Remove _has_gym checks

.cache/calibration/aloha_default/left_follower.json

@@ -1,68 +0,0 @@
-{
-    "homing_offset": [
-        2048,
-        3072,
-        3072,
-        -1024,
-        -1024,
-        2048,
-        -2048,
-        2048,
-        -2048
-    ],
-    "drive_mode": [
-        1,
-        1,
-        1,
-        0,
-        0,
-        1,
-        0,
-        1,
-        0
-    ],
-    "start_pos": [
-        2015,
-        3058,
-        3061,
-        1071,
-        1071,
-        2035,
-        2152,
-        2029,
-        2499
-    ],
-    "end_pos": [
-        -1008,
-        -1963,
-        -1966,
-        2141,
-        2143,
-        -971,
-        3043,
-        -1077,
-        3144
-    ],
-    "calib_mode": [
-        "DEGREE",
-        "DEGREE",
-        "DEGREE",
-        "DEGREE",
-        "DEGREE",
-        "DEGREE",
-        "DEGREE",
-        "DEGREE",
-        "LINEAR"
-    ],
-    "motor_names": [
-        "waist",
-        "shoulder",
-        "shoulder_shadow",
-        "elbow",
-        "elbow_shadow",
-        "forearm_roll",
-        "wrist_angle",
-        "wrist_rotate",
-        "gripper"
-    ]
-}

.cache/calibration/aloha_default/left_leader.json

@@ -1,68 +0,0 @@
-{
-    "homing_offset": [
-        2048,
-        3072,
-        3072,
-        -1024,
-        -1024,
-        2048,
-        -2048,
-        2048,
-        -1024
-    ],
-    "drive_mode": [
-        1,
-        1,
-        1,
-        0,
-        0,
-        1,
-        0,
-        1,
-        0
-    ],
-    "start_pos": [
-        2035,
-        3024,
-        3019,
-        979,
-        981,
-        1982,
-        2166,
-        2124,
-        1968
-    ],
-    "end_pos": [
-        -990,
-        -2017,
-        -2015,
-        2078,
-        2076,
-        -1030,
-        3117,
-        -1016,
-        2556
-    ],
-    "calib_mode": [
-        "DEGREE",
-        "DEGREE",
-        "DEGREE",
-        "DEGREE",
-        "DEGREE",
-        "DEGREE",
-        "DEGREE",
-        "DEGREE",
-        "LINEAR"
-    ],
-    "motor_names": [
-        "waist",
-        "shoulder",
-        "shoulder_shadow",
-        "elbow",
-        "elbow_shadow",
-        "forearm_roll",
-        "wrist_angle",
-        "wrist_rotate",
-        "gripper"
-    ]
-}

.cache/calibration/aloha_default/right_follower.json

@@ -1,68 +0,0 @@
-{
-    "homing_offset": [
-        2048,
-        3072,
-        3072,
-        -1024,
-        -1024,
-        2048,
-        -2048,
-        2048,
-        -2048
-    ],
-    "drive_mode": [
-        1,
-        1,
-        1,
-        0,
-        0,
-        1,
-        0,
-        1,
-        0
-    ],
-    "start_pos": [
-        2056,
-        2895,
-        2896,
-        1191,
-        1190,
-        2018,
-        2051,
-        2056,
-        2509
-    ],
-    "end_pos": [
-        -1040,
-        -2004,
-        -2006,
-        2126,
-        2127,
-        -1010,
-        3050,
-        -1117,
-        3143
-    ],
-    "calib_mode": [
-        "DEGREE",
-        "DEGREE",
-        "DEGREE",
-        "DEGREE",
-        "DEGREE",
-        "DEGREE",
-        "DEGREE",
-        "DEGREE",
-        "LINEAR"
-    ],
-    "motor_names": [
-        "waist",
-        "shoulder",
-        "shoulder_shadow",
-        "elbow",
-        "elbow_shadow",
-        "forearm_roll",
-        "wrist_angle",
-        "wrist_rotate",
-        "gripper"
-    ]
-}

.cache/calibration/aloha_default/right_leader.json

@@ -1,68 +0,0 @@
-{
-    "homing_offset": [
-        2048,
-        3072,
-        3072,
-        -1024,
-        -1024,
-        2048,
-        -2048,
-        2048,
-        -2048
-    ],
-    "drive_mode": [
-        1,
-        1,
-        1,
-        0,
-        0,
-        1,
-        0,
-        1,
-        0
-    ],
-    "start_pos": [
-        2068,
-        3034,
-        3030,
-        1038,
-        1041,
-        1991,
-        1948,
-        2090,
-        1985
-    ],
-    "end_pos": [
-        -1025,
-        -2014,
-        -2015,
-        2058,
-        2060,
-        -955,
-        3091,
-        -940,
-        2576
-    ],
-    "calib_mode": [
-        "DEGREE",
-        "DEGREE",
-        "DEGREE",
-        "DEGREE",
-        "DEGREE",
-        "DEGREE",
-        "DEGREE",
-        "DEGREE",
-        "LINEAR"
-    ],
-    "motor_names": [
-        "waist",
-        "shoulder",
-        "shoulder_shadow",
-        "elbow",
-        "elbow_shadow",
-        "forearm_roll",
-        "wrist_angle",
-        "wrist_rotate",
-        "gripper"
-    ]
-}

.dockerignore

@@ -1,160 +0,0 @@
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# Misc
-.git
-tmp
-wandb
-data
-outputs
-.vscode
-rl
-media
-
-
-# Logging
-logs
-
-# HPC
-nautilus/*.yaml
-*.key
-
-# Slurm
-sbatch*.sh
-
-# Byte-compiled / optimized / DLL files
-__pycache__/
-*.py[cod]
-*$py.class
-
-# C extensions
-*.so
-
-# Distribution / packaging
-.Python
-build/
-develop-eggs/
-dist/
-downloads/
-eggs/
-.eggs/
-lib/
-lib64/
-parts/
-sdist/
-var/
-wheels/
-pip-wheel-metadata/
-share/python-wheels/
-*.egg-info/
-.installed.cfg
-*.egg
-MANIFEST
-
-# PyInstaller
-#  Usually these files are written by a python script from a template
-#  before PyInstaller builds the exe, so as to inject date/other infos into it.
-*.manifest
-*.spec
-
-# Installer logs
-pip-log.txt
-pip-delete-this-directory.txt
-
-# Unit test / coverage reports
-!tests/artifacts
-htmlcov/
-.tox/
-.nox/
-.coverage
-.coverage.*
-nosetests.xml
-coverage.xml
-*.cover
-*.py,cover
-.hypothesis/
-.pytest_cache/
-
-# Ignore .cache except calibration
-.cache/*
-!.cache/calibration/
-!.cache/calibration/**
-
-# Translations
-*.mo
-*.pot
-
-# Django stuff:
-*.log
-local_settings.py
-db.sqlite3
-db.sqlite3-journal
-
-# Flask stuff:
-instance/
-.webassets-cache
-
-# Scrapy stuff:
-.scrapy
-
-# Sphinx documentation
-docs/_build/
-
-# PyBuilder
-target/
-
-# Jupyter Notebook
-.ipynb_checkpoints
-
-# IPython
-profile_default/
-ipython_config.py
-
-# pyenv
-.python-version
-
-# pipenv
-#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
-#   However, in case of collaboration, if having platform-specific dependencies or dependencies
-#   having no cross-platform support, pipenv may install dependencies that don't work, or not
-#   install all needed dependencies.
-#Pipfile.lock
-
-# PEP 582; used by e.g. github.com/David-OConnor/pyflow
-__pypackages__/
-
-# Celery stuff
-celerybeat-schedule
-celerybeat.pid
-
-# SageMath parsed files
-*.sage.py
-
-# Spyder project settings
-.spyderproject
-.spyproject
-
-# Rope project settings
-.ropeproject
-
-# mkdocs documentation
-/site
-
-# mypy
-.mypy_cache/
-.dmypy.json
-dmypy.json
-
-# Pyre type checker
-.pyre/

.gitattributes

@@ -1,21 +1,2 @@
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
 *.memmap filter=lfs diff=lfs merge=lfs -text
 *.stl filter=lfs diff=lfs merge=lfs -text
-*.safetensors filter=lfs diff=lfs merge=lfs -text
-*.mp4 filter=lfs diff=lfs merge=lfs -text
-*.arrow filter=lfs diff=lfs merge=lfs -text
-*.json !text !filter !merge !diff
-tests/artifacts/cameras/*.png filter=lfs diff=lfs merge=lfs -text
-tests/artifacts/cameras/*.bag filter=lfs diff=lfs merge=lfs -text

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -1,68 +0,0 @@
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-name: "\U0001F41B Bug Report"
-description: Submit a bug report to help us improve LeRobot
-body:
-  - type: markdown
-    attributes:
-      value: |
-        Thanks for taking the time to submit a bug report! 🐛
-        If this is not a bug related to the LeRobot library directly, but instead a general question about your code or the library specifically please use our [discord](https://discord.gg/s3KuuzsPFb).
-
-  - type: textarea
-    id: system-info
-    attributes:
-      label: System Info
-      description: If needed, you can share your lerobot configuration with us by running `python -m lerobot.scripts.display_sys_info` and copy-pasting its outputs below
-      render: Shell
-      placeholder: lerobot version, OS, python version, numpy version, torch version, and lerobot's configuration
-    validations:
-      required: true
-
-  - type: checkboxes
-    id: information-scripts-examples
-    attributes:
-      label: Information
-      description: 'The problem arises when using:'
-      options:
-        - label: "One of the scripts in the examples/ folder of LeRobot"
-        - label: "My own task or dataset (give details below)"
-
-  - type: textarea
-    id: reproduction
-    validations:
-      required: true
-    attributes:
-      label: Reproduction
-      description: |
-        If needed, provide a simple code sample that reproduces the problem you ran into. It can be a Colab link or just a code snippet.
-        Sharing error messages or stack traces could be useful as well!
-        Important! Use code tags to correctly format your code. See https://help.github.com/en/github/writing-on-github/creating-and-highlighting-code-blocks#syntax-highlighting
-        Try to avoid screenshots, as they are hard to read and don't allow copy-and-pasting.
-
-      placeholder: |
-        Steps to reproduce the behavior:
-
-          1.
-          2.
-          3.
-
-  - type: textarea
-    id: expected-behavior
-    validations:
-      required: true
-    attributes:
-      label: Expected behavior
-      description: "A clear and concise description of what you would expect to happen."

.github/PULL_REQUEST_TEMPLATE.md

@@ -1,34 +0,0 @@
-## What this does
-Explain what this PR does. Feel free to tag your PR with the appropriate label(s).
-
-Examples:
-|  Title               | Label           |
-|----------------------|-----------------|
-| Fixes #[issue]       | (🐛 Bug)        |
-| Adds new dataset     | (🗃️ Dataset)    |
-| Optimizes something  | (⚡️ Performance) |
-
-## How it was tested
-Explain/show how you tested your changes.
-
-Examples:
-- Added `test_something` in `tests/test_stuff.py`.
-- Added `new_feature` and checked that training converges with policy X on dataset/environment Y.
-- Optimized `some_function`, it now runs X times faster than previously.
-
-## How to checkout & try? (for the reviewer)
-Provide a simple way for the reviewer to try out your changes.
-
-Examples:
-```bash
-pytest -sx tests/test_stuff.py::test_something
-```
-```bash
-python lerobot/scripts/train.py --some.option=true
-```
-
-## SECTION TO REMOVE BEFORE SUBMITTING YOUR PR
-**Note**: Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
-members/contributors who may be interested in your PR. Try to avoid tagging more than 3 people.
-
-**Note**: Before submitting this PR, please read the [contributor guideline](https://github.com/huggingface/lerobot/blob/main/CONTRIBUTING.md#submitting-a-pull-request-pr).

.github/poetry/cpu/pyproject.toml

@@ -0,0 +1,118 @@
+[tool.poetry]
+name = "lerobot"
+version = "0.1.0"
+description = "🤗 LeRobot: State-of-the-art Machine Learning for Real-World Robotics in Pytorch"
+authors = [
+    "Rémi Cadène <re.cadene@gmail.com>",
+]
+maintainers = [
+    "Alexander Soare <alexander.soare159@gmail.com>",
+    "Quentin Gallouédec <quentin.gallouedec@ec-lyon.fr>",
+    "Simon Alibert <alibert.sim@gmail.com>",
+]
+repository = "https://github.com/Cadene/lerobot"
+readme = "README.md"
+license = "Apache-2.0"
+keywords = ["robotics, deep, reinforcement, learning, pytorch"]
+classifiers=[
+    "Development Status :: 3 - Alpha",
+    "Intended Audience :: Developers",
+    "Topic :: Software Development :: Build Tools",
+    "License :: OSI Approved :: Apache Software License",
+    "Programming Language :: Python :: 3.10",
+]
+packages = [{include = "lerobot"}]
+
+
+[tool.poetry.dependencies]
+python = "^3.10"
+termcolor = "^2.4.0"
+omegaconf = "^2.3.0"
+pandas = "^2.2.1"
+wandb = "^0.16.3"
+moviepy = "^1.0.3"
+imageio = {extras = ["pyav"], version = "^2.34.0"}
+gdown = "^5.1.0"
+hydra-core = "^1.3.2"
+einops = "^0.7.0"
+pygame = "^2.5.2"
+pymunk = "^6.6.0"
+zarr = "^2.17.0"
+numba = "^0.59.0"
+mpmath = "^1.3.0"
+torch = {version = "^2.2.1", source = "torch-cpu"}
+tensordict = {git = "https://github.com/pytorch/tensordict"}
+torchrl = {git = "https://github.com/pytorch/rl", rev = "13bef426dcfa5887c6e5034a6e9697993fa92c37"}
+opencv-python = "^4.9.0.80"
+diffusers = "^0.26.3"
+torchvision = {version = "^0.17.1", source = "torch-cpu"}
+h5py = "^3.10.0"
+robomimic = "0.2.0"
+huggingface-hub = "^0.21.4"
+cmake = "^3.29.0.1"
+sim-pusht = { version = "^0.1.0", optional = true}
+sim-xarm = { version = "^0.1.0", optional = true}
+sim-aloha = { version = "^0.1.2", optional = true}
+
+
+[tool.poetry.extras]
+pusht = ["sim-pusht"]
+xarm = ["sim-xarm"]
+aloha = ["sim-aloha"]
+
+
+[tool.poetry.group.dev.dependencies]
+pre-commit = "^3.6.2"
+debugpy = "^1.8.1"
+
+
+[tool.poetry.group.test.dependencies]
+pytest = "^8.1.0"
+pytest-cov = "^5.0.0"
+
+
+[[tool.poetry.source]]
+name = "torch-cpu"
+url = "https://download.pytorch.org/whl/cpu"
+priority = "supplemental"
+
+
+[tool.ruff]
+line-length = 110
+target-version = "py310"
+exclude = [
+    ".bzr",
+    ".direnv",
+    ".eggs",
+    ".git",
+    ".git-rewrite",
+    ".hg",
+    ".mypy_cache",
+    ".nox",
+    ".pants.d",
+    ".pytype",
+    ".ruff_cache",
+    ".svn",
+    ".tox",
+    ".venv",
+    "__pypackages__",
+    "_build",
+    "buck-out",
+    "build",
+    "dist",
+    "node_modules",
+    "venv",
+]
+
+
+[tool.ruff.lint]
+select = ["E4", "E7", "E9", "F", "I", "N", "B", "C4", "SIM"]
+
+
+[tool.poetry-dynamic-versioning]
+enable = true
+
+
+[build-system]
+requires = ["poetry-core>=1.0.0", "poetry-dynamic-versioning>=1.0.0,<2.0.0"]
+build-backend = "poetry_dynamic_versioning.backend"

.github/workflows/build-docker-images.yml

@@ -1,135 +0,0 @@
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# Inspired by
-# https://github.com/huggingface/peft/blob/main/.github/workflows/build_docker_images.yml
-name: Builds
-
-on:
-  workflow_dispatch:
-  workflow_call:
-  schedule:
-    - cron: "0 1 * * *"
-
-permissions: {}
-
-env:
-  PYTHON_VERSION: "3.10"
-
-jobs:
-  latest-cpu:
-    name: CPU
-    runs-on:
-      group: aws-general-8-plus
-    steps:
-      - name: Install Git LFS
-        run: |
-          sudo apt-get update
-          sudo apt-get install git-lfs
-          git lfs install
-
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
-        with:
-          cache-binary: false
-
-      - name: Check out code
-        uses: actions/checkout@v4
-        with:
-          lfs: true
-          persist-credentials: false
-
-      - name: Login to DockerHub
-        uses: docker/login-action@v3
-        with:
-          username: ${{ secrets.DOCKERHUB_USERNAME }}
-          password: ${{ secrets.DOCKERHUB_PASSWORD }}
-
-      - name: Build and Push CPU
-        uses: docker/build-push-action@v5
-        with:
-          context: .
-          file: ./docker/lerobot-cpu/Dockerfile
-          push: true
-          tags: huggingface/lerobot-cpu
-          build-args: PYTHON_VERSION=${{ env.PYTHON_VERSION }}
-
-
-  latest-cuda:
-    name: GPU
-    runs-on:
-      group: aws-general-8-plus
-    steps:
-      - name: Install Git LFS
-        run: |
-          sudo apt-get update
-          sudo apt-get install git-lfs
-          git lfs install
-
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
-        with:
-          cache-binary: false
-
-      - name: Check out code
-        uses: actions/checkout@v4
-        with:
-          lfs: true
-          persist-credentials: false
-
-      - name: Login to DockerHub
-        uses: docker/login-action@v3
-        with:
-          username: ${{ secrets.DOCKERHUB_USERNAME }}
-          password: ${{ secrets.DOCKERHUB_PASSWORD }}
-
-      - name: Build and Push GPU
-        uses: docker/build-push-action@v5
-        with:
-          context: .
-          file: ./docker/lerobot-gpu/Dockerfile
-          push: true
-          tags: huggingface/lerobot-gpu
-          build-args: PYTHON_VERSION=${{ env.PYTHON_VERSION }}
-
-
-  latest-cuda-dev:
-    name: GPU Dev
-    runs-on:
-      group: aws-general-8-plus
-    steps:
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
-        with:
-          cache-binary: false
-
-      - name: Check out code
-        uses: actions/checkout@v4
-        with:
-          persist-credentials: false
-
-      - name: Login to DockerHub
-        uses: docker/login-action@v3
-        with:
-          username: ${{ secrets.DOCKERHUB_USERNAME }}
-          password: ${{ secrets.DOCKERHUB_PASSWORD }}
-
-      - name: Build and Push GPU dev
-        uses: docker/build-push-action@v5
-        with:
-          context: .
-          file: ./docker/lerobot-gpu-dev/Dockerfile
-          push: true
-          tags: huggingface/lerobot-gpu:dev
-          build-args: PYTHON_VERSION=${{ env.PYTHON_VERSION }}

