl
 {"episode_index": 1, "tasks": ["Put the blue block in the green bowl"], "length": 266}
 
 NEW
-meta/episodes/chunk-000/episodes_000.parquet
+meta/episodes/chunk-000/file_000.parquet
 episode_index | video_chunk_index | video_file_index | data_chunk_index | data_file_index | tasks | length
 -------------------------
 OLD
@@ -113,15 +116,16 @@ tasks.jsonl
 {"task_index": 1, "task": "Put the blue block in the green bowl"}
 
 NEW
-meta/tasks/chunk-000/file_000.parquet
+meta/tasks.parquet
 task_index | task
 -------------------------
 OLD
 episodes_stats.jsonl
+{"episode_index": 1, "stats": {"feature_name": {"min": ..., "max": ..., "mean": ..., "std": ..., "count": ...}}}
 
 NEW
-meta/episodes_stats/chunk-000/file_000.parquet
+meta/episodes/chunk-000/file_000.parquet
-episode_index | mean | std | min | max
+episode_index | feature_name/min | feature_name/max | feature_name/mean | feature_name/std | feature_name/count
 -------------------------
 UPDATE
 meta/info.json
@@ -170,7 +174,7 @@ def convert_tasks(root, new_root):
     tasks, _ = legacy_load_tasks(root)
     task_indices = tasks.keys()
     task_strings = tasks.values()
-    df_tasks = pd.DataFrame({"task_index": task_indices}, index=task_strings)
+    df_tasks = pd.DataFrame({"task_index": task_indices}, index=pd.Index(task_strings, name="task"))
     write_tasks(df_tasks, new_root)
 
 
@@ -201,7 +205,6 @@ def convert_data(root: Path, new_root: Path, data_file_size_in_mb: int):
 
     image_keys = get_image_keys(root)
 
-    ep_idx = 0
     chunk_idx = 0
     file_idx = 0
     size_in_mb = 0
@@ -211,9 +214,23 @@ def convert_data(root: Path, new_root: Path, data_file_size_in_mb: int):
 
     logging.info(f"Converting data files from {len(ep_paths)} episodes")
 
-    for ep_path in tqdm.tqdm(ep_paths, desc="convert data files"):
+    for ep_idx, ep_path in enumerate(tqdm.tqdm(ep_paths, desc="convert data files")):
         ep_size_in_mb = get_parquet_file_size_in_mb(ep_path)
         ep_num_frames = get_parquet_num_frames(ep_path)
+
+        # Check if we need to start a new file BEFORE creating metadata
+        if size_in_mb + ep_size_in_mb >= data_file_size_in_mb and len(paths_to_cat) > 0:
+            # Write the accumulated data files
+            concat_data_files(paths_to_cat, new_root, chunk_idx, file_idx, image_keys)
+
+            # Move to next file
+            chunk_idx, file_idx = update_chunk_file_indices(chunk_idx, file_idx, DEFAULT_CHUNK_SIZE)
+
+            # Reset for the next file
+            size_in_mb = 0
+            paths_to_cat = []
+
+        # Now create metadata with correct chunk/file indices
         ep_metadata = {
             "episode_index": ep_idx,
             "data/chunk_index": chunk_idx,
@@ -224,20 +241,7 @@ def convert_data(root: Path, new_root: Path, data_file_size_in_mb: int):
         size_in_mb += ep_size_in_mb
         num_frames += ep_num_frames
         episodes_metadata.append(ep_metadata)
-        ep_idx += 1
-
-        if size_in_mb < data_file_size_in_mb:
         paths_to_cat.append(ep_path)
-            continue
-
-        if paths_to_cat:
-            concat_data_files(paths_to_cat, new_root, chunk_idx, file_idx, image_keys)
-
-        # Reset for the next file
-        size_in_mb = ep_size_in_mb
-        paths_to_cat = [ep_path]
-
-        chunk_idx, file_idx = update_chunk_file_indices(chunk_idx, file_idx, DEFAULT_CHUNK_SIZE)
 
     # Write remaining data if any
     if paths_to_cat:
@@ -469,7 +473,7 @@ def convert_dataset(
 
     # Set root based on whether local dataset path is provided
     use_local_dataset = False
-    root = HF_LEROBOT_HOME / repo_id if root is None else Path(root) / repo_id
+    root = HF_LEROBOT_HOME / repo_id if root is None else Path(root)
     if root.exists():
         validate_local_dataset_version(root)
         use_local_dataset = True
@@ -553,7 +557,7 @@ if __name__ == "__main__":
         "--root",
         type=str,
         default=None,
-        help="Local directory to use for downloading/writing the dataset.",
+        help="Local directory to use for downloading/writing the dataset. Defaults to $HF_LEROBOT_HOME/repo_id.",
     )
     parser.add_argument(
         "--push-to-hub",

src/lerobot/envs/configs.py

@@ -346,6 +346,105 @@ class LiberoEnv(EnvConfig):
         return kwargs
 
 
+@EnvConfig.register_subclass("libero_plus")
+@dataclass
+class LiberoPlusEnv(LiberoEnv):
+    """Alias config for LIBERO-plus benchmarks.
+
+    LIBERO-plus keeps the same Python package/module names as LIBERO, so this
+    config reuses the existing LIBERO env implementation while making intent explicit
+    in experiment configs (`env.type=libero_plus`).
+    """
+
+    task: str = "libero_spatial"
+
+
+@EnvConfig.register_subclass("robocasa")
+@dataclass
+class RoboCasaEnv(EnvConfig):
+    """RoboCasa kitchen composite-task environments.
+
+    Wraps ``robocasa.wrappers.gym_wrapper.RoboCasaGymEnv`` with a flat 12-D Box
+    action space and a structured pixel + state observation dict.
+
+    Selected benchmark tasks (3 short + 2 long):
+        Short:  PickPlaceCounterToCabinet, PrepareToast, CoffeeSetupMug
+        Long:   PrepareCoffee, RestockPantry
+    """
+
+    task: str = "PickPlaceCounterToCabinet"
+    tasks: list[str] | None = None  # multi-task: list of task names (without robocasa/ prefix)
+    fps: int = 20
+    episode_length: int = 500
+    image_size: int = 128
+    split: str = "target"  # "pretrain" or "target"
+    features: dict[str, PolicyFeature] = field(
+        default_factory=lambda: {
+            ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(12,)),
+        }
+    )
+    features_map: dict[str, str] = field(
+        default_factory=lambda: {
+            ACTION: ACTION,
+            "agentview_left":  f"{OBS_IMAGES}.agentview_left",
+            "agentview_right": f"{OBS_IMAGES}.agentview_right",
+            "eye_in_hand":     f"{OBS_IMAGES}.eye_in_hand",
+            "robot_state":     OBS_STATE,
+        }
+    )
+
+    def __post_init__(self):
+        for cam in ("agentview_left", "agentview_right", "eye_in_hand"):
+            self.features[cam] = PolicyFeature(
+                type=FeatureType.VISUAL, shape=(self.image_size, self.image_size, 3)
+            )
+        self.features["robot_state"] = PolicyFeature(type=FeatureType.STATE, shape=(16,))
+
+    @property
+    def gym_kwargs(self) -> dict:
+        return {"split": self.split}
+
+
+@EnvConfig.register_subclass("robomme")
+@dataclass
+class RoboMMEEnv(EnvConfig):
+    """RoboMME memory-augmented manipulation benchmark (ManiSkill/SAPIEN).
+
+    16 tasks across 4 suites: Counting, Permanence, Reference, Imitation.
+    Uses BenchmarkEnvBuilder from the robomme package.
+    """
+
+    task: str = "PickXtimes"
+    fps: int = 10
+    episode_length: int = 300
+    action_space: str = "joint_angle"
+    dataset_split: str = "test"
+    task_ids: list[int] | None = None
+    features: dict[str, PolicyFeature] = field(
+        default_factory=lambda: {
+            ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(8,)),
+            "front_rgb": PolicyFeature(type=FeatureType.VISUAL, shape=(256, 256, 3)),
+            "wrist_rgb": PolicyFeature(type=FeatureType.VISUAL, shape=(256, 256, 3)),
+            OBS_STATE: PolicyFeature(type=FeatureType.STATE, shape=(8,)),
+        }
+    )
+    features_map: dict[str, str] = field(
+        default_factory=lambda: {
+            ACTION: ACTION,
+            "front_rgb": f"{OBS_IMAGES}.front",
+            "wrist_rgb": f"{OBS_IMAGES}.wrist",
+            OBS_STATE: OBS_STATE,
+        }
+    )
+
+    @property
+    def gym_kwargs(self) -> dict:
+        return {
+            "action_space": self.action_space,
+            "dataset": self.dataset_split,
+        }
+
+
 @EnvConfig.register_subclass("metaworld")
 @dataclass
 class MetaworldEnv(EnvConfig):

src/lerobot/envs/factory.py

@@ -20,11 +20,21 @@ import gymnasium as gym
 from gymnasium.envs.registration import registry as gym_registry
 
 from lerobot.configs.policies import PreTrainedConfig
-from lerobot.envs.configs import AlohaEnv, EnvConfig, HubEnvConfig, IsaaclabArenaEnv, LiberoEnv, PushtEnv
+from lerobot.envs.configs import (
+    AlohaEnv,
+    EnvConfig,
+    HubEnvConfig,
+    IsaaclabArenaEnv,
+    LiberoEnv,
+    LiberoPlusEnv,
+    PushtEnv,
+    RoboCasaEnv,
+    RoboMMEEnv,
+)
 from lerobot.envs.utils import _call_make_env, _download_hub_file, _import_hub_module, _normalize_hub_result
 from lerobot.policies.xvla.configuration_xvla import XVLAConfig
 from lerobot.processor import ProcessorStep
-from lerobot.processor.env_processor import IsaaclabArenaProcessorStep, LiberoProcessorStep
+from lerobot.processor.env_processor import IsaaclabArenaProcessorStep, LiberoProcessorStep, RoboCasaProcessorStep
 from lerobot.processor.pipeline import PolicyProcessorPipeline
 
 
@@ -35,6 +45,12 @@ def make_env_config(env_type: str, **kwargs) -> EnvConfig:
         return PushtEnv(**kwargs)
     elif env_type == "libero":
         return LiberoEnv(**kwargs)
+    elif env_type == "libero_plus":
+        return LiberoPlusEnv(**kwargs)
+    elif env_type == "robocasa":
+        return RoboCasaEnv(**kwargs)
+    elif env_type == "robomme":
+        return RoboMMEEnv(**kwargs)
     else:
         raise ValueError(f"Policy type '{env_type}' is not available.")
 
@@ -70,9 +86,13 @@ def make_env_pre_post_processors(
         return make_xvla_libero_pre_post_processors()
 
     # For LIBERO environments, add the LiberoProcessorStep to preprocessor
-    if isinstance(env_cfg, LiberoEnv) or "libero" in env_cfg.type:
+    if isinstance(env_cfg, (LiberoEnv, LiberoPlusEnv)) or "libero" in env_cfg.type:
         preprocessor_steps.append(LiberoProcessorStep())
 
+    # For RoboCasa environments, add the RoboCasaProcessorStep to preprocessor
+    if isinstance(env_cfg, RoboCasaEnv) or "robocasa" in env_cfg.type:
+        preprocessor_steps.append(RoboCasaProcessorStep())
+
     # For Isaaclab Arena environments, add the IsaaclabArenaProcessorStep
     if isinstance(env_cfg, IsaaclabArenaEnv) or "isaaclab_arena" in env_cfg.type:
         # Parse comma-separated keys (handle None for state-based policies)
@@ -181,6 +201,33 @@ def make_env(
             control_mode=cfg.control_mode,
             episode_length=cfg.episode_length,
         )
+    elif "robocasa" in cfg.type:
+        from lerobot.envs.robocasa import create_robocasa_envs
+
+        tasks = cfg.tasks if cfg.tasks else [cfg.task]
+        return create_robocasa_envs(
+            tasks=tasks,
+            n_envs=n_envs,
+            image_size=cfg.image_size,
+            split=cfg.split,
+            episode_length=cfg.episode_length,
+            gym_kwargs=cfg.gym_kwargs,
+            env_cls=env_cls,
+        )
+
+    elif "robomme" in cfg.type:
+        from lerobot.envs.robomme import create_robomme_envs
+
+        return create_robomme_envs(
+            task=cfg.task,
+            n_envs=n_envs,
+            action_space_type=cfg.action_space,
+            dataset=cfg.dataset_split,
+            episode_length=cfg.episode_length,
+            task_ids=cfg.task_ids,
+            env_cls=env_cls,
+        )
+
     elif "metaworld" in cfg.type:
         from lerobot.envs.metaworld import create_metaworld_envs
 

src/lerobot/envs/libero.py

@@ -26,8 +26,14 @@ import gymnasium as gym
 import numpy as np
 import torch
 from gymnasium import spaces
+
+try:
     from libero.libero import benchmark, get_libero_path
     from libero.libero.envs import OffScreenRenderEnv
+except ImportError:
+    # LIBERO-plus may be installed from source with an extra nested package level.
+    from libero.libero.libero import benchmark, get_libero_path
+    from libero.libero.libero.envs import OffScreenRenderEnv
 
 from lerobot.processor import RobotObservation
 

src/lerobot/envs/robocasa.py

@@ -0,0 +1,273 @@
+#!/usr/bin/env python
+
+# Copyright 2025 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 __future__ import annotations
+
+from collections import defaultdict
+from collections.abc import Callable, Sequence
+from functools import partial
+from typing import Any
+
+import gymnasium as gym
+import numpy as np
+from gymnasium import spaces
+
+
+# Action layout (flat 12D, normalized to [-1, 1]):
+#   [0:3]   end_effector_position  (delta x, y, z)
+#   [3:6]   end_effector_rotation  (delta roll, pitch, yaw)
+#   [6:7]   gripper_close          (open=-1, close=+1)
+#   [7:11]  base_motion            (x, y, theta, torso_height)
+#   [11:12] control_mode           (arm=-1, base=+1)
+ACTION_DIM = 12
+ACTION_LOW = -1.0
+ACTION_HIGH = 1.0
+
+# Proprioceptive state layout (flat 16D):
+#   [0:2]   gripper_qpos
+#   [2:5]   base_position
+#   [5:9]   base_rotation (quaternion)
+#   [9:12]  end_effector_position_relative
+#   [12:16] end_effector_rotation_relative (quaternion)
+STATE_DIM = 16
+
+# Obs dict keys from RoboCasaGymEnv.get_observation()
+_CAM_KEYS = (
+    "video.robot0_agentview_left",
+    "video.robot0_agentview_right",
+    "video.robot0_eye_in_hand",
+)
+_STATE_KEYS_ORDERED = (
+    "state.gripper_qpos",           # (2,)
+    "state.base_position",          # (3,)
+    "state.base_rotation",          # (4,)
+    "state.end_effector_position_relative",   # (3,)
+    "state.end_effector_rotation_relative",   # (4,)
+)
+
+# Mapping from video.* key → short image name used in features_map
+CAM_KEY_TO_NAME = {
+    "video.robot0_agentview_left":  "agentview_left",
+    "video.robot0_agentview_right": "agentview_right",
+    "video.robot0_eye_in_hand":     "eye_in_hand",
+}
+
+
+def _flat_to_action_dict(flat: np.ndarray) -> dict[str, np.ndarray]:
+    """Convert a 12D flat action array to the Dict format expected by RoboCasaGymEnv."""
+    return {
+        "action.end_effector_position": flat[0:3],
+        "action.end_effector_rotation": flat[3:6],
+        "action.gripper_close":         flat[6:7],
+        "action.base_motion":           flat[7:11],
+        "action.control_mode":          flat[11:12],
+    }
+
+
+class RoboCasaEnv(gym.Env):
+    """Thin wrapper around RoboCasaGymEnv that provides a flat Box action space
+    and a structured observation dict compatible with LeRobot policies.
+
+    Observations returned by step/reset:
+        {
+            "pixels": {
+                "agentview_left":  (H, W, 3) uint8,
+                "agentview_right": (H, W, 3) uint8,
+                "eye_in_hand":     (H, W, 3) uint8,
+            },
+            "robot_state": (16,) float32,
+        }
+
+    Actions: flat float32 ndarray of shape (12,), normalized to [-1, 1].
+    """
+
+    metadata = {"render_modes": ["rgb_array"], "render_fps": 20}
+
+    def __init__(
+        self,
+        task: str,
+        split: str = "target",
+        image_size: int = 128,
+        render_mode: str = "rgb_array",
+        episode_length: int = 500,
+        **gym_kwargs: Any,
+    ):
+        super().__init__()
+        # Lazy import — robocasa is optional
+        import robocasa.environments  # noqa: F401 — registers all gym envs
+
+        self.task = task
+        self.render_mode = render_mode
+        self.image_size = image_size
+        self._max_episode_steps = episode_length
+        self._step_count = 0
+
+        self._env = gym.make(
+            f"robocasa/{task}",
+            split=split,
+            camera_widths=image_size,
+            camera_heights=image_size,
+            **gym_kwargs,
+        )
+
+        # Flat 12D Box action space
+        self.action_space = spaces.Box(
+            low=ACTION_LOW,
+            high=ACTION_HIGH,
+            shape=(ACTION_DIM,),
+            dtype=np.float32,
+        )
+
+        images = {
+            name: spaces.Box(low=0, high=255, shape=(image_size, image_size, 3), dtype=np.uint8)
+            for name in CAM_KEY_TO_NAME.values()
+        }
+        self.observation_space = spaces.Dict(
+            {
+                "pixels": spaces.Dict(images),
+                "robot_state": spaces.Box(
+                    low=-np.inf, high=np.inf, shape=(STATE_DIM,), dtype=np.float32
+                ),
+            }
+        )
+
+    def _format_obs(self, raw_obs: dict) -> dict:
+        pixels = {
+            CAM_KEY_TO_NAME[k]: raw_obs[k]
+            for k in _CAM_KEYS
+            if k in raw_obs
+        }
+        state_parts = [
+            np.asarray(raw_obs[k], dtype=np.float32)
+            for k in _STATE_KEYS_ORDERED
+            if k in raw_obs
+        ]
+        robot_state = np.concatenate(state_parts) if state_parts else np.zeros(STATE_DIM, dtype=np.float32)
+        return {"pixels": pixels, "robot_state": robot_state}
+
+    def reset(self, seed: int | None = None, **kwargs) -> tuple[dict, dict]:
+        super().reset(seed=seed)
+        self._step_count = 0
+        raw_obs, info = self._env.reset(seed=seed)
+        info.setdefault("is_success", False)
+        info["task"] = self.task
+        return self._format_obs(raw_obs), info
+
+    def step(self, action: np.ndarray) -> tuple[dict, float, bool, bool, dict]:
+        if action.ndim != 1 or action.shape[0] != ACTION_DIM:
+            raise ValueError(
+                f"Expected 1-D action of shape ({ACTION_DIM},), got {action.shape}"
+            )
+        action_dict = _flat_to_action_dict(action)
+        raw_obs, reward, terminated, truncated, info = self._env.step(action_dict)
+        self._step_count += 1
+
+        is_success = bool(info.get("success", False))
+        terminated = terminated or is_success
+        if self._step_count >= self._max_episode_steps:
+            truncated = True
+
+        info.update({"task": self.task, "is_success": is_success})
+        obs = self._format_obs(raw_obs)
+
+        if terminated or truncated:
+            info["final_info"] = {"task": self.task, "is_success": is_success}
+
+        return obs, reward, terminated, truncated, info
+
+    def render(self) -> np.ndarray | None:
+        if self.render_mode == "rgb_array":
+            return self._env.render()
+        return None
+
+    def close(self) -> None:
+        self._env.close()
+
+
+def _make_env_fns(
+    *,
+    task: str,
+    n_envs: int,
+    image_size: int,
+    split: str,
+    episode_length: int,
+    gym_kwargs: dict[str, Any],
+) -> list[Callable[[], RoboCasaEnv]]:
+    """Build n_envs factory callables for a single task."""
+    def _make(episode_index: int) -> RoboCasaEnv:  # noqa: ARG001
+        return RoboCasaEnv(
+            task=task,
+            split=split,
+            image_size=image_size,
+            episode_length=episode_length,
+            **gym_kwargs,
+        )
+
+    return [partial(_make, i) for i in range(n_envs)]
+
+
+def create_robocasa_envs(
+    tasks: str | Sequence[str],
+    n_envs: int,
+    image_size: int = 128,
+    split: str = "target",
+    episode_length: int = 500,
+    gym_kwargs: dict[str, Any] | None = None,
+    env_cls: Callable[[Sequence[Callable[[], Any]]], Any] | None = None,
+) -> dict[str, dict[int, Any]]:
+    """Create vectorized RoboCasa environments.
+
+    Args:
+        tasks: A single task name or list of task names (without "robocasa/" prefix).
+            E.g. "PickPlaceCounterToCabinet" or ["BoilPot", "PrepareCoffee"].
+        n_envs: Number of parallel envs per task.
+        image_size: Square image resolution for all cameras.
+        split: RoboCasa dataset split — "pretrain" or "target".
+        episode_length: Max steps per episode before truncation.
+        gym_kwargs: Extra kwargs forwarded to each RoboCasaEnv.
+        env_cls: Callable to wrap list of factory fns (SyncVectorEnv or AsyncVectorEnv).
+
+    Returns:
+        dict[task_name][task_id=0] -> vec_env
+    """
+    if env_cls is None or not callable(env_cls):
+        raise ValueError("env_cls must be a callable wrapping a list of env factory callables.")
+    if not isinstance(n_envs, int) or n_envs <= 0:
+        raise ValueError(f"n_envs must be a positive int; got {n_envs}.")
+
+    if isinstance(tasks, str):
+        task_list = [t.strip() for t in tasks.split(",") if t.strip()]
+    else:
+        task_list = [str(t).strip() for t in tasks if str(t).strip()]
+    if not task_list:
+        raise ValueError("`tasks` must contain at least one task name.")
+
+    gym_kwargs = dict(gym_kwargs or {})
+    out: dict[str, dict[int, Any]] = defaultdict(dict)
+
+    print(f"Creating RoboCasa envs | tasks={task_list} | n_envs(per task)={n_envs} | split={split}")
+    for task in task_list:
+        fns = _make_env_fns(
+            task=task,
+            n_envs=n_envs,
+            image_size=image_size,
+            split=split,
+            episode_length=episode_length,
+            gym_kwargs=gym_kwargs,
+        )
+        out["robocasa"][len(out["robocasa"])] = env_cls(fns)
+        print(f"  Built vec env | task={task} | n_envs={n_envs}")
+
+    return {suite: dict(task_map) for suite, task_map in out.items()}

src/lerobot/envs/robomme.py

@@ -0,0 +1,154 @@
+"""RoboMME environment wrapper for LeRobot evaluation.
+
+Wraps the RoboMME ``BenchmarkEnvBuilder`` into a Gymnasium-compatible
+``VectorEnv`` suitable for ``lerobot_eval``.
+
+RoboMME tasks:
+  Counting:    BinFill, PickXtimes, SwingXtimes, StopCube
+  Permanence:  VideoUnmask, VideoUnmaskSwap, ButtonUnmask, ButtonUnmaskSwap
+  Reference:   PickHighlight, VideoRepick, VideoPlaceButton, VideoPlaceOrder
+  Imitation:   MoveCube, InsertPeg, PatternLock, RouteStick
+
+Install: pip install robomme  (or from source: https://github.com/RoboMME/robomme_benchmark)
+"""
+
+from __future__ import annotations
+
+from typing import Any
+
+import gymnasium as gym
+import numpy as np
+from gymnasium import spaces
+
+ROBOMME_TASKS = [
+    "BinFill", "PickXtimes", "SwingXtimes", "StopCube",
+    "VideoUnmask", "VideoUnmaskSwap", "ButtonUnmask", "ButtonUnmaskSwap",
+    "PickHighlight", "VideoRepick", "VideoPlaceButton", "VideoPlaceOrder",
+    "MoveCube", "InsertPeg", "PatternLock", "RouteStick",
+]
+
+
+class RoboMMEGymEnv(gym.Env):
+    """Thin Gymnasium wrapper around a single RoboMME episode env."""
+
+    metadata = {"render_modes": ["rgb_array"]}
+
+    def __init__(
+        self,
+        task: str = "PickXtimes",
+        action_space_type: str = "joint_angle",
+        dataset: str = "test",
+        episode_idx: int = 0,
+        max_steps: int = 300,
+    ):
+        super().__init__()
+        from robomme.env_record_wrapper import BenchmarkEnvBuilder
+
+        self._task = task
+        self._action_space_type = action_space_type
+        self._dataset = dataset
+        self._episode_idx = episode_idx
+        self._max_steps = max_steps
+
+        self._builder = BenchmarkEnvBuilder(
+            env_id=task,
+            dataset=dataset,
+            action_space=action_space_type,
+            gui_render=False,
+            max_steps=max_steps,
+        )
+        self._env = None
+
+        action_dim = 8 if action_space_type == "joint_angle" else 7
+        self.action_space = spaces.Box(low=-1.0, high=1.0, shape=(action_dim,), dtype=np.float32)
+        self.observation_space = spaces.Dict({
+            "front_rgb": spaces.Box(0, 255, shape=(256, 256, 3), dtype=np.uint8),
+            "wrist_rgb": spaces.Box(0, 255, shape=(256, 256, 3), dtype=np.uint8),
+            "state": spaces.Box(-np.inf, np.inf, shape=(8,), dtype=np.float32),
+        })
+
+    def reset(self, *, seed=None, options=None):
+        super().reset(seed=seed)
+        self._env = self._builder.make_env_for_episode(
+            episode_idx=self._episode_idx, max_steps=self._max_steps,
+        )
+        obs, info = self._env.reset()
+        return self._convert_obs(obs), self._convert_info(info)
+
+    def step(self, action):
+        obs, reward, terminated, truncated, info = self._env.step(action)
+
+        terminated_bool = bool(terminated.item()) if hasattr(terminated, "item") else bool(terminated)
+        truncated_bool = bool(truncated.item()) if hasattr(truncated, "item") else bool(truncated)
+
+        status = info.get("status", "ongoing")
+        is_success = status == "success"
+        conv_info = self._convert_info(info)
+        conv_info["is_success"] = is_success
+
+        return self._convert_obs(obs), float(reward), terminated_bool, truncated_bool, conv_info
+
+    def _convert_obs(self, obs: dict) -> dict:
+        front_rgb = obs["front_rgb_list"][-1] if isinstance(obs["front_rgb_list"], list) else obs["front_rgb_list"]
+        wrist_rgb = obs["wrist_rgb_list"][-1] if isinstance(obs["wrist_rgb_list"], list) else obs["wrist_rgb_list"]
+        joint_state = obs["joint_state_list"][-1] if isinstance(obs["joint_state_list"], list) else obs["joint_state_list"]
+        gripper_state = obs["gripper_state_list"][-1] if isinstance(obs["gripper_state_list"], list) else obs["gripper_state_list"]
+
+        front_rgb = np.asarray(front_rgb, dtype=np.uint8)
+        wrist_rgb = np.asarray(wrist_rgb, dtype=np.uint8)
+        joint = np.asarray(joint_state, dtype=np.float32).flatten()[:7]
+        gripper = np.asarray(gripper_state, dtype=np.float32).flatten()[:1]
+        state = np.concatenate([joint, gripper])
+
+        return {
+            "front_rgb": front_rgb,
+            "wrist_rgb": wrist_rgb,
+            "state": state,
+        }
+
+    def _convert_info(self, info: dict) -> dict:
+        return {
+            "status": info.get("status", "ongoing"),
+            "task_goal": info.get("task_goal", ""),
+        }
+
+
+def create_robomme_envs(
+    task: str,
+    n_envs: int = 1,
+    action_space_type: str = "joint_angle",
+    dataset: str = "test",
+    episode_length: int = 300,
+    task_ids: list[int] | None = None,
+    env_cls=None,
+) -> dict[str, dict[int, gym.vector.VectorEnv]]:
+    """Create vectorized RoboMME environments for evaluation.
+
+    Returns {suite_name: {task_id: VectorEnv}} matching lerobot's expected format.
+    """
+    if env_cls is None:
+        env_cls = gym.vector.SyncVectorEnv
+
+    if task_ids is None:
+        task_ids = [0]
+
+    suite_name = "robomme"
+    envs_by_task = {}
+
+    for task_id in task_ids:
+        def _make_one(ep_idx=task_id):
+            return RoboMMEGymEnv(
+                task=task,
+                action_space_type=action_space_type,
+                dataset=dataset,
+                episode_idx=ep_idx,
+                max_steps=episode_length,
+            )
+
+        vec = env_cls(
+            [_make_one for _ in range(n_envs)],
+            autoreset_mode=gym.vector.AutoresetMode.SAME_STEP,
+        )
+        envs_by_task[task_id] = vec
+
+    return {suite_name: envs_by_task}

src/lerobot/motors/motors_bus.py

@@ -29,7 +29,7 @@ from dataclasses import dataclass
 from enum import Enum
 from functools import cached_property
 from pprint import pformat
-from typing import Protocol, TypeAlias
+from typing import Protocol
 
 import serial
 from deepdiff import DeepDiff
@@ -38,8 +38,8 @@ from tqdm import tqdm
 from lerobot.utils.decorators import check_if_already_connected, check_if_not_connected
 from lerobot.utils.utils import enter_pressed, move_cursor_up
 