.github/workflows/build_documentation.yml

@@ -1,23 +0,0 @@
-name: Build documentation
-
-on:
-  workflow_dispatch:
-  push:
-    paths:
-      - "docs/**"
-    branches:
-    - main
-    - doc-builder*
-    - v*-release
-
-
-jobs:
-  build:  # zizmor: ignore[excessive-permissions] We follow the same pattern as in Transformers
-    uses: huggingface/doc-builder/.github/workflows/build_main_documentation.yml@main
-    with:
-      commit_sha: ${{ github.sha }}
-      package: lerobot
-      additional_args: --not_python_module
-    secrets:
-      token: ${{ secrets.HUGGINGFACE_PUSH }}
-      hf_token: ${{ secrets.HF_DOC_BUILD_PUSH }}

.github/workflows/build_pr_documentation.yml

@@ -1,19 +0,0 @@
-name: Build PR Documentation
-
-on:
-  pull_request:
-    paths:
-      - "docs/**"
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
-  cancel-in-progress: true
-
-jobs:
-  build:  # zizmor: ignore[excessive-permissions] We follow the same pattern as in Transformers
-    uses: huggingface/doc-builder/.github/workflows/build_pr_documentation.yml@main
-    with:
-      commit_sha: ${{ github.event.pull_request.head.sha }}
-      pr_number: ${{ github.event.number }}
-      package: lerobot
-      additional_args: --not_python_module

.github/workflows/nightly-tests.yml

@@ -1,93 +0,0 @@
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# Inspired by
-# https://github.com/huggingface/peft/blob/main/.github/workflows/nightly.yml
-name: Nightly
-
-on:
-  workflow_dispatch:
-  schedule:
-    - cron: "0 2 * * *"
-
-permissions: {}
-
-# env:
-  # SLACK_API_TOKEN: ${{ secrets.SLACK_API_TOKEN }}
-jobs:
-  run_all_tests_cpu:
-    name: CPU
-    strategy:
-      fail-fast: false
-    runs-on:
-      group: aws-general-8-plus
-    container:
-      image: huggingface/lerobot-cpu:latest
-      options: --shm-size "16gb"
-      credentials:
-        username: ${{ secrets.DOCKERHUB_USERNAME }}
-        password: ${{ secrets.DOCKERHUB_PASSWORD }}
-    defaults:
-      run:
-        shell: bash
-        working-directory: /lerobot
-    steps:
-      - name: Tests
-        run: pytest -v --cov=./lerobot --disable-warnings tests
-
-      - name: Tests end-to-end
-        run: make test-end-to-end
-
-
-  run_all_tests_single_gpu:
-    name: GPU
-    strategy:
-      fail-fast: false
-    runs-on:
-      group: aws-g6-4xlarge-plus
-    env:
-      CUDA_VISIBLE_DEVICES: "0"
-      TEST_TYPE: "single_gpu"
-    container:
-      image: huggingface/lerobot-gpu:latest
-      options: --gpus all --shm-size "16gb"
-      credentials:
-        username: ${{ secrets.DOCKERHUB_USERNAME }}
-        password: ${{ secrets.DOCKERHUB_PASSWORD }}
-    defaults:
-      run:
-        shell: bash
-        working-directory: /lerobot
-    steps:
-      - name: Nvidia-smi
-        run: nvidia-smi
-
-      - name: Test
-        run: pytest -v --cov=./lerobot --cov-report=xml --disable-warnings tests
-      #   TODO(aliberts): Link with HF Codecov account
-      # - name: Upload coverage reports to Codecov with GitHub Action
-      #   uses: codecov/codecov-action@v4
-      #   with:
-      #     files: ./coverage.xml
-      #     verbose: true
-      - name: Tests end-to-end
-        env:
-          DEVICE: cuda
-        run: make test-end-to-end
-
-    #   - name: Generate Report
-    #     if: always()
-    #     run: |
-    #       pip install slack_sdk tabulate
-    #       python scripts/log_reports.py >> $GITHUB_STEP_SUMMARY

.github/workflows/quality.yml

@@ -1,72 +0,0 @@
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-name: Quality
-
-on:
-  workflow_dispatch:
-  workflow_call:
-  pull_request:
-  push:
-    branches:
-      - main
-
-permissions: {}
-
-env:
-  PYTHON_VERSION: "3.10"
-
-jobs:
-  style:
-    name: Style
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout Repository
-        uses: actions/checkout@v4
-        with:
-          persist-credentials: false
-
-      - name: Set up Python
-        uses: actions/setup-python@v4
-        with:
-          python-version: ${{ env.PYTHON_VERSION }}
-
-      - name: Get Ruff Version from pre-commit-config.yaml
-        id: get-ruff-version
-        run: |
-          RUFF_VERSION=$(awk '/repo: https:\/\/github.com\/astral-sh\/ruff-pre-commit/{flag=1;next}/rev:/{if(flag){print $2;exit}}' .pre-commit-config.yaml)
-          echo "ruff_version=${RUFF_VERSION}" >> $GITHUB_OUTPUT
-
-      - name: Install Ruff
-        env:
-          RUFF_VERSION: ${{ steps.get-ruff-version.outputs.ruff_version }}
-        run: python -m pip install "ruff==${RUFF_VERSION}"
-
-      - name: Ruff check
-        run: ruff check --output-format=github
-
-      - name: Ruff format
-        run: ruff format --diff
-
-  typos:
-    name: Typos
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout Repository
-        uses: actions/checkout@v4
-        with:
-          persist-credentials: false
-
-      - name: typos-action
-        uses: crate-ci/typos@v1.29.10

.github/workflows/test-docker-build.yml

@@ -1,82 +0,0 @@
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# Inspired by
-# https://github.com/huggingface/peft/blob/main/.github/workflows/test-docker-build.yml
-name: Test Dockerfiles
-
-on:
-  pull_request:
-    paths:
-      # Run only when DockerFile files are modified
-      - "docker/**"
-
-permissions: {}
-
-env:
-  PYTHON_VERSION: "3.10"
-
-jobs:
-  get_changed_files:
-    name: Detect modified Dockerfiles
-    runs-on: ubuntu-latest
-    outputs:
-      matrix: ${{ steps.set-matrix.outputs.matrix }}
-    steps:
-      - name: Check out code
-        uses: actions/checkout@v4
-        with:
-          persist-credentials: false
-
-      - name: Get changed files
-        id: changed-files
-        uses: tj-actions/changed-files@3f54ebb830831fc121d3263c1857cfbdc310cdb9 #v42
-        with:
-          files: docker/**
-          json: "true"
-
-      - name: Run step if only the files listed above change  # zizmor: ignore[template-injection]
-        if: steps.changed-files.outputs.any_changed == 'true'
-        id: set-matrix
-        run: |
-          echo "matrix=${{ steps.changed-files.outputs.all_changed_files}}" >> $GITHUB_OUTPUT
-
-  build_modified_dockerfiles:
-    name: Build modified Docker images
-    needs: get_changed_files
-    runs-on:
-      group: aws-general-8-plus
-    if: needs.get_changed_files.outputs.matrix != ''
-    strategy:
-      fail-fast: false
-      matrix:
-        docker-file: ${{ fromJson(needs.get_changed_files.outputs.matrix) }}
-    steps:
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
-        with:
-          cache-binary: false
-
-      - name: Check out code
-        uses: actions/checkout@v4
-        with:
-          persist-credentials: false
-
-      - name: Build Docker image
-        uses: docker/build-push-action@v5
-        with:
-          file: ${{ matrix.docker-file }}
-          context: .
-          push: False
-          build-args: PYTHON_VERSION=${{ env.PYTHON_VERSION }}

.github/workflows/test.yml

@@ -1,150 +1,234 @@
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
 name: Tests
 
 on:
   pull_request:
-    paths:
-      - "lerobot/**"
-      - "tests/**"
-      - "examples/**"
-      - ".github/**"
-      - "pyproject.toml"
-      - ".pre-commit-config.yaml"
-      - "Makefile"
-      - ".cache/**"
+    branches:
+      - main
+    types: [opened, synchronize, reopened, labeled]
   push:
     branches:
       - main
-    paths:
-      - "lerobot/**"
-      - "tests/**"
-      - "examples/**"
-      - ".github/**"
-      - "pyproject.toml"
-      - ".pre-commit-config.yaml"
-      - "Makefile"
-      - ".cache/**"
-
-permissions: {}
-
-env:
-  UV_VERSION: "0.6.0"
 
 jobs:
-  pytest:
-    name: Pytest
+  tests:
+    if: |
+      ${{ github.event_name == 'pull_request' && contains(github.event.pull_request.labels.*.name, 'CI') }} ||
+      ${{ github.event_name == 'push' }}
     runs-on: ubuntu-latest
     env:
+      POETRY_VERSION: 1.8.2
+      DATA_DIR: tests/data
       MUJOCO_GL: egl
     steps:
-      - uses: actions/checkout@v4
+      #----------------------------------------------
+      #       check-out repo and set-up python
+      #----------------------------------------------
+      - name: Check out repository
+        uses: actions/checkout@v4
         with:
-          lfs: true  # Ensure LFS files are pulled
-          persist-credentials: false
+          lfs: true
 
-      - name: Install apt dependencies
-      # portaudio19-dev is needed to install pyaudio
-        run: |
-          sudo apt-get update && \
-          sudo apt-get install -y libegl1-mesa-dev ffmpeg portaudio19-dev
-
-      - name: Install uv and python
-        uses: astral-sh/setup-uv@v5
+      - name: Set up python
+        id: setup-python
+        uses: actions/setup-python@v5
         with:
-          enable-cache: true
-          version: ${{ env.UV_VERSION }}
-          python-version: "3.10"
+          python-version: '3.10'
 
-      - name: Install lerobot (all extras)
-        run: uv sync --all-extras
-
-      - name: Test with pytest
-        run: |
-          uv run pytest tests -v --cov=./lerobot --durations=0 \
-            -W ignore::DeprecationWarning:imageio_ffmpeg._utils:7 \
-            -W ignore::UserWarning:torch.utils.data.dataloader:558 \
-            -W ignore::UserWarning:gymnasium.utils.env_checker:247 \
-            && rm -rf tests/outputs outputs
-
-  pytest-minimal:
-    name: Pytest (minimal install)
-    runs-on: ubuntu-latest
-    env:
-      MUJOCO_GL: egl
-    steps:
-      - uses: actions/checkout@v4
+      #----------------------------------------------
+      #         install & configure poetry
+      #----------------------------------------------
+      - name: Load cached Poetry installation
+        id: restore-poetry-cache
+        uses: actions/cache/restore@v3
         with:
-          lfs: true  # Ensure LFS files are pulled
-          persist-credentials: false
+          path: ~/.local
+          key: poetry-${{ env.POETRY_VERSION }}
 
-      - name: Install apt dependencies
-        run: sudo apt-get update && sudo apt-get install -y ffmpeg
-
-      - name: Install uv and python
-        uses: astral-sh/setup-uv@v5
+      - name: Install Poetry
+        if: steps.restore-poetry-cache.outputs.cache-hit != 'true'
+        uses: snok/install-poetry@v1
         with:
-          enable-cache: true
-          version: ${{ env.UV_VERSION }}
-          python-version: "3.10"
+          version: ${{ env.POETRY_VERSION }}
+          virtualenvs-create: true
+          installer-parallel: true
 
-      - name: Install lerobot
-        run: uv sync --extra "test"
-
-      - name: Test with pytest
-        run: |
-          uv run pytest tests -v --cov=./lerobot --durations=0 \
-            -W ignore::DeprecationWarning:imageio_ffmpeg._utils:7 \
-            -W ignore::UserWarning:torch.utils.data.dataloader:558 \
-            -W ignore::UserWarning:gymnasium.utils.env_checker:247 \
-            && rm -rf tests/outputs outputs
-
-  end-to-end:
-    name: End-to-end
-    runs-on: ubuntu-latest
-    env:
-      MUJOCO_GL: egl
-    steps:
-      - uses: actions/checkout@v4
+      - name: Save cached Poetry installation
+        if: |
+          steps.restore-poetry-cache.outputs.cache-hit != 'true' &&
+          github.ref_name == 'main'
+        id: save-poetry-cache
+        uses: actions/cache/save@v3
         with:
-          lfs: true  # Ensure LFS files are pulled
-          persist-credentials: false
+          path: ~/.local
+          key: poetry-${{ env.POETRY_VERSION }}
 
-      - name: Install apt dependencies
-      # portaudio19-dev is needed to install pyaudio
-        run: |
-          sudo apt-get update && \
-          sudo apt-get install -y libegl1-mesa-dev ffmpeg portaudio19-dev
+      - name: Configure Poetry
+        run: poetry config virtualenvs.in-project true
 
-      - name: Install uv and python
-        uses: astral-sh/setup-uv@v5
+      #----------------------------------------------
+      #           install dependencies
+      #----------------------------------------------
+      # TODO(aliberts): move to gpu runners
+      - name: Select cpu dependencies  # HACK
+        run: cp -t . .github/poetry/cpu/pyproject.toml .github/poetry/cpu/poetry.lock
+
+      - name: Load cached venv
+        id: restore-dependencies-cache
+        uses: actions/cache/restore@v3
         with:
-          enable-cache: true
-          version: ${{ env.UV_VERSION }}
-          python-version: "3.10"
+          path: .venv
+          key: venv-${{ steps.setup-python.outputs.python-version }}-${{ env.POETRY_VERSION }}-${{ hashFiles('**/poetry.lock') }}
 
-      - name: Install lerobot (all extras)
+      - name: Install dependencies
+        if: steps.restore-dependencies-cache.outputs.cache-hit != 'true'
+        env:
+          TMPDIR: ~/tmp
+          TEMP: ~/tmp
+          TMP: ~/tmp
         run: |
-          uv venv
-          uv sync --all-extras
+          mkdir ~/tmp
+          poetry install --no-interaction --no-root --without dev --all-extras
 
-      - name: venv
-        run: |
-          echo "PYTHON_PATH=${{ github.workspace }}/.venv/bin/python" >> $GITHUB_ENV
+      - name: Save cached venv
+        if: |
+            steps.restore-dependencies-cache.outputs.cache-hit != 'true' &&
+            github.ref_name == 'main'
+        id: save-dependencies-cache
+        uses: actions/cache/save@v3
+        with:
+          path: .venv
+          key: venv-${{ steps.setup-python.outputs.python-version }}-${{ env.POETRY_VERSION }}-${{ hashFiles('**/poetry.lock') }}
 
-      - name: Test end-to-end
+      - name: Install libegl1-mesa-dev (to use MUJOCO_GL=egl)
+        run: sudo apt-get update && sudo apt-get install -y libegl1-mesa-dev
+
+      #----------------------------------------------
+      #             install project
+      #----------------------------------------------
+      - name: Install project
+        run: poetry install --no-interaction --without dev --all-extras
+
+      #----------------------------------------------
+      #            run tests & coverage
+      #----------------------------------------------
+      - name: Run tests
+        env:
+          LEROBOT_TESTS_DEVICE: cpu
         run: |
-          make test-end-to-end \
-            && rm -rf outputs
+          source .venv/bin/activate
+          pytest --cov=./lerobot --cov-report=xml tests
+
+      #   TODO(aliberts): Link with HF Codecov account
+      # - name: Upload coverage reports to Codecov with GitHub Action
+      #   uses: codecov/codecov-action@v4
+      #   with:
+      #     files: ./coverage.xml
+      #     verbose: true
+
+      #----------------------------------------------
+      #            run end-to-end tests
+      #----------------------------------------------
+      - name: Test train ACT on ALOHA end-to-end
+        run: |
+          source .venv/bin/activate
+          python lerobot/scripts/train.py \
+            policy=act \
+            env=aloha \
+            wandb.enable=False \
+            offline_steps=2 \
+            online_steps=0 \
+            device=cpu \
+            save_model=true \
+            save_freq=2 \
+            horizon=20 \
+            policy.batch_size=2 \
+            hydra.run.dir=tests/outputs/act/
+
+      - name: Test eval ACT on ALOHA end-to-end
+        run: |
+          source .venv/bin/activate
+          python lerobot/scripts/eval.py \
+            --config tests/outputs/act/.hydra/config.yaml \
+            eval_episodes=1 \
+            env.episode_length=8 \
+            device=cpu \
+            policy.pretrained_model_path=tests/outputs/act/models/2.pt
+
+      # TODO(aliberts): This takes ~2mn to run, needs to be improved
+      # - name: Test eval ACT on ALOHA end-to-end (policy is None)
+      #   run: |
+      #     source .venv/bin/activate
+      #     python lerobot/scripts/eval.py \
+      #       --config lerobot/configs/default.yaml \
+      #       policy=act \
+      #       env=aloha \
+      #       eval_episodes=1 \
+      #       device=cpu
+
+      - name: Test train Diffusion on PushT end-to-end
+        run: |
+          source .venv/bin/activate
+          python lerobot/scripts/train.py \
+            policy=diffusion \
+            env=pusht \
+            wandb.enable=False \
+            offline_steps=2 \
+            online_steps=0 \
+            device=cpu \
+            save_model=true \
+            save_freq=2 \
+            hydra.run.dir=tests/outputs/diffusion/
+
+      - name: Test eval Diffusion on PushT end-to-end
+        run: |
+          source .venv/bin/activate
+          python lerobot/scripts/eval.py \
+            --config tests/outputs/diffusion/.hydra/config.yaml \
+            eval_episodes=1 \
+            env.episode_length=8 \
+            device=cpu \
+            policy.pretrained_model_path=tests/outputs/diffusion/models/2.pt
+
+      - name: Test eval Diffusion on PushT end-to-end (policy is None)
+        run: |
+          source .venv/bin/activate
+          python lerobot/scripts/eval.py \
+            --config lerobot/configs/default.yaml \
+            policy=diffusion  \
+            env=pusht \
+            eval_episodes=1 \
+            device=cpu
+
+      - name: Test train TDMPC on Simxarm end-to-end
+        run: |
+          source .venv/bin/activate
+          python lerobot/scripts/train.py \
+            policy=tdmpc \
+            env=simxarm \
+            wandb.enable=False \
+            offline_steps=1 \
+            online_steps=1 \
+            device=cpu \
+            save_model=true \
+            save_freq=2 \
+            hydra.run.dir=tests/outputs/tdmpc/
+
+      - name: Test eval TDMPC on Simxarm end-to-end
+        run: |
+          source .venv/bin/activate
+          python lerobot/scripts/eval.py \
+            --config tests/outputs/tdmpc/.hydra/config.yaml \
+            eval_episodes=1 \
+            env.episode_length=8 \
+            device=cpu \
+            policy.pretrained_model_path=tests/outputs/tdmpc/models/2.pt
+
+      - name: Test eval TDPMC on Simxarm end-to-end (policy is None)
+        run: |
+          source .venv/bin/activate
+          python lerobot/scripts/eval.py \
+            --config lerobot/configs/default.yaml \
+            policy=tdmpc  \
+            env=simxarm \
+            eval_episodes=1 \
+            device=cpu

.github/workflows/trufflehog.yml

@@ -1,35 +0,0 @@
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-on:
-  push:
-
-name: Secret Leaks
-
-permissions: {}
-
-jobs:
-  trufflehog:
-    runs-on: ubuntu-latest
-    steps:
-    - name: Checkout code
-      uses: actions/checkout@v4
-      with:
-        fetch-depth: 0
-        persist-credentials: false
-
-    - name: Secret Scanning
-      uses: trufflesecurity/trufflehog@main
-      with:
-        extra_args: --only-verified

.github/workflows/upload_pr_documentation.yml

@@ -1,16 +0,0 @@
-name: Upload PR Documentation
-
-on: # zizmor: ignore[dangerous-triggers] We follow the same pattern as in Transformers
-  workflow_run:
-    workflows: [ "Build PR Documentation" ]
-    types:
-    - completed
-
-jobs:
-  build:  # zizmor: ignore[excessive-permissions] We follow the same pattern as in Transformers
-    uses: huggingface/doc-builder/.github/workflows/upload_pr_documentation.yml@main
-    with:
-      package_name: lerobot
-    secrets:
-      hf_token: ${{ secrets.HF_DOC_BUILD_PUSH }}
-      comment_bot_token: ${{ secrets.COMMENT_BOT_TOKEN }}

.gitignore

@@ -1,39 +1,16 @@
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-.dev
 # Logging
 logs
 tmp
 wandb
-
-# Data
 data
 outputs
-
-# Apple
-.DS_Store
-
-# VS Code
 .vscode
+rl
 
 # HPC
 nautilus/*.yaml
 *.key
 
-# Slurm
-sbatch*.sh
-
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]
@@ -63,10 +40,6 @@ share/python-wheels/
 *.egg
 MANIFEST
 
-# uv/poetry lock files
-poetry.lock
-uv.lock
-
 # PyInstaller
 #  Usually these files are written by a python script from a template
 #  before PyInstaller builds the exe, so as to inject date/other infos into it.
@@ -78,12 +51,13 @@ pip-log.txt
 pip-delete-this-directory.txt
 
 # Unit test / coverage reports
-!tests/artifacts
+!tests/data
 htmlcov/
 .tox/
 .nox/
 .coverage
 .coverage.*
+.cache
 nosetests.xml
 coverage.xml
 *.cover
@@ -91,11 +65,6 @@ coverage.xml
 .hypothesis/
 .pytest_cache/
 
-# Ignore .cache except calibration
-.cache/*
-!.cache/calibration/
-!.cache/calibration/**
-
 # Translations
 *.mo
 *.pot
@@ -117,7 +86,6 @@ instance/
 docs/_build/
 
 # PyBuilder
-.pybuilder/
 target/
 
 # Jupyter Notebook
@@ -130,6 +98,13 @@ ipython_config.py
 # pyenv
 .python-version
 
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
 # PEP 582; used by e.g. github.com/David-OConnor/pyflow
 __pypackages__/
 
@@ -140,14 +115,6 @@ celerybeat.pid
 # SageMath parsed files
 *.sage.py
 
-# Environments
-.env
-.venv
-env/
-venv/
-env.bak/
-venv.bak/
-
 # Spyder project settings
 .spyderproject
 .spyproject
@@ -165,9 +132,3 @@ dmypy.json
 
 # Pyre type checker
 .pyre/
-
-# pytype static type analyzer
-.pytype/
-
-# Cython debug symbols
-cython_debug/

.pre-commit-config.yaml

@@ -1,31 +1,9 @@
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-exclude: "tests/artifacts/.*\\.safetensors$"
+exclude: ^(data/|tests/)
 default_language_version:
     python: python3.10
 repos:
-  ##### Meta #####
-  - repo: meta
-    hooks:
-      - id: check-useless-excludes
-      - id: check-hooks-apply
-
-
-  ##### Style / Misc. #####
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v5.0.0
+    rev: v4.5.0
     hooks:
       - id: check-added-large-files
       - id: debug-statements
@@ -35,40 +13,21 @@ repos:
       - id: check-toml
       - id: end-of-file-fixer
       - id: trailing-whitespace
-
-  - repo: https://github.com/adhtruong/mirrors-typos
-    rev: v1.31.1
-    hooks:
-      - id: typos
-        args: [--force-exclude]
-
   - repo: https://github.com/asottile/pyupgrade
-    rev: v3.19.1
+    rev: v3.15.2
     hooks:
     -   id: pyupgrade
-
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.11.5
+    rev: v0.3.4
     hooks:
       - id: ruff
         args: [--fix]
       - id: ruff-format
-
-
-  ##### Security #####
-  - repo: https://github.com/gitleaks/gitleaks
-    rev: v8.24.3
+  - repo: https://github.com/python-poetry/poetry
+    rev: 1.8.0
     hooks:
-      - id: gitleaks
-
-  - repo: https://github.com/woodruffw/zizmor-pre-commit
-    rev: v1.5.2
-    hooks:
-      - id: zizmor
-
-  - repo: https://github.com/PyCQA/bandit
-    rev: 1.8.3
-    hooks:
-    - id: bandit
-      args: ["-c", "pyproject.toml"]
-      additional_dependencies: ["bandit[toml]"]
+      - id: poetry-check
+      - id: poetry-lock
+        args:
+          - "--check"
+          - "--no-update"

CODE_OF_CONDUCT.md

@@ -1,133 +0,0 @@
-
-# Contributor Covenant Code of Conduct
-
-## Our Pledge
-
-We as members, contributors, and leaders pledge to make participation in our
-community a harassment-free experience for everyone, regardless of age, body
-size, visible or invisible disability, ethnicity, sex characteristics, gender
-identity and expression, level of experience, education, socio-economic status,
-nationality, personal appearance, race, caste, color, religion, or sexual
-identity and orientation.
-
-We pledge to act and interact in ways that contribute to an open, welcoming,
-diverse, inclusive, and healthy community.
-
-## Our Standards
-
-Examples of behavior that contributes to a positive environment for our
-community include:
-
-* Demonstrating empathy and kindness toward other people
-* Being respectful of differing opinions, viewpoints, and experiences
-* Giving and gracefully accepting constructive feedback
-* Accepting responsibility and apologizing to those affected by our mistakes,
-  and learning from the experience
-* Focusing on what is best not just for us as individuals, but for the overall
-  community
-
-Examples of unacceptable behavior include:
-
-* The use of sexualized language or imagery, and sexual attention or advances of
-  any kind
-* Trolling, insulting or derogatory comments, and personal or political attacks
-* Public or private harassment
-* Publishing others' private information, such as a physical or email address,
-  without their explicit permission
-* Other conduct which could reasonably be considered inappropriate in a
-  professional setting
-
-## Enforcement Responsibilities
-
-Community leaders are responsible for clarifying and enforcing our standards of
-acceptable behavior and will take appropriate and fair corrective action in
-response to any behavior that they deem inappropriate, threatening, offensive,
-or harmful.
-
-Community leaders have the right and responsibility to remove, edit, or reject
-comments, commits, code, wiki edits, issues, and other contributions that are
-not aligned to this Code of Conduct, and will communicate reasons for moderation
-decisions when appropriate.
-
-## Scope
-
-This Code of Conduct applies within all community spaces, and also applies when
-an individual is officially representing the community in public spaces.
-Examples of representing our community include using an official email address,
-posting via an official social media account, or acting as an appointed
-representative at an online or offline event.
-
-## Enforcement
-
-Instances of abusive, harassing, or otherwise unacceptable behavior may be
-reported to the community leaders responsible for enforcement at
-[feedback@huggingface.co](mailto:feedback@huggingface.co).
-All complaints will be reviewed and investigated promptly and fairly.
-
-All community leaders are obligated to respect the privacy and security of the
-reporter of any incident.
-
-## Enforcement Guidelines
-
-Community leaders will follow these Community Impact Guidelines in determining
-the consequences for any action they deem in violation of this Code of Conduct:
-
-### 1. Correction
-
-**Community Impact**: Use of inappropriate language or other behavior deemed
-unprofessional or unwelcome in the community.
-
-**Consequence**: A private, written warning from community leaders, providing
-clarity around the nature of the violation and an explanation of why the
-behavior was inappropriate. A public apology may be requested.
-
-### 2. Warning
-
-**Community Impact**: A violation through a single incident or series of
-actions.
-
-**Consequence**: A warning with consequences for continued behavior. No
-interaction with the people involved, including unsolicited interaction with
-those enforcing the Code of Conduct, for a specified period of time. This
-includes avoiding interactions in community spaces as well as external channels
-like social media. Violating these terms may lead to a temporary or permanent
-ban.
-
-### 3. Temporary Ban
-
-**Community Impact**: A serious violation of community standards, including
-sustained inappropriate behavior.
-
-**Consequence**: A temporary ban from any sort of interaction or public
-communication with the community for a specified period of time. No public or
-private interaction with the people involved, including unsolicited interaction
-with those enforcing the Code of Conduct, is allowed during this period.
-Violating these terms may lead to a permanent ban.
-
-### 4. Permanent Ban
-
-**Community Impact**: Demonstrating a pattern of violation of community
-standards, including sustained inappropriate behavior, harassment of an
-individual, or aggression toward or disparagement of classes of individuals.
-
-**Consequence**: A permanent ban from any sort of public interaction within the
-community.
-
-## Attribution
-
-This Code of Conduct is adapted from the [Contributor Covenant][homepage],
-version 2.1, available at
-[https://www.contributor-covenant.org/version/2/1/code_of_conduct.html][v2.1].
-
-Community Impact Guidelines were inspired by
-[Mozilla's code of conduct enforcement ladder][Mozilla CoC].
-
-For answers to common questions about this code of conduct, see the FAQ at
-[https://www.contributor-covenant.org/faq][FAQ]. Translations are available at
-[https://www.contributor-covenant.org/translations][translations].
-
-[homepage]: https://www.contributor-covenant.org
-[v2.1]: https://www.contributor-covenant.org/version/2/1/code_of_conduct.html
-[Mozilla CoC]: https://github.com/mozilla/diversity
-[FAQ]: https://www.contributor-covenant.org/faq
-[translations]: https://www.contributor-covenant.org/translations

CONTRIBUTING.md

@@ -1,308 +0,0 @@
-# How to contribute to 🤗 LeRobot?
-
-Everyone is welcome to contribute, and we value everybody's contribution. Code
-is thus not the only way to help the community. Answering questions, helping
-others, reaching out and improving the documentations are immensely valuable to
-the community.
-
-It also helps us if you spread the word: reference the library from blog posts
-on the awesome projects it made possible, shout out on Twitter when it has
-helped you, or simply ⭐️ the repo to say "thank you".
-
-Whichever way you choose to contribute, please be mindful to respect our
-[code of conduct](https://github.com/huggingface/lerobot/blob/main/CODE_OF_CONDUCT.md).
-
-## You can contribute in so many ways!
-
-Some of the ways you can contribute to 🤗 LeRobot:
-* Fixing outstanding issues with the existing code.
-* Implementing new models, datasets or simulation environments.
-* Contributing to the examples or to the documentation.
-* Submitting issues related to bugs or desired new features.
-
-Following the guides below, feel free to open issues and PRs and to coordinate your efforts with the community on our [Discord Channel](https://discord.gg/VjFz58wn3R). For specific inquiries, reach out to [Remi Cadene](mailto:remi.cadene@huggingface.co).
-
-If you are not sure how to contribute or want to know the next features we working on, look on this project page: [LeRobot TODO](https://github.com/orgs/huggingface/projects/46)
-
-## Submitting a new issue or feature request
-
-Do your best to follow these guidelines when submitting an issue or a feature
-request. It will make it easier for us to come back to you quickly and with good
-feedback.
-
-### Did you find a bug?
-
-The 🤗 LeRobot library is robust and reliable thanks to the users who notify us of
-the problems they encounter. So thank you for reporting an issue.
-
-First, we would really appreciate it if you could **make sure the bug was not
-already reported** (use the search bar on Github under Issues).
-
-Did not find it? :( So we can act quickly on it, please follow these steps:
-
-* Include your **OS type and version**, the versions of **Python** and **PyTorch**.
-* A short, self-contained, code snippet that allows us to reproduce the bug in
-  less than 30s.
-* The full traceback if an exception is raised.
-* Attach any other additional information, like screenshots, you think may help.
-
-### Do you want a new feature?
-
-A good feature request addresses the following points:
-
-1. Motivation first:
-* Is it related to a problem/frustration with the library? If so, please explain
-  why. Providing a code snippet that demonstrates the problem is best.
-* Is it related to something you would need for a project? We'd love to hear
-  about it!
-* Is it something you worked on and think could benefit the community?
-  Awesome! Tell us what problem it solved for you.
-2. Write a *paragraph* describing the feature.
-3. Provide a **code snippet** that demonstrates its future use.
-4. In case this is related to a paper, please attach a link.
-5. Attach any additional information (drawings, screenshots, etc.) you think may help.
-
-If your issue is well written we're already 80% of the way there by the time you
-post it.
-
-## Adding new policies, datasets or environments
-
-Look at our implementations for [datasets](./lerobot/common/datasets/), [policies](./lerobot/common/policies/),
-environments ([aloha](https://github.com/huggingface/gym-aloha),
-[xarm](https://github.com/huggingface/gym-xarm),
-[pusht](https://github.com/huggingface/gym-pusht))
-and follow the same api design.
-
-When implementing a new dataset loadable with LeRobotDataset follow these steps:
-- Update `available_datasets_per_env` in `lerobot/__init__.py`
-
-When implementing a new environment (e.g. `gym_aloha`), follow these steps:
-- Update `available_tasks_per_env` and `available_datasets_per_env` in `lerobot/__init__.py`
-
-When implementing a new policy class (e.g. `DiffusionPolicy`) follow these steps:
-- Update `available_policies` and `available_policies_per_env`, in `lerobot/__init__.py`
-- Set the required `name` class attribute.
-- Update variables in `tests/test_available.py` by importing your new Policy class
-
-## Submitting a pull request (PR)
-
-Before writing code, we strongly advise you to search through the existing PRs or
-issues to make sure that nobody is already working on the same thing. If you are
-unsure, it is always a good idea to open an issue to get some feedback.
-
-You will need basic `git` proficiency to be able to contribute to
-🤗 LeRobot. `git` is not the easiest tool to use but it has the greatest
-manual. Type `git --help` in a shell and enjoy. If you prefer books, [Pro
-Git](https://git-scm.com/book/en/v2) is a very good reference.
-
-Follow these steps to start contributing:
-
-1. Fork the [repository](https://github.com/huggingface/lerobot) by
-   clicking on the 'Fork' button on the repository's page. This creates a copy of the code
-   under your GitHub user account.
-
-2. Clone your fork to your local disk, and add the base repository as a remote. The following command
-   assumes you have your public SSH key uploaded to GitHub. See the following guide for more
-   [information](https://docs.github.com/en/repositories/creating-and-managing-repositories/cloning-a-repository).
-
-   ```bash
-   git clone git@github.com:<your Github handle>/lerobot.git
-   cd lerobot
-   git remote add upstream https://github.com/huggingface/lerobot.git
-   ```
-
-3. Create a new branch to hold your development changes, and do this for every new PR you work on.
-
-   Start by synchronizing your `main` branch with the `upstream/main` branch (more details in the [GitHub Docs](https://docs.github.com/en/github/collaborating-with-issues-and-pull-requests/syncing-a-fork)):
-
-   ```bash
-   git checkout main
-   git fetch upstream
-   git rebase upstream/main
-   ```
-
-   Once your `main` branch is synchronized, create a new branch from it:
-
-   ```bash
-   git checkout -b a-descriptive-name-for-my-changes
-   ```
-
-   🚨 **Do not** work on the `main` branch.
-
-4. for development, we advise to use a tool like `poetry` or `uv` instead of just `pip` to easily track our dependencies.
-   Follow the instructions to [install poetry](https://python-poetry.org/docs/#installation) (use a version >=2.1.0) or to [install uv](https://docs.astral.sh/uv/getting-started/installation/#installation-methods) if you don't have one of them already.
-
-   Set up a development environment with conda or miniconda:
-   ```bash
-   conda create -y -n lerobot-dev python=3.10 && conda activate lerobot-dev
-   ```
-
-   If you're using `uv`, it can manage python versions so you can instead do:
-   ```bash
-   uv venv --python 3.10 && source .venv/bin/activate
-   ```
-
-   To develop on 🤗 LeRobot, you will at least need to install the `dev` and `test` extras dependencies along with the core library:
-
-   using `poetry`
-   ```bash
-   poetry sync --extras "dev test"
-   ```
-
-   using `uv`
-   ```bash
-   uv sync --extra dev --extra test
-   ```
-
-   You can also install the project with all its dependencies (including environments):
-
-   using `poetry`
-   ```bash
-   poetry sync --all-extras
-   ```
-
-   using `uv`
-   ```bash
-   uv sync --all-extras
-   ```
-
-   > **Note:** If you don't install simulation environments with `--all-extras`, the tests that require them will be skipped when running the pytest suite locally. However, they *will* be tested in the CI. In general, we advise you to install everything and test locally before pushing.
-
-   Whichever command you chose to install the project (e.g. `poetry sync --all-extras`), you should run it again when pulling code with an updated version of `pyproject.toml` and `poetry.lock` in order to synchronize your virtual environment with the new dependencies.
-
-   The equivalent of `pip install some-package`, would just be:
-
-   using `poetry`
-   ```bash
-   poetry add some-package
-   ```
-
-   using `uv`
-   ```bash
-   uv add some-package
-   ```
-
-   When making changes to the poetry sections of the `pyproject.toml`, you should run the following command to lock dependencies.
-   using `poetry`
-   ```bash
-   poetry lock
-   ```
-
-   using `uv`
-   ```bash
-   uv lock
-   ```
-
-
-5. Develop the features on your branch.
-
-   As you work on the features, you should make sure that the test suite
-   passes. You should run the tests impacted by your changes like this (see
-   below an explanation regarding the environment variable):
-
-   ```bash
-   pytest tests/<TEST_TO_RUN>.py
-   ```
-
-6. Follow our style.
-
-   `lerobot` relies on `ruff` to format its source code
-   consistently. Set up [`pre-commit`](https://pre-commit.com/) to run these checks
-   automatically as Git commit hooks.
-
-   Install `pre-commit` hooks:
-   ```bash
-   pre-commit install
-   ```
-
-   You can run these hooks whenever you need on staged files with:
-   ```bash
-   pre-commit
-   ```
-
-   Once you're happy with your changes, add changed files using `git add` and
-   make a commit with `git commit` to record your changes locally:
-
-   ```bash
-   git add modified_file.py
-   git commit
-   ```
-
-   Note, if you already committed some changes that have a wrong formatting, you can use:
-   ```bash
-   pre-commit run --all-files
-   ```
-
-   Please write [good commit messages](https://chris.beams.io/posts/git-commit/).
-
-   It is a good idea to sync your copy of the code with the original
-   repository regularly. This way you can quickly account for changes:
-
-   ```bash
-   git fetch upstream
-   git rebase upstream/main
-   ```
-
-   Push the changes to your account using:
-
-   ```bash
-   git push -u origin a-descriptive-name-for-my-changes
-   ```
-
-6. Once you are satisfied (**and the checklist below is happy too**), go to the
-   webpage of your fork on GitHub. Click on 'Pull request' to send your changes
-   to the project maintainers for review.
-
-7. It's ok if maintainers ask you for changes. It happens to core contributors
-   too! So everyone can see the changes in the Pull request, work in your local
-   branch and push the changes to your fork. They will automatically appear in
-   the pull request.
-
-
-### Checklist
-
-1. The title of your pull request should be a summary of its contribution;
-2. If your pull request addresses an issue, please mention the issue number in
-   the pull request description to make sure they are linked (and people
-   consulting the issue know you are working on it);
-3. To indicate a work in progress please prefix the title with `[WIP]`, or preferably mark
-   the PR as a draft PR. These are useful to avoid duplicated work, and to differentiate
-   it from PRs ready to be merged;
-4. Make sure existing tests pass;
-<!-- 5. Add high-coverage tests. No quality testing = no merge.
-
-See an example of a good PR here: https://github.com/huggingface/lerobot/pull/ -->
-
-### Tests
-
-An extensive test suite is included to test the library behavior and several examples. Library tests can be found in the [tests folder](https://github.com/huggingface/lerobot/tree/main/tests).
-
-Install [git lfs](https://git-lfs.com/) to retrieve test artifacts (if you don't have it already).
-
-On Mac:
-```bash
-brew install git-lfs
-git lfs install
-```
-
-On Ubuntu:
-```bash
-sudo apt-get install git-lfs
-git lfs install
-```
-
-Pull artifacts if they're not in [tests/artifacts](tests/artifacts)
-```bash
-git lfs pull
-```
-
-We use `pytest` in order to run the tests. From the root of the
-repository, here's how to run tests with `pytest` for the library:
-
-```bash
-python -m pytest -sv ./tests
-```
-
-
-You can specify a smaller set of tests in order to test only the feature
-you're working on.