-NameOrID: TypeAlias = str | int
+type NameOrID = str | int
-Value: TypeAlias = int | float
+type Value = int | float
 
 logger = logging.getLogger(__name__)
 
@@ -1277,4 +1277,4 @@ class SerialMotorsBus(MotorsBusBase):
 
 
 # Backward compatibility alias
-MotorsBus: TypeAlias = SerialMotorsBus
+MotorsBus = SerialMotorsBus

src/lerobot/policies/diffusion/configuration_diffusion.py

@@ -55,10 +55,16 @@ class DiffusionConfig(PreTrainedConfig):
         normalization_mapping: A dictionary that maps from a str value of FeatureType (e.g., "STATE", "VISUAL") to
             a corresponding NormalizationMode (e.g., NormalizationMode.MIN_MAX)
         vision_backbone: Name of the torchvision resnet backbone to use for encoding images.
-        crop_shape: (H, W) shape to crop images to as a preprocessing step for the vision backbone. Must fit
+        resize_shape: (H, W) shape to resize images to as a preprocessing step for the vision
-            within the image size. If None, no cropping is done.
+            backbone. If None, no resizing is done and the original image resolution is used.
-        crop_is_random: Whether the crop should be random at training time (it's always a center crop in eval
+        crop_ratio: Ratio in (0, 1] used to derive the crop size from resize_shape
-            mode).
+            (crop_h = int(resize_shape[0] * crop_ratio), likewise for width).
+            Set to 1.0 to disable cropping. Only takes effect when resize_shape is not None.
+        crop_shape: (H, W) shape to crop images to. When resize_shape is set and crop_ratio < 1.0,
+            this is computed automatically. Can also be set directly for legacy configs that use
+            crop-only (without resize). If None and no derivation applies, no cropping is done.
+        crop_is_random: Whether the crop should be random at training time (it's always a center
+            crop in eval mode).
         pretrained_backbone_weights: Pretrained weights from torchvision to initialize the backbone.
             `None` means no pretrained weights.
         use_group_norm: Whether to replace batch normalization with group normalization in the backbone.
@@ -114,7 +120,9 @@ class DiffusionConfig(PreTrainedConfig):
     # Architecture / modeling.
     # Vision backbone.
     vision_backbone: str = "resnet18"
-    crop_shape: tuple[int, int] | None = (84, 84)
+    resize_shape: tuple[int, int] | None = None
+    crop_ratio: float = 1.0
+    crop_shape: tuple[int, int] | None = None
     crop_is_random: bool = True
     pretrained_backbone_weights: str | None = None
     use_group_norm: bool = True
@@ -175,6 +183,25 @@ class DiffusionConfig(PreTrainedConfig):
                 f"Got {self.noise_scheduler_type}."
             )
 
+        if self.resize_shape is not None and (
+            len(self.resize_shape) != 2 or any(d <= 0 for d in self.resize_shape)
+        ):
+            raise ValueError(f"`resize_shape` must be a pair of positive integers. Got {self.resize_shape}.")
+        if not (0 < self.crop_ratio <= 1.0):
+            raise ValueError(f"`crop_ratio` must be in (0, 1]. Got {self.crop_ratio}.")
+
+        if self.resize_shape is not None:
+            if self.crop_ratio < 1.0:
+                self.crop_shape = (
+                    int(self.resize_shape[0] * self.crop_ratio),
+                    int(self.resize_shape[1] * self.crop_ratio),
+                )
+            else:
+                # Explicitly disable cropping for resize+ratio path when crop_ratio == 1.0.
+                self.crop_shape = None
+        if self.crop_shape is not None and (self.crop_shape[0] <= 0 or self.crop_shape[1] <= 0):
+            raise ValueError(f"`crop_shape` must have positive dimensions. Got {self.crop_shape}.")
+
         # Check that the horizon size and U-Net downsampling is compatible.
         # U-Net downsamples by 2 with each stage.
         downsampling_factor = 2 ** len(self.down_dims)
@@ -202,13 +229,12 @@ class DiffusionConfig(PreTrainedConfig):
         if len(self.image_features) == 0 and self.env_state_feature is None:
             raise ValueError("You must provide at least one image or the environment state among the inputs.")
 
-        if self.crop_shape is not None:
+        if self.resize_shape is None and self.crop_shape is not None:
             for key, image_ft in self.image_features.items():
                 if self.crop_shape[0] > image_ft.shape[1] or self.crop_shape[1] > image_ft.shape[2]:
                     raise ValueError(
-                        f"`crop_shape` should fit within the images shapes. Got {self.crop_shape} "
+                        f"`crop_shape` should fit within the image shapes. Got {self.crop_shape} "
-                        f"for `crop_shape` and {image_ft.shape} for "
+                        f"for `crop_shape` and {image_ft.shape} for `{key}`."
-                        f"`{key}`."
                     )
 
         # Check that all input images have the same shape.

src/lerobot/policies/diffusion/modeling_diffusion.py

@@ -454,12 +454,18 @@ class DiffusionRgbEncoder(nn.Module):
     def __init__(self, config: DiffusionConfig):
         super().__init__()
         # Set up optional preprocessing.
-        if config.crop_shape is not None:
+        if config.resize_shape is not None:
+            self.resize = torchvision.transforms.Resize(config.resize_shape)
+        else:
+            self.resize = None
+
+        crop_shape = config.crop_shape
+        if crop_shape is not None:
             self.do_crop = True
             # Always use center crop for eval
-            self.center_crop = torchvision.transforms.CenterCrop(config.crop_shape)
+            self.center_crop = torchvision.transforms.CenterCrop(crop_shape)
             if config.crop_is_random:
-                self.maybe_random_crop = torchvision.transforms.RandomCrop(config.crop_shape)
+                self.maybe_random_crop = torchvision.transforms.RandomCrop(crop_shape)
             else:
                 self.maybe_random_crop = self.center_crop
         else:
@@ -485,13 +491,16 @@ class DiffusionRgbEncoder(nn.Module):
 
         # Set up pooling and final layers.
         # Use a dry run to get the feature map shape.
-        # The dummy input should take the number of image channels from `config.image_features` and it should
+        # The dummy shape mirrors the runtime preprocessing order: resize -> crop.
-        # use the height and width from `config.crop_shape` if it is provided, otherwise it should use the
-        # height and width from `config.image_features`.
 
         # Note: we have a check in the config class to make sure all images have the same shape.
         images_shape = next(iter(config.image_features.values())).shape
-        dummy_shape_h_w = config.crop_shape if config.crop_shape is not None else images_shape[1:]
+        if config.crop_shape is not None:
+            dummy_shape_h_w = config.crop_shape
+        elif config.resize_shape is not None:
+            dummy_shape_h_w = config.resize_shape
+        else:
+            dummy_shape_h_w = images_shape[1:]
         dummy_shape = (1, images_shape[0], *dummy_shape_h_w)
         feature_map_shape = get_output_shape(self.backbone, dummy_shape)[1:]
 
@@ -507,7 +516,10 @@ class DiffusionRgbEncoder(nn.Module):
         Returns:
             (B, D) image feature.
         """
-        # Preprocess: maybe crop (if it was set up in the __init__).
+        # Preprocess: resize if configured, then crop if configured.
+
+        if self.resize is not None:
+            x = self.resize(x)
         if self.do_crop:
             if self.training:  # noqa: SIM108
                 x = self.maybe_random_crop(x)

src/lerobot/policies/factory.py

@@ -18,10 +18,9 @@ from __future__ import annotations
 
 import importlib
 import logging
-from typing import Any, TypedDict
+from typing import Any, TypedDict, Unpack
 
 import torch
-from typing_extensions import Unpack
 
 from lerobot.configs.policies import PreTrainedConfig
 from lerobot.configs.types import FeatureType

src/lerobot/policies/groot/eagle2_hg_model/image_processing_eagle2_5_vl_fast.py

@@ -4,17 +4,16 @@
 # Licensed under The MIT License [see LICENSE for details]
 # --------------------------------------------------------
 
+from __future__ import annotations
 
 # copy from https://github.com/huggingface/transformers/blob/main/src/transformers/models/llava_onevision/image_processing_llava_onevision_fast.py
-from typing import Optional
-
 from transformers.image_processing_utils import (
     BatchFeature,
     get_patch_output_size,
 )
 from transformers.image_processing_utils_fast import (
     BaseImageProcessorFast,
-    DefaultFastImageProcessorKwargs,
+    ImagesKwargs,
     group_images_by_shape,
     reorder_images,
 )
@@ -77,7 +76,7 @@ def crop(img: torch.Tensor, left: int, top: int, right: int, bottom: int) -> tor
     return img[:, top:bottom, left:right]
 
 
-class Eagle25VLFastImageProcessorKwargs(DefaultFastImageProcessorKwargs):
+class Eagle25VLFastImageProcessorKwargs(ImagesKwargs):
     max_dynamic_tiles: int | None
     min_dynamic_tiles: int | None
     use_thumbnail: bool | None
@@ -165,11 +164,11 @@ class Eagle25VLImageProcessorFast(BaseImageProcessorFast):
 
     def _resize_for_patching(
         self,
-        image: "torch.Tensor",
+        image: torch.Tensor,
         target_resolution: tuple,
-        interpolation: "F.InterpolationMode",
+        interpolation: F.InterpolationMode,
         input_data_format: ChannelDimension,
-    ) -> "torch.Tensor":
+    ) -> torch.Tensor:
         """
         Resizes an image to a target resolution while maintaining aspect ratio.
 
@@ -219,8 +218,8 @@ class Eagle25VLImageProcessorFast(BaseImageProcessorFast):
         return best_ratio
 
     def _pad_for_patching(
-        self, image: "torch.Tensor", target_resolution: tuple, input_data_format: ChannelDimension
+        self, image: torch.Tensor, target_resolution: tuple, input_data_format: ChannelDimension
-    ) -> "torch.Tensor":
+    ) -> torch.Tensor:
         """
         Pad an image to a target resolution while maintaining aspect ratio.
         """
@@ -236,15 +235,15 @@ class Eagle25VLImageProcessorFast(BaseImageProcessorFast):
 
     def _get_image_patches(
         self,
-        image: "torch.Tensor",
+        image: torch.Tensor,
         min_num: int,
         max_num: int,
         size: tuple,
         tile_size: int,
         use_thumbnail: bool,
-        interpolation: "F.InterpolationMode",
+        interpolation: F.InterpolationMode,
         pad_during_tiling: bool,
-    ) -> list["torch.Tensor"]:
+    ) -> list[torch.Tensor]:
         image_size = get_image_size(image, channel_dim=ChannelDimension.FIRST)
         orig_height, orig_width = image_size
         aspect_ratio = orig_width / orig_height
@@ -305,8 +304,8 @@ class Eagle25VLImageProcessorFast(BaseImageProcessorFast):
 
     def _pad_for_batching(
         self,
-        pixel_values: list["torch.Tensor"],
+        pixel_values: list[torch.Tensor],
-    ) -> list["torch.Tensor"]:
+    ) -> list[torch.Tensor]:
         """
         Pads images on the `num_of_patches` dimension with zeros to form a batch of same number of patches.
 
@@ -327,14 +326,14 @@ class Eagle25VLImageProcessorFast(BaseImageProcessorFast):
 
     def _preprocess(
         self,
-        images: list["torch.Tensor"],
+        images: list[torch.Tensor],
         do_resize: bool,
         size: SizeDict,
         max_dynamic_tiles: int,
         min_dynamic_tiles: int,
         use_thumbnail: bool,
         pad_during_tiling: bool,
-        interpolation: Optional["F.InterpolationMode"],
+        interpolation: F.InterpolationMode | None,
         do_center_crop: bool,
         crop_size: SizeDict,
         do_rescale: bool,

src/lerobot/policies/pi0/modeling_pi0.py

@@ -15,16 +15,16 @@
 # limitations under the License.
 
 import builtins
+import copy
 import logging
 import math
 from collections import deque
 from pathlib import Path
-from typing import TYPE_CHECKING, Literal, TypedDict
+from typing import TYPE_CHECKING, Literal, TypedDict, Unpack
 
 import torch
 import torch.nn.functional as F  # noqa: N812
 from torch import Tensor, nn
-from typing_extensions import Unpack
 
 from lerobot.utils.import_utils import _transformers_available
 
@@ -32,13 +32,21 @@ from lerobot.utils.import_utils import _transformers_available
 if TYPE_CHECKING or _transformers_available:
     from transformers.models.auto import CONFIG_MAPPING
     from transformers.models.gemma import modeling_gemma
-    from transformers.models.gemma.modeling_gemma import GemmaForCausalLM
+
-    from transformers.models.paligemma.modeling_paligemma import PaliGemmaForConditionalGeneration
+    from lerobot.policies.pi_gemma import (
+        PaliGemmaForConditionalGenerationWithPiGemma,
+        PiGemmaForCausalLM,
+        _gated_residual,
+        layernorm_forward,
+    )
 else:
     CONFIG_MAPPING = None
     modeling_gemma = None
-    GemmaForCausalLM = None
+    PiGemmaForCausalLM = None
-    PaliGemmaForConditionalGeneration = None
+    _gated_residual = None
+    layernorm_forward = None
+    PaliGemmaForConditionalGenerationWithPiGemma = None
+
 
 from lerobot.configs.policies import PreTrainedConfig
 from lerobot.policies.pi0.configuration_pi0 import DEFAULT_IMAGE_SIZE, PI0Config
@@ -191,7 +199,7 @@ def resize_with_pad_torch(  # see openpi `resize_with_pad_torch` (exact copy)
     if images.dtype == torch.uint8:
         resized_images = torch.round(resized_images).clamp(0, 255).to(torch.uint8)
     elif images.dtype == torch.float32:
-        resized_images = resized_images.clamp(-1.0, 1.0)
+        resized_images = resized_images.clamp(0.0, 1.0)
     else:
         raise ValueError(f"Unsupported image dtype: {images.dtype}")
 
@@ -202,7 +210,7 @@ def resize_with_pad_torch(  # see openpi `resize_with_pad_torch` (exact copy)
     pad_w1 = pad_w0 + remainder_w
 
     # Pad
-    constant_value = 0 if images.dtype == torch.uint8 else -1.0
+    constant_value = 0 if images.dtype == torch.uint8 else 0.0
     padded_images = F.pad(
         resized_images,
         (pad_w0, pad_w1, pad_h0, pad_h1),  # left, right, top, bottom
@@ -221,14 +229,14 @@ def resize_with_pad_torch(  # see openpi `resize_with_pad_torch` (exact copy)
 def compute_layer_complete(
     layer_idx, inputs_embeds, attention_mask, position_ids, adarms_cond, paligemma, gemma_expert
 ):
-    models = [paligemma.language_model, gemma_expert.model]
+    models = [paligemma.model.language_model, gemma_expert.model]
     query_states = []
     key_states = []
     value_states = []
     gates = []
     for i, hidden_states in enumerate(inputs_embeds):
         layer = models[i].layers[layer_idx]
-        hidden_states, gate = layer.input_layernorm(hidden_states, cond=adarms_cond[i])  # noqa: PLW2901
+        hidden_states, gate = layernorm_forward(layer.input_layernorm, hidden_states, adarms_cond[i])
         gates.append(gate)
         input_shape = hidden_states.shape[:-1]
         hidden_shape = (*input_shape, -1, layer.self_attn.head_dim)
@@ -254,10 +262,10 @@ def compute_layer_complete(
         query_states, key_states, cos, sin, unsqueeze_dim=1
     )
     batch_size = query_states.shape[0]
-    scaling = paligemma.language_model.layers[layer_idx].self_attn.scaling
+    scaling = paligemma.model.language_model.layers[layer_idx].self_attn.scaling
     # Attention computation
     att_output, _ = modeling_gemma.eager_attention_forward(
-        paligemma.language_model.layers[layer_idx].self_attn,
+        paligemma.model.language_model.layers[layer_idx].self_attn,
         query_states,
         key_states,
         value_states,
@@ -265,7 +273,7 @@ def compute_layer_complete(
         scaling,
     )
     # Get head_dim from the current layer, not from the model
-    head_dim = paligemma.language_model.layers[layer_idx].self_attn.head_dim
+    head_dim = paligemma.model.language_model.layers[layer_idx].self_attn.head_dim
     att_output = att_output.reshape(batch_size, -1, 1 * 8 * head_dim)
     # Process layer outputs
     outputs_embeds = []
@@ -277,15 +285,15 @@ def compute_layer_complete(
             att_output = att_output.to(layer.self_attn.o_proj.weight.dtype)
         out_emb = layer.self_attn.o_proj(att_output[:, start_pos:end_pos])
         # first residual
-        out_emb = modeling_gemma._gated_residual(hidden_states, out_emb, gates[i])  # noqa: SLF001
+        out_emb = _gated_residual(hidden_states, out_emb, gates[i])
         after_first_residual = out_emb.clone()
-        out_emb, gate = layer.post_attention_layernorm(out_emb, cond=adarms_cond[i])
+        out_emb, gate = layernorm_forward(layer.post_attention_layernorm, out_emb, adarms_cond[i])
         # Convert to bfloat16 if the next layer (mlp) uses bfloat16
         if layer.mlp.up_proj.weight.dtype == torch.bfloat16:
             out_emb = out_emb.to(dtype=torch.bfloat16)
         out_emb = layer.mlp(out_emb)
         # second residual
-        out_emb = modeling_gemma._gated_residual(after_first_residual, out_emb, gate)  # noqa: SLF001
+        out_emb = _gated_residual(after_first_residual, out_emb, gate)
         outputs_embeds.append(out_emb)
         start_pos = end_pos
     return outputs_embeds
@@ -358,7 +366,7 @@ class PaliGemmaWithExpertModel(
         vlm_config_hf.text_config.num_hidden_layers = vlm_config.depth
         vlm_config_hf.text_config.num_key_value_heads = vlm_config.num_kv_heads
         vlm_config_hf.text_config.hidden_activation = "gelu_pytorch_tanh"
-        vlm_config_hf.text_config.torch_dtype = "float32"
+        vlm_config_hf.text_config.dtype = "float32"
         vlm_config_hf.text_config.vocab_size = 257152
         vlm_config_hf.text_config.use_adarms = use_adarms[0]
         vlm_config_hf.text_config.adarms_cond_dim = vlm_config.width if use_adarms[0] else None
@@ -366,7 +374,7 @@ class PaliGemmaWithExpertModel(
         vlm_config_hf.vision_config.intermediate_size = 4304
         vlm_config_hf.vision_config.projection_dim = 2048
         vlm_config_hf.vision_config.projector_hidden_act = "gelu_fast"
-        vlm_config_hf.vision_config.torch_dtype = "float32"
+        vlm_config_hf.vision_config.dtype = "float32"
 
         action_expert_config_hf = CONFIG_MAPPING["gemma"](
             head_dim=action_expert_config.head_dim,
@@ -377,13 +385,13 @@ class PaliGemmaWithExpertModel(
             num_key_value_heads=action_expert_config.num_kv_heads,
             vocab_size=257152,
             hidden_activation="gelu_pytorch_tanh",
-            torch_dtype="float32",
+            dtype="float32",
             use_adarms=use_adarms[1],
             adarms_cond_dim=action_expert_config.width if use_adarms[1] else None,
         )
 
-        self.paligemma = PaliGemmaForConditionalGeneration(config=vlm_config_hf)
+        self.paligemma = PaliGemmaForConditionalGenerationWithPiGemma(config=vlm_config_hf)
-        self.gemma_expert = GemmaForCausalLM(config=action_expert_config_hf)
+        self.gemma_expert = PiGemmaForCausalLM(config=action_expert_config_hf)
         self.gemma_expert.model.embed_tokens = None
 
         self.to_bfloat16_for_selected_params(precision)
@@ -398,10 +406,11 @@ class PaliGemmaWithExpertModel(
         else:
             raise ValueError(f"Invalid precision: {precision}")
 
+        # Keep full vision path in float32 so we never toggle (toggle causes optimizer
+        # "same dtype" error). Align with PI05.
         params_to_keep_float32 = [
-            "vision_tower.vision_model.embeddings.patch_embedding.weight",
+            "vision_tower",
-            "vision_tower.vision_model.embeddings.patch_embedding.bias",
+            "multi_modal_projector",
-            "vision_tower.vision_model.embeddings.position_embedding.weight",
             "input_layernorm",
             "post_attention_layernorm",
             "model.norm",
@@ -413,8 +422,8 @@ class PaliGemmaWithExpertModel(
 
     def _set_requires_grad(self):
         if self.freeze_vision_encoder:
-            self.paligemma.vision_tower.eval()
+            self.paligemma.model.vision_tower.eval()
-            for param in self.paligemma.vision_tower.parameters():
+            for param in self.paligemma.model.vision_tower.parameters():
                 param.requires_grad = False
         if self.train_expert_only:
             self.paligemma.eval()
@@ -424,15 +433,23 @@ class PaliGemmaWithExpertModel(
     def train(self, mode: bool = True):
         super().train(mode)
         if self.freeze_vision_encoder:
-            self.paligemma.vision_tower.eval()
+            self.paligemma.model.vision_tower.eval()
         if self.train_expert_only:
             self.paligemma.eval()
 
     def embed_image(self, image: torch.Tensor):
-        return self.paligemma.model.get_image_features(image)
+        # Vision tower and multi_modal_projector are kept in float32 (params_to_keep_float32). Align with PI05.
+        out_dtype = image.dtype
+        if image.dtype != torch.float32:
+            image = image.to(torch.float32)
+        image_outputs = self.paligemma.model.get_image_features(image)
+        features = image_outputs.pooler_output * self.paligemma.config.text_config.hidden_size**0.5
+        if features.dtype != out_dtype:
+            features = features.to(out_dtype)
+        return features
 
     def embed_language_tokens(self, tokens: torch.Tensor):
-        return self.paligemma.language_model.embed_tokens(tokens)
+        return self.paligemma.model.language_model.embed_tokens(tokens)
 
     def forward(
         self,
@@ -446,7 +463,7 @@ class PaliGemmaWithExpertModel(
         if adarms_cond is None:
             adarms_cond = [None, None]
         if inputs_embeds[1] is None:
-            prefix_output = self.paligemma.language_model.forward(
+            prefix_output = self.paligemma.model.language_model.forward(
                 inputs_embeds=inputs_embeds[0],
                 attention_mask=attention_mask,
                 position_ids=position_ids,
@@ -470,7 +487,7 @@ class PaliGemmaWithExpertModel(
             prefix_output = None
             prefix_past_key_values = None
         else:
-            models = [self.paligemma.language_model, self.gemma_expert.model]
+            models = [self.paligemma.model.language_model, self.gemma_expert.model]
             num_layers = self.paligemma.config.text_config.num_hidden_layers
 