Makefile

@@ -1,142 +0,0 @@
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-.PHONY: tests
-
-PYTHON_PATH := $(shell which python)
-
-# If uv is installed and a virtual environment exists, use it
-UV_CHECK := $(shell command -v uv)
-ifneq ($(UV_CHECK),)
-	PYTHON_PATH := $(shell .venv/bin/python)
-endif
-
-export PATH := $(dir $(PYTHON_PATH)):$(PATH)
-
-DEVICE ?= cpu
-
-build-cpu:
-	docker build -t lerobot:latest -f docker/lerobot-cpu/Dockerfile .
-
-build-gpu:
-	docker build -t lerobot:latest -f docker/lerobot-gpu/Dockerfile .
-
-test-end-to-end:
-	${MAKE} DEVICE=$(DEVICE) test-act-ete-train
-	${MAKE} DEVICE=$(DEVICE) test-act-ete-train-resume
-	${MAKE} DEVICE=$(DEVICE) test-act-ete-eval
-	${MAKE} DEVICE=$(DEVICE) test-diffusion-ete-train
-	${MAKE} DEVICE=$(DEVICE) test-diffusion-ete-eval
-	${MAKE} DEVICE=$(DEVICE) test-tdmpc-ete-train
-	${MAKE} DEVICE=$(DEVICE) test-tdmpc-ete-eval
-
-test-act-ete-train:
-	python lerobot/scripts/train.py \
-		--policy.type=act \
-		--policy.dim_model=64 \
-		--policy.n_action_steps=20 \
-		--policy.chunk_size=20 \
-		--policy.device=$(DEVICE) \
-		--env.type=aloha \
-		--env.episode_length=5 \
-		--dataset.repo_id=lerobot/aloha_sim_transfer_cube_human \
-		--dataset.image_transforms.enable=true \
-		--dataset.episodes="[0]" \
-		--batch_size=2 \
-		--steps=4 \
-		--eval_freq=2 \
-		--eval.n_episodes=1 \
-		--eval.batch_size=1 \
-		--save_freq=2 \
-		--save_checkpoint=true \
-		--log_freq=1 \
-		--wandb.enable=false \
-		--output_dir=tests/outputs/act/
-
-test-act-ete-train-resume:
-	python lerobot/scripts/train.py \
-		--config_path=tests/outputs/act/checkpoints/000002/pretrained_model/train_config.json \
-		--resume=true
-
-test-act-ete-eval:
-	python lerobot/scripts/eval.py \
-		--policy.path=tests/outputs/act/checkpoints/000004/pretrained_model \
-		--policy.device=$(DEVICE) \
-		--env.type=aloha \
-		--env.episode_length=5 \
-		--eval.n_episodes=1 \
-		--eval.batch_size=1
-
-test-diffusion-ete-train:
-	python lerobot/scripts/train.py \
-		--policy.type=diffusion \
-		--policy.down_dims='[64,128,256]' \
-		--policy.diffusion_step_embed_dim=32 \
-		--policy.num_inference_steps=10 \
-		--policy.device=$(DEVICE) \
-		--env.type=pusht \
-		--env.episode_length=5 \
-		--dataset.repo_id=lerobot/pusht \
-		--dataset.image_transforms.enable=true \
-		--dataset.episodes="[0]" \
-		--batch_size=2 \
-		--steps=2 \
-		--eval_freq=2 \
-		--eval.n_episodes=1 \
-		--eval.batch_size=1 \
-		--save_checkpoint=true \
-		--save_freq=2 \
-		--log_freq=1 \
-		--wandb.enable=false \
-		--output_dir=tests/outputs/diffusion/
-
-test-diffusion-ete-eval:
-	python lerobot/scripts/eval.py \
-		--policy.path=tests/outputs/diffusion/checkpoints/000002/pretrained_model \
-		--policy.device=$(DEVICE) \
-		--env.type=pusht \
-		--env.episode_length=5 \
-		--eval.n_episodes=1 \
-		--eval.batch_size=1
-
-test-tdmpc-ete-train:
-	python lerobot/scripts/train.py \
-		--policy.type=tdmpc \
-		--policy.device=$(DEVICE) \
-		--env.type=xarm \
-		--env.task=XarmLift-v0 \
-		--env.episode_length=5 \
-		--dataset.repo_id=lerobot/xarm_lift_medium \
-		--dataset.image_transforms.enable=true \
-		--dataset.episodes="[0]" \
-		--batch_size=2 \
-		--steps=2 \
-		--eval_freq=2 \
-		--eval.n_episodes=1 \
-		--eval.batch_size=1 \
-		--save_checkpoint=true \
-		--save_freq=2 \
-		--log_freq=1 \
-		--wandb.enable=false \
-		--output_dir=tests/outputs/tdmpc/
-
-test-tdmpc-ete-eval:
-	python lerobot/scripts/eval.py \
-		--policy.path=tests/outputs/tdmpc/checkpoints/000002/pretrained_model \
-		--policy.device=$(DEVICE) \
-		--env.type=xarm \
-		--env.episode_length=5 \
-		--env.task=XarmLift-v0 \
-		--eval.n_episodes=1 \
-		--eval.batch_size=1

README.md

@@ -10,76 +10,39 @@
 
 <div align="center">
 
-[![Tests](https://github.com/huggingface/lerobot/actions/workflows/nightly-tests.yml/badge.svg?branch=main)](https://github.com/huggingface/lerobot/actions/workflows/nightly-tests.yml?query=branch%3Amain)
+[![Tests](https://github.com/huggingface/lerobot/actions/workflows/test.yml/badge.svg?branch=main)](https://github.com/huggingface/lerobot/actions/workflows/test.yml?query=branch%3Amain)
 [![Coverage](https://codecov.io/gh/huggingface/lerobot/branch/main/graph/badge.svg?token=TODO)](https://codecov.io/gh/huggingface/lerobot)
 [![Python versions](https://img.shields.io/pypi/pyversions/lerobot)](https://www.python.org/downloads/)
 [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://github.com/huggingface/lerobot/blob/main/LICENSE)
 [![Status](https://img.shields.io/pypi/status/lerobot)](https://pypi.org/project/lerobot/)
 [![Version](https://img.shields.io/pypi/v/lerobot)](https://pypi.org/project/lerobot/)
 [![Examples](https://img.shields.io/badge/Examples-green.svg)](https://github.com/huggingface/lerobot/tree/main/examples)
-[![Contributor Covenant](https://img.shields.io/badge/Contributor%20Covenant-v2.1%20adopted-ff69b4.svg)](https://github.com/huggingface/lerobot/blob/main/CODE_OF_CONDUCT.md)
 [![Discord](https://dcbadge.vercel.app/api/server/C5P34WJ68S?style=flat)](https://discord.gg/s3KuuzsPFb)
 
 </div>
 
-<h2 align="center">
-    <p><a href="https://github.com/huggingface/lerobot/blob/main/examples/12_use_so101.md">
-        Build Your Own SO-101 Robot!</a></p>
-</h2>
-
-<div align="center">
-  <div style="display: flex; gap: 1rem; justify-content: center; align-items: center;" >
-    <img
-      src="media/so101/so101.webp?raw=true"
-      alt="SO-101 follower arm"
-      title="SO-101 follower arm"
-      style="width: 40%;"
-    />
-    <img
-      src="media/so101/so101-leader.webp?raw=true"
-      alt="SO-101 leader arm"
-      title="SO-101 leader arm"
-      style="width: 40%;"
-    />
-  </div>
-
-
-  <p><strong>Meet the updated SO100, the SO-101 – Just €114 per arm!</strong></p>
-  <p>Train it in minutes with a few simple moves on your laptop.</p>
-  <p>Then sit back and watch your creation act autonomously! 🤯</p>
-
-  <p><a href="https://github.com/huggingface/lerobot/blob/main/examples/12_use_so101.md">
-      See the full SO-101 tutorial here.</a></p>
-
-  <p>Want to take it to the next level? Make your SO-101 mobile by building LeKiwi!</p>
-  <p>Check out the <a href="https://github.com/huggingface/lerobot/blob/main/examples/11_use_lekiwi.md">LeKiwi tutorial</a> and bring your robot to life on wheels.</p>
-
-  <img src="media/lekiwi/kiwi.webp?raw=true" alt="LeKiwi mobile robot" title="LeKiwi mobile robot" width="50%">
-</div>
-
-<br/>
-
 <h3 align="center">
-    <p>LeRobot: State-of-the-art AI for real-world robotics</p>
+    <p>State-of-the-art Machine Learning for real-world robotics</p>
 </h3>
 
 ---
 
-🤗 LeRobot aims to provide models, datasets, and tools for real-world robotics in PyTorch. The goal is to lower the barrier to entry to robotics so that everyone can contribute and benefit from sharing datasets and pretrained models.
+
+🤗 LeRobot aims to provide models, datasets, and tools for real-world robotics in PyTorch. The goal is to lower the barrier for entry to robotics so that everyone can contribute and benefit from sharing datasets and pretrained models.
 
 🤗 LeRobot contains state-of-the-art approaches that have been shown to transfer to the real-world with a focus on imitation learning and reinforcement learning.
 
-🤗 LeRobot already provides a set of pretrained models, datasets with human collected demonstrations, and simulation environments to get started without assembling a robot. In the coming weeks, the plan is to add more and more support for real-world robotics on the most affordable and capable robots out there.
+🤗 LeRobot already provides a set of pretrained models, datasets with human collected demonstrations, and simulated environments so that everyone can get started. In the coming weeks, the plan is to add more and more support for real-world robotics on the most affordable and capable robots out there.
 
-🤗 LeRobot hosts pretrained models and datasets on this Hugging Face community page: [huggingface.co/lerobot](https://huggingface.co/lerobot)
+🤗 LeRobot hosts pretrained models and datasets on this HuggingFace community page: [huggingface.co/lerobot](https://huggingface.co/lerobot)
 
-#### Examples of pretrained models on simulation environments
+#### Examples of pretrained models and environments
 
 <table>
   <tr>
-    <td><img src="media/gym/aloha_act.gif" width="100%" alt="ACT policy on ALOHA env"/></td>
-    <td><img src="media/gym/simxarm_tdmpc.gif" width="100%" alt="TDMPC policy on SimXArm env"/></td>
-    <td><img src="media/gym/pusht_diffusion.gif" width="100%" alt="Diffusion policy on PushT env"/></td>
+    <td><img src="http://remicadene.com/assets/gif/aloha_act.gif" width="100%" alt="ACT policy on ALOHA env"/></td>
+    <td><img src="http://remicadene.com/assets/gif/simxarm_tdmpc.gif" width="100%" alt="TDMPC policy on SimXArm env"/></td>
+    <td><img src="http://remicadene.com/assets/gif/pusht_diffusion.gif" width="100%" alt="Diffusion policy on PushT env"/></td>
   </tr>
   <tr>
     <td align="center">ACT policy on ALOHA env</td>
@@ -90,12 +53,10 @@
 
 ### Acknowledgment
 
-- Thanks to Tony Zhao, Zipeng Fu and colleagues for open sourcing ACT policy, ALOHA environments and datasets. Ours are adapted from [ALOHA](https://tonyzhaozh.github.io/aloha) and [Mobile ALOHA](https://mobile-aloha.github.io).
-- Thanks to Cheng Chi, Zhenjia Xu and colleagues for open sourcing Diffusion policy, Pusht environment and datasets, as well as UMI datasets. Ours are adapted from [Diffusion Policy](https://diffusion-policy.cs.columbia.edu) and [UMI Gripper](https://umi-gripper.github.io).
-- Thanks to Nicklas Hansen, Yunhai Feng and colleagues for open sourcing TDMPC policy, Simxarm environments and datasets. Ours are adapted from [TDMPC](https://github.com/nicklashansen/tdmpc) and [FOWM](https://www.yunhaifeng.com/FOWM).
-- Thanks to Antonio Loquercio and Ashish Kumar for their early support.
-- Thanks to [Seungjae (Jay) Lee](https://sjlee.cc/), [Mahi Shafiullah](https://mahis.life/) and colleagues for open sourcing [VQ-BeT](https://sjlee.cc/vq-bet/) policy and helping us adapt the codebase to our repository. The policy is adapted from [VQ-BeT repo](https://github.com/jayLEE0301/vq_bet_official).
-
+- ACT policy and ALOHA environment are adapted from [ALOHA](https://tonyzhaozh.github.io/aloha/)
+- Diffusion policy and Pusht environment are adapted from [Diffusion Policy](https://diffusion-policy.cs.columbia.edu/)
+- TDMPC policy and Simxarm environment are adapted from [FOWM](https://www.yunhaifeng.com/FOWM/)
+- Abstractions and utilities for Reinforcement Learning come from [TorchRL](https://github.com/pytorch/rl)
 
 ## Installation
 
@@ -111,224 +72,298 @@ conda create -y -n lerobot python=3.10
 conda activate lerobot
 ```
 
-When using `miniconda`, install `ffmpeg` in your environment:
+Then, install 🤗 LeRobot:
 ```bash
-conda install ffmpeg -c conda-forge
+python -m pip install .
 ```
 
-> **NOTE:** This usually installs `ffmpeg 7.X` for your platform compiled with the `libsvtav1` encoder. If `libsvtav1` is not supported (check supported encoders with `ffmpeg -encoders`), you can:
->  - _[On any platform]_ Explicitly install `ffmpeg 7.X` using:
->  ```bash
->  conda install ffmpeg=7.1.1 -c conda-forge
->  ```
->  - _[On Linux only]_ Install [ffmpeg build dependencies](https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu#GettheDependencies) and [compile ffmpeg from source with libsvtav1](https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu#libsvtav1), and make sure you use the corresponding ffmpeg binary to your install with `which ffmpeg`.
-
-Install 🤗 LeRobot:
-```bash
-pip install -e .
-```
-
-> **NOTE:** If you encounter build errors, you may need to install additional dependencies (`cmake`, `build-essential`, and `ffmpeg libs`). On Linux, run:
-`sudo apt-get install cmake build-essential python3-dev pkg-config libavformat-dev libavcodec-dev libavdevice-dev libavutil-dev libswscale-dev libswresample-dev libavfilter-dev pkg-config`. For other systems, see: [Compiling PyAV](https://pyav.org/docs/develop/overview/installation.html#bring-your-own-ffmpeg)
-
-For simulations, 🤗 LeRobot comes with gymnasium environments that can be installed as extras:
-- [aloha](https://github.com/huggingface/gym-aloha)
-- [xarm](https://github.com/huggingface/gym-xarm)
-- [pusht](https://github.com/huggingface/gym-pusht)
-
-For instance, to install 🤗 LeRobot with aloha and pusht, use:
-```bash
-pip install -e ".[aloha, pusht]"
-```
-
-To use [Weights and Biases](https://docs.wandb.ai/quickstart) for experiment tracking, log in with
+To use [Weights and Biases](https://docs.wandb.ai/quickstart) for experiments tracking, log in with
 ```bash
 wandb login
 ```
 
-(note: you will also need to enable WandB in the configuration. See below.)
-
 ## Walkthrough
 
 ```
 .
-├── examples             # contains demonstration examples, start here to learn about LeRobot
-|   └── advanced         # contains even more examples for those who have mastered the basics
 ├── lerobot
-|   ├── configs          # contains config classes with all options that you can override in the command line
+|   ├── configs          # contains hydra yaml files with all options that you can override in the command line
+|   |   ├── default.yaml   # selected by default, it loads pusht environment and diffusion policy
+|   |   ├── env            # various sim environments and their datasets: aloha.yaml, pusht.yaml, simxarm.yaml
+|   |   └── policy         # various policies: act.yaml, diffusion.yaml, tdmpc.yaml
 |   ├── common           # contains classes and utilities
-|   |   ├── datasets       # various datasets of human demonstrations: aloha, pusht, xarm
-|   |   ├── envs           # various sim environments: aloha, pusht, xarm
-|   |   ├── policies       # various policies: act, diffusion, tdmpc
-|   |   ├── robot_devices  # various real devices: dynamixel motors, opencv cameras, koch robots
-|   |   └── utils          # various utilities
-|   └── scripts          # contains functions to execute via command line
-|       ├── eval.py                 # load policy and evaluate it on an environment
-|       ├── train.py                # train a policy via imitation learning and/or reinforcement learning
-|       ├── control_robot.py        # teleoperate a real robot, record data, run a policy
-|       ├── push_dataset_to_hub.py  # convert your dataset into LeRobot dataset format and upload it to the Hugging Face hub
-|       └── visualize_dataset.py    # load a dataset and render its demonstrations
+|   |   ├── datasets       # various datasets of human demonstrations: aloha, pusht, simxarm
+|   |   ├── envs           # various sim environments: aloha, pusht, simxarm
+|   |   └── policies       # various policies: act, diffusion, tdmpc
+|   └── scripts                  # contains functions to execute via command line
+|       ├── visualize_dataset.py  # load a dataset and render its demonstrations
+|       ├── eval.py               # load policy and evaluate it on an environment
+|       └── train.py              # train a policy via imitation learning and/or reinforcement learning
 ├── outputs               # contains results of scripts execution: logs, videos, model checkpoints
+├── .github
+|   └── workflows
+|       └── test.yml      # defines install settings for continuous integration and specifies end-to-end tests
 └── tests                 # contains pytest utilities for continuous integration
+
 ```
 
 ### Visualize datasets
 
-Check out [example 1](./examples/1_load_lerobot_dataset.py) that illustrates how to use our dataset class which automatically downloads data from the Hugging Face hub.
+You can import our dataset class, download the data from the HuggingFace hub and use our rendering utilities:
+```python
+""" Copy pasted from `examples/1_visualize_dataset.py` """
+import lerobot
+from lerobot.common.datasets.aloha import AlohaDataset
+from torchrl.data.replay_buffers import SamplerWithoutReplacement
+from lerobot.scripts.visualize_dataset import render_dataset
 
-You can also locally visualize episodes from a dataset on the hub by executing our script from the command line:
+print(lerobot.available_datasets)
+# >>> ['aloha_sim_insertion_human', 'aloha_sim_insertion_scripted', 'aloha_sim_transfer_cube_human', 'aloha_sim_transfer_cube_scripted', 'pusht', 'xarm_lift_medium']
+
+# we use this sampler to sample 1 frame after the other
+sampler = SamplerWithoutReplacement(shuffle=False)
+
+dataset = AlohaDataset("aloha_sim_transfer_cube_human", sampler=sampler)
+
+video_paths = render_dataset(
+    dataset,
+    out_dir="outputs/visualize_dataset/example",
+    max_num_samples=300,
+    fps=50,
+)
+print(video_paths)
+# >>> ['outputs/visualize_dataset/example/episode_0.mp4']
+```
+
+Or you can achieve the same result by executing our script from the command line:
 ```bash
 python lerobot/scripts/visualize_dataset.py \
-    --repo-id lerobot/pusht \
-    --episode-index 0
+env=aloha \
+task=sim_sim_transfer_cube_human \
+hydra.run.dir=outputs/visualize_dataset/example
+# >>> ['outputs/visualize_dataset/example/episode_0.mp4']
 ```
 
-or from a dataset in a local folder with the `root` option and the `--local-files-only` (in the following case the dataset will be searched for in `./my_local_data_dir/lerobot/pusht`)
-```bash
-python lerobot/scripts/visualize_dataset.py \
-    --repo-id lerobot/pusht \
-    --root ./my_local_data_dir \
-    --local-files-only 1 \
-    --episode-index 0
-```
-
-
-It will open `rerun.io` and display the camera streams, robot states and actions, like this:
-
-https://github-production-user-asset-6210df.s3.amazonaws.com/4681518/328035972-fd46b787-b532-47e2-bb6f-fd536a55a7ed.mov?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAVCODYLSA53PQK4ZA%2F20240505%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20240505T172924Z&X-Amz-Expires=300&X-Amz-Signature=d680b26c532eeaf80740f08af3320d22ad0b8a4e4da1bcc4f33142c15b509eda&X-Amz-SignedHeaders=host&actor_id=24889239&key_id=0&repo_id=748713144
-
-
-Our script can also visualize datasets stored on a distant server. See `python lerobot/scripts/visualize_dataset.py --help` for more instructions.
-
-### The `LeRobotDataset` format
-
-A dataset in `LeRobotDataset` format is very simple to use. It can be loaded from a repository on the Hugging Face hub or a local folder simply with e.g. `dataset = LeRobotDataset("lerobot/aloha_static_coffee")` and can be indexed into like any Hugging Face and PyTorch dataset. For instance `dataset[0]` will retrieve a single temporal frame from the dataset containing observation(s) and an action as PyTorch tensors ready to be fed to a model.
-
-A specificity of `LeRobotDataset` is that, rather than retrieving a single frame by its index, we can retrieve several frames based on their temporal relationship with the indexed frame, by setting `delta_timestamps` to a list of relative times with respect to the indexed frame. For example, with `delta_timestamps = {"observation.image": [-1, -0.5, -0.2, 0]}`  one can retrieve, for a given index, 4 frames: 3 "previous" frames 1 second, 0.5 seconds, and 0.2 seconds before the indexed frame, and the indexed frame itself (corresponding to the 0 entry). See example [1_load_lerobot_dataset.py](examples/1_load_lerobot_dataset.py) for more details on `delta_timestamps`.
-
-Under the hood, the `LeRobotDataset` format makes use of several ways to serialize data which can be useful to understand if you plan to work more closely with this format. We tried to make a flexible yet simple dataset format that would cover most type of features and specificities present in reinforcement learning and robotics, in simulation and in real-world, with a focus on cameras and robot states but easily extended to other types of sensory inputs as long as they can be represented by a tensor.
-
-Here are the important details and internal structure organization of a typical `LeRobotDataset` instantiated with `dataset = LeRobotDataset("lerobot/aloha_static_coffee")`. The exact features will change from dataset to dataset but not the main aspects:
-
-```
-dataset attributes:
-  ├ hf_dataset: a Hugging Face dataset (backed by Arrow/parquet). Typical features example:
-  │  ├ observation.images.cam_high (VideoFrame):
-  │  │   VideoFrame = {'path': path to a mp4 video, 'timestamp' (float32): timestamp in the video}
-  │  ├ observation.state (list of float32): position of an arm joints (for instance)
-  │  ... (more observations)
-  │  ├ action (list of float32): goal position of an arm joints (for instance)
-  │  ├ episode_index (int64): index of the episode for this sample
-  │  ├ frame_index (int64): index of the frame for this sample in the episode ; starts at 0 for each episode
-  │  ├ timestamp (float32): timestamp in the episode
-  │  ├ next.done (bool): indicates the end of an episode ; True for the last frame in each episode
-  │  └ index (int64): general index in the whole dataset
-  ├ episode_data_index: contains 2 tensors with the start and end indices of each episode
-  │  ├ from (1D int64 tensor): first frame index for each episode — shape (num episodes,) starts with 0
-  │  └ to: (1D int64 tensor): last frame index for each episode — shape (num episodes,)
-  ├ stats: a dictionary of statistics (max, mean, min, std) for each feature in the dataset, for instance
-  │  ├ observation.images.cam_high: {'max': tensor with same number of dimensions (e.g. `(c, 1, 1)` for images, `(c,)` for states), etc.}
-  │  ...
-  ├ info: a dictionary of metadata on the dataset
-  │  ├ codebase_version (str): this is to keep track of the codebase version the dataset was created with
-  │  ├ fps (float): frame per second the dataset is recorded/synchronized to
-  │  ├ video (bool): indicates if frames are encoded in mp4 video files to save space or stored as png files
-  │  └ encoding (dict): if video, this documents the main options that were used with ffmpeg to encode the videos
-  ├ videos_dir (Path): where the mp4 videos or png images are stored/accessed
-  └ camera_keys (list of string): the keys to access camera features in the item returned by the dataset (e.g. `["observation.images.cam_high", ...]`)
-```
-
-A `LeRobotDataset` is serialised using several widespread file formats for each of its parts, namely:
-- hf_dataset stored using Hugging Face datasets library serialization to parquet
-- videos are stored in mp4 format to save space
-- metadata are stored in plain json/jsonl files
-
-Dataset can be uploaded/downloaded from the HuggingFace hub seamlessly. To work on a local dataset, you can specify its location with the `root` argument if it's not in the default `~/.cache/huggingface/lerobot` location.
-
 ### Evaluate a pretrained policy
 
-Check out [example 2](./examples/2_evaluate_pretrained_policy.py) that illustrates how to download a pretrained policy from Hugging Face hub, and run an evaluation on its corresponding environment.
-
-We also provide a more capable script to parallelize the evaluation over multiple environments during the same rollout. Here is an example with a pretrained model hosted on [lerobot/diffusion_pusht](https://huggingface.co/lerobot/diffusion_pusht):
-```bash
-python lerobot/scripts/eval.py \
-    --policy.path=lerobot/diffusion_pusht \
-    --env.type=pusht \
-    --eval.batch_size=10 \
-    --eval.n_episodes=10 \
-    --policy.use_amp=false \
-    --policy.device=cuda
+You can import our environment class, download pretrained policies from the HuggingFace hub, and use our rollout utilities with rendering:
+```python
+""" Copy pasted from `examples/2_evaluate_pretrained_policy.py`
+# TODO
 ```
 
-Note: After training your own policy, you can re-evaluate the checkpoints with:
-
+Or you can achieve the same result by executing our script from the command line:
 ```bash
-python lerobot/scripts/eval.py --policy.path={OUTPUT_DIR}/checkpoints/last/pretrained_model
+python lerobot/scripts/eval.py \
+--hub-id lerobot/diffusion_policy_pusht_image \
+eval_episodes=10 \
+hydra.run.dir=outputs/eval/example_hub
+```
+
+After launching training of your own policy, you can also re-evaluate the checkpoints with:
+```bash
+python lerobot/scripts/eval.py \
+--config PATH/TO/FOLDER/config.yaml \
+policy.pretrained_model_path=PATH/TO/FOLDER/weights.pth \
+eval_episodes=10 \
+hydra.run.dir=outputs/eval/example_dir
 ```
 
 See `python lerobot/scripts/eval.py --help` for more instructions.
 
 ### Train your own policy
 
-Check out [example 3](./examples/3_train_policy.py) that illustrates how to train a model using our core library in python, and [example 4](./examples/4_train_policy_with_script.md) that shows how to use our training script from command line.
-
-To use wandb for logging training and evaluation curves, make sure you've run `wandb login` as a one-time setup step. Then, when running the training command above, enable WandB in the configuration by adding `--wandb.enable=true`.
-
-A link to the wandb logs for the run will also show up in yellow in your terminal. Here is an example of what they look like in your browser. Please also check [here](./examples/4_train_policy_with_script.md#typical-logs-and-metrics) for the explanation of some commonly used metrics in logs.
-
-![](media/wandb.png)
-
-Note: For efficiency, during training every checkpoint is evaluated on a low number of episodes. You may use `--eval.n_episodes=500` to evaluate on more episodes than the default. Or, after training, you may want to re-evaluate your best checkpoints on more episodes or change the evaluation settings. See `python lerobot/scripts/eval.py --help` for more instructions.
-
-#### Reproduce state-of-the-art (SOTA)
-
-We provide some pretrained policies on our [hub page](https://huggingface.co/lerobot) that can achieve state-of-the-art performances.
-You can reproduce their training by loading the config from their run. Simply running:
-```bash
-python lerobot/scripts/train.py --config_path=lerobot/diffusion_pusht
+You can import our dataset, environment, policy classes, and use our training utilities (if some data is missing, it will be automatically downloaded from HuggingFace hub):
+```python
+""" Copy pasted from `examples/3_train_policy.py`
+# TODO
+```
+
+Or you can achieve the same result by executing our script from the command line:
+```bash
+python lerobot/scripts/train.py \
+hydra.run.dir=outputs/train/example
+```
+
+You can easily train any policy on any environment:
+```bash
+python lerobot/scripts/train.py \
+env=aloha \
+task=sim_insertion \
+dataset_id=aloha_sim_insertion_scripted \
+policy=act \
+hydra.run.dir=outputs/train/aloha_act
 ```
-reproduces SOTA results for Diffusion Policy on the PushT task.
 
 ## Contribute
 
-If you would like to contribute to 🤗 LeRobot, please check out our [contribution guide](https://github.com/huggingface/lerobot/blob/main/CONTRIBUTING.md).
+Feel free to open issues and PRs, and to coordinate your efforts with the community on our [Discord Channel](https://discord.gg/VjFz58wn3R). For specific inquiries, reach out to [Remi Cadene](remi.cadene@huggingface.co).
 
-<!-- ### Add a new dataset
+### TODO
 
-To add a dataset to the hub, you need to login using a write-access token, which can be generated from the [Hugging Face settings](https://huggingface.co/settings/tokens):
+If you are not sure how to contribute or want to know the next features we working on, look on this project page: [LeRobot TODO](https://github.com/orgs/huggingface/projects/46)
+
+### Follow our style
+
+```bash
+# install if needed
+pre-commit install
+# apply style and linter checks before git commit
+pre-commit
+```
+
+### Add dependencies
+
+Instead of using `pip` directly, we use `poetry` for development purposes to easily track our dependencies.
+If you don't have it already, follow the [instructions](https://python-poetry.org/docs/#installation) to install it.
+
+Install the project with:
+```bash
+poetry install
+```
+
+Then, the equivalent of `pip install some-package`, would just be:
+```bash
+poetry add some-package
+```
+
+**NOTE:** Currently, to ensure the CI works properly, any new package must also be added in the CPU-only environment dedicated to the CI. To do this, you should create a separate environment and add the new package there as well. For example:
+```bash
+# Add the new package to your main poetry env
+poetry add some-package
+# Add the same package to the CPU-only env dedicated to CI
+conda create -y -n lerobot-ci python=3.10
+conda activate lerobot-ci
+cd .github/poetry/cpu
+poetry add some-package
+```
+
+### Run tests locally
+
+Install [git lfs](https://git-lfs.com/) to retrieve test artifacts (if you don't have it already).
+
+On Mac:
+```bash
+brew install git-lfs
+git lfs install
+```
+
+On Ubuntu:
+```bash
+sudo apt-get install git-lfs
+git lfs install
+```
+
+Pull artifacts if they're not in [tests/data](tests/data)
+```bash
+git lfs pull
+```
+
+When adding a new dataset, mock it with
+```bash
+python tests/scripts/mock_dataset.py --in-data-dir data/$DATASET --out-data-dir tests/data/$DATASET
+```
+
+Run tests
+```bash
+DATA_DIR="tests/data" pytest -sx tests
+```
+
+### Add a new dataset
+
+To add a dataset to the hub, first login and use a token generated from [huggingface settings](https://huggingface.co/settings/tokens) with write access:
 ```bash
 huggingface-cli login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential
 ```
 
-Then point to your raw dataset folder (e.g. `data/aloha_static_pingpong_test_raw`), and push your dataset to the hub with:
+Then you can upload it to the hub with:
 ```bash
-python lerobot/scripts/push_dataset_to_hub.py \
---raw-dir data/aloha_static_pingpong_test_raw \
---out-dir data \
---repo-id lerobot/aloha_static_pingpong_test \
---raw-format aloha_hdf5
+HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli upload $HF_USER/$DATASET data/$DATASET \
+--repo-type dataset  \
+--revision v1.0
 ```
 
-See `python lerobot/scripts/push_dataset_to_hub.py --help` for more instructions.
+You will need to set the corresponding version as a default argument in your dataset class:
+```python
+  version: str | None = "v1.0",
+```
+See: [`lerobot/common/datasets/pusht.py`](https://github.com/Cadene/lerobot/blob/main/lerobot/common/datasets/pusht.py)
 
-If your dataset format is not supported, implement your own in `lerobot/common/datasets/push_dataset_to_hub/${raw_format}_format.py` by copying examples like [pusht_zarr](https://github.com/huggingface/lerobot/blob/main/lerobot/common/datasets/push_dataset_to_hub/pusht_zarr_format.py), [umi_zarr](https://github.com/huggingface/lerobot/blob/main/lerobot/common/datasets/push_dataset_to_hub/umi_zarr_format.py), [aloha_hdf5](https://github.com/huggingface/lerobot/blob/main/lerobot/common/datasets/push_dataset_to_hub/aloha_hdf5_format.py), or [xarm_pkl](https://github.com/huggingface/lerobot/blob/main/lerobot/common/datasets/push_dataset_to_hub/xarm_pkl_format.py). -->
+For instance, for [lerobot/pusht](https://huggingface.co/datasets/lerobot/pusht), we used:
+```bash
+HF_USER=lerobot
+DATASET=pusht
+```
 
+If you want to improve an existing dataset, you can download it locally with:
+```bash
+mkdir -p data/$DATASET
+HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download ${HF_USER}/$DATASET \
+--repo-type dataset \
+--local-dir data/$DATASET \
+--local-dir-use-symlinks=False \
+--revision v1.0
+```
+
+Iterate on your code and dataset with:
+```bash
+DATA_DIR=data python train.py
+```
+
+Upload a new version (v2.0 or v1.1 if the changes are respectively more or less significant):
+```bash
+HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli upload $HF_USER/$DATASET data/$DATASET \
+--repo-type dataset \
+--revision v1.1 \
+--delete "*"
+```
+
+Then you will need to set the corresponding version as a default argument in your dataset class:
+```python
+  version: str | None = "v1.1",
+```
+See: [`lerobot/common/datasets/pusht.py`](https://github.com/Cadene/lerobot/blob/main/lerobot/common/datasets/pusht.py)
+
+
+Finally, you might want to mock the dataset if you need to update the unit tests as well:
+```bash
+python tests/scripts/mock_dataset.py --in-data-dir data/$DATASET --out-data-dir tests/data/$DATASET
+```
 
 ### Add a pretrained policy
 
-Once you have trained a policy you may upload it to the Hugging Face hub using a hub id that looks like `${hf_user}/${repo_name}` (e.g. [lerobot/diffusion_pusht](https://huggingface.co/lerobot/diffusion_pusht)).
+Once you have trained a policy you may upload it to the HuggingFace hub.
 
-You first need to find the checkpoint folder located inside your experiment directory (e.g. `outputs/train/2024-05-05/20-21-12_aloha_act_default/checkpoints/002500`). Within that there is a `pretrained_model` directory which should contain:
-- `config.json`: A serialized version of the policy configuration (following the policy's dataclass config).
-- `model.safetensors`: A set of `torch.nn.Module` parameters, saved in [Hugging Face Safetensors](https://huggingface.co/docs/safetensors/index) format.
-- `train_config.json`: A consolidated configuration containing all parameters used for training. The policy configuration should match `config.json` exactly. This is useful for anyone who wants to evaluate your policy or for reproducibility.
+Firstly, make sure you have a model repository set up on the hub. The hub ID looks like HF_USER/REPO_NAME.
 
-To upload these to the hub, run the following:
-```bash
-huggingface-cli upload ${hf_user}/${repo_name} path/to/pretrained_model
+Secondly, assuming you have trained a policy, you need:
+
+- `config.yaml` which you can get from the `.hydra` directory of your training output folder.
+- `model.pt` which should be one of the saved models in the `models` directory of your training output folder (they won't be named `model.pt` but you will need to choose one).
+- `stats.pth` which should point to the same file in the dataset directory (found in `data/{dataset_name}`).
+
+To upload these to the hub, prepare a folder with the following structure (you can use symlinks rather than copying):
+
+```
+to_upload
+    ├── config.yaml
+    ├── model.pt
+    └── stats.pth
 ```
 
-See [eval.py](https://github.com/huggingface/lerobot/blob/main/lerobot/scripts/eval.py) for an example of how other people may use your policy.
+With the folder prepared, run the following with a desired revision ID.
+
+```bash
+huggingface-cli upload $HUB_ID to_upload --revision $REVISION_ID
+```
+
+If you want this to be the default revision also run the following (don't worry, it won't upload the files again; it will just adjust the file pointers):
+
+```bash
+huggingface-cli upload $HUB_ID to_upload
+```
+
+See `eval.py` for an example of how a user may use your policy.
 