             # Check if gradient checkpointing is enabled for any of the models
@@ -510,7 +527,7 @@ class PaliGemmaWithExpertModel(
             def compute_final_norms(inputs_embeds, adarms_cond):
                 outputs_embeds = []
                 for i, hidden_states in enumerate(inputs_embeds):
-                    out_emb, _ = models[i].norm(hidden_states, cond=adarms_cond[i])
+                    out_emb, _ = layernorm_forward(models[i].norm, hidden_states, adarms_cond[i])
                     outputs_embeds.append(out_emb)
                 return outputs_embeds
 
@@ -576,29 +593,19 @@ class PI0Pytorch(nn.Module):  # see openpi `PI0Pytorch`
             # Also compile the main forward pass used during training
             self.forward = torch.compile(self.forward, mode=config.compile_mode)
 
-        msg = """An incorrect transformer version is used, please create an issue on https://github.com/huggingface/lerobot/issues"""
-
-        try:
-            from transformers.models.siglip import check
-
-            if not check.check_whether_transformers_replace_is_installed_correctly():
-                raise ValueError(msg)
-        except ImportError:
-            raise ValueError(msg) from None
-
     def gradient_checkpointing_enable(self):
         """Enable gradient checkpointing for memory optimization."""
         self.gradient_checkpointing_enabled = True
-        self.paligemma_with_expert.paligemma.language_model.gradient_checkpointing = True
+        self.paligemma_with_expert.paligemma.model.language_model.gradient_checkpointing = True
-        self.paligemma_with_expert.paligemma.vision_tower.gradient_checkpointing = True
+        self.paligemma_with_expert.paligemma.model.vision_tower.gradient_checkpointing = True
         self.paligemma_with_expert.gemma_expert.model.gradient_checkpointing = True
         logging.info("Enabled gradient checkpointing for PI0Pytorch model")
 
     def gradient_checkpointing_disable(self):
         """Disable gradient checkpointing."""
         self.gradient_checkpointing_enabled = False
-        self.paligemma_with_expert.paligemma.language_model.gradient_checkpointing = False
+        self.paligemma_with_expert.paligemma.model.language_model.gradient_checkpointing = False
-        self.paligemma_with_expert.paligemma.vision_tower.gradient_checkpointing = False
+        self.paligemma_with_expert.paligemma.model.vision_tower.gradient_checkpointing = False
         self.paligemma_with_expert.gemma_expert.model.gradient_checkpointing = False
         logging.info("Disabled gradient checkpointing for PI0Pytorch model")
 
@@ -760,7 +767,7 @@ class PI0Pytorch(nn.Module):  # see openpi `PI0Pytorch`
         suffix_embs, suffix_pad_masks, suffix_att_masks, adarms_cond = self.embed_suffix(state, x_t, time)
 
         if (
-            self.paligemma_with_expert.paligemma.language_model.layers[0].self_attn.q_proj.weight.dtype
+            self.paligemma_with_expert.paligemma.model.language_model.layers[0].self_attn.q_proj.weight.dtype
             == torch.bfloat16
         ):
             suffix_embs = suffix_embs.to(dtype=torch.bfloat16)
@@ -834,7 +841,7 @@ class PI0Pytorch(nn.Module):  # see openpi `PI0Pytorch`
         prefix_position_ids = torch.cumsum(prefix_pad_masks, dim=1) - 1
 
         prefix_att_2d_masks_4d = self._prepare_attention_masks_4d(prefix_att_2d_masks)
-        self.paligemma_with_expert.paligemma.language_model.config._attn_implementation = "eager"  # noqa: SLF001
+        self.paligemma_with_expert.paligemma.model.language_model.config._attn_implementation = "eager"  # noqa: SLF001
 
         _, past_key_values = self.paligemma_with_expert.forward(
             attention_mask=prefix_att_2d_masks_4d,
@@ -908,6 +915,7 @@ class PI0Pytorch(nn.Module):  # see openpi `PI0Pytorch`
         full_att_2d_masks_4d = self._prepare_attention_masks_4d(full_att_2d_masks)
         self.paligemma_with_expert.gemma_expert.model.config._attn_implementation = "eager"  # noqa: SLF001
 
+        past_key_values = copy.deepcopy(past_key_values)
         outputs_embeds, _ = self.paligemma_with_expert.forward(
             attention_mask=full_att_2d_masks_4d,
             position_ids=position_ids,
@@ -997,14 +1005,12 @@ class PI0Policy(PreTrainedPolicy):
         # Check if dataset_stats were provided in kwargs
         model = cls(config, **kwargs)
 
-        # Now manually load and remap the state dict
+        # Load state dict (expects keys with "model." prefix)
         try:
-            # Try to load the pytorch_model.bin or model.safetensors file
             print(f"Loading model from: {pretrained_name_or_path}")
             try:
                 from transformers.utils import cached_file
 
-                # Try safetensors first
                 resolved_file = cached_file(
                     pretrained_name_or_path,
                     "model.safetensors",
@@ -1012,7 +1018,7 @@ class PI0Policy(PreTrainedPolicy):
                     force_download=kwargs.get("force_download", False),
                     resume_download=kwargs.get("resume_download"),
                     proxies=kwargs.get("proxies"),
-                    use_auth_token=kwargs.get("use_auth_token"),
+                    token=kwargs.get("token"),
                     revision=kwargs.get("revision"),
                     local_files_only=kwargs.get("local_files_only", False),
                 )
@@ -1025,7 +1031,7 @@ class PI0Policy(PreTrainedPolicy):
                 print("Returning model without loading pretrained weights")
                 return model
 
-            # First, fix any key differences # see openpi `model.py, _fix_pytorch_state_dict_keys`
+            # First, fix any key differences (see openpi model.py, _fix_pytorch_state_dict_keys)
             fixed_state_dict = model._fix_pytorch_state_dict_keys(original_state_dict, model.config)
 
             # Then add "model." prefix for all keys that don't already have it
@@ -1070,7 +1076,7 @@ class PI0Policy(PreTrainedPolicy):
                 print("All keys loaded successfully!")
 
         except Exception as e:
-            print(f"Warning: Could not remap state dict keys: {e}")
+            print(f"Warning: Could not load state dict: {e}")
 
         return model
 
@@ -1120,6 +1126,14 @@ class PI0Policy(PreTrainedPolicy):
                 # Some checkpoints might have this, but current model expects different structure
                 logging.warning(f"Vision embedding key might need handling: {key}")
 
+            if (
+                key == "model.paligemma_with_expert.paligemma.lm_head.weight"
+                or key == "paligemma_with_expert.paligemma.lm_head.weight"
+            ):
+                fixed_state_dict[
+                    "model.paligemma_with_expert.paligemma.model.language_model.embed_tokens.weight"
+                ] = value.clone()
+
             fixed_state_dict[new_key] = value
 
         return fixed_state_dict

src/lerobot/policies/pi05/modeling_pi05.py

@@ -15,16 +15,16 @@
 # limitations under the License.
 
 import builtins
+import copy
 import logging
 import math
 from collections import deque
 from pathlib import Path
-from typing import TYPE_CHECKING, Literal, TypedDict
+from typing import TYPE_CHECKING, Literal, TypedDict, Unpack
 
 import torch
 import torch.nn.functional as F  # noqa: N812
 from torch import Tensor, nn
-from typing_extensions import Unpack
 
 from lerobot.utils.import_utils import _transformers_available
 
@@ -32,14 +32,20 @@ from lerobot.utils.import_utils import _transformers_available
 if TYPE_CHECKING or _transformers_available:
     from transformers.models.auto import CONFIG_MAPPING
     from transformers.models.gemma import modeling_gemma
-    from transformers.models.gemma.modeling_gemma import GemmaForCausalLM
+
-    from transformers.models.paligemma.modeling_paligemma import PaliGemmaForConditionalGeneration
+    from lerobot.policies.pi_gemma import (
+        PaliGemmaForConditionalGenerationWithPiGemma,
+        PiGemmaForCausalLM,
+        _gated_residual,
+        layernorm_forward,
+    )
 else:
     CONFIG_MAPPING = None
     modeling_gemma = None
-    GemmaForCausalLM = None
+    PiGemmaForCausalLM = None
-    PaliGemmaForConditionalGeneration = None
+    _gated_residual = None
-
+    layernorm_forward = None
+    PaliGemmaForConditionalGenerationWithPiGemma = None
 from lerobot.configs.policies import PreTrainedConfig
 from lerobot.policies.pi05.configuration_pi05 import DEFAULT_IMAGE_SIZE, PI05Config
 from lerobot.policies.pretrained import PreTrainedPolicy, T
@@ -92,10 +98,11 @@ def create_sinusoidal_pos_embedding(  # see openpi `create_sinusoidal_pos_embedd
 
 
 def sample_beta(alpha, beta, bsize, device):  # see openpi `sample_beta` (exact copy)
-    alpha_t = torch.as_tensor(alpha, dtype=torch.float32, device=device)
+    # Beta sampling uses _sample_dirichlet which isn't implemented for MPS, so sample on CPU
-    beta_t = torch.as_tensor(beta, dtype=torch.float32, device=device)
+    alpha_t = torch.tensor(alpha, dtype=torch.float32)
+    beta_t = torch.tensor(beta, dtype=torch.float32)
     dist = torch.distributions.Beta(alpha_t, beta_t)
-    return dist.sample((bsize,))
+    return dist.sample((bsize,)).to(device)
 
 
 def make_att_2d_masks(pad_masks, att_masks):  # see openpi `make_att_2d_masks` (exact copy)
@@ -189,7 +196,7 @@ def resize_with_pad_torch(  # see openpi `resize_with_pad_torch` (exact copy)
     if images.dtype == torch.uint8:
         resized_images = torch.round(resized_images).clamp(0, 255).to(torch.uint8)
     elif images.dtype == torch.float32:
-        resized_images = resized_images.clamp(-1.0, 1.0)
+        resized_images = resized_images.clamp(0.0, 1.0)
     else:
         raise ValueError(f"Unsupported image dtype: {images.dtype}")
 
@@ -200,7 +207,7 @@ def resize_with_pad_torch(  # see openpi `resize_with_pad_torch` (exact copy)
     pad_w1 = pad_w0 + remainder_w
 
     # Pad
-    constant_value = 0 if images.dtype == torch.uint8 else -1.0
+    constant_value = 0 if images.dtype == torch.uint8 else 0.0
     padded_images = F.pad(
         resized_images,
         (pad_w0, pad_w1, pad_h0, pad_h1),  # left, right, top, bottom
@@ -219,14 +226,14 @@ def resize_with_pad_torch(  # see openpi `resize_with_pad_torch` (exact copy)
 def compute_layer_complete(
     layer_idx, inputs_embeds, attention_mask, position_ids, adarms_cond, paligemma, gemma_expert
 ):
-    models = [paligemma.language_model, gemma_expert.model]
+    models = [paligemma.model.language_model, gemma_expert.model]
     query_states = []
     key_states = []
     value_states = []
     gates = []
     for i, hidden_states in enumerate(inputs_embeds):
         layer = models[i].layers[layer_idx]
-        hidden_states, gate = layer.input_layernorm(hidden_states, cond=adarms_cond[i])  # noqa: PLW2901
+        hidden_states, gate = layernorm_forward(layer.input_layernorm, hidden_states, adarms_cond[i])
         gates.append(gate)
         input_shape = hidden_states.shape[:-1]
         hidden_shape = (*input_shape, -1, layer.self_attn.head_dim)
@@ -252,10 +259,10 @@ def compute_layer_complete(
         query_states, key_states, cos, sin, unsqueeze_dim=1
     )
     batch_size = query_states.shape[0]
-    scaling = paligemma.language_model.layers[layer_idx].self_attn.scaling
+    scaling = paligemma.model.language_model.layers[layer_idx].self_attn.scaling
     # Attention computation
     att_output, _ = modeling_gemma.eager_attention_forward(
-        paligemma.language_model.layers[layer_idx].self_attn,
+        paligemma.model.language_model.layers[layer_idx].self_attn,
         query_states,
         key_states,
         value_states,
@@ -263,7 +270,7 @@ def compute_layer_complete(
         scaling,
     )
     # Get head_dim from the current layer, not from the model
-    head_dim = paligemma.language_model.layers[layer_idx].self_attn.head_dim
+    head_dim = paligemma.model.language_model.layers[layer_idx].self_attn.head_dim
     att_output = att_output.reshape(batch_size, -1, 1 * 8 * head_dim)
     # Process layer outputs
     outputs_embeds = []
@@ -275,15 +282,15 @@ def compute_layer_complete(
             att_output = att_output.to(layer.self_attn.o_proj.weight.dtype)
         out_emb = layer.self_attn.o_proj(att_output[:, start_pos:end_pos])
         # first residual
-        out_emb = modeling_gemma._gated_residual(hidden_states, out_emb, gates[i])  # noqa: SLF001
+        out_emb = _gated_residual(hidden_states, out_emb, gates[i])
         after_first_residual = out_emb.clone()
-        out_emb, gate = layer.post_attention_layernorm(out_emb, cond=adarms_cond[i])
+        out_emb, gate = layernorm_forward(layer.post_attention_layernorm, out_emb, adarms_cond[i])
         # Convert to bfloat16 if the next layer (mlp) uses bfloat16
         if layer.mlp.up_proj.weight.dtype == torch.bfloat16:
             out_emb = out_emb.to(dtype=torch.bfloat16)
         out_emb = layer.mlp(out_emb)
         # second residual
-        out_emb = modeling_gemma._gated_residual(after_first_residual, out_emb, gate)  # noqa: SLF001
+        out_emb = _gated_residual(after_first_residual, out_emb, gate)
         outputs_embeds.append(out_emb)
         start_pos = end_pos
     return outputs_embeds
@@ -356,7 +363,7 @@ class PaliGemmaWithExpertModel(
         vlm_config_hf.text_config.num_hidden_layers = vlm_config.depth
         vlm_config_hf.text_config.num_key_value_heads = vlm_config.num_kv_heads
         vlm_config_hf.text_config.hidden_activation = "gelu_pytorch_tanh"
-        vlm_config_hf.text_config.torch_dtype = "float32"
+        vlm_config_hf.text_config.dtype = "float32"
         vlm_config_hf.text_config.vocab_size = 257152
         vlm_config_hf.text_config.use_adarms = use_adarms[0]
         vlm_config_hf.text_config.adarms_cond_dim = vlm_config.width if use_adarms[0] else None
@@ -364,7 +371,7 @@ class PaliGemmaWithExpertModel(
         vlm_config_hf.vision_config.intermediate_size = 4304
         vlm_config_hf.vision_config.projection_dim = 2048
         vlm_config_hf.vision_config.projector_hidden_act = "gelu_fast"
-        vlm_config_hf.vision_config.torch_dtype = "float32"
+        vlm_config_hf.vision_config.dtype = "float32"
 
         action_expert_config_hf = CONFIG_MAPPING["gemma"](
             head_dim=action_expert_config.head_dim,
@@ -375,13 +382,13 @@ class PaliGemmaWithExpertModel(
             num_key_value_heads=action_expert_config.num_kv_heads,
             vocab_size=257152,
             hidden_activation="gelu_pytorch_tanh",
-            torch_dtype="float32",
+            dtype="float32",
             use_adarms=use_adarms[1],
             adarms_cond_dim=action_expert_config.width if use_adarms[1] else None,
         )
 
-        self.paligemma = PaliGemmaForConditionalGeneration(config=vlm_config_hf)
+        self.paligemma = PaliGemmaForConditionalGenerationWithPiGemma(config=vlm_config_hf)
-        self.gemma_expert = GemmaForCausalLM(config=action_expert_config_hf)
+        self.gemma_expert = PiGemmaForCausalLM(config=action_expert_config_hf)
         self.gemma_expert.model.embed_tokens = None
 
         self.to_bfloat16_for_selected_params(precision)
@@ -396,10 +403,11 @@ class PaliGemmaWithExpertModel(
         else:
             raise ValueError(f"Invalid precision: {precision}")
 
+        # Keep full vision path in float32 so we never toggle (toggle causes optimizer
+        # "same dtype" error). Saves memory vs full float32; more memory than only 3 params.
         params_to_keep_float32 = [
-            "vision_tower.vision_model.embeddings.patch_embedding.weight",
+            "vision_tower",
-            "vision_tower.vision_model.embeddings.patch_embedding.bias",
+            "multi_modal_projector",
-            "vision_tower.vision_model.embeddings.position_embedding.weight",
             "input_layernorm",
             "post_attention_layernorm",
             "model.norm",
@@ -411,8 +419,8 @@ class PaliGemmaWithExpertModel(
 
     def _set_requires_grad(self):
         if self.freeze_vision_encoder:
-            self.paligemma.vision_tower.eval()
+            self.paligemma.model.vision_tower.eval()
-            for param in self.paligemma.vision_tower.parameters():
+            for param in self.paligemma.model.vision_tower.parameters():
                 param.requires_grad = False
         if self.train_expert_only:
             self.paligemma.eval()
@@ -422,15 +430,23 @@ class PaliGemmaWithExpertModel(
     def train(self, mode: bool = True):
         super().train(mode)
         if self.freeze_vision_encoder:
-            self.paligemma.vision_tower.eval()
+            self.paligemma.model.vision_tower.eval()
         if self.train_expert_only:
             self.paligemma.eval()
 
     def embed_image(self, image: torch.Tensor):
-        return self.paligemma.model.get_image_features(image)
+        # Vision tower and multi_modal_projector are kept in float32 (params_to_keep_float32).
+        out_dtype = image.dtype
+        if image.dtype != torch.float32:
+            image = image.to(torch.float32)
+        image_outputs = self.paligemma.model.get_image_features(image)
+        features = image_outputs.pooler_output * self.paligemma.config.text_config.hidden_size**0.5
+        if features.dtype != out_dtype:
+            features = features.to(out_dtype)
+        return features
 
     def embed_language_tokens(self, tokens: torch.Tensor):
-        return self.paligemma.language_model.embed_tokens(tokens)
+        return self.paligemma.model.language_model.embed_tokens(tokens)
 
     def forward(
         self,
@@ -444,7 +460,7 @@ class PaliGemmaWithExpertModel(
         if adarms_cond is None:
             adarms_cond = [None, None]
         if inputs_embeds[1] is None:
-            prefix_output = self.paligemma.language_model.forward(
+            prefix_output = self.paligemma.model.language_model.forward(
                 inputs_embeds=inputs_embeds[0],
                 attention_mask=attention_mask,
                 position_ids=position_ids,
@@ -468,7 +484,7 @@ class PaliGemmaWithExpertModel(
             prefix_output = None
             prefix_past_key_values = None
         else:
-            models = [self.paligemma.language_model, self.gemma_expert.model]
+            models = [self.paligemma.model.language_model, self.gemma_expert.model]
             num_layers = self.paligemma.config.text_config.num_hidden_layers
 
             # Check if gradient checkpointing is enabled for any of the models
@@ -508,7 +524,7 @@ class PaliGemmaWithExpertModel(
             def compute_final_norms(inputs_embeds, adarms_cond):
                 outputs_embeds = []
                 for i, hidden_states in enumerate(inputs_embeds):
-                    out_emb, _ = models[i].norm(hidden_states, cond=adarms_cond[i])
+                    out_emb, _ = layernorm_forward(models[i].norm, hidden_states, adarms_cond[i])
                     outputs_embeds.append(out_emb)
                 return outputs_embeds
 
@@ -573,29 +589,19 @@ class PI05Pytorch(nn.Module):  # see openpi `PI0Pytorch`
             # Also compile the main forward pass used during training
             self.forward = torch.compile(self.forward, mode=config.compile_mode)
 
-        msg = """An incorrect transformer version is used, please create an issue on https://github.com/huggingface/lerobot/issues"""
-
-        try:
-            from transformers.models.siglip import check
-
-            if not check.check_whether_transformers_replace_is_installed_correctly():
-                raise ValueError(msg)
-        except ImportError:
-            raise ValueError(msg) from None
-
     def gradient_checkpointing_enable(self):
         """Enable gradient checkpointing for memory optimization."""
         self.gradient_checkpointing_enabled = True
-        self.paligemma_with_expert.paligemma.language_model.gradient_checkpointing = True
+        self.paligemma_with_expert.paligemma.model.language_model.gradient_checkpointing = True
-        self.paligemma_with_expert.paligemma.vision_tower.gradient_checkpointing = True
+        self.paligemma_with_expert.paligemma.model.vision_tower.gradient_checkpointing = True
         self.paligemma_with_expert.gemma_expert.model.gradient_checkpointing = True
         logging.info("Enabled gradient checkpointing for PI05Pytorch model")
 
     def gradient_checkpointing_disable(self):
         """Disable gradient checkpointing."""
         self.gradient_checkpointing_enabled = False
-        self.paligemma_with_expert.paligemma.language_model.gradient_checkpointing = False
+        self.paligemma_with_expert.paligemma.model.language_model.gradient_checkpointing = False
-        self.paligemma_with_expert.paligemma.vision_tower.gradient_checkpointing = False
+        self.paligemma_with_expert.paligemma.model.vision_tower.gradient_checkpointing = False
         self.paligemma_with_expert.gemma_expert.model.gradient_checkpointing = False
         logging.info("Disabled gradient checkpointing for PI05Pytorch model")
 
@@ -737,7 +743,7 @@ class PI05Pytorch(nn.Module):  # see openpi `PI0Pytorch`
         suffix_embs, suffix_pad_masks, suffix_att_masks, adarms_cond = self.embed_suffix(x_t, time)
 
         if (
-            self.paligemma_with_expert.paligemma.language_model.layers[0].self_attn.q_proj.weight.dtype
+            self.paligemma_with_expert.paligemma.model.language_model.layers[0].self_attn.q_proj.weight.dtype
             == torch.bfloat16
         ):
             suffix_embs = suffix_embs.to(dtype=torch.bfloat16)
@@ -808,7 +814,7 @@ class PI05Pytorch(nn.Module):  # see openpi `PI0Pytorch`
         prefix_position_ids = torch.cumsum(prefix_pad_masks, dim=1) - 1
 
         prefix_att_2d_masks_4d = self._prepare_attention_masks_4d(prefix_att_2d_masks)
-        self.paligemma_with_expert.paligemma.language_model.config._attn_implementation = "eager"  # noqa: SLF001
+        self.paligemma_with_expert.paligemma.model.language_model.config._attn_implementation = "eager"  # noqa: SLF001
 
         _, past_key_values = self.paligemma_with_expert.forward(
             attention_mask=prefix_att_2d_masks_4d,
@@ -880,6 +886,7 @@ class PI05Pytorch(nn.Module):  # see openpi `PI0Pytorch`
         full_att_2d_masks_4d = self._prepare_attention_masks_4d(full_att_2d_masks)
         self.paligemma_with_expert.gemma_expert.model.config._attn_implementation = "eager"  # noqa: SLF001
 
+        past_key_values = copy.deepcopy(past_key_values)
         outputs_embeds, _ = self.paligemma_with_expert.forward(
             attention_mask=full_att_2d_masks_4d,
             position_ids=position_ids,
@@ -969,14 +976,12 @@ class PI05Policy(PreTrainedPolicy):
         # Check if dataset_stats were provided in kwargs
         model = cls(config, **kwargs)
 
-        # Now manually load and remap the state dict
+        # Load state dict (expects keys with "model." prefix)
         try:
-            # Try to load the pytorch_model.bin or model.safetensors file
             print(f"Loading model from: {pretrained_name_or_path}")
             try:
                 from transformers.utils import cached_file
 
-                # Try safetensors first
                 resolved_file = cached_file(
                     pretrained_name_or_path,
                     "model.safetensors",
@@ -984,7 +989,7 @@ class PI05Policy(PreTrainedPolicy):
                     force_download=kwargs.get("force_download", False),
                     resume_download=kwargs.get("resume_download"),
                     proxies=kwargs.get("proxies"),
-                    use_auth_token=kwargs.get("use_auth_token"),
+                    token=kwargs.get("token"),
                     revision=kwargs.get("revision"),
                     local_files_only=kwargs.get("local_files_only", False),
                 )
@@ -997,7 +1002,7 @@ class PI05Policy(PreTrainedPolicy):
                 print("Returning model without loading pretrained weights")
                 return model
 
-            # First, fix any key differences # see openpi `model.py, _fix_pytorch_state_dict_keys`
+            # First, fix any key differences (see openpi model.py, _fix_pytorch_state_dict_keys)
             fixed_state_dict = model._fix_pytorch_state_dict_keys(original_state_dict, model.config)
 
             # Then add "model." prefix for all keys that don't already have it
@@ -1009,8 +1014,6 @@ class PI05Policy(PreTrainedPolicy):
                     new_key = f"model.{key}"
                     remapped_state_dict[new_key] = value
                     remap_count += 1
-                    if remap_count <= 10:  # Only print first 10 to avoid spam
-                        print(f"Remapped: {key} -> {new_key}")
                 else:
                     remapped_state_dict[key] = value
 
@@ -1044,7 +1047,7 @@ class PI05Policy(PreTrainedPolicy):
                 print("All keys loaded successfully!")
 
         except Exception as e:
-            print(f"Warning: Could not remap state dict keys: {e}")
+            print(f"Warning: Could not load state dict: {e}")
 
         return model
 
@@ -1098,6 +1101,14 @@ class PI05Policy(PreTrainedPolicy):
                 # Some checkpoints might have this, but current model expects different structure
                 logging.warning(f"Vision embedding key might need handling: {key}")
 
+            if (
+                key == "model.paligemma_with_expert.paligemma.lm_head.weight"
+                or key == "paligemma_with_expert.paligemma.lm_head.weight"
+            ):
+                fixed_state_dict[
+                    "model.paligemma_with_expert.paligemma.model.language_model.embed_tokens.weight"
+                ] = value.clone()
+
             fixed_state_dict[new_key] = value
 
         return fixed_state_dict

src/lerobot/policies/pi05/processor_pi05.py

@@ -23,7 +23,6 @@ import torch
 
 from lerobot.configs.types import PipelineFeatureType, PolicyFeature
 from lerobot.policies.pi05.configuration_pi05 import PI05Config
-from lerobot.policies.pi05.modeling_pi05 import pad_vector
 from lerobot.processor import (
     AddBatchDimensionProcessorStep,
     DeviceProcessorStep,
@@ -68,9 +67,6 @@ class Pi05PrepareStateTokenizerProcessorStep(ProcessorStep):
         # TODO: check if this necessary
         state = deepcopy(state)
 
-        # Prepare state (pad to max_state_dim)
-        state = pad_vector(state, self.max_state_dim)
-
         # State should already be normalized to [-1, 1] by the NormalizerProcessorStep that runs before this step
         # Discretize into 256 bins (see openpi `PaligemmaTokenizer.tokenize()`)
         state_np = state.cpu().numpy()

src/lerobot/policies/pi0_fast/configuration_pi0_fast.py

@@ -54,7 +54,7 @@ class PI0FastConfig(PreTrainedConfig):
 
     tokenizer_max_length: int = 200  # see openpi `__post_init__`
     text_tokenizer_name: str = "google/paligemma-3b-pt-224"
-    action_tokenizer_name: str = "physical-intelligence/fast"
+    action_tokenizer_name: str = "lerobot/fast-action-tokenizer"
     temperature: float = 0.0
     max_decoding_steps: int = 256
     fast_skip_tokens: int = 128

src/lerobot/policies/pi0_fast/modeling_pi0_fast.py

@@ -19,13 +19,12 @@ import logging
 import math
 from collections import deque
 from pathlib import Path
-from typing import TYPE_CHECKING, Literal, TypedDict
+from typing import TYPE_CHECKING, Literal, TypedDict, Unpack
 
 import numpy as np
 import torch
 import torch.nn.functional as F  # noqa: N812
 from torch import Tensor, nn
-from typing_extensions import Unpack
 
 from lerobot.utils.import_utils import _scipy_available, _transformers_available
 
@@ -38,11 +37,16 @@ else:
 if TYPE_CHECKING or _transformers_available:
     from transformers import AutoTokenizer
     from transformers.models.auto import CONFIG_MAPPING
-    from transformers.models.paligemma.modeling_paligemma import PaliGemmaForConditionalGeneration
+
+    from lerobot.policies.pi_gemma import (
+        PaliGemmaForConditionalGenerationWithPiGemma,
+        PiGemmaModel,
+    )
 else:
     CONFIG_MAPPING = None
-    PaliGemmaForConditionalGeneration = None
     AutoTokenizer = None
+    PiGemmaModel = None
+    PaliGemmaForConditionalGenerationWithPiGemma = None
 
 from lerobot.configs.policies import PreTrainedConfig
 from lerobot.policies.pi0_fast.configuration_pi0_fast import PI0FastConfig
@@ -121,7 +125,7 @@ def resize_with_pad_torch(  # see openpi `resize_with_pad_torch` (exact copy)
     if images.dtype == torch.uint8:
         resized_images = torch.round(resized_images).clamp(0, 255).to(torch.uint8)
     elif images.dtype == torch.float32:
-        resized_images = resized_images.clamp(-1.0, 1.0)
+        resized_images = resized_images.clamp(0.0, 1.0)
     else:
         raise ValueError(f"Unsupported image dtype: {images.dtype}")
 
@@ -132,7 +136,7 @@ def resize_with_pad_torch(  # see openpi `resize_with_pad_torch` (exact copy)
     pad_w1 = pad_w0 + remainder_w
 
     # Pad
-    constant_value = 0 if images.dtype == torch.uint8 else -1.0
+    constant_value = 0 if images.dtype == torch.uint8 else 0.0
     padded_images = F.pad(
         resized_images,
         (pad_w0, pad_w1, pad_h0, pad_h1),  # left, right, top, bottom
@@ -206,16 +210,22 @@ class PI0FastPaliGemma(nn.Module):
         vlm_config_hf.text_config.num_hidden_layers = vlm_config.depth
         vlm_config_hf.text_config.num_key_value_heads = vlm_config.num_kv_heads
         vlm_config_hf.text_config.hidden_activation = "gelu_pytorch_tanh"
-        vlm_config_hf.text_config.torch_dtype = "float32"
+        vlm_config_hf.text_config.dtype = "float32"
         vlm_config_hf.text_config.vocab_size = 257152
         vlm_config_hf.text_config.use_adarms = use_adarms[0]
         vlm_config_hf.text_config.adarms_cond_dim = vlm_config.width if use_adarms[0] else None
         vlm_config_hf.vision_config.intermediate_size = 4304
         vlm_config_hf.vision_config.projection_dim = 2048
         vlm_config_hf.vision_config.projector_hidden_act = "gelu_fast"
-        vlm_config_hf.vision_config.torch_dtype = "float32"
+        vlm_config_hf.vision_config.dtype = "float32"
 
-        self.paligemma = PaliGemmaForConditionalGeneration(config=vlm_config_hf)
+        self.paligemma = PaliGemmaForConditionalGenerationWithPiGemma(config=vlm_config_hf)
+
+        # Use PI Gemma (AdaRMS) as language model when use_adarms[0] is True so that
+        # forward(..., adarms_cond=...) is supported (same as pi0/pi05).
+        if use_adarms[0]:
+            text_config = self.paligemma.config.text_config
+            self.paligemma.model.language_model = PiGemmaModel(text_config)
 
         self.to_bfloat16_for_selected_params(precision)
 
@@ -228,10 +238,11 @@ class PI0FastPaliGemma(nn.Module):
         else:
             raise ValueError(f"Invalid precision: {precision}")
 
+        # Keep full vision path in float32 so we never toggle (toggle causes optimizer
+        # "same dtype" error). Align with PI05.
         params_to_keep_float32 = [
-            "vision_tower.vision_model.embeddings.patch_embedding.weight",
+            "vision_tower",
-            "vision_tower.vision_model.embeddings.patch_embedding.bias",
+            "multi_modal_projector",
-            "vision_tower.vision_model.embeddings.position_embedding.weight",
             "input_layernorm",
             "post_attention_layernorm",
             "model.norm",
@@ -242,10 +253,18 @@ class PI0FastPaliGemma(nn.Module):
                 param.data = param.data.to(dtype=torch.float32)
 
     def embed_image(self, image: torch.Tensor):
-        return self.paligemma.model.get_image_features(image)
+        # Vision tower and multi_modal_projector are kept in float32 (params_to_keep_float32). Align with PI05.
+        out_dtype = image.dtype
+        if image.dtype != torch.float32:
+            image = image.to(torch.float32)
+        image_outputs = self.paligemma.model.get_image_features(image)
+        features = image_outputs.pooler_output * self.paligemma.config.text_config.hidden_size**0.5
+        if features.dtype != out_dtype:
+            features = features.to(out_dtype)
+        return features
 
     def embed_language_tokens(self, tokens: torch.Tensor):
-        return self.paligemma.language_model.embed_tokens(tokens)
+        return self.paligemma.model.language_model.embed_tokens(tokens)
 
     def forward(
         self,
@@ -259,7 +278,7 @@ class PI0FastPaliGemma(nn.Module):
         if adarms_cond is None:
             adarms_cond = [None, None]
         if inputs_embeds[1] is None:
-            prefix_output = self.paligemma.language_model.forward(
+            prefix_output = self.paligemma.model.language_model.forward(
                 inputs_embeds=inputs_embeds[0],
                 attention_mask=attention_mask,
                 position_ids=position_ids,
@@ -306,24 +325,14 @@ class PI0FastPytorch(nn.Module):  # see openpi `PI0Pytorch`
             self.sample_actions_fast = torch.compile(self.sample_actions_fast, mode=config.compile_mode)
             self.forward = torch.compile(self.forward, mode=config.compile_mode)
 
-        msg = """An incorrect transformer version is used, please create an issue on https://github.com/huggingface/lerobot/issues"""
-
-        try:
-            from transformers.models.siglip import check
-
-            if not check.check_whether_transformers_replace_is_installed_correctly():
-                raise ValueError(msg)
-        except ImportError:
-            raise ValueError(msg) from None
-
     def gradient_checkpointing_enable(self):
         """Enable gradient checkpointing for memory optimization."""
         self.gradient_checkpointing_enabled = True
         # Call the proper gradient_checkpointing_enable() method with use_reentrant=False for better memory efficiency
-        self.paligemma_with_expert.paligemma.language_model.gradient_checkpointing_enable(
+        self.paligemma_with_expert.paligemma.model.language_model.gradient_checkpointing_enable(
             gradient_checkpointing_kwargs={"use_reentrant": False}
         )
-        self.paligemma_with_expert.paligemma.vision_tower.gradient_checkpointing_enable(
+        self.paligemma_with_expert.paligemma.model.vision_tower.gradient_checkpointing_enable(
             gradient_checkpointing_kwargs={"use_reentrant": False}
         )
         logging.info("Enabled gradient checkpointing for PI0FastPytorch model")
@@ -332,8 +341,8 @@ class PI0FastPytorch(nn.Module):  # see openpi `PI0Pytorch`
         """Disable gradient checkpointing."""
         self.gradient_checkpointing_enabled = False
         # Call the proper gradient_checkpointing_disable() method
-        self.paligemma_with_expert.paligemma.language_model.gradient_checkpointing_disable()
+        self.paligemma_with_expert.paligemma.model.language_model.gradient_checkpointing_disable()
-        self.paligemma_with_expert.paligemma.vision_tower.gradient_checkpointing_disable()
+        self.paligemma_with_expert.paligemma.model.vision_tower.gradient_checkpointing_disable()
         logging.info("Disabled gradient checkpointing for PI0FastPytorch model")
 
     def _apply_checkpoint(self, func, *args, **kwargs):
@@ -523,7 +532,7 @@ class PI0FastPytorch(nn.Module):  # see openpi `PI0Pytorch`
 
         # Convert embeddings to bfloat16 if needed
         if (
-            self.paligemma_with_expert.paligemma.language_model.layers[0].self_attn.q_proj.weight.dtype
+            self.paligemma_with_expert.paligemma.model.language_model.layers[0].self_attn.q_proj.weight.dtype
             == torch.bfloat16
         ):
             prefix_embs = prefix_embs.to(dtype=torch.bfloat16)
@@ -616,7 +625,7 @@ class PI0FastPytorch(nn.Module):  # see openpi `PI0Pytorch`
         )
 
         if (
-            self.paligemma_with_expert.paligemma.language_model.layers[0].self_attn.q_proj.weight.dtype
+            self.paligemma_with_expert.paligemma.model.language_model.layers[0].self_attn.q_proj.weight.dtype
             == torch.bfloat16
         ):
             prefix_embs = prefix_embs.to(dtype=torch.bfloat16)
@@ -714,7 +723,7 @@ class PI0FastPytorch(nn.Module):  # see openpi `PI0Pytorch`
 
         # Ensure correct precision (bfloat16/float32)
         if (
-            self.paligemma_with_expert.paligemma.language_model.layers[0].self_attn.q_proj.weight.dtype
+            self.paligemma_with_expert.paligemma.model.language_model.layers[0].self_attn.q_proj.weight.dtype
             == torch.bfloat16
         ):
             prefix_embs = prefix_embs.to(dtype=torch.bfloat16)
@@ -897,14 +906,12 @@ class PI0FastPolicy(PreTrainedPolicy):
         # Check if dataset_stats were provided in kwargs
         model = cls(config, **kwargs)
 
-        # Now manually load and remap the state dict
+        # Load state dict (expects keys with "model." prefix)
         try:
-            # Try to load the pytorch_model.bin or model.safetensors file
             print(f"Loading model from: {pretrained_name_or_path}")
             try:
                 from transformers.utils import cached_file
 
-                # Try safetensors first
                 resolved_file = cached_file(
                     pretrained_name_or_path,
                     "model.safetensors",
@@ -912,7 +919,7 @@ class PI0FastPolicy(PreTrainedPolicy):
                     force_download=kwargs.get("force_download", False),
                     resume_download=kwargs.get("resume_download"),
                     proxies=kwargs.get("proxies"),
-                    use_auth_token=kwargs.get("use_auth_token"),
+                    token=kwargs.get("token"),
                     revision=kwargs.get("revision"),
                     local_files_only=kwargs.get("local_files_only", False),
                 )
@@ -925,8 +932,9 @@ class PI0FastPolicy(PreTrainedPolicy):
                 print("Returning model without loading pretrained weights")
                 return model
 
-            # First, fix any key differences # see openpi `model.py, _fix_pytorch_state_dict_keys`
+            # First, fix any key differences (see openpi model.py, _fix_pytorch_state_dict_keys)
             fixed_state_dict = model._fix_pytorch_state_dict_keys(original_state_dict, model.config)
+
             # Then add "model." prefix for all keys that don't already have it
             remapped_state_dict = {}
             remap_count = 0
@@ -936,8 +944,6 @@ class PI0FastPolicy(PreTrainedPolicy):
                     new_key = f"model.{key}"
                     remapped_state_dict[new_key] = value
                     remap_count += 1
-                    if remap_count <= 10:  # Only print first 10 to avoid spam
-                        print(f"Remapped: {key} -> {new_key}")
                 else:
                     remapped_state_dict[key] = value
 
@@ -971,7 +977,7 @@ class PI0FastPolicy(PreTrainedPolicy):
                 print("All keys loaded successfully!")
 
         except Exception as e:
-            print(f"Warning: Could not remap state dict keys: {e}")
+            print(f"Warning: Could not load state dict: {e}")
 
         return model
 

src/lerobot/policies/pi0_fast/processor_pi0_fast.py

@@ -23,7 +23,6 @@ import torch
 
 from lerobot.configs.types import PipelineFeatureType, PolicyFeature
 from lerobot.policies.pi0_fast.configuration_pi0_fast import PI0FastConfig
-from lerobot.policies.pi0_fast.modeling_pi0_fast import pad_vector
 from lerobot.processor import (
     ActionTokenizerProcessorStep,
     AddBatchDimensionProcessorStep,
@@ -69,9 +68,6 @@ class Pi0FastPrepareStateAndLanguageTokenizerProcessorStep(ProcessorStep):
         # TODO: check if this necessary
         state = deepcopy(state)
 
-        # Prepare state (pad to max_state_dim)
-        state = pad_vector(state, self.max_state_dim)
-
         # State should already be normalized to [-1, 1] by the NormalizerProcessorStep that runs before this step
         # Discretize into 256 bins (see openpi `PaligemmaTokenizer.tokenize()`)
         state_np = state.cpu().numpy()

src/lerobot/policies/pi_gemma.py

@@ -0,0 +1,363 @@
+# Copyright 2025 Physical Intelligence and 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 __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import torch
+from torch import nn
+
+from lerobot.utils.import_utils import _transformers_available
+
+if TYPE_CHECKING or _transformers_available:
+    from transformers.cache_utils import DynamicCache
+    from transformers.masking_utils import create_causal_mask
+    from transformers.modeling_layers import GradientCheckpointingLayer
+    from transformers.modeling_outputs import BaseModelOutputWithPast
+    from transformers.models.gemma.modeling_gemma import (
+        GemmaAttention,
+        GemmaConfig,
+        GemmaForCausalLM,
+        GemmaMLP,
+        GemmaModel,
+    )
+    from transformers.models.paligemma.modeling_paligemma import (
+        PaliGemmaForConditionalGeneration,
+        PaliGemmaModel,
+    )
+else:
+    GemmaAttention = None
+    GemmaConfig = None
+    GemmaForCausalLM = None
+    GemmaMLP = None
+    GemmaModel = None
+    PaliGemmaModel = None
+    PaliGemmaForConditionalGeneration = None
+    DynamicCache = None
+    GradientCheckpointingLayer = None
+    BaseModelOutputWithPast = None
+    create_causal_mask = None
+
+
+def _gated_residual(
+    x: torch.Tensor | None,
+    y: torch.Tensor | None,
+    gate: torch.Tensor | None,
+) -> torch.Tensor | None:
+    """Gated residual: x + y when gate is None, else x + y * gate."""
+    if x is None and y is None:
+        return None
+    if x is None or y is None:
+        return x if x is not None else y
+    if gate is None:
+        return x + y
+    return x + y * gate
+
+
+def layernorm_forward(
+    layernorm: nn.Module,
+    x: torch.Tensor,
+    cond: torch.Tensor | None = None,
+):
+    """
+    call layernorm and return hidden states and gate
+    if cond is not None, use conditional norm
+    otherwise, use normal gemma norm
+    """
+    if cond is not None:
+        return layernorm(x, cond=cond)
+    else:
+        return layernorm(x)
+
+
+class PiGemmaRMSNorm(nn.Module):
+    """
+    Adaptive RMSNorm for PI Gemma (AdaRMS).
+    When cond_dim is set, uses cond to modulate scale/shift/gate; otherwise behaves like standard GemmaRMSNorm.
+    forward(x, cond=None) returns (output, gate) for use with _gated_residual.
+    """
+
+    def __init__(self, dim: int, eps: float = 1e-6, cond_dim: int | None = None):
+        super().__init__()
+        self.eps = eps
+        self.dim = dim
+        self.cond_dim = cond_dim
+        if cond_dim is not None:
+            self.dense = nn.Linear(cond_dim, dim * 3, bias=True)
+            nn.init.zeros_(self.dense.weight)
+        else:
+            self.weight = nn.Parameter(torch.zeros(dim))
+            self.dense = None
+
+    def _norm(self, x):
+        # Compute variance in float32 (like the source implementation)
+        var = torch.mean(torch.square(x.float()), dim=-1, keepdim=True)
+        # Compute normalization in float32
+        normed_inputs = x * torch.rsqrt(var + self.eps)
+        return normed_inputs
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        cond: torch.Tensor | None = None,
+    ) -> tuple[torch.Tensor, torch.Tensor | None]:
+        dtype = x.dtype
+        normed = self._norm(x)
+        if cond is None or self.dense is None:
+            normed = normed * (1.0 + self.weight.float())
+            return normed.type_as(x), None
+        if cond.shape[-1] != self.cond_dim:
+            raise ValueError(f"Expected cond dim {self.cond_dim}, got {cond.shape[-1]}")
+        modulation = self.dense(cond)
+        if len(x.shape) == 3:
+            modulation = modulation.unsqueeze(1)
+        scale, shift, gate = modulation.chunk(3, dim=-1)
+        normed = normed * (1 + scale.float()) + shift.float()
+        return normed.to(dtype), gate.to(dtype)
+
+    def extra_repr(self) -> str:
+        if self.dense is not None:
+            return f"dim={self.dim}, eps={self.eps}, adaptive=True, cond_dim={self.cond_dim}"
+        return f"dim={self.dim}, eps={self.eps}"
+
+
+def _get_pi_gemma_decoder_layer_base():
+    """base for PiGemmaDecoderLayer"""
+
+    class _PiGemmaDecoderLayerBase(GradientCheckpointingLayer):
+        """Decoder layer that uses PiGemmaRMSNorm and _gated_residual, compatible with v5 Gemma."""
+
+        def __init__(self, config: GemmaConfig, layer_idx: int):
+            super().__init__()
+            self.hidden_size = config.hidden_size
+            self.self_attn = GemmaAttention(config=config, layer_idx=layer_idx)
+            self.mlp = GemmaMLP(config)
+            cond_dim = (
+                getattr(config, "adarms_cond_dim", None) if getattr(config, "use_adarms", False) else None
+            )
+            self.input_layernorm = PiGemmaRMSNorm(
+                config.hidden_size, eps=config.rms_norm_eps, cond_dim=cond_dim
+            )
+            self.post_attention_layernorm = PiGemmaRMSNorm(
+                config.hidden_size, eps=config.rms_norm_eps, cond_dim=cond_dim
+            )
+
+        def forward(
+            self,
+            hidden_states: torch.Tensor,
+            attention_mask: torch.Tensor | None = None,
+            position_ids: torch.LongTensor | None = None,
+            past_key_values=None,
+            use_cache: bool = False,
+            cache_position: torch.LongTensor | None = None,
+            position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None,
+            adarms_cond: torch.Tensor | None = None,
+            **kwargs,
+        ) -> torch.Tensor:
+            residual = hidden_states
+            hidden_states, gate = self.input_layernorm(hidden_states, cond=adarms_cond)
+            hidden_states, _ = self.self_attn(
+                hidden_states,
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                past_key_values=past_key_values,
+                use_cache=use_cache,
+                cache_position=cache_position,
+                position_embeddings=position_embeddings,
+                **kwargs,
+            )
+
+            hidden_states = _gated_residual(residual, hidden_states, gate)
+
+            residual = hidden_states
+            hidden_states, gate = self.post_attention_layernorm(hidden_states, cond=adarms_cond)
+            hidden_states = self.mlp(hidden_states)
+            hidden_states = _gated_residual(residual, hidden_states, gate)
+            return hidden_states
+
+    return _PiGemmaDecoderLayerBase
+
+
+class PiGemmaModel(GemmaModel):  # type: ignore[misc]
+    """
+    GemmaModel extended with AdaRMS (adaptive RMSNorm) and gated residuals when config.use_adarms is True.
+    """
+
+    def __init__(self, config: GemmaConfig, **kwargs):
+        super().__init__(config, **kwargs)
+        # if not getattr(config, "use_adarms", False):
+        #     return
+        cond_dim = getattr(config, "adarms_cond_dim", None)
+        pi_gemma_decoder_layer_base = _get_pi_gemma_decoder_layer_base()
+        self.layers = nn.ModuleList(
+            [pi_gemma_decoder_layer_base(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+        self.norm = PiGemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps, cond_dim=cond_dim)
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: DynamicCache | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        use_cache: bool | None = None,
+        output_attentions: bool | None = None,
+        output_hidden_states: bool | None = None,
+        cache_position: torch.LongTensor | None = None,
+        adarms_cond: torch.Tensor | None = None,
+        **kwargs,
+    ) -> BaseModelOutputWithPast:
+        """
+        adarms_cond (`torch.Tensor` of shape `(batch_size, cond_dim)`, *optional*):
+            Condition for ADARMS.
+        """
+        output_attentions = (
+            output_attentions if output_attentions is not None else self.config.output_attentions
+        )
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+        if self.gradient_checkpointing and self.training and use_cache:
+            import logging
+
+            logging.warning(
+                "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
+            )
+            use_cache = False
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+
+        if use_cache and past_key_values is None:
+            past_key_values = DynamicCache()
+
+        if cache_position is None:
+            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+            cache_position = torch.arange(
+                past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
+            )
+
+        if position_ids is None:
+            position_ids = cache_position.unsqueeze(0)
+
+        causal_mask = create_causal_mask(
+            config=self.config,
+            inputs_embeds=inputs_embeds,
+            attention_mask=attention_mask,
+            cache_position=cache_position,
+            past_key_values=past_key_values,
+            position_ids=position_ids,
+        )
+
+        # embed positions
+        hidden_states = inputs_embeds
+        # Convert to bfloat16 if the first layer uses bfloat16
+        if len(self.layers) > 0 and self.layers[0].self_attn.q_proj.weight.dtype == torch.bfloat16:
+            hidden_states = hidden_states.to(torch.bfloat16)
+
+        # create position embeddings to be shared across the decoder layers
+        position_embeddings = self.rotary_emb(hidden_states, position_ids)
+
+        # normalized
+        # Gemma downcasts the below to float16, causing sqrt(3072)=55.4256 to become 55.5
+        # See https://github.com/huggingface/transformers/pull/29402
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+
+        for decoder_layer in self.layers[: self.config.num_hidden_layers]:
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            layer_outputs = decoder_layer(
+                hidden_states,
+                attention_mask=causal_mask,
+                position_ids=position_ids,
+                past_key_values=past_key_values,
+                output_attentions=output_attentions,
+                use_cache=use_cache,
+                cache_position=cache_position,
+                position_embeddings=position_embeddings,
+                adarms_cond=adarms_cond,
+                **kwargs,
+            )
+
+            hidden_states = layer_outputs
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+        hidden_states, _ = self.norm(hidden_states, adarms_cond)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=past_key_values if use_cache else None,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+
+
+class PiGemmaForCausalLM(GemmaForCausalLM):  # type: ignore[misc]
+    """
+    Causal LM wrapper using PiGemmaModel as the backbone, for consistency with GemmaForCausalLM
+    and the language model used in pi0_fast. Use this for the action expert in pi0/pi05.
+    """
+
+    def __init__(self, config: GemmaConfig, **kwargs):
+        super().__init__(config, **kwargs)
+        self.model = PiGemmaModel(config)
+
+
+class PaliGemmaModelWithPiGemma(PaliGemmaModel):
+    """PaliGemmaModel whose language_model is PiGemmaModel (custom decoder with PiGemmaRMSNorm and gated residuals)."""
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.language_model = PiGemmaModel(config.text_config)
+
+
+class PaliGemmaForConditionalGenerationWithPiGemma(PaliGemmaForConditionalGeneration):
+    """PaliGemmaForConditionalGeneration using PiGemma decoder for the language model."""
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = PaliGemmaModelWithPiGemma(config)
+
+    # Make modules available through conditional class for BC
+    @property
+    def language_model(self):
+        return self.model.language_model
+
+
+__all__ = [
+    "PiGemmaModel",
+    "PiGemmaForCausalLM",
+    "PiGemmaRMSNorm",
+    "_gated_residual",
+    "layernorm_forward",
+    "PaliGemmaModelWithPiGemma",
+    "PaliGemmaForConditionalGenerationWithPiGemma",
+]

src/lerobot/policies/pretrained.py

@@ -19,7 +19,7 @@ import os
 from importlib.resources import files
 from pathlib import Path
 from tempfile import TemporaryDirectory
-from typing import TypedDict, TypeVar
+from typing import TypedDict, TypeVar, Unpack
 
 import packaging
 import safetensors
@@ -28,7 +28,6 @@ from huggingface_hub.constants import SAFETENSORS_SINGLE_FILE
 from huggingface_hub.errors import HfHubHTTPError
 from safetensors.torch import load_model as load_model_as_safetensor, save_model as save_model_as_safetensor
 from torch import Tensor, nn
-from typing_extensions import Unpack
 
 from lerobot.configs.policies import PreTrainedConfig
 from lerobot.configs.train import TrainPipelineConfig

src/lerobot/policies/sac/reward_model/configuration_classifier.py

@@ -33,7 +33,7 @@ class RewardClassifierConfig(PreTrainedConfig):
     latent_dim: int = 256
     image_embedding_pooling_dim: int = 8
     dropout_rate: float = 0.1
-    model_name: str = "helper2424/resnet10"
+    model_name: str = "helper2424/resnet10"  # TODO: This needs to be updated. The model on the Hub doesn't call self.post_init() in its __init__, which is required by transformers v5 to set all_tied_weights_keys. The from_pretrained call fails when it tries to access this attribute during _finalize_model_loading.
     device: str = "cpu"
     model_type: str = "cnn"  # "transformer" or "cnn"
     num_cameras: int = 2

src/lerobot/policies/smolvla/configuration_smolvla.py

@@ -106,6 +106,9 @@ class SmolVLAConfig(PreTrainedConfig):
     # Real-Time Chunking (RTC) configuration
     rtc_config: RTCConfig | None = None
 