 
 ### Improve your code with profiling
@@ -355,59 +390,9 @@ with profile(
             # insert code to profile, potentially whole body of eval_policy function
 ```
 
-## Citation
-
-If you want, you can cite this work with:
-```bibtex
-@misc{cadene2024lerobot,
-    author = {Cadene, Remi and Alibert, Simon and Soare, Alexander and Gallouedec, Quentin and Zouitine, Adil and Wolf, Thomas},
-    title = {LeRobot: State-of-the-art Machine Learning for Real-World Robotics in Pytorch},
-    howpublished = "\url{https://github.com/huggingface/lerobot}",
-    year = {2024}
-}
+```bash
+python lerobot/scripts/eval.py \
+--config outputs/pusht/.hydra/config.yaml \
+pretrained_model_path=outputs/pusht/model.pt \
+eval_episodes=7
 ```
-
-Additionally, if you are using any of the particular policy architecture, pretrained models, or datasets, it is recommended to cite the original authors of the work as they appear below:
-
-- [Diffusion Policy](https://diffusion-policy.cs.columbia.edu)
-```bibtex
-@article{chi2024diffusionpolicy,
-	author = {Cheng Chi and Zhenjia Xu and Siyuan Feng and Eric Cousineau and Yilun Du and Benjamin Burchfiel and Russ Tedrake and Shuran Song},
-	title ={Diffusion Policy: Visuomotor Policy Learning via Action Diffusion},
-	journal = {The International Journal of Robotics Research},
-	year = {2024},
-}
-```
-- [ACT or ALOHA](https://tonyzhaozh.github.io/aloha)
-```bibtex
-@article{zhao2023learning,
-  title={Learning fine-grained bimanual manipulation with low-cost hardware},
-  author={Zhao, Tony Z and Kumar, Vikash and Levine, Sergey and Finn, Chelsea},
-  journal={arXiv preprint arXiv:2304.13705},
-  year={2023}
-}
-```
-
-- [TDMPC](https://www.nicklashansen.com/td-mpc/)
-
-```bibtex
-@inproceedings{Hansen2022tdmpc,
-	title={Temporal Difference Learning for Model Predictive Control},
-	author={Nicklas Hansen and Xiaolong Wang and Hao Su},
-	booktitle={ICML},
-	year={2022}
-}
-```
-
-- [VQ-BeT](https://sjlee.cc/vq-bet/)
-```bibtex
-@article{lee2024behavior,
-  title={Behavior generation with latent actions},
-  author={Lee, Seungjae and Wang, Yibin and Etukuru, Haritheja and Kim, H Jin and Shafiullah, Nur Muhammad Mahi and Pinto, Lerrel},
-  journal={arXiv preprint arXiv:2403.03181},
-  year={2024}
-}
-```
-## Star History
-
-[![Star History Chart](https://api.star-history.com/svg?repos=huggingface/lerobot&type=Timeline)](https://star-history.com/#huggingface/lerobot&Timeline)

benchmarks/video/README.md

@@ -1,271 +0,0 @@
-# Video benchmark
-
-
-## Questions
-What is the optimal trade-off between:
-- maximizing loading time with random access,
-- minimizing memory space on disk,
-- maximizing success rate of policies,
-- compatibility across devices/platforms for decoding videos (e.g. video players, web browsers).
-
-How to encode videos?
-- Which video codec (`-vcodec`) to use? h264, h265, AV1?
-- What pixel format to use (`-pix_fmt`)? `yuv444p` or `yuv420p`?
-- How much compression (`-crf`)? No compression with `0`, intermediate compression with `25` or extreme with `50+`?
-- Which frequency to chose for key frames (`-g`)? A key frame every `10` frames?
-
-How to decode videos?
-- Which `decoder`? `torchvision`, `torchaudio`, `ffmpegio`, `decord`, or `nvc`?
-- What scenarios to use for the requesting timestamps during benchmark? (`timestamps_mode`)
-
-
-## Variables
-**Image content & size**
-We don't expect the same optimal settings for a dataset of images from a simulation, or from real-world in an apartment, or in a factory, or outdoor, or with lots of moving objects in the scene, etc. Similarly, loading times might not vary linearly with the image size (resolution).
-For these reasons, we run this benchmark on four representative datasets:
-- `lerobot/pusht_image`: (96 x 96 pixels) simulation with simple geometric shapes, fixed camera.
-- `aliberts/aloha_mobile_shrimp_image`: (480 x 640 pixels) real-world indoor, moving camera.
-- `aliberts/paris_street`: (720 x 1280 pixels) real-world outdoor, moving camera.
-- `aliberts/kitchen`: (1080 x 1920 pixels) real-world indoor, fixed camera.
-
-Note: The datasets used for this benchmark need to be image datasets, not video datasets.
-
-**Data augmentations**
-We might revisit this benchmark and find better settings if we train our policies with various data augmentations to make them more robust (e.g. robust to color changes, compression, etc.).
-
-### Encoding parameters
-| parameter   | values                                                       |
-|-------------|--------------------------------------------------------------|
-| **vcodec**  | `libx264`, `libx265`, `libsvtav1`                            |
-| **pix_fmt** | `yuv444p`, `yuv420p`                                         |
-| **g**       | `1`, `2`, `3`, `4`, `5`, `6`, `10`, `15`, `20`, `40`, `None` |
-| **crf**     | `0`, `5`, `10`, `15`, `20`, `25`, `30`, `40`, `50`, `None`   |
-
-Note that `crf` value might be interpreted differently by various video codecs. In other words, the same value used with one codec doesn't necessarily translate into the same compression level with another codec. In fact, the default value (`None`) isn't the same amongst the different video codecs. Importantly, it is also the case for many other ffmpeg arguments like `g` which specifies the frequency of the key frames.
-
-For a comprehensive list and documentation of these parameters, see the ffmpeg documentation depending on the video codec used:
-- h264: https://trac.ffmpeg.org/wiki/Encode/H.264
-- h265: https://trac.ffmpeg.org/wiki/Encode/H.265
-- AV1: https://trac.ffmpeg.org/wiki/Encode/AV1
-
-### Decoding parameters
-**Decoder**
-We tested two video decoding backends from torchvision:
-- `pyav`
-- `video_reader` (requires to build torchvision from source)
-
-**Requested timestamps**
-Given the way video decoding works, once a keyframe has been loaded, the decoding of subsequent frames is fast.
-This of course is affected by the `-g` parameter during encoding, which specifies the frequency of the keyframes. Given our typical use cases in robotics policies which might request a few timestamps in different random places, we want to replicate these use cases with the following scenarios:
-- `1_frame`: 1 frame,
-- `2_frames`: 2 consecutive frames (e.g. `[t, t + 1 / fps]`),
-- `6_frames`: 6 consecutive frames (e.g. `[t + i / fps for i in range(6)]`)
-
-Note that this differs significantly from a typical use case like watching a movie, in which every frame is loaded sequentially from the beginning to the end and it's acceptable to have big values for `-g`.
-
-Additionally, because some policies might request single timestamps that are a few frames apart, we also have the following scenario:
-- `2_frames_4_space`: 2 frames with 4 consecutive frames of spacing in between (e.g `[t, t + 5 / fps]`),
-
-However, due to how video decoding is implemented with `pyav`, we don't have access to an accurate seek so in practice this scenario is essentially the same as `6_frames` since all 6 frames between `t` and `t + 5 / fps` will be decoded.
-
-
-## Metrics
-**Data compression ratio (lower is better)**
-`video_images_size_ratio` is the ratio of the memory space on disk taken by the encoded video over the memory space taken by the original images. For instance, `video_images_size_ratio=25%` means that the video takes 4 times less memory space on disk compared to the original images.
-
-**Loading time ratio (lower is better)**
-`video_images_load_time_ratio` is the ratio of the time it takes to decode frames from the video at a given timestamps over the time it takes to load the exact same original images. Lower is better. For instance, `video_images_load_time_ratio=200%` means that decoding from video is 2 times slower than loading the original images.
-
-**Average Mean Square Error (lower is better)**
-`avg_mse` is the average mean square error between each decoded frame and its corresponding original image over all requested timestamps, and also divided by the number of pixels in the image to be comparable when switching to different image sizes.
-
-**Average Peak Signal to Noise Ratio (higher is better)**
-`avg_psnr` measures the ratio between the maximum possible power of a signal and the power of corrupting noise that affects the fidelity of its representation. Higher PSNR indicates better quality.
-
-**Average Structural Similarity Index Measure (higher is better)**
-`avg_ssim` evaluates the perceived quality of images by comparing luminance, contrast, and structure. SSIM values range from -1 to 1, where 1 indicates perfect similarity.
-
-One aspect that can't be measured here with those metrics is the compatibility of the encoding across platforms, in particular on web browser, for visualization purposes.
-h264, h265 and AV1 are all commonly used codecs and should not pose an issue. However, the chroma subsampling (`pix_fmt`) format might affect compatibility:
-- `yuv420p` is more widely supported across various platforms, including web browsers.
-- `yuv444p` offers higher color fidelity but might not be supported as broadly.
-
-
-<!-- **Loss of a pretrained policy (higher is better)** (not available)
-`loss_pretrained` is the result of evaluating with the selected encoding/decoding settings a policy pretrained on original images. It is easier to understand than `avg_l2_error`.
-
-**Success rate after retraining (higher is better)** (not available)
-`success_rate` is the result of training and evaluating a policy with the selected encoding/decoding settings. It is the most difficult metric to get but also the very best. -->
-
-
-## How the benchmark works
-The benchmark evaluates both encoding and decoding of video frames on the first episode of each dataset.
-
-**Encoding:** for each `vcodec` and `pix_fmt` pair, we use a default value for `g` and `crf` upon which we change a single value (either `g` or `crf`) to one of the specified values (we don't test every combination of those as this would be computationally too heavy).
-This gives a unique set of encoding parameters which is used to encode the episode.
-
-**Decoding:** Then, for each of those unique encodings, we iterate through every combination of the decoding parameters `backend` and `timestamps_mode`. For each of them, we record the metrics of a number of samples (given by `--num-samples`). This is parallelized for efficiency and the number of processes can be controlled with `--num-workers`. Ideally, it's best to have a `--num-samples` that is divisible by `--num-workers`.
-
-Intermediate results saved for each `vcodec` and `pix_fmt` combination in csv tables.
-These are then all concatenated to a single table ready for analysis.
-
-## Caveats
-We tried to measure the most impactful parameters for both encoding and decoding. However, for computational reasons we can't test out every combination.
-
-Additional encoding parameters exist that are not included in this benchmark. In particular:
-- `-preset` which allows for selecting encoding presets. This represents a collection of options that will provide a certain encoding speed to compression ratio. By leaving this parameter unspecified, it is considered to be `medium` for libx264 and libx265 and `8` for libsvtav1.
-- `-tune` which allows to optimize the encoding for certain aspects (e.g. film quality, fast decoding, etc.).
-
-See the documentation mentioned above for more detailed info on these settings and for a more comprehensive list of other parameters.
-
-Similarly on the decoding side, other decoders exist but are not implemented in our current benchmark. To name a few:
-- `torchaudio`
-- `ffmpegio`
-- `decord`
-- `nvc`
-
-Note as well that since we are mostly interested in the performance at decoding time (also because encoding is done only once before uploading a dataset), we did not measure encoding times nor have any metrics regarding encoding.
-However, besides the necessity to build ffmpeg from source, encoding did not pose any issue and it didn't take a significant amount of time during this benchmark.
-
-
-## Install
-Building ffmpeg from source is required to include libx265 and libaom/libsvtav1 (av1) video codecs ([compilation guide](https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu)).
-
-**Note:** While you still need to build torchvision with a conda-installed `ffmpeg<4.3` to use the `video_reader` decoder (as described in [#220](https://github.com/huggingface/lerobot/pull/220)), you also need another version which is custom-built with all the video codecs for encoding. For the script to then use that version, you can prepend the command above with `PATH="$HOME/bin:$PATH"`, which is where ffmpeg should be built.
-
-
-## Adding a video decoder
-Right now, we're only benchmarking the two video decoder available with torchvision: `pyav` and `video_reader`.
-You can easily add a new decoder to benchmark by adding it to this function in the script:
-```diff
-def decode_video_frames(
-    video_path: str,
-    timestamps: list[float],
-    tolerance_s: float,
-    backend: str,
-) -> torch.Tensor:
-    if backend in ["pyav", "video_reader"]:
-        return decode_video_frames_torchvision(
-            video_path, timestamps, tolerance_s, backend
-        )
-+    elif backend == ["your_decoder"]:
-+        return your_decoder_function(
-+            video_path, timestamps, tolerance_s, backend
-+        )
-    else:
-        raise NotImplementedError(backend)
-```
-
-
-## Example
-For a quick run, you can try these parameters:
-```bash
-python benchmark/video/run_video_benchmark.py \
-    --output-dir outputs/video_benchmark \
-    --repo-ids \
-        lerobot/pusht_image \
-        aliberts/aloha_mobile_shrimp_image \
-    --vcodec libx264 libx265 \
-    --pix-fmt yuv444p yuv420p \
-    --g 2 20 None \
-    --crf 10 40 None \
-    --timestamps-modes 1_frame 2_frames \
-    --backends pyav video_reader \
-    --num-samples 5 \
-    --num-workers 5 \
-    --save-frames 0
-```
-
-
-## Results
-
-### Reproduce
-We ran the benchmark with the following parameters:
-```bash
-# h264 and h265 encodings
-python benchmark/video/run_video_benchmark.py \
-    --output-dir outputs/video_benchmark \
-    --repo-ids \
-        lerobot/pusht_image \
-        aliberts/aloha_mobile_shrimp_image \
-        aliberts/paris_street \
-        aliberts/kitchen \
-    --vcodec libx264 libx265 \
-    --pix-fmt yuv444p yuv420p \
-    --g 1 2 3 4 5 6 10 15 20 40 None \
-    --crf 0 5 10 15 20 25 30 40 50 None \
-    --timestamps-modes 1_frame 2_frames 6_frames \
-    --backends pyav video_reader \
-    --num-samples 50 \
-    --num-workers 5 \
-    --save-frames 1
-
-# av1 encoding (only compatible with yuv420p and pyav decoder)
-python benchmark/video/run_video_benchmark.py \
-    --output-dir outputs/video_benchmark \
-    --repo-ids \
-        lerobot/pusht_image \
-        aliberts/aloha_mobile_shrimp_image \
-        aliberts/paris_street \
-        aliberts/kitchen \
-    --vcodec libsvtav1 \
-    --pix-fmt yuv420p \
-    --g 1 2 3 4 5 6 10 15 20 40 None \
-    --crf 0 5 10 15 20 25 30 40 50 None \
-    --timestamps-modes 1_frame 2_frames 6_frames \
-    --backends pyav \
-    --num-samples 50 \
-    --num-workers 5 \
-    --save-frames 1
-```
-
-The full results are available [here](https://docs.google.com/spreadsheets/d/1OYJB43Qu8fC26k_OyoMFgGBBKfQRCi4BIuYitQnq3sw/edit?usp=sharing)
-
-
-### Parameters selected for LeRobotDataset
-Considering these results, we chose what we think is the best set of encoding parameter:
-- vcodec: `libsvtav1`
-- pix-fmt: `yuv420p`
-- g: `2`
-- crf: `30`
-
-Since we're using av1 encoding, we're choosing the `pyav` decoder as `video_reader` does not support it (and `pyav` doesn't require a custom build of `torchvision`).
-
-### Summary
-
-These tables show the results for `g=2` and `crf=30`, using `timestamps-modes=6_frames` and `backend=pyav`
-
-| video_images_size_ratio            | vcodec     | pix_fmt |           |           |           |
-|------------------------------------|------------|---------|-----------|-----------|-----------|
-|                                    | libx264    |         | libx265   |           | libsvtav1 |
-| repo_id                            | yuv420p    | yuv444p | yuv420p   | yuv444p   | yuv420p   |
-| lerobot/pusht_image                | **16.97%** | 17.58%  | 18.57%    | 18.86%    | 22.06%    |
-| aliberts/aloha_mobile_shrimp_image | 2.14%      | 2.11%   | 1.38%     | **1.37%** | 5.59%     |
-| aliberts/paris_street              | 2.12%      | 2.13%   | **1.54%** | **1.54%** | 4.43%     |
-| aliberts/kitchen                   | 1.40%      | 1.39%   | **1.00%** | **1.00%** | 2.52%     |
-
-| video_images_load_time_ratio       | vcodec  | pix_fmt |          |         |           |
-|------------------------------------|---------|---------|----------|---------|-----------|
-|                                    | libx264 |         | libx265  |         | libsvtav1 |
-| repo_id                            | yuv420p | yuv444p | yuv420p  | yuv444p | yuv420p   |
-| lerobot/pusht_image                | 6.45    | 5.19    | **1.90** | 2.12    | 2.47      |
-| aliberts/aloha_mobile_shrimp_image | 11.80   | 7.92    | 0.71     | 0.85    | **0.48**  |
-| aliberts/paris_street              | 2.21    | 2.05    | 0.36     | 0.49    | **0.30**  |
-| aliberts/kitchen                   | 1.46    | 1.46    | 0.28     | 0.51    | **0.26**  |
-
-|                                    |          | vcodec   | pix_fmt      |          |           |              |
-|------------------------------------|----------|----------|--------------|----------|-----------|--------------|
-|                                    |          | libx264  |              | libx265  |           | libsvtav1    |
-| repo_id                            | metric   | yuv420p  | yuv444p      | yuv420p  | yuv444p   | yuv420p      |
-| lerobot/pusht_image                | avg_mse  | 2.90E-04 | **2.03E-04** | 3.13E-04 | 2.29E-04  | 2.19E-04     |
-|                                    | avg_psnr | 35.44    | 37.07        | 35.49    | **37.30** | 37.20        |
-|                                    | avg_ssim | 98.28%   | **98.85%**   | 98.31%   | 98.84%    | 98.72%       |
-| aliberts/aloha_mobile_shrimp_image | avg_mse  | 2.76E-04 | 2.59E-04     | 3.17E-04 | 3.06E-04  | **1.30E-04** |
-|                                    | avg_psnr | 35.91    | 36.21        | 35.88    | 36.09     | **40.17**    |
-|                                    | avg_ssim | 95.19%   | 95.18%       | 95.00%   | 95.05%    | **97.73%**   |
-| aliberts/paris_street              | avg_mse  | 6.89E-04 | 6.70E-04     | 4.03E-03 | 4.02E-03  | **3.09E-04** |
-|                                    | avg_psnr | 33.48    | 33.68        | 32.05    | 32.15     | **35.40**    |
-|                                    | avg_ssim | 93.76%   | 93.75%       | 89.46%   | 89.46%    | **95.46%**   |
-| aliberts/kitchen                   | avg_mse  | 2.50E-04 | 2.24E-04     | 4.28E-04 | 4.18E-04  | **1.53E-04** |
-|                                    | avg_psnr | 36.73    | 37.33        | 36.56    | 36.75     | **39.12**    |
-|                                    | avg_ssim | 95.47%   | 95.58%       | 95.52%   | 95.53%    | **96.82%**   |