+    compile_model: bool = False  # Whether to use torch.compile for model optimization
+    compile_mode: str = "max-autotune"  # Torch compile mode
+
     def __post_init__(self):
         super().__post_init__()
 

src/lerobot/policies/smolvla/modeling_smolvla.py

@@ -54,12 +54,11 @@ policy = SmolVLAPolicy.from_pretrained("lerobot/smolvla_base")
 
 import math
 from collections import deque
-from typing import TypedDict
+from typing import TypedDict, Unpack
 
 import torch
 import torch.nn.functional as F  # noqa: N812
 from torch import Tensor, nn
-from typing_extensions import Unpack
 
 from lerobot.policies.pretrained import PreTrainedPolicy
 from lerobot.policies.rtc.modeling_rtc import RTCProcessor
@@ -593,6 +592,12 @@ class VLAFlowMatching(nn.Module):
         self.prefix_length = self.config.prefix_length
         self.rtc_processor = rtc_processor
 
+        # Compile model if requested
+        if config.compile_model:
+            torch.set_float32_matmul_precision("high")
+            self.sample_actions = torch.compile(self.sample_actions, mode=config.compile_mode)
+            self.forward = torch.compile(self.forward, mode=config.compile_mode)
+
     def _rtc_enabled(self):
         return self.config.rtc_config is not None and self.config.rtc_config.enabled
 

src/lerobot/policies/wall_x/configuration_wall_x.py

@@ -55,7 +55,7 @@ class WallXConfig(PreTrainedConfig):
     pretrained_name_or_path: str = "x-square-robot/wall-oss-flow"
 
     # Tokenizer settings
-    action_tokenizer_path: str | None = "physical-intelligence/fast"
+    action_tokenizer_path: str | None = "lerobot/fast-action-tokenizer"
 
     # Action prediction mode: "diffusion" or "fast"
     prediction_mode: str = "diffusion"

src/lerobot/policies/wall_x/modeling_wall_x.py

@@ -261,10 +261,15 @@ class Qwen2_5_VLMoEForAction(Qwen2_5_VLForConditionalGeneration):
     and optional LoRA fine-tuning support.
     """
 
-    _tied_weights_keys = ["lm_head.weight"]
+    _tied_weights_keys = {"lm_head.weight": "model.embed_tokens.weight"}
     config_class = Qwen2_5_VLConfig
     _no_split_modules = ["Qwen2_5_VLDecoderLayer_with_MoE", "Qwen2_5_VLVisionBlock"]
 
+    def init_weights(self):
+        if getattr(self.model, "language_model", None) is not None:
+            return
+        super().init_weights()
+
     @classmethod
     def from_pretrained(
         cls,
@@ -312,6 +317,11 @@ class Qwen2_5_VLMoEForAction(Qwen2_5_VLForConditionalGeneration):
             processor.action_processor = action_tokenizer
         else:
             action_tokenizer = None
+
+        # add pad_token_id to config
+        config.pad_token_id = processor.tokenizer.pad_token_id
+        config.text_config.pad_token_id = processor.tokenizer.pad_token_id
+
         # Initialize model with configuration and processor
         model = cls(config, processor=processor, action_tokenizer=action_tokenizer, **kwargs)
 
@@ -331,7 +341,7 @@ class Qwen2_5_VLMoEForAction(Qwen2_5_VLForConditionalGeneration):
                 force_download=kwargs.get("force_download", False),
                 resume_download=kwargs.get("resume_download"),
                 proxies=kwargs.get("proxies"),
-                use_auth_token=kwargs.get("use_auth_token"),
+                token=kwargs.get("token"),
                 revision=kwargs.get("revision"),
                 local_files_only=kwargs.get("local_files_only", False),
             )

src/lerobot/policies/wall_x/qwen_model/configuration_qwen2_5_vl.py

@@ -21,6 +21,7 @@ class Qwen2_5_VLVisionConfig(PretrainedConfig):
         window_size=112,
         out_hidden_size=3584,
         fullatt_block_indexes=[7, 15, 23, 31],
+        initializer_range=0.02,
         **kwargs,
     ):
         super().__init__(**kwargs)
@@ -38,6 +39,7 @@ class Qwen2_5_VLVisionConfig(PretrainedConfig):
         self.window_size = window_size
         self.fullatt_block_indexes = fullatt_block_indexes
         self.out_hidden_size = out_hidden_size
+        self.initializer_range = initializer_range
 
 
 class Qwen2_5_VLConfig(PretrainedConfig):

src/lerobot/policies/wall_x/qwen_model/qwen2_5_vl_moe.py

@@ -11,7 +11,6 @@ from transformers.activations import ACT2FN
 from transformers.cache_utils import (
     Cache,
     DynamicCache,
-    SlidingWindowCache,
     StaticCache,
 )
 from transformers.generation import GenerationMixin
@@ -31,6 +30,15 @@ from transformers.utils import (
 
 from .configuration_qwen2_5_vl import Qwen2_5_VLConfig, Qwen2_5_VLVisionConfig
 
+
+# TODO(Steven): SlidingWindowCache was removed in transformers v5. Define a placeholder so isinstance checks
+# always return False (which is the correct behavior when no sliding window cache is in use).
+class _SlidingWindowCachePlaceholder:
+    pass
+
+
+SlidingWindowCache = _SlidingWindowCachePlaceholder
+
 if is_flash_attn_2_available():
     from flash_attn import flash_attn_func, flash_attn_varlen_func
     from flash_attn.layers.rotary import apply_rotary_emb
@@ -594,19 +602,40 @@ class Qwen2_5_VisionTransformerPretrainedModel(Qwen2_5_VLPreTrainedModel):
         return hidden_states
 
 
+def _compute_default_rope_parameters_qwen2_5_vl(config, device=None):
+    """
+    compute default rope parameters for Qwen2_5_VL
+    """
+    base = config.text_config.rope_parameters["rope_theta"]
+    dim = config.hidden_size // config.num_attention_heads
+    inv_freq = 1.0 / (
+        base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim)
+    )
+    return inv_freq, 1.0
+
+
 class Qwen2_5_VLRotaryEmbedding(nn.Module):
     def __init__(self, config: Qwen2_5_VLConfig, device=None):
         super().__init__()
         # BC: "rope_type" was originally "type"
         if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
             self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
+        elif hasattr(config, "rope_parameters") and config.rope_parameters is not None:
+            self.rope_type = config.rope_parameters.get("rope_type", "default")
         else:
             self.rope_type = "default"
         self.max_seq_len_cached = config.max_position_embeddings
         self.original_max_seq_len = config.max_position_embeddings
 
         self.config = config
-        self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
+
+        if self.rope_type == "default":
+            self.rope_init_fn = _compute_default_rope_parameters_qwen2_5_vl
+            self.rope_kwargs = {}
+        else:
+            rope_type_key = "linear" if self.rope_type == "linear" else self.rope_type
+            self.rope_init_fn = ROPE_INIT_FUNCTIONS[rope_type_key]
+            self.rope_kwargs = {}
 
         inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
         self.register_buffer("inv_freq", inv_freq, persistent=False)
@@ -1567,7 +1596,7 @@ QWEN2_5_VL_INPUTS_DOCSTRING = r"""
 
 
 class Qwen2_5_VLForConditionalGeneration(Qwen2_5_VLPreTrainedModel, GenerationMixin):
-    _tied_weights_keys = ["lm_head.weight"]
+    _tied_weights_keys = {"lm_head.weight": "model.embed_tokens.weight"}
     config_class = Qwen2_5_VLConfig
     _no_split_modules = ["Qwen2_5_VLDecoderLayer", "Qwen2_5_VLVisionBlock"]
 

src/lerobot/policies/wall_x/utils.py

@@ -144,7 +144,7 @@ def preprocesser_call(
     """
     # Process image inputs
     if images is not None and len(images) > 0:
-        image_inputs = processor.image_processor(images=images, videos=None, return_tensors=return_tensors)
+        image_inputs = processor.image_processor(images=images, return_tensors=return_tensors)
         image_grid_thw = image_inputs["image_grid_thw"]
     else:
         image_inputs = {}
@@ -152,7 +152,7 @@ def preprocesser_call(
 
     # Process video inputs
     if videos is not None:
-        videos_inputs = processor.image_processor(images=None, videos=videos, return_tensors=return_tensors)
+        videos_inputs = processor.image_processor(videos=videos, return_tensors=return_tensors)
         video_grid_thw = videos_inputs["video_grid_thw"]
     else:
         videos_inputs = {}

src/lerobot/policies/xvla/configuration_florence2.py

@@ -276,6 +276,8 @@ class Florence2LanguageConfig(PretrainedConfig):
         )
 
         # ensure backward compatibility for BART CNN models
+        if not hasattr(self, "forced_bos_token_id"):
+            self.forced_bos_token_id = None
         if self.forced_bos_token_id is None and kwargs.get("force_bos_token_to_be_generated", False):
             self.forced_bos_token_id = self.bos_token_id
             warnings.warn(

src/lerobot/policies/xvla/modeling_florence2.py

@@ -1951,7 +1951,10 @@ class Florence2Decoder(Florence2LanguagePreTrainedModel):
 
 
 class Florence2LanguageModel(Florence2LanguagePreTrainedModel):
-    _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
+    _tied_weights_keys = {
+        "encoder.embed_tokens.weight": "shared.weight",
+        "decoder.embed_tokens.weight": "shared.weight",
+    }
 
     def __init__(self, config: Florence2LanguageConfig):
         super().__init__(config)
@@ -2076,7 +2079,10 @@ class Florence2LanguageModel(Florence2LanguagePreTrainedModel):
 
 class Florence2LanguageForConditionalGeneration(Florence2LanguagePreTrainedModel, GenerationMixin):
     base_model_prefix = "model"
-    _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight", "lm_head.weight"]
+    _tied_weights_keys = {
+        "model.encoder.embed_tokens.weight": "model.shared.weight",
+        "model.decoder.embed_tokens.weight": "model.shared.weight",
+    }
     _keys_to_ignore_on_load_missing = ["final_logits_bias"]
 
     def __init__(self, config: Florence2LanguageConfig):
@@ -2436,11 +2442,10 @@ FLORENCE2_INPUTS_DOCSTRING = r"""
     FLORENCE2_START_DOCSTRING,
 )
 class Florence2ForConditionalGeneration(Florence2PreTrainedModel):
-    _tied_weights_keys = [
+    _tied_weights_keys = {
-        "language_model.encoder.embed_tokens.weight",
+        "language_model.model.encoder.embed_tokens.weight": "language_model.model.shared.weight",
-        "language_model.decoder.embed_tokens.weight",
+        "language_model.model.decoder.embed_tokens.weight": "language_model.model.shared.weight",
-        "language_model.lm_head.weight",
+    }
-    ]
 
     def __init__(self, config: Florence2Config):
         super().__init__(config)

src/lerobot/processor/core.py

@@ -17,7 +17,7 @@
 from __future__ import annotations
 
 from enum import Enum
-from typing import Any, TypeAlias, TypedDict
+from typing import Any, TypedDict
 
 import numpy as np
 import torch
@@ -36,10 +36,10 @@ class TransitionKey(str, Enum):
     COMPLEMENTARY_DATA = "complementary_data"
 
 
-PolicyAction: TypeAlias = torch.Tensor
+PolicyAction = torch.Tensor
-RobotAction: TypeAlias = dict[str, Any]
+RobotAction = dict[str, Any]
-EnvAction: TypeAlias = np.ndarray
+EnvAction = np.ndarray
-RobotObservation: TypeAlias = dict[str, Any]
+RobotObservation = dict[str, Any]
 
 
 EnvTransition = TypedDict(

src/lerobot/processor/env_processor.py

@@ -153,6 +153,44 @@ class LiberoProcessorStep(ObservationProcessorStep):
         return result
 
 
+@dataclass
+@ProcessorStepRegistry.register(name="robocasa_processor")
+class RoboCasaProcessorStep(ObservationProcessorStep):
+    """
+    Processes RoboCasa observations into LeRobot format.
+
+    The RoboCasaEnv wrapper returns:
+        - ``pixels.<cam_name>``: (B, C, H, W) float32 images (already converted by vectorenv)
+        - ``observation.robot_state``: (B, 16) float32 proprioception
+
+    This step remaps them to:
+        - ``observation.images.<cam_name>``   (unchanged tensor)
+        - ``observation.state``               (robot_state renamed)
+    """
+
+    def _process_observation(self, observation: dict) -> dict:
+        processed = {}
+        obs_prefix = OBS_PREFIX  # "observation."
+
+        for key, value in observation.items():
+            if key.startswith(f"{OBS_IMAGES}."):
+                # Already in the right place; pass through
+                processed[key] = value
+            elif key == OBS_STATE or key == f"{obs_prefix}robot_state":
+                # Rename robot_state → observation.state
+                processed[OBS_STATE] = value.float() if hasattr(value, "float") else value
+
+        return processed
+
+    def transform_features(
+        self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
+    ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
+        return features
+
+    def observation(self, observation: dict) -> dict:
+        return self._process_observation(observation)
+
+
 @dataclass
 @ProcessorStepRegistry.register(name="isaaclab_arena_processor")
 class IsaaclabArenaProcessorStep(ObservationProcessorStep):

src/lerobot/processor/pipeline.py

@@ -39,7 +39,7 @@ from collections.abc import Callable, Iterable, Sequence
 from copy import deepcopy
 from dataclasses import dataclass, field
 from pathlib import Path
-from typing import Any, Generic, TypeAlias, TypedDict, TypeVar, cast
+from typing import Any, TypedDict, TypeVar, cast
 
 import torch
 from huggingface_hub import hf_hub_download
@@ -251,7 +251,7 @@ class ProcessorMigrationError(Exception):
 
 
 @dataclass
-class DataProcessorPipeline(HubMixin, Generic[TInput, TOutput]):
+class DataProcessorPipeline[TInput, TOutput](HubMixin):
     """A sequential pipeline for processing data, integrated with the Hugging Face Hub.
 
     This class chains together multiple `ProcessorStep` instances to form a complete
@@ -1432,8 +1432,8 @@ class DataProcessorPipeline(HubMixin, Generic[TInput, TOutput]):
 
 
 # Type aliases for semantic clarity.
-RobotProcessorPipeline: TypeAlias = DataProcessorPipeline[TInput, TOutput]
+RobotProcessorPipeline = DataProcessorPipeline[TInput, TOutput]
-PolicyProcessorPipeline: TypeAlias = DataProcessorPipeline[TInput, TOutput]
+PolicyProcessorPipeline = DataProcessorPipeline[TInput, TOutput]
 
 
 class ObservationProcessorStep(ProcessorStep, ABC):

src/lerobot/processor/tokenizer_processor.py

@@ -336,7 +336,7 @@ class ActionTokenizerProcessorStep(ActionProcessorStep):
     Requires the `transformers` library to be installed.
 
     Attributes:
-        tokenizer_name: The name of a pretrained processor from the Hugging Face Hub (e.g., "physical-intelligence/fast").
+        tokenizer_name: The name of a pretrained processor from the Hugging Face Hub (e.g., "lerobot/fast-action-tokenizer").
         tokenizer: A pre-initialized processor/tokenizer object. If provided, `tokenizer_name` is ignored.
         trust_remote_code: Whether to trust remote code when loading the tokenizer (required for some tokenizers).
         action_tokenizer: The internal tokenizer/processor instance, loaded during initialization.

src/lerobot/robots/so_follower/config_so_follower.py

@@ -15,7 +15,6 @@
 # limitations under the License.
 
 from dataclasses import dataclass, field
-from typing import TypeAlias
 
 from lerobot.cameras import CameraConfig
 
@@ -50,5 +49,5 @@ class SOFollowerRobotConfig(RobotConfig, SOFollowerConfig):
     pass
 
 
-SO100FollowerConfig: TypeAlias = SOFollowerRobotConfig
+SO100FollowerConfig = SOFollowerRobotConfig
-SO101FollowerConfig: TypeAlias = SOFollowerRobotConfig
+SO101FollowerConfig = SOFollowerRobotConfig

src/lerobot/robots/so_follower/so_follower.py

@@ -17,7 +17,6 @@
 import logging
 import time
 from functools import cached_property
-from typing import TypeAlias
 
 from lerobot.cameras.utils import make_cameras_from_configs
 from lerobot.motors import Motor, MotorCalibration, MotorNormMode
@@ -230,5 +229,5 @@ class SOFollower(Robot):
         logger.info(f"{self} disconnected.")
 
 
-SO100Follower: TypeAlias = SOFollower
+SO100Follower = SOFollower
-SO101Follower: TypeAlias = SOFollower
+SO101Follower = SOFollower

src/lerobot/robots/unitree_g1/__init__.py

@@ -16,3 +16,5 @@
 
 from .config_unitree_g1 import UnitreeG1Config
 from .unitree_g1 import UnitreeG1
+
+__all__ = ["UnitreeG1", "UnitreeG1Config"]

src/lerobot/robots/unitree_g1/config_unitree_g1.py

@@ -27,11 +27,10 @@ _GAINS: dict[str, dict[str, list[float]]] = {
     },  # pitch, roll, yaw, knee, ankle_pitch, ankle_roll
     "right_leg": {"kp": [150, 150, 150, 300, 40, 40], "kd": [2, 2, 2, 4, 2, 2]},
     "waist": {"kp": [250, 250, 250], "kd": [5, 5, 5]},  # yaw, roll, pitch
-    "left_arm": {"kp": [80, 80, 80, 80], "kd": [3, 3, 3, 3]},  # shoulder_pitch/roll/yaw, elbow
+    "left_arm": {"kp": [50, 50, 80, 80], "kd": [3, 3, 3, 3]},  # shoulder_pitch/roll/yaw, elbow
     "left_wrist": {"kp": [40, 40, 40], "kd": [1.5, 1.5, 1.5]},  # roll, pitch, yaw
-    "right_arm": {"kp": [80, 80, 80, 80], "kd": [3, 3, 3, 3]},
+    "right_arm": {"kp": [50, 50, 80, 80], "kd": [3, 3, 3, 3]},
     "right_wrist": {"kp": [40, 40, 40], "kd": [1.5, 1.5, 1.5]},
-    "other": {"kp": [80, 80, 80, 80, 80, 80], "kd": [3, 3, 3, 3, 3, 3]},
 }
 
 
@@ -68,3 +67,7 @@ class UnitreeG1Config(RobotConfig):
 
     # Compensates for gravity on the unitree's arms using the arm ik solver
     gravity_compensation: bool = False
+
+    # Lower-body controller class name, e.g. "GrootLocomotionController" or
+    # "HolosomaLocomotionController". None disables it.
+    controller: str | None = None

src/lerobot/robots/unitree_g1/g1_kinematics.py

@@ -16,13 +16,11 @@
 
 import logging
 import os
-import sys
+from collections import deque
 
 import numpy as np
 
 logger = logging.getLogger(__name__)
-parent2_dir = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-sys.path.append(parent2_dir)
 
 
 class WeightedMovingFilter:
@@ -31,18 +29,14 @@ class WeightedMovingFilter:
         self._weights = np.array(weights)
         self._data_size = data_size
         self._filtered_data = np.zeros(self._data_size)
-        self._data_queue = []
+        self._data_queue = deque(maxlen=self._window_size)
 
     def _apply_filter(self):
         if len(self._data_queue) < self._window_size:
             return self._data_queue[-1]
 
         data_array = np.array(self._data_queue)
-        temp_filtered_data = np.zeros(self._data_size)
+        return data_array.T @ self._weights
-        for i in range(self._data_size):
-            temp_filtered_data[i] = np.convolve(data_array[:, i], self._weights, mode="valid")[-1]
-
-        return temp_filtered_data
 
     def add_data(self, new_data):
         assert len(new_data) == self._data_size
@@ -52,9 +46,6 @@ class WeightedMovingFilter:
         ):  # skip duplicate data
             return
 
-        if len(self._data_queue) >= self._window_size:
-            self._data_queue.pop(0)
-
         self._data_queue.append(new_data)
         self._filtered_data = self._apply_filter()
 
@@ -71,8 +62,6 @@ class G1_29_ArmIK:  # noqa: N801
         from pinocchio import casadi as cpin
 
         self._pin = pin
-        np.set_printoptions(precision=5, suppress=True, linewidth=200)
-
         self.unit_test = unit_test
 
         self.repo_path = snapshot_download("lerobot/unitree-g1-mujoco")
@@ -249,50 +238,35 @@ class G1_29_ArmIK:  # noqa: N801
         self.opti.set_value(self.param_tf_r, right_wrist)
         self.opti.set_value(self.var_q_last, self.init_data)  # for smooth
 
+        converged = True
         try:
             self.opti.solve()
-
             sol_q = self.opti.value(self.var_q)
+        except Exception as e:
+            converged = False
+            logger.error(f"IK convergence error: {e}")
+            sol_q = self.opti.debug.value(self.var_q)
+
         self.smooth_filter.add_data(sol_q)
         sol_q = self.smooth_filter.filtered_data
-
-            if current_lr_arm_motor_dq is not None:
-                v = current_lr_arm_motor_dq * 0.0
-            else:
-                v = (sol_q - self.init_data) * 0.0
-
         self.init_data = sol_q
 
+        if not converged:
+            logger.error(
+                f"sol_q:{sol_q} \nmotorstate: \n{current_lr_arm_motor_q} \nleft_pose: \n{left_wrist} \nright_pose: \n{right_wrist}"
+            )
+            return current_lr_arm_motor_q, np.zeros(self.reduced_robot.model.nv)
+
         sol_tauff = self._pin.rnea(
             self.reduced_robot.model,
             self.reduced_robot.data,
             sol_q,
-                v,
+            np.zeros(self.reduced_robot.model.nv),
             np.zeros(self.reduced_robot.model.nv),
         )
 
         return sol_q, sol_tauff
 
-        except Exception as e:
-            logger.error(f"ERROR in convergence, plotting debug info.{e}")
-
-            sol_q = self.opti.debug.value(self.var_q)
-            self.smooth_filter.add_data(sol_q)
-            sol_q = self.smooth_filter.filtered_data
-
-            if current_lr_arm_motor_dq is not None:
-                v = current_lr_arm_motor_dq * 0.0
-            else:
-                v = (sol_q - self.init_data) * 0.0
-
-            self.init_data = sol_q
-
-            logger.error(
-                f"sol_q:{sol_q} \nmotorstate: \n{current_lr_arm_motor_q} \nleft_pose: \n{left_wrist} \nright_pose: \n{right_wrist}"
-            )
-
-            return current_lr_arm_motor_q, np.zeros(self.reduced_robot.model.nv)
-
     def solve_tau(self, current_lr_arm_motor_q=None, current_lr_arm_motor_dq=None):
         try:
             q_g1 = np.array(current_lr_arm_motor_q, dtype=float)

src/lerobot/robots/unitree_g1/g1_utils.py

@@ -14,12 +14,34 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import importlib
 from enum import IntEnum
 