benchmarks/video/capture_camera_feed.py

@@ -1,102 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Capture video feed from a camera as raw images."""
-
-import argparse
-import datetime as dt
-import os
-import time
-from pathlib import Path
-
-import cv2
-import rerun as rr
-
-# see https://rerun.io/docs/howto/visualization/limit-ram
-RERUN_MEMORY_LIMIT = os.getenv("LEROBOT_RERUN_MEMORY_LIMIT", "5%")
-
-
-def display_and_save_video_stream(output_dir: Path, fps: int, width: int, height: int, duration: int):
-    rr.init("lerobot_capture_camera_feed")
-    rr.spawn(memory_limit=RERUN_MEMORY_LIMIT)
-
-    now = dt.datetime.now()
-    capture_dir = output_dir / f"{now:%Y-%m-%d}" / f"{now:%H-%M-%S}"
-    if not capture_dir.exists():
-        capture_dir.mkdir(parents=True, exist_ok=True)
-
-    # Opens the default webcam
-    cap = cv2.VideoCapture(0)
-    if not cap.isOpened():
-        print("Error: Could not open video stream.")
-        return
-
-    cap.set(cv2.CAP_PROP_FPS, fps)
-    cap.set(cv2.CAP_PROP_FRAME_WIDTH, width)
-    cap.set(cv2.CAP_PROP_FRAME_HEIGHT, height)
-
-    frame_index = 0
-    start_time = time.time()
-    while time.time() - start_time < duration:
-        ret, frame = cap.read()
-
-        if not ret:
-            print("Error: Could not read frame.")
-            break
-        rr.log("video/stream", rr.Image(frame.numpy()), static=True)
-        cv2.imwrite(str(capture_dir / f"frame_{frame_index:06d}.png"), frame)
-        frame_index += 1
-
-    # Release the capture
-    cap.release()
-
-    # TODO(Steven): Add a graceful shutdown via a close() method for the Viewer context, though not currently supported in the Rerun API.
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument(
-        "--output-dir",
-        type=Path,
-        default=Path("outputs/cam_capture/"),
-        help="Directory where the capture images are written. A subfolder named with the current date & time will be created inside it for each capture.",
-    )
-    parser.add_argument(
-        "--fps",
-        type=int,
-        default=30,
-        help="Frames Per Second of the capture.",
-    )
-    parser.add_argument(
-        "--width",
-        type=int,
-        default=1280,
-        help="Width of the captured images.",
-    )
-    parser.add_argument(
-        "--height",
-        type=int,
-        default=720,
-        help="Height of the captured images.",
-    )
-    parser.add_argument(
-        "--duration",
-        type=int,
-        default=20,
-        help="Duration in seconds for which the video stream should be captured.",
-    )
-    args = parser.parse_args()
-    display_and_save_video_stream(**vars(args))

benchmarks/video/run_video_benchmark.py

@@ -1,490 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Assess the performance of video decoding in various configurations.
-
-This script will benchmark different video encoding and decoding parameters.
-See the provided README.md or run `python benchmark/video/run_video_benchmark.py --help` for usage info.
-"""
-
-import argparse
-import datetime as dt
-import random
-import shutil
-from collections import OrderedDict
-from concurrent.futures import ThreadPoolExecutor, as_completed
-from pathlib import Path
-
-import einops
-import numpy as np
-import pandas as pd
-import PIL
-import torch
-from skimage.metrics import mean_squared_error, peak_signal_noise_ratio, structural_similarity
-from tqdm import tqdm
-
-from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
-from lerobot.common.datasets.video_utils import (
-    decode_video_frames_torchvision,
-    encode_video_frames,
-)
-from lerobot.common.utils.benchmark import TimeBenchmark
-
-BASE_ENCODING = OrderedDict(
-    [
-        ("vcodec", "libx264"),
-        ("pix_fmt", "yuv444p"),
-        ("g", 2),
-        ("crf", None),
-        # TODO(aliberts): Add fastdecode
-        # ("fastdecode", 0),
-    ]
-)
-
-
-# TODO(rcadene, aliberts): move to `utils.py` folder when we want to refactor
-def parse_int_or_none(value) -> int | None:
-    if value.lower() == "none":
-        return None
-    try:
-        return int(value)
-    except ValueError as e:
-        raise argparse.ArgumentTypeError(f"Invalid int or None: {value}") from e
-
-
-def check_datasets_formats(repo_ids: list) -> None:
-    for repo_id in repo_ids:
-        dataset = LeRobotDataset(repo_id)
-        if len(dataset.meta.video_keys) > 0:
-            raise ValueError(
-                f"Use only image dataset for running this benchmark. Video dataset provided: {repo_id}"
-            )
-
-
-def get_directory_size(directory: Path) -> int:
-    total_size = 0
-    for item in directory.rglob("*"):
-        if item.is_file():
-            total_size += item.stat().st_size
-    return total_size
-
-
-def load_original_frames(imgs_dir: Path, timestamps: list[float], fps: int) -> torch.Tensor:
-    frames = []
-    for ts in timestamps:
-        idx = int(ts * fps)
-        frame = PIL.Image.open(imgs_dir / f"frame_{idx:06d}.png")
-        frame = torch.from_numpy(np.array(frame))
-        frame = frame.type(torch.float32) / 255
-        frame = einops.rearrange(frame, "h w c -> c h w")
-        frames.append(frame)
-    return torch.stack(frames)
-
-
-def save_decoded_frames(
-    imgs_dir: Path, save_dir: Path, frames: torch.Tensor, timestamps: list[float], fps: int
-) -> None:
-    if save_dir.exists() and len(list(save_dir.glob("frame_*.png"))) == len(timestamps):
-        return
-
-    save_dir.mkdir(parents=True, exist_ok=True)
-    for i, ts in enumerate(timestamps):
-        idx = int(ts * fps)
-        frame_hwc = (frames[i].permute((1, 2, 0)) * 255).type(torch.uint8).cpu().numpy()
-        PIL.Image.fromarray(frame_hwc).save(save_dir / f"frame_{idx:06d}_decoded.png")
-        shutil.copyfile(imgs_dir / f"frame_{idx:06d}.png", save_dir / f"frame_{idx:06d}_original.png")
-
-
-def save_first_episode(imgs_dir: Path, dataset: LeRobotDataset) -> None:
-    ep_num_images = dataset.episode_data_index["to"][0].item()
-    if imgs_dir.exists() and len(list(imgs_dir.glob("frame_*.png"))) == ep_num_images:
-        return
-
-    imgs_dir.mkdir(parents=True, exist_ok=True)
-    hf_dataset = dataset.hf_dataset.with_format(None)
-
-    # We only save images from the first camera
-    img_keys = [key for key in hf_dataset.features if key.startswith("observation.image")]
-    imgs_dataset = hf_dataset.select_columns(img_keys[0])
-
-    for i, item in enumerate(
-        tqdm(imgs_dataset, desc=f"saving {dataset.repo_id} first episode images", leave=False)
-    ):
-        img = item[img_keys[0]]
-        img.save(str(imgs_dir / f"frame_{i:06d}.png"), quality=100)
-
-        if i >= ep_num_images - 1:
-            break
-
-
-def sample_timestamps(timestamps_mode: str, ep_num_images: int, fps: int) -> list[float]:
-    # Start at 5 to allow for 2_frames_4_space and 6_frames
-    idx = random.randint(5, ep_num_images - 1)
-    match timestamps_mode:
-        case "1_frame":
-            frame_indexes = [idx]
-        case "2_frames":
-            frame_indexes = [idx - 1, idx]
-        case "2_frames_4_space":
-            frame_indexes = [idx - 5, idx]
-        case "6_frames":
-            frame_indexes = [idx - i for i in range(6)][::-1]
-        case _:
-            raise ValueError(timestamps_mode)
-
-    return [idx / fps for idx in frame_indexes]
-
-
-def decode_video_frames(
-    video_path: str,
-    timestamps: list[float],
-    tolerance_s: float,
-    backend: str,
-) -> torch.Tensor:
-    if backend in ["pyav", "video_reader"]:
-        return decode_video_frames_torchvision(video_path, timestamps, tolerance_s, backend)
-    else:
-        raise NotImplementedError(backend)
-
-
-def benchmark_decoding(
-    imgs_dir: Path,
-    video_path: Path,
-    timestamps_mode: str,
-    backend: str,
-    ep_num_images: int,
-    fps: int,
-    num_samples: int = 50,
-    num_workers: int = 4,
-    save_frames: bool = False,
-) -> dict:
-    def process_sample(sample: int):
-        time_benchmark = TimeBenchmark()
-        timestamps = sample_timestamps(timestamps_mode, ep_num_images, fps)
-        num_frames = len(timestamps)
-        result = {
-            "psnr_values": [],
-            "ssim_values": [],
-            "mse_values": [],
-        }
-
-        with time_benchmark:
-            frames = decode_video_frames(video_path, timestamps=timestamps, tolerance_s=5e-1, backend=backend)
-        result["load_time_video_ms"] = time_benchmark.result_ms / num_frames
-
-        with time_benchmark:
-            original_frames = load_original_frames(imgs_dir, timestamps, fps)
-        result["load_time_images_ms"] = time_benchmark.result_ms / num_frames
-
-        frames_np, original_frames_np = frames.numpy(), original_frames.numpy()
-        for i in range(num_frames):
-            result["mse_values"].append(mean_squared_error(original_frames_np[i], frames_np[i]))
-            result["psnr_values"].append(
-                peak_signal_noise_ratio(original_frames_np[i], frames_np[i], data_range=1.0)
-            )
-            result["ssim_values"].append(
-                structural_similarity(original_frames_np[i], frames_np[i], data_range=1.0, channel_axis=0)
-            )
-
-        if save_frames and sample == 0:
-            save_dir = video_path.with_suffix("") / f"{timestamps_mode}_{backend}"
-            save_decoded_frames(imgs_dir, save_dir, frames, timestamps, fps)
-
-        return result
-
-    load_times_video_ms = []
-    load_times_images_ms = []
-    mse_values = []
-    psnr_values = []
-    ssim_values = []
-
-    # A sample is a single set of decoded frames specified by timestamps_mode (e.g. a single frame, 2 frames, etc.).
-    # For each sample, we record metrics (loading time and quality metrics) which are then averaged over all samples.
-    # As these samples are independent, we run them in parallel threads to speed up the benchmark.
-    with ThreadPoolExecutor(max_workers=num_workers) as executor:
-        futures = [executor.submit(process_sample, i) for i in range(num_samples)]
-        for future in tqdm(as_completed(futures), total=num_samples, desc="samples", leave=False):
-            result = future.result()
-            load_times_video_ms.append(result["load_time_video_ms"])
-            load_times_images_ms.append(result["load_time_images_ms"])
-            psnr_values.extend(result["psnr_values"])
-            ssim_values.extend(result["ssim_values"])
-            mse_values.extend(result["mse_values"])
-
-    avg_load_time_video_ms = float(np.array(load_times_video_ms).mean())
-    avg_load_time_images_ms = float(np.array(load_times_images_ms).mean())
-    video_images_load_time_ratio = avg_load_time_video_ms / avg_load_time_images_ms
-
-    return {
-        "avg_load_time_video_ms": avg_load_time_video_ms,
-        "avg_load_time_images_ms": avg_load_time_images_ms,
-        "video_images_load_time_ratio": video_images_load_time_ratio,
-        "avg_mse": float(np.mean(mse_values)),
-        "avg_psnr": float(np.mean(psnr_values)),
-        "avg_ssim": float(np.mean(ssim_values)),
-    }
-
-
-def benchmark_encoding_decoding(
-    dataset: LeRobotDataset,
-    video_path: Path,
-    imgs_dir: Path,
-    encoding_cfg: dict,
-    decoding_cfg: dict,
-    num_samples: int,
-    num_workers: int,
-    save_frames: bool,
-    overwrite: bool = False,
-    seed: int = 1337,
-) -> list[dict]:
-    fps = dataset.fps
-
-    if overwrite or not video_path.is_file():
-        tqdm.write(f"encoding {video_path}")
-        encode_video_frames(
-            imgs_dir=imgs_dir,
-            video_path=video_path,
-            fps=fps,
-            vcodec=encoding_cfg["vcodec"],
-            pix_fmt=encoding_cfg["pix_fmt"],
-            g=encoding_cfg.get("g"),
-            crf=encoding_cfg.get("crf"),
-            # fast_decode=encoding_cfg.get("fastdecode"),
-            overwrite=True,
-        )
-
-    ep_num_images = dataset.episode_data_index["to"][0].item()
-    width, height = tuple(dataset[0][dataset.meta.camera_keys[0]].shape[-2:])
-    num_pixels = width * height
-    video_size_bytes = video_path.stat().st_size
-    images_size_bytes = get_directory_size(imgs_dir)
-    video_images_size_ratio = video_size_bytes / images_size_bytes
-
-    random.seed(seed)
-    benchmark_table = []
-    for timestamps_mode in tqdm(
-        decoding_cfg["timestamps_modes"], desc="decodings (timestamps_modes)", leave=False
-    ):
-        for backend in tqdm(decoding_cfg["backends"], desc="decodings (backends)", leave=False):
-            benchmark_row = benchmark_decoding(
-                imgs_dir,
-                video_path,
-                timestamps_mode,
-                backend,
-                ep_num_images,
-                fps,
-                num_samples,
-                num_workers,
-                save_frames,
-            )
-            benchmark_row.update(
-                **{
-                    "repo_id": dataset.repo_id,
-                    "resolution": f"{width} x {height}",
-                    "num_pixels": num_pixels,
-                    "video_size_bytes": video_size_bytes,
-                    "images_size_bytes": images_size_bytes,
-                    "video_images_size_ratio": video_images_size_ratio,
-                    "timestamps_mode": timestamps_mode,
-                    "backend": backend,
-                },
-                **encoding_cfg,
-            )
-            benchmark_table.append(benchmark_row)
-
-    return benchmark_table
-
-
-def main(
-    output_dir: Path,
-    repo_ids: list[str],
-    vcodec: list[str],
-    pix_fmt: list[str],
-    g: list[int],
-    crf: list[int],
-    # fastdecode: list[int],
-    timestamps_modes: list[str],
-    backends: list[str],
-    num_samples: int,
-    num_workers: int,
-    save_frames: bool,
-):
-    check_datasets_formats(repo_ids)
-    encoding_benchmarks = {
-        "g": g,
-        "crf": crf,
-        # "fastdecode": fastdecode,
-    }
-    decoding_benchmarks = {
-        "timestamps_modes": timestamps_modes,
-        "backends": backends,
-    }
-    headers = ["repo_id", "resolution", "num_pixels"]
-    headers += list(BASE_ENCODING.keys())
-    headers += [
-        "timestamps_mode",
-        "backend",
-        "video_size_bytes",
-        "images_size_bytes",
-        "video_images_size_ratio",
-        "avg_load_time_video_ms",
-        "avg_load_time_images_ms",
-        "video_images_load_time_ratio",
-        "avg_mse",
-        "avg_psnr",
-        "avg_ssim",
-    ]
-    file_paths = []
-    for video_codec in tqdm(vcodec, desc="encodings (vcodec)"):
-        for pixel_format in tqdm(pix_fmt, desc="encodings (pix_fmt)", leave=False):
-            benchmark_table = []
-            for repo_id in tqdm(repo_ids, desc="encodings (datasets)", leave=False):
-                dataset = LeRobotDataset(repo_id)
-                imgs_dir = output_dir / "images" / dataset.repo_id.replace("/", "_")
-                # We only use the first episode
-                save_first_episode(imgs_dir, dataset)
-                for key, values in tqdm(encoding_benchmarks.items(), desc="encodings (g, crf)", leave=False):
-                    for value in tqdm(values, desc=f"encodings ({key})", leave=False):
-                        encoding_cfg = BASE_ENCODING.copy()
-                        encoding_cfg["vcodec"] = video_codec
-                        encoding_cfg["pix_fmt"] = pixel_format
-                        encoding_cfg[key] = value
-                        args_path = Path("_".join(str(value) for value in encoding_cfg.values()))
-                        video_path = output_dir / "videos" / args_path / f"{repo_id.replace('/', '_')}.mp4"
-                        benchmark_table += benchmark_encoding_decoding(
-                            dataset,
-                            video_path,
-                            imgs_dir,
-                            encoding_cfg,
-                            decoding_benchmarks,
-                            num_samples,
-                            num_workers,
-                            save_frames,
-                        )
-
-            # Save intermediate results
-            benchmark_df = pd.DataFrame(benchmark_table, columns=headers)
-            now = dt.datetime.now()
-            csv_path = (
-                output_dir
-                / f"{now:%Y-%m-%d}_{now:%H-%M-%S}_{video_codec}_{pixel_format}_{num_samples}-samples.csv"
-            )
-            benchmark_df.to_csv(csv_path, header=True, index=False)
-            file_paths.append(csv_path)
-            del benchmark_df
-
-    # Concatenate all results
-    df_list = [pd.read_csv(csv_path) for csv_path in file_paths]
-    concatenated_df = pd.concat(df_list, ignore_index=True)
-    concatenated_path = output_dir / f"{now:%Y-%m-%d}_{now:%H-%M-%S}_all_{num_samples}-samples.csv"
-    concatenated_df.to_csv(concatenated_path, header=True, index=False)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--output-dir",
-        type=Path,
-        default=Path("outputs/video_benchmark"),
-        help="Directory where the video benchmark outputs are written.",
-    )
-    parser.add_argument(
-        "--repo-ids",
-        type=str,
-        nargs="*",
-        default=[
-            "lerobot/pusht_image",
-            "aliberts/aloha_mobile_shrimp_image",
-            "aliberts/paris_street",
-            "aliberts/kitchen",
-        ],
-        help="Datasets repo-ids to test against. First episodes only are used. Must be images.",
-    )
-    parser.add_argument(
-        "--vcodec",
-        type=str,
-        nargs="*",
-        default=["libx264", "hevc", "libsvtav1"],
-        help="Video codecs to be tested",
-    )
-    parser.add_argument(
-        "--pix-fmt",
-        type=str,
-        nargs="*",
-        default=["yuv444p", "yuv420p"],
-        help="Pixel formats (chroma subsampling) to be tested",
-    )
-    parser.add_argument(
-        "--g",
-        type=parse_int_or_none,
-        nargs="*",
-        default=[1, 2, 3, 4, 5, 6, 10, 15, 20, 40, 100, None],
-        help="Group of pictures sizes to be tested.",
-    )
-    parser.add_argument(
-        "--crf",
-        type=parse_int_or_none,
-        nargs="*",
-        default=[0, 5, 10, 15, 20, 25, 30, 40, 50, None],
-        help="Constant rate factors to be tested.",
-    )
-    # parser.add_argument(
-    #     "--fastdecode",
-    #     type=int,
-    #     nargs="*",
-    #     default=[0, 1],
-    #     help="Use the fastdecode tuning option. 0 disables it. "
-    #         "For libx264 and libx265/hevc, only 1 is possible. "
-    #         "For libsvtav1, 1, 2 or 3 are possible values with a higher number meaning a faster decoding optimization",
-    # )
-    parser.add_argument(
-        "--timestamps-modes",
-        type=str,
-        nargs="*",
-        default=[
-            "1_frame",
-            "2_frames",
-            "2_frames_4_space",
-            "6_frames",
-        ],
-        help="Timestamps scenarios to be tested.",
-    )
-    parser.add_argument(
-        "--backends",
-        type=str,
-        nargs="*",
-        default=["pyav", "video_reader"],
-        help="Torchvision decoding backend to be tested.",
-    )
-    parser.add_argument(
-        "--num-samples",
-        type=int,
-        default=50,
-        help="Number of samples for each encoding x decoding config.",
-    )
-    parser.add_argument(
-        "--num-workers",
-        type=int,
-        default=10,
-        help="Number of processes for parallelized sample processing.",
-    )
-    parser.add_argument(
-        "--save-frames",
-        type=int,
-        default=0,
-        help="Whether to save decoded frames or not. Enter a non-zero number for true.",
-    )
-    args = parser.parse_args()
-    main(**vars(args))