+import numpy as np
+
 # ruff: noqa: N801, N815
 
 NUM_MOTORS = 29
 
+REMOTE_AXES = ("remote.lx", "remote.ly", "remote.rx", "remote.ry")
+REMOTE_BUTTONS = tuple(f"remote.button.{i}" for i in range(16))
+REMOTE_KEYS = REMOTE_AXES + REMOTE_BUTTONS
+
+
+def default_remote_input() -> dict[str, float]:
+    """Return a zeroed-out remote input dict (axes + buttons)."""
+    return dict.fromkeys(REMOTE_KEYS, 0.0)
+
+
+def get_gravity_orientation(quaternion: list[float] | np.ndarray) -> np.ndarray:
+    """Get gravity orientation from quaternion [w, x, y, z]."""
+    qw, qx, qy, qz = quaternion
+    gravity_orientation = np.zeros(3, dtype=np.float32)
+    gravity_orientation[0] = 2 * (-qz * qx + qw * qy)
+    gravity_orientation[1] = -2 * (qz * qy + qw * qx)
+    gravity_orientation[2] = 1 - 2 * (qw * qw + qz * qz)
+    return gravity_orientation
+
 
 class G1_29_JointArmIndex(IntEnum):
     # Left arm
@@ -29,7 +51,7 @@ class G1_29_JointArmIndex(IntEnum):
     kLeftElbow = 18
     kLeftWristRoll = 19
     kLeftWristPitch = 20
-    kLeftWristyaw = 21
+    kLeftWristYaw = 21
 
     # Right arm
     kRightShoulderPitch = 22
@@ -41,6 +63,21 @@ class G1_29_JointArmIndex(IntEnum):
     kRightWristYaw = 28
 
 
+def make_locomotion_controller(name: str | None):
+    """Instantiate a locomotion controller by class name. Returns None if name is None."""
+    if name is None:
+        return None
+    controllers = {
+        "GrootLocomotionController": "lerobot.robots.unitree_g1.gr00t_locomotion",
+        "HolosomaLocomotionController": "lerobot.robots.unitree_g1.holosoma_locomotion",
+    }
+    module_path = controllers.get(name)
+    if module_path is None:
+        raise ValueError(f"Unknown controller: {name!r}. Available: {list(controllers)}")
+    module = importlib.import_module(module_path)
+    return getattr(module, name)()
+
+
 class G1_29_JointIndex(IntEnum):
     # Left leg
     kLeftHipPitch = 0
@@ -69,7 +106,7 @@ class G1_29_JointIndex(IntEnum):
     kLeftElbow = 18
     kLeftWristRoll = 19
     kLeftWristPitch = 20
-    kLeftWristyaw = 21
+    kLeftWristYaw = 21
 
     # Right arm
     kRightShoulderPitch = 22

src/lerobot/robots/unitree_g1/gr00t_locomotion.py

@@ -14,20 +14,20 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import argparse
 import logging
-import time
 from collections import deque
 
 import numpy as np
 import onnxruntime as ort
 from huggingface_hub import hf_hub_download
 
-from lerobot.robots.unitree_g1.config_unitree_g1 import UnitreeG1Config
+from lerobot.robots.unitree_g1.g1_utils import (
-from lerobot.robots.unitree_g1.g1_utils import G1_29_JointIndex
+    REMOTE_AXES,
-from lerobot.robots.unitree_g1.unitree_g1 import UnitreeG1
+    REMOTE_BUTTONS,
+    G1_29_JointIndex,
+    get_gravity_orientation,
+)
 
-logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
 
@@ -36,18 +36,13 @@ GROOT_DEFAULT_ANGLES[[0, 6]] = -0.1  # Hip pitch
 GROOT_DEFAULT_ANGLES[[3, 9]] = 0.3  # Knee
 GROOT_DEFAULT_ANGLES[[4, 10]] = -0.2  # Ankle pitch
 
-MISSING_JOINTS = []
-G1_MODEL = "g1_23"  # Or "g1_29"
-if G1_MODEL == "g1_23":
-    MISSING_JOINTS = [12, 14, 20, 21, 27, 28]  # Waist yaw/pitch, wrist pitch/yaw
-
 # Control parameters
 ACTION_SCALE = 0.25
 CONTROL_DT = 0.02  # 50Hz
 ANG_VEL_SCALE: float = 0.25
 DOF_POS_SCALE: float = 1.0
 DOF_VEL_SCALE: float = 0.05
-CMD_SCALE: list = [2.0, 2.0, 0.25]
+CMD_SCALE: list[float] = [2.0, 2.0, 0.25]
 
 
 DEFAULT_GROOT_REPO_ID = "nepyope/GR00T-WholeBodyControl_g1"
@@ -85,11 +80,11 @@ def load_groot_policies(
 class GrootLocomotionController:
     """GR00T lower-body locomotion controller for the Unitree G1."""
 
-    def __init__(self, policy_balance, policy_walk, robot, config):
+    control_dt = CONTROL_DT  # Expose for unitree_g1.py
-        self.policy_balance = policy_balance
+
-        self.policy_walk = policy_walk
+    def __init__(self):
-        self.robot = robot
+        # Load policies
-        self.config = config
+        self.policy_balance, self.policy_walk = load_groot_policies()
 
         self.cmd = np.array([0.0, 0.0, 0.0], dtype=np.float32)  # vx, vy, theta_dot
 
@@ -109,45 +104,60 @@ class GrootLocomotionController:
 
         logger.info("GrootLocomotionController initialized")
 
-    def run_step(self):
+    def reset(self) -> None:
-        # Get current observation
+        """Reset internal state for a new episode."""
-        obs = self.robot.get_observation()
+        self.cmd[:] = 0.0
+        self.groot_qj_all[:] = 0.0
+        self.groot_dqj_all[:] = 0.0
+        self.groot_action[:] = 0.0
+        self.groot_obs_single[:] = 0.0
+        self.groot_obs_stacked[:] = 0.0
+        self.groot_height_cmd = 0.74
+        self.groot_orientation_cmd[:] = 0.0
+        self.groot_obs_history.clear()
+        for _ in range(6):
+            self.groot_obs_history.append(np.zeros(86, dtype=np.float32))
 
-        if not obs:
+    def run_step(self, action: dict, lowstate) -> dict:
-            return
+        """Run one step of the locomotion controller.
 
-        # Get command from remote controller
+        Args:
-        if obs["remote.buttons"][0]:  # R1 - raise waist
+            action: Action dict containing remote.lx/ly/rx/ry and buttons
+            lowstate: Robot lowstate containing motor positions/velocities and IMU
+
+        Returns:
+            Action dict for lower body joints (0-14)
+        """
+        if lowstate is None:
+            return {}
+
+        buttons = [int(action.get(k, 0)) for k in REMOTE_BUTTONS]
+        if buttons[0]:  # R1 - raise waist
             self.groot_height_cmd += 0.001
             self.groot_height_cmd = np.clip(self.groot_height_cmd, 0.50, 1.00)
-        if obs["remote.buttons"][4]:  # R2 - lower waist
+        if buttons[4]:  # R2 - lower waist
             self.groot_height_cmd -= 0.001
             self.groot_height_cmd = np.clip(self.groot_height_cmd, 0.50, 1.00)
 
-        self.cmd[0] = obs["remote.ly"]  # Forward/backward
+        lx, ly, rx, _ry = (action.get(k, 0.0) for k in REMOTE_AXES)
-        self.cmd[1] = obs["remote.lx"] * -1  # Left/right
+        self.cmd[0] = ly  # Forward/backward
-        self.cmd[2] = obs["remote.rx"] * -1  # Rotation rate
+        self.cmd[1] = -lx  # Left/right (negated)
+        self.cmd[2] = -rx  # Rotation rate (negated)
 
-        # Get joint positions and velocities from flat dict
+        # Get joint positions and velocities from lowstate
         for motor in G1_29_JointIndex:
-            name = motor.name
             idx = motor.value
-            self.groot_qj_all[idx] = obs[f"{name}.q"]
+            self.groot_qj_all[idx] = lowstate.motor_state[idx].q
-            self.groot_dqj_all[idx] = obs[f"{name}.dq"]
+            self.groot_dqj_all[idx] = lowstate.motor_state[idx].dq
-
-        # Adapt observation for g1_23dof
-        for idx in MISSING_JOINTS:
-            self.groot_qj_all[idx] = 0.0
-            self.groot_dqj_all[idx] = 0.0
 
         # Scale joint positions and velocities
         qj_obs = self.groot_qj_all.copy()
         dqj_obs = self.groot_dqj_all.copy()
 
         # Express IMU data in gravity frame of reference
-        quat = [obs["imu.quat.w"], obs["imu.quat.x"], obs["imu.quat.y"], obs["imu.quat.z"]]
+        quat = lowstate.imu_state.quaternion
-        ang_vel = np.array([obs["imu.gyro.x"], obs["imu.gyro.y"], obs["imu.gyro.z"]], dtype=np.float32)
+        ang_vel = np.array(lowstate.imu_state.gyroscope, dtype=np.float32)
-        gravity_orientation = self.robot.get_gravity_orientation(quat)
+        gravity_orientation = get_gravity_orientation(quat)
 
         # Scale joint positions and velocities before policy inference
         qj_obs = (qj_obs - GROOT_DEFAULT_ANGLES) * DOF_POS_SCALE
@@ -186,73 +196,10 @@ class GrootLocomotionController:
         # Transform action back to target joint positions
         target_dof_pos_15 = GROOT_DEFAULT_ANGLES[:15] + self.groot_action * ACTION_SCALE
 
-        # Build action dict (only first 15 joints for GR00T)
+        # Build action dict
         action_dict = {}
         for i in range(15):
             motor_name = G1_29_JointIndex(i).name
             action_dict[f"{motor_name}.q"] = float(target_dof_pos_15[i])
 
-        # Zero out missing joints for g1_23dof
+        return action_dict
-        for joint_idx in MISSING_JOINTS:
-            motor_name = G1_29_JointIndex(joint_idx).name
-            action_dict[f"{motor_name}.q"] = 0.0
-
-        # Send action to robot
-        self.robot.send_action(action_dict)
-
-
-def run(repo_id: str = DEFAULT_GROOT_REPO_ID) -> None:
-    """Main function to run the GR00T locomotion controller.
-
-    Args:
-        repo_id: Hugging Face Hub repository ID for GR00T policies.
-    """
-    # Load policies
-    policy_balance, policy_walk = load_groot_policies(repo_id=repo_id)
-
-    # Initialize robot
-    config = UnitreeG1Config()
-    robot = UnitreeG1(config)
-
-    robot.connect()
-
-    # Initialize gr00T locomotion controller
-    groot_controller = GrootLocomotionController(
-        policy_balance=policy_balance,
-        policy_walk=policy_walk,
-        robot=robot,
-        config=config,
-    )
-
-    try:
-        robot.reset(CONTROL_DT, GROOT_DEFAULT_ANGLES)
-
-        logger.info("Use joystick: LY=fwd/back, LX=left/right, RX=rotate, R1=raise waist, R2=lower waist")
-        logger.info("Press Ctrl+C to stop")
-
-        # Run step
-        while not robot._shutdown_event.is_set():
-            start_time = time.time()
-            groot_controller.run_step()
-            elapsed = time.time() - start_time
-            sleep_time = max(0, CONTROL_DT - elapsed)
-            time.sleep(sleep_time)
-    except KeyboardInterrupt:
-        logger.info("Stopping locomotion...")
-    finally:
-        if robot.is_connected:
-            robot.disconnect()
-        logger.info("Done!")
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="GR00T Locomotion Controller for Unitree G1")
-    parser.add_argument(
-        "--repo-id",
-        type=str,
-        default=DEFAULT_GROOT_REPO_ID,
-        help=f"Hugging Face Hub repo ID for GR00T policies (default: {DEFAULT_GROOT_REPO_ID})",
-    )
-    args = parser.parse_args()
-
-    run(repo_id=args.repo_id)

src/lerobot/robots/unitree_g1/holosoma_locomotion.py

@@ -14,21 +14,21 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import argparse
 import json
 import logging
-import time
 
 import numpy as np
 import onnx
 import onnxruntime as ort
 from huggingface_hub import hf_hub_download
 
-from lerobot.robots.unitree_g1.config_unitree_g1 import UnitreeG1Config
+from lerobot.robots.unitree_g1.g1_utils import (
-from lerobot.robots.unitree_g1.g1_utils import G1_29_JointIndex
+    REMOTE_AXES,
-from lerobot.robots.unitree_g1.unitree_g1 import UnitreeG1
+    G1_29_JointArmIndex,
+    G1_29_JointIndex,
+    get_gravity_orientation,
+)
 
-logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
 DEFAULT_ANGLES = np.zeros(29, dtype=np.float32)
@@ -40,18 +40,13 @@ DEFAULT_ANGLES[16] = 0.2  # Left shoulder roll
 DEFAULT_ANGLES[23] = -0.2  # Right shoulder roll
 DEFAULT_ANGLES[[18, 25]] = 0.6  # Elbow
 
-MISSING_JOINTS = []
-G1_MODEL = "g1_23"  # Or "g1_29"
-if G1_MODEL == "g1_23":
-    MISSING_JOINTS = [12, 14, 20, 21, 27, 28]  # Waist yaw/pitch, wrist pitch/yaw
-
 # Control parameters
 ACTION_SCALE = 0.25
-CONTROL_DT = 0.02  # 50Hz
+CONTROL_DT = 0.005  # 200Hz
 ANG_VEL_SCALE = 0.25
 DOF_POS_SCALE = 1.0
 DOF_VEL_SCALE = 0.05
-GAIT_PERIOD = 1.0
+GAIT_PERIOD = 0.5
 
 
 DEFAULT_HOLOSOMA_REPO_ID = "nepyope/holosoma_locomotion"
@@ -87,7 +82,7 @@ def load_policy(
     logger.info(f"Policy loaded: {policy.get_inputs()[0].shape} → {policy.get_outputs()[0].shape}")
 
     # Extract KP/KD from ONNX metadata
-    model = onnx.load(policy_path)
+    model = onnx.load(policy_path, load_external_data=False)
     metadata = {prop.key: prop.value for prop in model.metadata_props}
 
     if "kp" not in metadata or "kd" not in metadata:
@@ -101,15 +96,13 @@ def load_policy(
 
 
 class HolosomaLocomotionController:
-    """Holosoma whole-body locomotion controller for Unitree G1."""
+    """Holosoma lower-body locomotion controller for Unitree G1."""
 
-    def __init__(self, policy, robot, kp: np.ndarray, kd: np.ndarray):
+    control_dt = CONTROL_DT  # Expose for unitree_g1.py
-        self.policy = policy
-        self.robot = robot
 
-        # Override robot's PD gains with policy gains
+    def __init__(self):
-        self.robot.kp = kp
+        # Load policy and gains
-        self.robot.kd = kd
+        self.policy, self.kp, self.kd = load_policy()
 
         self.cmd = np.zeros(3, dtype=np.float32)
 
@@ -124,35 +117,55 @@ class HolosomaLocomotionController:
         self.phase_dt = 2 * np.pi / ((1.0 / CONTROL_DT) * GAIT_PERIOD)
         self.is_standing = True
 
-    def run_step(self):
+        logger.info("HolosomaLocomotionController initialized")
-        # Get current observation
-        obs = self.robot.get_observation()
 
-        if not obs:
+    def reset(self) -> None:
-            return
+        """Reset internal state for a new episode."""
+        self.cmd[:] = 0.0
+        self.qj[:] = 0.0
+        self.dqj[:] = 0.0
+        self.obs[:] = 0.0
+        self.last_action[:] = 0.0
+        self.phase = np.array([[0.0, np.pi]], dtype=np.float32)
+        self.is_standing = True
 
-        # Get command from remote controller
+    def run_step(self, action: dict, lowstate) -> dict:
-        ly = obs["remote.ly"] if abs(obs["remote.ly"]) > 0.1 else 0.0
+        """Run one step of the locomotion controller.
-        lx = obs["remote.lx"] if abs(obs["remote.lx"]) > 0.1 else 0.0
+
-        rx = obs["remote.rx"] if abs(obs["remote.rx"]) > 0.1 else 0.0
+        Args:
+            action: Action dict containing remote.lx/ly/rx/ry
+            lowstate: Robot lowstate containing motor positions/velocities and IMU
+
+        Returns:
+            Action dict for lower body joints (0-14)
+        """
+        if lowstate is None:
+            return {}
+
+        lx, ly, rx, _ry = (action.get(k, 0.0) for k in REMOTE_AXES)
+        ly = ly if abs(ly) > 0.1 else 0.0
+        lx = lx if abs(lx) > 0.1 else 0.0
+        rx = rx if abs(rx) > 0.1 else 0.0
+        ly = np.clip(ly, -0.3, 0.3)
+        lx = np.clip(lx, -0.3, 0.3)
         self.cmd[:] = [ly, -lx, -rx]
 
-        # Get joint positions and velocities
+        # Get joint positions and velocities from lowstate
         for motor in G1_29_JointIndex:
-            name = motor.name
             idx = motor.value
-            self.qj[idx] = obs[f"{name}.q"]
+            self.qj[idx] = lowstate.motor_state[idx].q
-            self.dqj[idx] = obs[f"{name}.dq"]
+            self.dqj[idx] = lowstate.motor_state[idx].dq
 
-        # Adapt observation for g1_23dof
+        # Hide arm positions from policy (show DEFAULT_ANGLES instead)
-        for idx in MISSING_JOINTS:
+        # This prevents policy from reacting to teleop arm movements
-            self.qj[idx] = 0.0
+        for arm_joint in G1_29_JointArmIndex:
-            self.dqj[idx] = 0.0
+            self.qj[arm_joint.value] = DEFAULT_ANGLES[arm_joint.value]
+            self.dqj[arm_joint.value] = 0.0
 
         # Express IMU data in gravity frame of reference
-        quat = [obs["imu.quat.w"], obs["imu.quat.x"], obs["imu.quat.y"], obs["imu.quat.z"]]
+        quat = lowstate.imu_state.quaternion
-        ang_vel = np.array([obs["imu.gyro.x"], obs["imu.gyro.y"], obs["imu.gyro.z"]], dtype=np.float32)
+        ang_vel = np.array(lowstate.imu_state.gyroscope, dtype=np.float32)
-        gravity = self.robot.get_gravity_orientation(quat)
+        gravity = get_gravity_orientation(quat)
 
         # Scale joint positions and velocities before policy inference
         qj_obs = (self.qj - DEFAULT_ANGLES) * DOF_POS_SCALE
@@ -186,79 +199,16 @@ class HolosomaLocomotionController:
         # Run policy inference
         ort_in = {self.policy.get_inputs()[0].name: self.obs.reshape(1, -1).astype(np.float32)}
         raw_action = self.policy.run(None, ort_in)[0].squeeze()
-        action = np.clip(raw_action, -100.0, 100.0)
+        policy_action = np.clip(raw_action, -100.0, 100.0)
-        self.last_action = action.copy()
+        self.last_action = policy_action.copy()
 
         # Transform action back to target joint positions
-        target = DEFAULT_ANGLES + action * ACTION_SCALE
+        target = DEFAULT_ANGLES + policy_action * ACTION_SCALE
 
-        # Build action dict
+        # Build action dict (first 15 joints only)
         action_dict = {}
-        for motor in G1_29_JointIndex:
+        for i in range(15):
-            action_dict[f"{motor.name}.q"] = float(target[motor.value])
+            motor_name = G1_29_JointIndex(i).name
+            action_dict[f"{motor_name}.q"] = float(target[i])
 
-        # Zero out missing joints for g1_23dof
+        return action_dict
-        for joint_idx in MISSING_JOINTS:
-            motor_name = G1_29_JointIndex(joint_idx).name
-            action_dict[f"{motor_name}.q"] = 0.0
-
-        # Send action to robot
-        self.robot.send_action(action_dict)
-
-
-def run(repo_id: str = DEFAULT_HOLOSOMA_REPO_ID, policy_type: str = "fastsac") -> None:
-    """Main function to run the Holosoma locomotion controller.
-
-    Args:
-        repo_id: Hugging Face Hub repository ID for Holosoma policies.
-        policy_type: Policy type to use ('fastsac' or 'ppo').
-    """
-    # Load policy and gains
-    policy, kp, kd = load_policy(repo_id=repo_id, policy_type=policy_type)
-
-    # Initialize robot
-    config = UnitreeG1Config()
-    robot = UnitreeG1(config)
-    robot.connect()
-
-    holosoma_controller = HolosomaLocomotionController(policy, robot, kp, kd)
-
-    try:
-        robot.reset(CONTROL_DT, DEFAULT_ANGLES)
-
-        logger.info("Use joystick: LY=fwd/back, LX=left/right, RX=rotate")
-        logger.info("Press Ctrl+C to stop")
-
-        # Run step
-        while not robot._shutdown_event.is_set():
-            start_time = time.time()
-            holosoma_controller.run_step()
-            elapsed = time.time() - start_time
-            sleep_time = max(0, CONTROL_DT - elapsed)
-            time.sleep(sleep_time)
-    except KeyboardInterrupt:
-        logger.info("Stopping locomotion...")
-    finally:
-        if robot.is_connected:
-            robot.disconnect()
-        logger.info("Done!")
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Holosoma Locomotion Controller for Unitree G1")
-    parser.add_argument(
-        "--repo-id",
-        type=str,
-        default=DEFAULT_HOLOSOMA_REPO_ID,
-        help=f"Hugging Face Hub repo ID for Holosoma policies (default: {DEFAULT_HOLOSOMA_REPO_ID})",
-    )
-    parser.add_argument(
-        "--policy",
-        type=str,
-        choices=["fastsac", "ppo"],
-        default="fastsac",
-        help="Policy type to use: 'fastsac' (default) or 'ppo'",
-    )
-    args = parser.parse_args()
-
-    run(repo_id=args.repo_id, policy_type=args.policy)

src/lerobot/robots/unitree_g1/run_g1_server.py

@@ -24,6 +24,7 @@ This server runs on the robot and forwards:
 Uses JSON for secure serialization instead of pickle.
 """
 
+import argparse
 import base64
 import contextlib
 import json
@@ -38,6 +39,8 @@ from unitree_sdk2py.idl.default import unitree_hg_msg_dds__LowCmd_
 from unitree_sdk2py.idl.unitree_hg.msg.dds_ import LowCmd_ as hg_LowCmd, LowState_ as hg_LowState
 from unitree_sdk2py.utils.crc import CRC
 
+from lerobot.cameras.zmq.image_server import ImageServer
+
 # DDS topic names follow Unitree SDK naming conventions
 # ruff: noqa: N816
 kTopicLowCommand_Debug = "rt/lowcmd"  # action to robot
@@ -150,6 +153,32 @@ def cmd_forward_loop(
 
 def main() -> None:
     """Main entry point for the robot server bridge."""
+    parser = argparse.ArgumentParser(description="DDS-to-ZMQ bridge server for Unitree G1")
+    parser.add_argument("--camera", action="store_true", help="Also launch camera server")
+    parser.add_argument("--camera-device", type=int, default=4, help="Camera device ID (default: 4)")
+    parser.add_argument("--camera-fps", type=int, default=30, help="Camera FPS (default: 30)")
+    parser.add_argument("--camera-width", type=int, default=640, help="Camera width (default: 640)")
+    parser.add_argument("--camera-height", type=int, default=480, help="Camera height (default: 480)")
+    parser.add_argument("--camera-port", type=int, default=5555, help="Camera ZMQ port (default: 5555)")
+    args = parser.parse_args()
+
+    # Optionally start camera server in background thread
+    camera_thread = None
+    if args.camera:
+        camera_config = {
+            "fps": args.camera_fps,
+            "cameras": {
+                "head_camera": {
+                    "device_id": args.camera_device,
+                    "shape": [args.camera_height, args.camera_width],
+                }
+            },
+        }
+        camera_server = ImageServer(camera_config, port=args.camera_port)
+        camera_thread = threading.Thread(target=camera_server.run, daemon=True)
+        camera_thread.start()
+        print(f"Camera server started on port {args.camera_port} (device {args.camera_device})")
+
     # initialize DDS
     ChannelFactoryInitialize(0)
 
@@ -206,6 +235,8 @@ def main() -> None:
         shutdown_event.set()
         ctx.term()  # terminates blocking zmq.recv() calls
         t_state.join(timeout=2.0)
+        if camera_thread is not None:
+            camera_thread.join(timeout=2.0)
 
 
 if __name__ == "__main__":

src/lerobot/robots/unitree_g1/unitree_g1.py

@@ -14,27 +14,67 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from __future__ import annotations
+
 import logging
-import struct
 import threading
 import time
 from dataclasses import dataclass, field
 from functools import cached_property
-from typing import Any
+from typing import TYPE_CHECKING, Protocol, runtime_checkable
 
 import numpy as np
 
 from lerobot.cameras.utils import make_cameras_from_configs
 from lerobot.envs.factory import make_env
 from lerobot.processor import RobotAction, RobotObservation
-from lerobot.robots.unitree_g1.g1_utils import G1_29_JointArmIndex, G1_29_JointIndex
+from lerobot.robots.unitree_g1.g1_kinematics import G1_29_ArmIK
-from lerobot.robots.unitree_g1.robot_kinematic_processor import G1_29_ArmIK
+from lerobot.robots.unitree_g1.g1_utils import (
+    REMOTE_AXES,
+    REMOTE_KEYS,
+    G1_29_JointArmIndex,
+    G1_29_JointIndex,
+    default_remote_input,
+    make_locomotion_controller,
+)
+from lerobot.utils.import_utils import _unitree_sdk_available
 
 from ..robot import Robot
 from .config_unitree_g1 import UnitreeG1Config
 
+if TYPE_CHECKING or _unitree_sdk_available:
+    from unitree_sdk2py.core.channel import (
+        ChannelFactoryInitialize as _SDKChannelFactoryInitialize,
+        ChannelPublisher as _SDKChannelPublisher,
+        ChannelSubscriber as _SDKChannelSubscriber,
+    )
+    from unitree_sdk2py.idl.default import unitree_hg_msg_dds__LowCmd_
+    from unitree_sdk2py.idl.unitree_hg.msg.dds_ import (
+        LowCmd_ as hg_LowCmd,
+        LowState_ as hg_LowState,
+    )
+    from unitree_sdk2py.utils.crc import CRC
+else:
+    _SDKChannelFactoryInitialize = None
+    _SDKChannelPublisher = None
+    _SDKChannelSubscriber = None
+    unitree_hg_msg_dds__LowCmd_ = None
+    hg_LowCmd = None
+    hg_LowState = None
+    CRC = None
+
 logger = logging.getLogger(__name__)
 
+
+@runtime_checkable
+class LocomotionController(Protocol):
+    control_dt: float
+
+    def run_step(self, action: dict, lowstate) -> dict: ...
+
+    def reset(self) -> None: ...
+
+
 # DDS topic names follow Unitree SDK naming conventions
 # ruff: noqa: N816
 kTopicLowCommand_Debug = "rt/lowcmd"
@@ -63,7 +103,7 @@ class IMUState:
 class G1_29_LowState:  # noqa: N801
     motor_state: list[MotorState] = field(default_factory=lambda: [MotorState() for _ in G1_29_JointIndex])
     imu_state: IMUState = field(default_factory=IMUState)
-    wireless_remote: Any = None  # Raw wireless remote data
+    wireless_remote: bytes | None = None  # Raw wireless remote data
     mode_machine: int = 0  # Robot mode
 
 
@@ -71,25 +111,6 @@ class UnitreeG1(Robot):
     config_class = UnitreeG1Config
     name = "unitree_g1"
 
-    # unitree remote controller
-    class RemoteController:
-        def __init__(self):
-            self.lx = 0
-            self.ly = 0
-            self.rx = 0
-            self.ry = 0
-            self.button = [0] * 16
-
-        def set(self, data):
-            # wireless_remote
-            keys = struct.unpack("H", data[2:4])[0]
-            for i in range(16):
-                self.button[i] = (keys & (1 << i)) >> i
-            self.lx = struct.unpack("f", data[4:8])[0]
-            self.rx = struct.unpack("f", data[8:12])[0]
-            self.ry = struct.unpack("f", data[12:16])[0]
-            self.ly = struct.unpack("f", data[20:24])[0]
-
     def __init__(self, config: UnitreeG1Config):
         super().__init__(config)
 
@@ -103,11 +124,9 @@ class UnitreeG1(Robot):
 
         # Import channel classes based on mode
         if config.is_simulation:
-            from unitree_sdk2py.core.channel import (
+            self._ChannelFactoryInitialize = _SDKChannelFactoryInitialize
-                ChannelFactoryInitialize,
+            self._ChannelPublisher = _SDKChannelPublisher
-                ChannelPublisher,
+            self._ChannelSubscriber = _SDKChannelSubscriber
-                ChannelSubscriber,
-            )
         else:
             from lerobot.robots.unitree_g1.unitree_sdk2_socket import (
                 ChannelFactoryInitialize,
@@ -115,7 +134,6 @@ class UnitreeG1(Robot):
                 ChannelSubscriber,
             )
 
-        # Store for use in connect()
             self._ChannelFactoryInitialize = ChannelFactoryInitialize
             self._ChannelPublisher = ChannelPublisher
             self._ChannelSubscriber = ChannelSubscriber
@@ -124,13 +142,22 @@ class UnitreeG1(Robot):
         self.sim_env = None
         self._env_wrapper = None
         self._lowstate = None
+        self._lowstate_lock = threading.Lock()
         self._shutdown_event = threading.Event()
         self.subscribe_thread = None
-        self.remote_controller = self.RemoteController()
 
-        self.arm_ik = G1_29_ArmIK()
+        self.arm_ik = G1_29_ArmIK() if config.gravity_compensation else None
 
-    def _subscribe_motor_state(self):  # polls robot state @ 250Hz
+        # Lower-body controller loaded dynamically
+        self.controller: LocomotionController | None = make_locomotion_controller(config.controller)
+
+        # Controller thread state
+        self._controller_thread = None
+        self._controller_action_lock = threading.Lock()
+        self.controller_input = default_remote_input()
+        self.controller_output = {}
+
+    def _subscribe_lowstate(self):  # polls robot state @ 250Hz
         while not self._shutdown_event.is_set():
             start_time = time.time()
 
@@ -143,11 +170,11 @@ class UnitreeG1(Robot):
                 lowstate = G1_29_LowState()
 
                 # Capture motor states using jointindex
-                for id in G1_29_JointIndex:
+                for joint in G1_29_JointIndex:
-                    lowstate.motor_state[id].q = msg.motor_state[id].q
+                    lowstate.motor_state[joint].q = msg.motor_state[joint].q
-                    lowstate.motor_state[id].dq = msg.motor_state[id].dq
+                    lowstate.motor_state[joint].dq = msg.motor_state[joint].dq
-                    lowstate.motor_state[id].tau_est = msg.motor_state[id].tau_est
+                    lowstate.motor_state[joint].tau_est = msg.motor_state[joint].tau_est
-                    lowstate.motor_state[id].temperature = msg.motor_state[id].temperature
+                    lowstate.motor_state[joint].temperature = msg.motor_state[joint].temperature
 
                 # Capture IMU state
                 lowstate.imu_state.quaternion = list(msg.imu_state.quaternion)
@@ -162,6 +189,7 @@ class UnitreeG1(Robot):
                 # Capture mode_machine
                 lowstate.mode_machine = msg.mode_machine
 
+                with self._lowstate_lock:
                     self._lowstate = lowstate
 
             current_time = time.time()
@@ -169,24 +197,98 @@ class UnitreeG1(Robot):
             sleep_time = max(0, (self.control_dt - all_t_elapsed))  # maintain constant control dt
             time.sleep(sleep_time)
 
+    def publish_lowcmd(
+        self,
+        action: RobotAction,
+        kp: np.ndarray | list[float] | None = None,
+        kd: np.ndarray | list[float] | None = None,
+        tau: np.ndarray | list[float] | None = None,
+    ) -> None:  # writes robot command whenever requested
+        for motor in G1_29_JointIndex:
+            key = f"{motor.name}.q"
+            if key in action:
+                self.msg.motor_cmd[motor.value].q = action[key]
+                self.msg.motor_cmd[motor.value].qd = 0
+                self.msg.motor_cmd[motor.value].kp = (
+                    kp[motor.value] if kp is not None else self.kp[motor.value]
+                )
+                self.msg.motor_cmd[motor.value].kd = (
+                    kd[motor.value] if kd is not None else self.kd[motor.value]
+                )
+                self.msg.motor_cmd[motor.value].tau = tau[motor.value] if tau is not None else 0.0
+
+        self.msg.crc = self.crc.Crc(self.msg)
+        self.lowcmd_publisher.Write(self.msg)
+
+    @property
+    def _cameras_ft(self) -> dict[str, tuple]:
+        return {
+            cam: (self.config.cameras[cam].height, self.config.cameras[cam].width, 3) for cam in self.cameras
+        }
+
+    @cached_property
+    def observation_features(self) -> dict[str, type | tuple]:
+        return {**self._motors_ft, **self._cameras_ft}
+
     @cached_property
     def action_features(self) -> dict[str, type]:
+        if self.controller is None:
             return {f"{G1_29_JointIndex(motor).name}.q": float for motor in G1_29_JointIndex}
 
-    def calibrate(self) -> None:  # robot is already calibrated
+        arm_features = {f"{G1_29_JointArmIndex(motor).name}.q": float for motor in G1_29_JointArmIndex}
+        remote_features = dict.fromkeys(REMOTE_AXES, float)
+        return {**arm_features, **remote_features}
+
+    def _controller_loop(self):
+        """Background thread that runs controller at policy's control_dt."""
+        control_dt = self.controller.control_dt
+        logger.info(f"Controller loop starting with control_dt={control_dt} ({1.0 / control_dt:.1f}Hz)")
+
+        loop_count = 0
+        last_log_time = time.time()
+
+        while not self._shutdown_event.is_set():
+            start_time = time.time()
+
+            with self._lowstate_lock:
+                lowstate = self._lowstate
+
+            if lowstate is not None and self.controller is not None:
+                loop_count += 1
+                if time.time() - last_log_time >= 5.0:  # Log every 5 seconds
+                    actual_hz = loop_count / (time.time() - last_log_time)
+                    logger.info(
+                        f"Controller actual rate: {actual_hz:.1f}Hz (target: {1.0 / control_dt:.1f}Hz)"
+                    )
+                    loop_count = 0
+                    last_log_time = time.time()
+                # Read controller input snapshot
+                with self._controller_action_lock:
+                    controller_input = dict(self.controller_input)
+
+                # Run controller step
+                controller_action = self.controller.run_step(controller_input, lowstate)
+
+                # Write controller output snapshot
+                with self._controller_action_lock:
+                    self.controller_output = dict(controller_action)
+
+                ctrl_kp = self.controller.kp if hasattr(self.controller, "kp") else None
+                ctrl_kd = self.controller.kd if hasattr(self.controller, "kd") else None
+                self.publish_lowcmd(controller_action, kp=ctrl_kp, kd=ctrl_kd)
+
+            elapsed = time.time() - start_time
+            sleep_time = max(0, control_dt - elapsed)
+            time.sleep(sleep_time)
+
+    def calibrate(self) -> None:
+        # TODO: implement g1_29 calibration
         pass
 
     def configure(self) -> None:
         pass
 
     def connect(self, calibrate: bool = True) -> None:  # connect to DDS
-        from unitree_sdk2py.idl.default import unitree_hg_msg_dds__LowCmd_
-        from unitree_sdk2py.idl.unitree_hg.msg.dds_ import (
-            LowCmd_ as hg_LowCmd,
-            LowState_ as hg_LowState,
-        )
-        from unitree_sdk2py.utils.crc import CRC
-
         # Initialize DDS channel and simulation environment
         if self.config.is_simulation:
             self._ChannelFactoryInitialize(0, "lo")
@@ -194,7 +296,7 @@ class UnitreeG1(Robot):
             # Extract the actual gym env from the dict structure
             self.sim_env = self._env_wrapper["hub_env"][0].envs[0]
         else:
-            self._ChannelFactoryInitialize(0)
+            self._ChannelFactoryInitialize(0, config=self.config)
 
         # Initialize direct motor control interface
         self.lowcmd_publisher = self._ChannelPublisher(kTopicLowCommand_Debug, hg_LowCmd)
@@ -203,7 +305,7 @@ class UnitreeG1(Robot):
         self.lowstate_subscriber.Init()
 
         # Start subscribe thread to read robot state
-        self.subscribe_thread = threading.Thread(target=self._subscribe_motor_state)
+        self.subscribe_thread = threading.Thread(target=self._subscribe_lowstate)
         self.subscribe_thread.start()
 
         # Connect cameras
@@ -220,25 +322,53 @@ class UnitreeG1(Robot):
 
         # Wait for first state message to arrive
         lowstate = None
+        deadline = time.time() + 10.0
         while lowstate is None:
+            with self._lowstate_lock:
                 lowstate = self._lowstate
             if lowstate is None:
-                time.sleep(0.01)
+                if time.time() > deadline:
+                    raise TimeoutError("Timed out waiting for robot state (10s)")
                 logger.warning("[UnitreeG1] Waiting for robot state...")
-        logger.warning("[UnitreeG1] Connected to robot.")
+                time.sleep(0.01)
+        logger.info("[UnitreeG1] Connected to robot.")
         self.msg.mode_machine = lowstate.mode_machine
 
-        # Initialize all motors with unified kp/kd from config
         self.kp = np.array(self.config.kp, dtype=np.float32)
         self.kd = np.array(self.config.kd, dtype=np.float32)
 
-        for id in G1_29_JointIndex:
+        for joint in G1_29_JointIndex:
-            self.msg.motor_cmd[id].mode = 1
+            self.msg.motor_cmd[joint].mode = 1
-            self.msg.motor_cmd[id].kp = self.kp[id.value]
+            self.msg.motor_cmd[joint].kp = self.kp[joint.value]
-            self.msg.motor_cmd[id].kd = self.kd[id.value]
+            self.msg.motor_cmd[joint].kd = self.kd[joint.value]
-            self.msg.motor_cmd[id].q = lowstate.motor_state[id.value].q
+            self.msg.motor_cmd[joint].q = lowstate.motor_state[joint.value].q
+
+        # Start controller thread if enabled
+        if self.controller is not None:
+            self._controller_thread = threading.Thread(target=self._controller_loop, daemon=True)
+            self._controller_thread.start()
+            fps = int(1.0 / self.controller.control_dt)
+            logger.info(f"Controller thread started ({fps}Hz)")
+
+    def _send_zero_torque(self) -> None:
+        """Send a zero-gain command to make joints passive before shutting down."""
+        try:
+            with self._lowstate_lock:
+                lowstate = self._lowstate
+            if lowstate is None:
+                return
+            action = {f"{motor.name}.q": lowstate.motor_state[motor.value].q for motor in G1_29_JointIndex}
+            zero_gains = np.zeros(29, dtype=np.float32)
+            self.publish_lowcmd(action, kp=zero_gains, kd=zero_gains, tau=zero_gains)
+            logger.info("Sent zero-torque command for safe shutdown")
+        except Exception as e:
+            logger.warning(f"Failed to send zero-torque on disconnect: {e}")
 
     def disconnect(self):
+        # Put robot in passive mode before stopping threads
+        if not self.config.is_simulation:
+            self._send_zero_torque()
+
         # Signal thread to stop and unblock any waits
         self._shutdown_event.set()
 
@@ -248,6 +378,12 @@ class UnitreeG1(Robot):
             if self.subscribe_thread.is_alive():
                 logger.warning("Subscribe thread did not stop cleanly")
 
+        # Wait for controller thread to finish
+        if self._controller_thread is not None:
+            self._controller_thread.join(timeout=2.0)
+            if self._controller_thread.is_alive():
+                logger.warning("Controller thread did not stop cleanly")
+
         # Close simulation environment
         if self.config.is_simulation and self.sim_env is not None:
             try:
@@ -274,6 +410,7 @@ class UnitreeG1(Robot):
             cam.disconnect()
 
     def get_observation(self) -> RobotObservation:
+        with self._lowstate_lock:
             lowstate = self._lowstate
         if lowstate is None:
             return {}
@@ -313,14 +450,9 @@ class UnitreeG1(Robot):
             obs["imu.rpy.pitch"] = lowstate.imu_state.rpy[1]
             obs["imu.rpy.yaw"] = lowstate.imu_state.rpy[2]
 
-        # Controller - parse wireless_remote and add to obs
+        # Wireless remote (raw bytes for teleoperator)
-        if lowstate.wireless_remote and len(lowstate.wireless_remote) >= 24:
+        if lowstate.wireless_remote:
-            self.remote_controller.set(lowstate.wireless_remote)
+            obs["wireless_remote"] = lowstate.wireless_remote
-        obs["remote.buttons"] = self.remote_controller.button.copy()
-        obs["remote.lx"] = self.remote_controller.lx
-        obs["remote.ly"] = self.remote_controller.ly
-        obs["remote.rx"] = self.remote_controller.rx
-        obs["remote.ry"] = self.remote_controller.ry
 
         # Cameras - read images from ZMQ cameras
         for cam_name, cam in self._cameras.items():
@@ -328,73 +460,63 @@ class UnitreeG1(Robot):
 
         return obs
 
+    def send_action(self, action: RobotAction) -> RobotAction:
+        action_to_publish = action
+        if self.controller is not None:
+            # Controller thread owns legs/waist. Here we only update joystick inputs
+            # and publish arm targets from the teleoperator.
+            self._update_controller_action(action)
+            arm_prefixes = tuple(j.name for j in G1_29_JointArmIndex)
+            action_to_publish = {
+                key: value
+                for key, value in action.items()
+                if key.endswith(".q") and key.startswith(arm_prefixes)
+            }
+
+        tau = None
+        if self.config.gravity_compensation and self.arm_ik is not None:
+            tau = np.zeros(29, dtype=np.float32)
+            action_np = np.array(
+                [
+                    action_to_publish.get(f"{joint.name}.q", self.msg.motor_cmd[joint.value].q)
+                    for joint in G1_29_JointArmIndex
+                ],
+                dtype=np.float32,
+            )
+            arm_tau = self.arm_ik.solve_tau(action_np)
+            arm_start_idx = G1_29_JointArmIndex.kLeftShoulderPitch.value
+            for joint in G1_29_JointArmIndex:
+                local_idx = joint.value - arm_start_idx
+                tau[joint.value] = arm_tau[local_idx]
+
+        self.publish_lowcmd(action_to_publish, tau=tau)
+        return action
+
+    def _update_controller_action(self, action: RobotAction) -> None:
+        """Update controller input state from incoming teleop action."""
+        with self._controller_action_lock:
+            for key in REMOTE_KEYS:
+                if key in action:
+                    self.controller_input[key] = action[key]
+
     @property
     def is_calibrated(self) -> bool:
         return True
 
     @property
     def is_connected(self) -> bool:
+        with self._lowstate_lock:
             return self._lowstate is not None
 
     @property
     def _motors_ft(self) -> dict[str, type]:
+        """Joint positions for all 29 joints."""
         return {f"{G1_29_JointIndex(motor).name}.q": float for motor in G1_29_JointIndex}
 
     @property
     def cameras(self) -> dict:
         return self._cameras
 
-    @property
-    def _cameras_ft(self) -> dict[str, tuple]:
-        return {
-            cam: (self.config.cameras[cam].height, self.config.cameras[cam].width, 3) for cam in self.cameras
-        }
-
-    @cached_property
-    def observation_features(self) -> dict[str, type | tuple]:
-        return {**self._motors_ft, **self._cameras_ft}
-
-    def send_action(self, action: RobotAction) -> RobotAction:
-        for motor in G1_29_JointIndex:
-            key = f"{motor.name}.q"
-            if key in action:
-                self.msg.motor_cmd[motor.value].q = action[key]
-                self.msg.motor_cmd[motor.value].qd = 0
-                self.msg.motor_cmd[motor.value].kp = self.kp[motor.value]
-                self.msg.motor_cmd[motor.value].kd = self.kd[motor.value]
-                self.msg.motor_cmd[motor.value].tau = 0
-
-        if self.config.gravity_compensation:
-            # Build action_np from motor commands (arm joints are indices 15-28, local indices 0-13)
-            action_np = np.zeros(14)
-            arm_start_idx = G1_29_JointArmIndex.kLeftShoulderPitch.value  # 15
-            for joint in G1_29_JointArmIndex:
-                local_idx = joint.value - arm_start_idx
-                action_np[local_idx] = self.msg.motor_cmd[joint.value].q
-            tau = self.arm_ik.solve_tau(action_np)
-
-            # Apply tau back to motor commands
-            for joint in G1_29_JointArmIndex:
-                local_idx = joint.value - arm_start_idx
-                self.msg.motor_cmd[joint.value].tau = tau[local_idx]
-
-        self.msg.crc = self.crc.Crc(self.msg)
-        self.lowcmd_publisher.Write(self.msg)
-        return action
-
-    def get_gravity_orientation(self, quaternion):  # get gravity orientation from quaternion
-        """Get gravity orientation from quaternion."""
-        qw = quaternion[0]
-        qx = quaternion[1]
-        qy = quaternion[2]
-        qz = quaternion[3]
-
-        gravity_orientation = np.zeros(3)
-        gravity_orientation[0] = 2 * (-qz * qx + qw * qy)
-        gravity_orientation[1] = -2 * (qz * qy + qw * qx)
-        gravity_orientation[2] = 1 - 2 * (qw * qw + qz * qz)
-        return gravity_orientation
-
     def reset(
         self,
         control_dt: float | None = None,
@@ -407,15 +529,9 @@ class UnitreeG1(Robot):
 
         if self.config.is_simulation and self.sim_env is not None:
             self.sim_env.reset()
-
+            self.publish_lowcmd(
-            for motor in G1_29_JointIndex:
+                {f"{motor.name}.q": float(default_positions[motor.value]) for motor in G1_29_JointIndex}
-                self.msg.motor_cmd[motor.value].q = default_positions[motor.value]
+            )
-                self.msg.motor_cmd[motor.value].qd = 0
-                self.msg.motor_cmd[motor.value].kp = self.kp[motor.value]
-                self.msg.motor_cmd[motor.value].kd = self.kd[motor.value]
-                self.msg.motor_cmd[motor.value].tau = 0
-            self.msg.crc = self.crc.Crc(self.msg)
-            self.lowcmd_publisher.Write(self.msg)
         else:
             total_time = 3.0
             num_steps = int(total_time / control_dt)
@@ -446,4 +562,8 @@ class UnitreeG1(Robot):
                 sleep_time = max(0, control_dt - elapsed)
                 time.sleep(sleep_time)
 
+        # Reset controller internal state (gait phase, obs history, etc.)
+        if self.controller is not None and hasattr(self.controller, "reset"):
+            self.controller.reset()
+
         logger.info("Reached default position")

src/lerobot/robots/unitree_g1/unitree_sdk2_socket.py

@@ -22,6 +22,8 @@ import zmq
 
 from lerobot.robots.unitree_g1.config_unitree_g1 import UnitreeG1Config
 
+# Module-level ZMQ state mirrors the Unitree SDK's global ChannelFactory Singleton.
+# Only one robot connection per process is supported.
 _ctx: zmq.Context | None = None
 _lowcmd_sock: zmq.Socket | None = None
 _lowstate_sock: zmq.Socket | None = None
@@ -97,16 +99,21 @@ def lowcmd_to_dict(topic: str, msg: Any) -> dict[str, Any]:
     }
 
 
-def ChannelFactoryInitialize(*args: Any, **kwargs: Any) -> None:  # noqa: N802
+def ChannelFactoryInitialize(domain_id: int = 0, config: Any = None) -> None:  # noqa: N802
     """
     Initialize ZMQ sockets for robot communication.
 
     This function mimics the Unitree SDK's ChannelFactoryInitialize but uses
     ZMQ sockets to connect to the robot server bridge instead of DDS.
+
+    Args:
+        domain_id: Ignored (for API compatibility with Unitree SDK)
+        config: UnitreeG1Config instance with robot_ip
     """
     global _ctx, _lowcmd_sock, _lowstate_sock
 
     # read socket config
+    if config is None:
         config = UnitreeG1Config()
     robot_ip = config.robot_ip
 

src/lerobot/scripts/lerobot_dataset_viz.py

@@ -132,10 +132,13 @@ def visualize_dataset(
 
     logging.info("Logging to Rerun")
 
+    first_index = None
     for batch in tqdm.tqdm(dataloader, total=len(dataloader)):
+        if first_index is None:
+            first_index = batch["index"][0].item()
         # iterate over the batch
         for i in range(len(batch["index"])):
-            rr.set_time("frame_index", sequence=batch["frame_index"][i].item())
+            rr.set_time("frame_index", sequence=batch["index"][i].item() - first_index)
             rr.set_time("timestamp", timestamp=batch["timestamp"][i].item())
 
             # display each camera image

src/lerobot/scripts/lerobot_edit_dataset.py

@@ -21,6 +21,9 @@ This script allows you to delete episodes, split datasets, merge datasets,
 remove features, modify tasks, and convert image datasets to video format.
 When new_repo_id is specified, creates a new dataset.
 
+Path semantics (v2): --root and --new_root are exact dataset folders containing
+meta/, data/, videos/. When omitted, defaults to $HF_LEROBOT_HOME/{repo_id}.
+
 Usage Examples:
 
 Delete episodes 0, 2, and 5 from a dataset:
@@ -29,16 +32,31 @@ Delete episodes 0, 2, and 5 from a dataset:
         --operation.type delete_episodes \
         --operation.episode_indices "[0, 2, 5]"
 
-Delete episodes and save to a new dataset:
+Delete episodes from a local dataset at a specific path:
     lerobot-edit-dataset \
         --repo_id lerobot/pusht \
-        --new_repo_id lerobot/pusht_filtered \
+        --root /path/to/pusht \
         --operation.type delete_episodes \
         --operation.episode_indices "[0, 2, 5]"
 
-Split dataset by fractions:
+Delete episodes and save to a new dataset at a specific path and with a new repo_id:
     lerobot-edit-dataset \
         --repo_id lerobot/pusht \
+        --new_repo_id lerobot/pusht_filtered \
+        --new_root /path/to/pusht_filtered \
+        --operation.type delete_episodes \
+        --operation.episode_indices "[0, 2, 5]"
+
+Split dataset by fractions (pusht_train, pusht_val):
+    lerobot-edit-dataset \
+        --repo_id lerobot/pusht \
+        --operation.type split \
+        --operation.splits '{"train": 0.8, "val": 0.2}'
+
+Split dataset by fractions and save split datasets to a specific folder (base_folder/train, base_folder/val):
+    lerobot-edit-dataset \
+        --repo_id lerobot/pusht \
+        --new_root /path/to/base_folder \
         --operation.type split \
         --operation.splits '{"train": 0.8, "val": 0.2}'
 
@@ -56,15 +74,29 @@ Split into more than two splits:
 
 Merge multiple datasets:
     lerobot-edit-dataset \
-        --repo_id lerobot/pusht_merged \
+        --new_repo_id lerobot/pusht_merged \
         --operation.type merge \
         --operation.repo_ids "['lerobot/pusht_train', 'lerobot/pusht_val']"
 
+Merge multiple datasets to a specific output path:
+    lerobot-edit-dataset \
+        --new_repo_id lerobot/pusht_merged \
+        --new_root /path/to/pusht_merged \
+        --operation.type merge \
+        --operation.repo_ids "['lerobot/pusht_train', 'lerobot/pusht_val']"
+
+Merge multiple datasets from a list of local dataset paths:
+    lerobot-edit-dataset \
+        --new_repo_id lerobot/pusht_merged \
+        --operation.type merge \
+        --operation.repo_ids "['pusht_train', 'pusht_val']" \
+        --operation.roots "['/path/to/pusht_train', '/path/to/pusht_val']"
+
 Remove camera feature:
     lerobot-edit-dataset \
         --repo_id lerobot/pusht \
         --operation.type remove_feature \
-        --operation.feature_names "['observation.images.top']"
+        --operation.feature_names "['observation.image']"
 
 Modify tasks - set a single task for all episodes (WARNING: modifies in-place):
     lerobot-edit-dataset \
@@ -88,8 +120,8 @@ Modify tasks - set default task with overrides for specific episodes (WARNING: m
 Convert image dataset to video format and save locally:
     lerobot-edit-dataset \
         --repo_id lerobot/pusht_image \
-        --operation.type convert_image_to_video \
+        --new_root /path/to/output/pusht_video \
-        --operation.output_dir /path/to/output/pusht_video
+        --operation.type convert_image_to_video
 
 Convert image dataset to video format and save with new repo_id:
     lerobot-edit-dataset \
@@ -167,6 +199,7 @@ class SplitConfig(OperationConfig):
 @dataclass
 class MergeConfig(OperationConfig):
     repo_ids: list[str] | None = None
+    roots: list[str] | None = None
 
 
 @OperationConfig.register_subclass("remove_feature")
@@ -200,36 +233,46 @@ class ConvertImageToVideoConfig(OperationConfig):
 @OperationConfig.register_subclass("info")
 @dataclass
 class InfoConfig(OperationConfig):
-    type: str = "info"
     show_features: bool = False
 
 
 @dataclass
 class EditDatasetConfig:
-    repo_id: str
+    # Operation configuration.
     operation: OperationConfig
+    # Input dataset identifier. Always required unless for Merge operation.
+    repo_id: str | None = None
+    # Root directory where the input dataset is stored. If not specified, defaults to $HF_LEROBOT_HOME/repo_id.
     root: str | None = None
+    # Edited dataset identifier. When both new_repo_id (resp. new_root) and repo_id (resp. root) are identical, modifications are applied in-place and a backup of the original dataset is created. Required for Merge operation.
     new_repo_id: str | None = None
+    # Root directory where the edited dataset will be stored. If not specified, defaults to $HF_LEROBOT_HOME/new_repo_id. For Split operation, this is the base directory for the split datasets.
+    new_root: str | None = None
+    # Upload dataset to Hugging Face hub.
     push_to_hub: bool = False
 
 
-def get_output_path(repo_id: str, new_repo_id: str | None, root: Path | None) -> tuple[str, Path]:
+def get_output_path(
-    if new_repo_id:
+    repo_id: str,
-        output_repo_id = new_repo_id
+    new_repo_id: str | None,
-        output_dir = root / new_repo_id if root else HF_LEROBOT_HOME / new_repo_id
+    root: Path | str | None,
-    else:
+    new_root: Path | str | None,
-        output_repo_id = repo_id
+) -> tuple[str, Path]:
-        dataset_path = root / repo_id if root else HF_LEROBOT_HOME / repo_id
+    input_path = Path(root) if root else HF_LEROBOT_HOME / repo_id
-        old_path = Path(str(dataset_path) + "_old")
 
-        if dataset_path.exists():
+    output_repo_id = new_repo_id if new_repo_id else repo_id
-            if old_path.exists():
+    output_path = Path(new_root) if new_root else HF_LEROBOT_HOME / output_repo_id
-                shutil.rmtree(old_path)
-            shutil.move(str(dataset_path), str(old_path))
 
-        output_dir = dataset_path
+    # In case of in-place modification, create a backup of the original dataset (if it exists)
+    if output_path == input_path:
+        backup_path = input_path.with_name(input_path.name + "_old")
 
-    return output_repo_id, output_dir
+        if input_path.exists():
+            if backup_path.exists():
+                shutil.rmtree(backup_path)
+            shutil.move(input_path, backup_path)
+
+    return output_repo_id, output_path
 
 
 def handle_delete_episodes(cfg: EditDatasetConfig) -> None:
@@ -241,11 +284,15 @@ def handle_delete_episodes(cfg: EditDatasetConfig) -> None:
 
     dataset = LeRobotDataset(cfg.repo_id, root=cfg.root)
     output_repo_id, output_dir = get_output_path(
-        cfg.repo_id, cfg.new_repo_id, Path(cfg.root) if cfg.root else None
+        cfg.repo_id,
+        new_repo_id=cfg.new_repo_id,
+        root=cfg.root,
+        new_root=cfg.new_root,
     )
 
-    if cfg.new_repo_id is None:
+    # In case of in-place modification, make the dataset point to the backup directory
-        dataset.root = Path(str(dataset.root) + "_old")
+    if output_dir == dataset.root:
+        dataset.root = dataset.root.with_name(dataset.root.name + "_old")
 
     logging.info(f"Deleting episodes {cfg.operation.episode_indices} from {cfg.repo_id}")
     new_dataset = delete_episodes(
@@ -272,19 +319,27 @@ def handle_split(cfg: EditDatasetConfig) -> None:
             "splits dict must be specified with split names as keys and fractions/episode lists as values"
         )
 
+    if cfg.new_repo_id is not None:
+        logging.warning(
+            "split uses the original dataset identifier --repo_id to generate split names. The --new_repo_id parameter is ignored."
+        )
+
     dataset = LeRobotDataset(cfg.repo_id, root=cfg.root)
 
     logging.info(f"Splitting dataset {cfg.repo_id} with splits: {cfg.operation.splits}")
-    split_datasets = split_dataset(dataset, splits=cfg.operation.splits)
+    split_datasets = split_dataset(
+        dataset,
+        splits=cfg.operation.splits,
+        output_dir=cfg.new_root,
+    )
 
     for split_name, split_ds in split_datasets.items():
-        split_repo_id = f"{cfg.repo_id}_{split_name}"
         logging.info(
             f"{split_name}: {split_ds.meta.total_episodes} episodes, {split_ds.meta.total_frames} frames"
         )
 
         if cfg.push_to_hub:
-            logging.info(f"Pushing {split_name} split to hub as {split_repo_id}")
+            logging.info(f"Pushing {split_name} split to hub as {split_ds.repo_id}")
             LeRobotDataset(split_ds.repo_id, root=split_ds.root).push_to_hub()
 
 
@@ -295,18 +350,29 @@ def handle_merge(cfg: EditDatasetConfig) -> None:
     if not cfg.operation.repo_ids:
         raise ValueError("repo_ids must be specified for merge operation")
 
-    if not cfg.repo_id:
+    if cfg.repo_id is not None or cfg.root is not None:
-        raise ValueError("repo_id must be specified as the output repository for merged dataset")
+        logging.warning(
+            "merge uses --new_repo_id and --new_root for the merged dataset. The --repo_id and --root parameters are ignored."
+        )
 
+    if cfg.operation.roots:
+        if len(cfg.operation.roots) != len(cfg.operation.repo_ids):
+            raise ValueError("repo_ids and roots must have the same length for merge operation")
+        logging.info(f"Loading {len(cfg.operation.roots)} datasets to merge")
+        datasets = [
+            LeRobotDataset(repo_id=repo_id, root=root)
+            for repo_id, root in zip(cfg.operation.repo_ids, cfg.operation.roots, strict=True)
+        ]
+    else:
         logging.info(f"Loading {len(cfg.operation.repo_ids)} datasets to merge")
-    datasets = [LeRobotDataset(repo_id, root=cfg.root) for repo_id in cfg.operation.repo_ids]
+        datasets = [LeRobotDataset(repo_id) for repo_id in cfg.operation.repo_ids]
 
-    output_dir = Path(cfg.root) / cfg.repo_id if cfg.root else HF_LEROBOT_HOME / cfg.repo_id
+    output_dir = Path(cfg.new_root) if cfg.new_root else HF_LEROBOT_HOME / cfg.new_repo_id
 
-    logging.info(f"Merging datasets into {cfg.repo_id}")
+    logging.info(f"Merging datasets into {cfg.new_repo_id}")
     merged_dataset = merge_datasets(
         datasets,
-        output_repo_id=cfg.repo_id,
+        output_repo_id=cfg.new_repo_id,
         output_dir=output_dir,
     )
 
@@ -316,7 +382,7 @@ def handle_merge(cfg: EditDatasetConfig) -> None:
     )
 
     if cfg.push_to_hub:
-        logging.info(f"Pushing to hub as {cfg.repo_id}")
+        logging.info(f"Pushing to hub as {cfg.new_repo_id}")
         LeRobotDataset(merged_dataset.repo_id, root=output_dir).push_to_hub()
 
 
@@ -329,11 +395,15 @@ def handle_remove_feature(cfg: EditDatasetConfig) -> None:
 
     dataset = LeRobotDataset(cfg.repo_id, root=cfg.root)
     output_repo_id, output_dir = get_output_path(
-        cfg.repo_id, cfg.new_repo_id, Path(cfg.root) if cfg.root else None
+        cfg.repo_id,
+        new_repo_id=cfg.new_repo_id,
+        root=cfg.root,
+        new_root=cfg.new_root,
     )
 
-    if cfg.new_repo_id is None:
+    # In case of in-place modification, make the dataset point to the backup directory
-        dataset.root = Path(str(dataset.root) + "_old")
+    if output_dir == dataset.root:
+        dataset.root = dataset.root.with_name(dataset.root.name + "_old")
 
     logging.info(f"Removing features {cfg.operation.feature_names} from {cfg.repo_id}")
     new_dataset = remove_feature(
@@ -361,9 +431,10 @@ def handle_modify_tasks(cfg: EditDatasetConfig) -> None:
     if new_task is None and episode_tasks_raw is None:
         raise ValueError("Must specify at least one of new_task or episode_tasks for modify_tasks operation")
 
-    # Warn about in-place modification behavior
+    if cfg.new_repo_id is not None or cfg.new_root is not None:
-    if cfg.new_repo_id is not None:
+        logging.warning(
-        logging.warning("modify_tasks modifies datasets in-place. The --new_repo_id parameter is ignored.")
+            "modify_tasks modifies datasets in-place. The --new_repo_id and --new_root parameters are ignored."
+        )
 
     dataset = LeRobotDataset(cfg.repo_id, root=cfg.root)
     logging.warning(f"Modifying dataset in-place at {dataset.root}. Original data will be overwritten.")
@@ -399,32 +470,30 @@ def handle_convert_image_to_video(cfg: EditDatasetConfig) -> None:
     dataset = LeRobotDataset(cfg.repo_id, root=cfg.root)
 
     # Determine output directory and repo_id
-    # Priority: 1) new_repo_id, 2) operation.output_dir, 3) auto-generated name
+    # Priority: 1) new_root, 2) new_repo_id, 3) operation.output_dir, 4) auto-generated name
     output_dir_config = getattr(cfg.operation, "output_dir", None)
+    if output_dir_config:
+        logging.warning(
+            "--operation.output_dir is deprecated and will be removed in future versions. "
+            "Please use --new_root instead."
+        )
 
-    if cfg.new_repo_id:
+    if cfg.new_root:
-        # Use new_repo_id for both local storage and hub push
+        output_dir = Path(cfg.new_root)
+        output_repo_id = cfg.new_repo_id or f"{cfg.repo_id}_video"
+        logging.info(f"Saving to new_root: {output_dir} as {output_repo_id}")
+    elif cfg.new_repo_id:
         output_repo_id = cfg.new_repo_id
-        # Place new dataset as a sibling to the original dataset
+        output_dir = HF_LEROBOT_HOME / cfg.new_repo_id
-        # Get the parent of the actual dataset root (not cfg.root which might be the lerobot cache dir)
-        # Extract just the dataset name (after last slash) for the local directory
-        local_dir_name = cfg.new_repo_id.split("/")[-1]
-        output_dir = dataset.root.parent / local_dir_name
         logging.info(f"Saving to new dataset: {cfg.new_repo_id} at {output_dir}")
     elif output_dir_config:
-        # Use custom output directory for local-only storage
         output_dir = Path(output_dir_config)
-        # Extract repo name from output_dir for the dataset
         output_repo_id = output_dir.name
-        logging.info(f"Saving to local directory: {output_dir}")
+        logging.info(f"Saving to local directory: {output_dir} as {output_repo_id}")
     else:
-        # Auto-generate name: append "_video" to original repo_id
         output_repo_id = f"{cfg.repo_id}_video"
-        # Place new dataset as a sibling to the original dataset
+        output_dir = HF_LEROBOT_HOME / output_repo_id
-        # Extract just the dataset name (after last slash) for the local directory
+        logging.info(f"Saving to auto-generated location: {output_dir} as {output_repo_id}")
-        local_dir_name = output_repo_id.split("/")[-1]
-        output_dir = dataset.root.parent / local_dir_name
-        logging.info(f"Saving to auto-generated location: {output_dir}")
 
     logging.info(f"Converting dataset {cfg.repo_id} to video format")
 
@@ -499,8 +568,20 @@ def handle_info(cfg: EditDatasetConfig):
         sys.stdout.write(f"{feature_dump_str}\n")
 
 
+def _validate_config(cfg: EditDatasetConfig) -> None:
+    if isinstance(cfg.operation, MergeConfig):
+        if not cfg.new_repo_id:
+            raise ValueError("--new_repo_id is required for merge operation (the merged dataset identifier)")
+    else:
+        if not cfg.repo_id:
+            raise ValueError(
+                f"--repo_id is required for {cfg.operation.type} operation (the input dataset identifier)"
+            )
+
+
 @parser.wrap()
 def edit_dataset(cfg: EditDatasetConfig) -> None:
+    _validate_config(cfg)
     operation_type = cfg.operation.type
 
     if operation_type == "delete_episodes":

src/lerobot/scripts/lerobot_record.py

@@ -155,7 +155,7 @@ class DatasetRecordConfig:
     repo_id: str
     # A short but accurate description of the task performed during the recording (e.g. "Pick the Lego block and drop it in the box on the right.")
     single_task: str
-    # Root directory where the dataset will be stored (e.g. 'dataset/path').
+    # Root directory where the dataset will be stored (e.g. 'dataset/path'). If None, defaults to $HF_LEROBOT_HOME/repo_id.
     root: str | Path | None = None
     # Limit the frames per second.
     fps: int = 30
@@ -369,6 +369,8 @@ def record_loop(
             act_processed_policy: RobotAction = make_robot_action(action_values, dataset.features)
 
         elif policy is None and isinstance(teleop, Teleoperator):
+            if robot.name == "unitree_g1":
+                teleop.send_feedback(obs)
             act = teleop.get_action()
 
             # Applies a pipeline to the raw teleop action, default is IdentityProcessor
@@ -556,10 +558,6 @@ def record(cfg: RecordConfig) -> LeRobotDataset:
                 ):
                     log_say("Reset the environment", cfg.play_sounds)
 
-                    # reset g1 robot
-                    if robot.name == "unitree_g1":
-                        robot.reset()
-
                     record_loop(
                         robot=robot,
                         events=events,

src/lerobot/scripts/lerobot_replay.py

@@ -80,7 +80,7 @@ class DatasetReplayConfig:
     repo_id: str
     # Episode to replay.
     episode: int
-    # Root directory where the dataset will be stored (e.g. 'dataset/path').
+    # Root directory where the dataset will be stored (e.g. 'dataset/path'). If None, defaults to $HF_LEROBOT_HOME/repo_id.
     root: str | Path | None = None
     # Limit the frames per second. By default, uses the policy fps.
     fps: int = 30

src/lerobot/scripts/lerobot_teleoperate.py

@@ -60,6 +60,7 @@ import rerun as rr
 
 from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig  # noqa: F401
 from lerobot.cameras.realsense.configuration_realsense import RealSenseCameraConfig  # noqa: F401
+from lerobot.cameras.zmq.configuration_zmq import ZMQCameraConfig  # noqa: F401
 from lerobot.configs import parser
 from lerobot.processor import (
     RobotAction,
@@ -153,7 +154,6 @@ def teleop_loop(
 
     display_len = max(len(key) for key in robot.action_features)
     start = time.perf_counter()
-
     while True:
         loop_start = time.perf_counter()
 
@@ -163,6 +163,9 @@ def teleop_loop(
         # given that it is the identity processor as default
         obs = robot.get_observation()
 
+        if robot.name == "unitree_g1":
+            teleop.send_feedback(obs)
+
         # Get teleop action
         raw_action = teleop.get_action()
 

src/lerobot/scripts/lerobot_train.py

@@ -29,7 +29,8 @@ from tqdm import tqdm
 from lerobot.configs import parser
 from lerobot.configs.train import TrainPipelineConfig
 from lerobot.datasets.factory import make_dataset
-from lerobot.datasets.sampler import EpisodeAwareSampler
+from lerobot.datasets.multi_dataset import NewMultiLeRobotDataset
+from lerobot.datasets.sampler import EpisodeAwareSampler, WeightedEpisodeAwareSampler
 from lerobot.datasets.utils import cycle
 from lerobot.envs.factory import make_env, make_env_pre_post_processors
 from lerobot.envs.utils import close_envs
@@ -209,6 +210,10 @@ def train(cfg: TrainPipelineConfig, accelerator: Accelerator | None = None):
 
     # Use accelerator's device
     device = accelerator.device
+    if cfg.cudnn_deterministic:
+        torch.backends.cudnn.deterministic = True
+        torch.backends.cudnn.benchmark = False
+    else:
         torch.backends.cudnn.benchmark = True
     torch.backends.cuda.matmul.allow_tf32 = True
 
@@ -339,13 +344,25 @@ def train(cfg: TrainPipelineConfig, accelerator: Accelerator | None = None):
         logging.info(f"{num_total_params=} ({format_big_number(num_total_params)})")
 
     # create dataloader for offline training
-    if hasattr(cfg.policy, "drop_n_last_frames"):
+    drop_n_last = getattr(cfg.policy, "drop_n_last_frames", 0)
+
+    if isinstance(dataset, NewMultiLeRobotDataset):
+        shuffle = False
+        sampler = WeightedEpisodeAwareSampler(
+            dataset.meta.episodes["dataset_from_index"],
+            dataset.meta.episodes["dataset_to_index"],
+            dataset_membership=dataset.meta.episodes["dataset_source"],
+            dataset_weights=dataset.dataset_weights,
+            drop_n_last_frames=drop_n_last,
+            shuffle=True,
+        )
+    elif drop_n_last > 0:
         shuffle = False
         sampler = EpisodeAwareSampler(
             dataset.meta.episodes["dataset_from_index"],
             dataset.meta.episodes["dataset_to_index"],
             episode_indices_to_use=dataset.episodes,
-            drop_n_last_frames=cfg.policy.drop_n_last_frames,
+            drop_n_last_frames=drop_n_last,
             shuffle=True,
         )
     else:
@@ -356,7 +373,7 @@ def train(cfg: TrainPipelineConfig, accelerator: Accelerator | None = None):
         dataset,
         num_workers=cfg.num_workers,
         batch_size=cfg.batch_size,
-        shuffle=shuffle and not cfg.dataset.streaming,
+        shuffle=shuffle and not getattr(cfg.dataset, "streaming", False),
         sampler=sampler,
         pin_memory=device.type == "cuda",
         drop_last=False,
@@ -380,10 +397,10 @@ def train(cfg: TrainPipelineConfig, accelerator: Accelerator | None = None):
         "dataloading_s": AverageMeter("data_s", ":.3f"),
     }
 
-    # Use effective batch size for proper epoch calculation in distributed training
+    # Keep global batch size for logging; MetricsTracker handles world size internally.
     effective_batch_size = cfg.batch_size * accelerator.num_processes
     train_tracker = MetricsTracker(
-        effective_batch_size,
+        cfg.batch_size,
         dataset.num_frames,
         dataset.num_episodes,
         train_metrics,

src/lerobot/scripts/lerobot_train_tokenizer.py

@@ -306,7 +306,7 @@ def train_fast_tokenizer(
 
     # download the tokenizer source code (not pretrained weights)
     # we'll train a new tokenizer on our own data
-    base_tokenizer = AutoProcessor.from_pretrained("physical-intelligence/fast", trust_remote_code=True)
+    base_tokenizer = AutoProcessor.from_pretrained("lerobot/fast-action-tokenizer", trust_remote_code=True)
 
     # convert action_chunks array to list of arrays (expected by .fit())
     action_data_list = [action_chunks[i] for i in range(len(action_chunks))]

src/lerobot/teleoperators/so_leader/config_so_leader.py

@@ -15,7 +15,6 @@
 # limitations under the License.
 
 from dataclasses import dataclass
-from typing import TypeAlias
 
 from ..config import TeleoperatorConfig
 
@@ -38,5 +37,5 @@ class SOLeaderTeleopConfig(TeleoperatorConfig, SOLeaderConfig):
     pass
 
 
-SO100LeaderConfig: TypeAlias = SOLeaderTeleopConfig
+SO100LeaderConfig = SOLeaderTeleopConfig
-SO101LeaderConfig: TypeAlias = SOLeaderTeleopConfig
+SO101LeaderConfig = SOLeaderTeleopConfig

src/lerobot/teleoperators/so_leader/so_leader.py

@@ -16,7 +16,6 @@
 
 import logging
 import time
-from typing import TypeAlias
 
 from lerobot.motors import Motor, MotorCalibration, MotorNormMode
 from lerobot.motors.feetech import (
@@ -156,5 +155,5 @@ class SOLeader(Teleoperator):
         logger.info(f"{self} disconnected.")
 
 
-SO100Leader: TypeAlias = SOLeader
+SO100Leader = SOLeader
-SO101Leader: TypeAlias = SOLeader
+SO101Leader = SOLeader

src/lerobot/teleoperators/unitree_g1/__init__.py

@@ -19,3 +19,13 @@ from .exo_calib import ExoskeletonCalibration, ExoskeletonJointCalibration
 from .exo_ik import ExoskeletonIKHelper
 from .exo_serial import ExoskeletonArm
 from .unitree_g1 import UnitreeG1Teleoperator
+
+__all__ = [
+    "ExoskeletonArmPortConfig",
+    "ExoskeletonCalibration",
+    "ExoskeletonIKHelper",
+    "ExoskeletonJointCalibration",
+    "ExoskeletonArm",
+    "UnitreeG1Teleoperator",
+    "UnitreeG1TeleoperatorConfig",
+]

src/lerobot/teleoperators/unitree_g1/exo_calib.py

@@ -35,6 +35,9 @@ import serial
 logger = logging.getLogger(__name__)
 
 
+ADC_MAX = 2**12 - 1
+ADC_HALF = ADC_MAX / 2
+
 # exoskeleton joint names -> ADC channel pairs. TODO: add wrist pitch and wrist yaw
 JOINTS = {
     "shoulder_pitch": (0, 1),
@@ -59,7 +62,7 @@ class ExoskeletonCalibration:
 
     version: int = 2
     side: str = ""
-    adc_max: int = 2**12 - 1
+    adc_max: int = ADC_MAX
     joints: list[ExoskeletonJointCalibration] = field(default_factory=list)
 
     def to_dict(self) -> dict:
@@ -92,7 +95,7 @@ class ExoskeletonCalibration:
         return cls(
             version=data.get("version", 2),
             side=data.get("side", ""),
-            adc_max=data.get("adc_max", 2**12 - 1),
+            adc_max=data.get("adc_max", ADC_MAX),
             joints=joints,
         )
 
@@ -112,11 +115,8 @@ class CalibParams:
 
 
 def normalize_angle(angle: float) -> float:
-    while angle > np.pi:
+    """Normalize angle to [-pi, pi]."""
-        angle -= 2 * np.pi
+    return float(np.arctan2(np.sin(angle), np.cos(angle)))
-    while angle < -np.pi:
-        angle += 2 * np.pi
-    return angle
 
 
 def joint_z_and_angle(raw16: list[int], j: ExoskeletonJointCalibration) -> tuple[np.ndarray, float]:
@@ -125,7 +125,7 @@ def joint_z_and_angle(raw16: list[int], j: ExoskeletonJointCalibration) -> tuple
     """
     pair = JOINTS[j.name]
     s, c = raw16[pair[0]], raw16[pair[1]]  # get sin and cos
-    p = np.array([float(c) - (2**12 - 1) / 2, float(s) - (2**12 - 1) / 2])  # center the raw values
+    p = np.array([float(c) - ADC_HALF, float(s) - ADC_HALF])  # center the raw values
     z = np.asarray(j.T) @ (
         p - np.asarray(j.center_fit)
     )  # center the ellipse and invert the transformation matrix to get unit circle coords
@@ -167,7 +167,7 @@ def run_exo_calibration(
 
     def read_joint_point(raw16: list[int], pair: tuple[int, int]):
         s, c = raw16[pair[0]], raw16[pair[1]]
-        return float(c) - (2**12 - 1) / 2, float(s) - (2**12 - 1) / 2, float(s), float(c)
+        return float(c) - ADC_HALF, float(s) - ADC_HALF, float(s), float(c)
 
     def select_fit_subset(xs, ys):
         """Select and filter points for ellipse fitting. Trims outliers by radius and downsamples."""
@@ -317,7 +317,7 @@ def run_exo_calibration(
                         calib = ExoskeletonCalibration(
                             version=2,
                             side=side,
-                            adc_max=2**12 - 1,
+                            adc_max=ADC_MAX,
                             joints=[
                                 ExoskeletonJointCalibration(
                                     name=j["name"],
@@ -367,8 +367,8 @@ def run_exo_calibration(
                     state["win_s"].append(s_raw)
                     state["win_c"].append(c_raw)
                     if len(state["win_s"]) >= max(3, params.median_window):
-                        state["ys"].append(running_median(state["win_s"]) - (2**12 - 1) / 2)
+                        state["ys"].append(running_median(state["win_s"]) - ADC_HALF)
-                        state["xs"].append(running_median(state["win_c"]) - (2**12 - 1) / 2)
+                        state["xs"].append(running_median(state["win_c"]) - ADC_HALF)
                 else:
                     jdata = joints_out[-1]
                     z = np.array(jdata["T"]) @ (np.array([x_raw, y_raw]) - np.array(jdata["center_fit"]))