docker/lerobot-cpu/Dockerfile

@@ -1,29 +0,0 @@
-# Configure image
-ARG PYTHON_VERSION=3.10
-FROM python:${PYTHON_VERSION}-slim
-
-# Configure environment variables
-ARG PYTHON_VERSION
-ENV DEBIAN_FRONTEND=noninteractive
-ENV MUJOCO_GL="egl"
-ENV PATH="/opt/venv/bin:$PATH"
-
-# Install dependencies and set up Python in a single layer
-RUN apt-get update && apt-get install -y --no-install-recommends \
-    build-essential cmake git \
-    libglib2.0-0 libgl1-mesa-glx libegl1-mesa ffmpeg \
-    speech-dispatcher libgeos-dev \
-    && ln -s /usr/bin/python${PYTHON_VERSION} /usr/bin/python \
-    && python -m venv /opt/venv \
-    && apt-get clean && rm -rf /var/lib/apt/lists/* \
-    && echo "source /opt/venv/bin/activate" >> /root/.bashrc
-
-# Clone repository and install LeRobot in a single layer
-COPY . /lerobot
-WORKDIR /lerobot
-RUN /opt/venv/bin/pip install --upgrade --no-cache-dir pip \
-    && /opt/venv/bin/pip install --no-cache-dir ".[test, aloha, xarm, pusht, dynamixel]" \
-        --extra-index-url https://download.pytorch.org/whl/cpu
-
-# Execute in bash shell rather than python
-CMD ["/bin/bash"]

docker/lerobot-gpu-dev/Dockerfile

@@ -1,68 +0,0 @@
-FROM nvidia/cuda:12.2.2-devel-ubuntu22.04
-
-# Configure image
-ARG PYTHON_VERSION=3.10
-ARG DEBIAN_FRONTEND=noninteractive
-
-# Install apt dependencies
-RUN apt-get update && apt-get install -y --no-install-recommends \
-    build-essential cmake \
-    git git-lfs openssh-client \
-    nano vim less util-linux tree \
-    htop atop nvtop \
-    sed gawk grep curl wget zip unzip \
-    tcpdump sysstat screen tmux \
-    libglib2.0-0 libgl1-mesa-glx libegl1-mesa \
-    speech-dispatcher portaudio19-dev libgeos-dev \
-    python${PYTHON_VERSION} python${PYTHON_VERSION}-venv python${PYTHON_VERSION}-dev \
-    && apt-get clean && rm -rf /var/lib/apt/lists/*
-
-# Install ffmpeg build dependencies. See:
-# https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu
-# TODO(aliberts): create image to build dependencies from source instead
-RUN apt-get update && apt-get install -y --no-install-recommends \
-    autoconf automake yasm \
-    libass-dev \
-    libfreetype6-dev \
-    libgnutls28-dev \
-    libunistring-dev \
-    libmp3lame-dev \
-    libtool \
-    libvorbis-dev \
-    meson \
-    ninja-build \
-    pkg-config \
-    texinfo \
-    yasm \
-    zlib1g-dev \
-    nasm \
-    libx264-dev \
-    libx265-dev libnuma-dev \
-    libvpx-dev \
-    libfdk-aac-dev \
-    libopus-dev \
-    libsvtav1-dev libsvtav1enc-dev libsvtav1dec-dev \
-    libdav1d-dev
-
-# Install gh cli tool
-RUN (type -p wget >/dev/null || (apt update && apt-get install wget -y)) \
-    && mkdir -p -m 755 /etc/apt/keyrings \
-    && wget -qO- https://cli.github.com/packages/githubcli-archive-keyring.gpg | tee /etc/apt/keyrings/githubcli-archive-keyring.gpg > /dev/null \
-    && chmod go+r /etc/apt/keyrings/githubcli-archive-keyring.gpg \
-    && echo "deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/githubcli-archive-keyring.gpg] https://cli.github.com/packages stable main" | tee /etc/apt/sources.list.d/github-cli.list > /dev/null \
-    && apt update \
-    && apt install gh -y \
-    && apt clean && rm -rf /var/lib/apt/lists/*
-
-# Setup `python`
-RUN ln -s /usr/bin/python3 /usr/bin/python
-
-# Install poetry
-RUN curl -sSL https://install.python-poetry.org | python -
-ENV PATH="/root/.local/bin:$PATH"
-RUN echo 'if [ "$HOME" != "/root" ]; then ln -sf /root/.local/bin/poetry $HOME/.local/bin/poetry; fi' >> /root/.bashrc
-RUN poetry config virtualenvs.create false
-RUN poetry config virtualenvs.in-project true
-
-# Set EGL as the rendering backend for MuJoCo
-ENV MUJOCO_GL="egl"

docker/lerobot-gpu/Dockerfile

@@ -1,24 +0,0 @@
-FROM nvidia/cuda:12.4.1-base-ubuntu22.04
-
-# Configure environment variables
-ARG PYTHON_VERSION=3.10
-ENV DEBIAN_FRONTEND=noninteractive
-ENV MUJOCO_GL="egl"
-ENV PATH="/opt/venv/bin:$PATH"
-
-# Install dependencies and set up Python in a single layer
-RUN apt-get update && apt-get install -y --no-install-recommends \
-    build-essential cmake git \
-    libglib2.0-0 libgl1-mesa-glx libegl1-mesa ffmpeg \
-    speech-dispatcher libgeos-dev \
-    python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
-    && ln -s /usr/bin/python${PYTHON_VERSION} /usr/bin/python \
-    && python -m venv /opt/venv \
-    && apt-get clean && rm -rf /var/lib/apt/lists/* \
-    && echo "source /opt/venv/bin/activate" >> /root/.bashrc
-
-# Clone repository and install LeRobot in a single layer
-COPY . /lerobot
-WORKDIR /lerobot
-RUN /opt/venv/bin/pip install --upgrade --no-cache-dir pip \
-    && /opt/venv/bin/pip install --no-cache-dir ".[test, aloha, xarm, pusht, dynamixel]"

docs/README.md

@@ -1,137 +0,0 @@
-<!---
-Copyright 2020 The HuggingFace 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.
--->
-
-# Generating the documentation
-
-To generate the documentation, you first have to build it. Several packages are necessary to build the doc,
-you can install them with the following command, at the root of the code repository:
-
-```bash
-pip install -e ".[docs]"
-```
-
-You will also need `nodejs`. Please refer to their [installation page](https://nodejs.org/en/download)
-
----
-**NOTE**
-
-You only need to generate the documentation to inspect it locally (if you're planning changes and want to
-check how they look before committing for instance). You don't have to `git commit` the built documentation.
-
----
-
-## Building the documentation
-
-Once you have setup the `doc-builder` and additional packages, you can generate the documentation by
-typing the following command:
-
-```bash
-doc-builder build lerobot docs/source/ --build_dir ~/tmp/test-build
-```
-
-You can adapt the `--build_dir` to set any temporary folder that you prefer. This command will create it and generate
-the MDX files that will be rendered as the documentation on the main website. You can inspect them in your favorite
-Markdown editor.
-
-## Previewing the documentation
-
-To preview the docs, first install the `watchdog` module with:
-
-```bash
-pip install watchdog
-```
-
-Then run the following command:
-
-```bash
-doc-builder preview lerobot docs/source/
-```
-
-The docs will be viewable at [http://localhost:3000](http://localhost:3000). You can also preview the docs once you have opened a PR. You will see a bot add a comment to a link where the documentation with your changes lives.
-
----
-**NOTE**
-
-The `preview` command only works with existing doc files. When you add a completely new file, you need to update `_toctree.yml` & restart `preview` command (`ctrl-c` to stop it & call `doc-builder preview ...` again).
-
----
-
-## Adding a new element to the navigation bar
-
-Accepted files are Markdown (.md).
-
-Create a file with its extension and put it in the source directory. You can then link it to the toc-tree by putting
-the filename without the extension in the [`_toctree.yml`](https://github.com/huggingface/lerobot/blob/main/docs/source/_toctree.yml) file.
-
-## Renaming section headers and moving sections
-
-It helps to keep the old links working when renaming the section header and/or moving sections from one document to another. This is because the old links are likely to be used in Issues, Forums, and Social media and it'd make for a much more superior user experience if users reading those months later could still easily navigate to the originally intended information.
-
-Therefore, we simply keep a little map of moved sections at the end of the document where the original section was. The key is to preserve the original anchor.
-
-So if you renamed a section from: "Section A" to "Section B", then you can add at the end of the file:
-
-```
-Sections that were moved:
-
-[ <a href="#section-b">Section A</a><a id="section-a"></a> ]
-```
-and of course, if you moved it to another file, then:
-
-```
-Sections that were moved:
-
-[ <a href="../new-file#section-b">Section A</a><a id="section-a"></a> ]
-```
-
-Use the relative style to link to the new file so that the versioned docs continue to work.
-
-For an example of a rich moved sections set please see the very end of [the transformers Trainer doc](https://github.com/huggingface/transformers/blob/main/docs/source/en/main_classes/trainer.md).
-
-### Adding a new tutorial
-
-Adding a new tutorial or section is done in two steps:
-
-- Add a new file under `./source`. This file can either be ReStructuredText (.rst) or Markdown (.md).
-- Link that file in `./source/_toctree.yml` on the correct toc-tree.
-
-Make sure to put your new file under the proper section. If you have a doubt, feel free to ask in a Github Issue or PR.
-
-### Writing source documentation
-
-Values that should be put in `code` should either be surrounded by backticks: \`like so\`. Note that argument names
-and objects like True, None or any strings should usually be put in `code`.
-
-#### Writing a multi-line code block
-
-Multi-line code blocks can be useful for displaying examples. They are done between two lines of three backticks as usual in Markdown:
-
-
-````
-```
-# first line of code
-# second line
-# etc
-```
-````
-
-#### Adding an image
-
-Due to the rapidly growing repository, it is important to make sure that no files that would significantly weigh down the repository are added. This includes images, videos, and other non-text files. We prefer to leverage a hf.co hosted `dataset` like
-the ones hosted on [`hf-internal-testing`](https://huggingface.co/hf-internal-testing) in which to place these files and reference
-them by URL. We recommend putting them in the following dataset: [huggingface/documentation-images](https://huggingface.co/datasets/huggingface/documentation-images).
-If an external contribution, feel free to add the images to your PR and ask a Hugging Face member to migrate your images
-to this dataset.

docs/source/_toctree.yml

@@ -1,12 +0,0 @@
-- sections:
-  - local: index
-    title: LeRobot
-  - local: installation
-    title: Installation
-  title: Get started
-- sections:
-  - local: assemble_so101
-    title: Assemble SO-101
-  - local: getting_started_real_world_robot
-    title: Getting Started with Real-World Robots
-  title: "Tutorials"

docs/source/assemble_so101.mdx

@@ -1,348 +0,0 @@
-# Assemble SO-101
-
-In the steps below we explain how to assemble our flagship robot, the SO-101.
-
-## Source the parts
-
-Follow this [README](https://github.com/TheRobotStudio/SO-ARM100). It contains the bill of materials, with a link to source the parts, as well as the instructions to 3D print the parts,
-and advice if it's your first time printing or if you don't own a 3D printer.
-
-Before assembling, you will first need to configure your motors. To this end, we provide a nice script, so let's first install LeRobot. After configuration, we will also guide you through assembly.
-
-## Install LeRobot
-
-To install LeRobot follow our [Installation Guide](./installation)
-
-## Configure motors
-
-To configure the motors designate one bus servo adapter and 6 motors for your leader arm, and similarly the other bus servo adapter and 6 motors for the follower arm. It's convenient to label them and write on each motor if it's for the follower `F` or for the leader `L` and it's ID from 1 to 6.
-
-You now should plug the 5V or 12V power supply to the motor bus. 5V for the STS3215 7.4V motors and 12V for the STS3215 12V motors. Note that the leader arm always uses the 7.4V motors, so watch out that you plug in the right power supply if you have 12V and 7.4V motors, otherwise you might burn your motors! Now, connect the motor bus to your computer via USB. Note that the USB doesn't provide any power, and both the power supply and USB have to be plugged in.
-
-### Find the USB ports associated to each arm
-
-To find the port for each bus servo adapter, run this script:
-```bash
-python lerobot/scripts/find_motors_bus_port.py
-```
-##### Example outputs of script
-
-<hfoptions id="example">
-<hfoption id="Mac">
-
-Example output leader arm's port: `/dev/tty.usbmodem575E0031751`
-
-```bash
-Finding all available ports for the MotorBus.
-['/dev/tty.usbmodem575E0032081', '/dev/tty.usbmodem575E0031751']
-Remove the usb cable from your MotorsBus and press Enter when done.
-
-[...Disconnect leader arm and press Enter...]
-
-The port of this MotorsBus is /dev/tty.usbmodem575E0031751
-Reconnect the usb cable.
-```
-
-Example output follower arm port: `/dev/tty.usbmodem575E0032081`
-
-```
-Finding all available ports for the MotorBus.
-['/dev/tty.usbmodem575E0032081', '/dev/tty.usbmodem575E0031751']
-Remove the usb cable from your MotorsBus and press Enter when done.
-
-[...Disconnect follower arm and press Enter...]
-
-The port of this MotorsBus is /dev/tty.usbmodem575E0032081
-Reconnect the usb cable.
-```
-
-</hfoption>
-<hfoption id="Linux">
-
-On Linux, you might need to give access to the USB ports by running:
-```bash
-sudo chmod 666 /dev/ttyACM0
-sudo chmod 666 /dev/ttyACM1
-```
-
-Example output leader arm port: `/dev/ttyACM0`
-
-```bash
-Finding all available ports for the MotorBus.
-['/dev/ttyACM0', '/dev/ttyACM1']
-Remove the usb cable from your MotorsBus and press Enter when done.
-
-[...Disconnect leader arm and press Enter...]
-
-The port of this MotorsBus is /dev/ttyACM0
-Reconnect the usb cable.
-```
-
-Example output follower arm port: `/dev/ttyACM1`
-
-```
-Finding all available ports for the MotorBus.
-['/dev/ttyACM0', '/dev/ttyACM1']
-Remove the usb cable from your MotorsBus and press Enter when done.
-
-[...Disconnect follower arm and press Enter...]
-
-The port of this MotorsBus is /dev/ttyACM1
-Reconnect the usb cable.
-```
-</hfoption>
-</hfoptions>
-
-#### Update config file
-
-Now that you have your ports, update the **port** default values of [`SO101RobotConfig`](https://github.com/huggingface/lerobot/blob/main/lerobot/common/robot_devices/robots/configs.py).
-You will find a class called `so101` where you can update the `port` values with your actual motor ports:
-```diff
-@RobotConfig.register_subclass("so101")
-@dataclass
-class So101RobotConfig(ManipulatorRobotConfig):
-    calibration_dir: str = ".cache/calibration/so101"
-    # `max_relative_target` limits the magnitude of the relative positional target vector for safety purposes.
-    # Set this to a positive scalar to have the same value for all motors, or a list that is the same length as
-    # the number of motors in your follower arms.
-    max_relative_target: int | None = None
-
-    leader_arms: dict[str, MotorsBusConfig] = field(
-        default_factory=lambda: {
-            "main": FeetechMotorsBusConfig(
--               port="/dev/tty.usbmodem58760431091",
-+               port="{ADD YOUR LEADER PORT}",
-                motors={
-                    # name: (index, model)
-                    "shoulder_pan": [1, "sts3215"],
-                    "shoulder_lift": [2, "sts3215"],
-                    "elbow_flex": [3, "sts3215"],
-                    "wrist_flex": [4, "sts3215"],
-                    "wrist_roll": [5, "sts3215"],
-                    "gripper": [6, "sts3215"],
-                },
-            ),
-        }
-    )
-
-    follower_arms: dict[str, MotorsBusConfig] = field(
-        default_factory=lambda: {
-            "main": FeetechMotorsBusConfig(
--                port="/dev/tty.usbmodem585A0076891",
-+                port="{ADD YOUR FOLLOWER PORT}",
-                motors={
-                    # name: (index, model)
-                    "shoulder_pan": [1, "sts3215"],
-                    "shoulder_lift": [2, "sts3215"],
-                    "elbow_flex": [3, "sts3215"],
-                    "wrist_flex": [4, "sts3215"],
-                    "wrist_roll": [5, "sts3215"],
-                    "gripper": [6, "sts3215"],
-                },
-            ),
-        }
-    )
-```
-
-Here is a video of the process:
-<div class="video-container">
-  <video controls width="600">
-    <source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/lerobot-find-motorbus.mp4" type="video/mp4" />
-  </video>
- </div>
-
-## Step-by-Step Assembly Instructions
-
-The follower arm uses 6x STS3215 motors with 1/345 gearing. The leader however uses three differently geared motors to make sure it can both sustain its own weight and it can be moved without requiring much force. Which motor is needed for which joint is shown in table below.
-
-| Leader-Arm Axis | Motor | Gear Ratio |
-|-----------------|:-------:|:----------:|
-| Base / Shoulder Yaw | 1 | 1 / 191 |
-| Shoulder Pitch      | 2 | 1 / 345 |
-| Elbow               | 3 | 1 / 191 |
-| Wrist Roll          | 4 | 1 / 147 |
-| Wrist Pitch         | 5 | 1 / 147 |
-| Gripper             | 6 | 1 / 147 |
-
-### Set motor IDs
-
-Plug your motor in one of the two ports of the motor bus and run this script to set its ID to 1. Replace the text after --port to the corresponding control board port.
-```bash
-python lerobot/scripts/configure_motor.py \
-  --port /dev/tty.usbmodem58760432961 \
-  --brand feetech \
-  --model sts3215 \
-  --baudrate 1000000 \
-  --ID 1
-```
-
-Then unplug your motor and plug the second motor and set its ID to 2.
-```bash
-python lerobot/scripts/configure_motor.py \
-  --port /dev/tty.usbmodem58760432961 \
-  --brand feetech \
-  --model sts3215 \
-  --baudrate 1000000 \
-  --ID 2
-```
-
-Redo this process for all your motors until ID 6. Do the same for the 6 motors of the leader arm, but make sure to change the power supply if you use motors with different voltage and make sure you give the right ID to the right motor according to the table above.
-
-Here is a video of the process:
-<div class="video-container">
-  <video controls width="600">
-    <source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/lerobot-configure-motor.mp4" type="video/mp4" />
-  </video>
-</div>
-
-### Clean Parts
-Remove all support material from the 3D-printed parts, the easiest way to do this is using a small screwdriver to get underneath the support material.
-
-### Joint 1
-
-- Place the first motor into the base.
-- Fasten the motor with 4 M2x6mm screws (smallest screws). Two from the top and two from bottom.
-- Slide over the first motor holder and fasten it using two M2x6mm screws (one on each side).
-- Install both motor horns, securing the top horn with a M3x6mm screw.
-- Attach the shoulder part.
-- Tighten the shoulder part with 4 M3x6mm screws on top and 4 M3x6mm screws on the bottom
-- Add the shoulder motor holder.
-
-<div class="video-container">
-  <video controls width="600">
-    <source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/Joint1_v2.mp4" type="video/mp4" />
-  </video>
-</div>
-
-### Joint 2
-
-- Slide the second motor in from the top.
-- Fasten the second motor with 4 M2x6mm screws.
-- Attach both motor horns to motor 2, again use the M3x6mm horn screw.
-- Attach the upper arm with 4 M3x6mm screws on each side.
-
-<div class="video-container">
-  <video controls width="600">
-    <source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/Joint2_v2.mp4" type="video/mp4" />
-  </video>
-</div>
-
-### Joint 3
-
-- Insert motor 3 and fasten using 4 M2x6mm screws
-- Attach both motor horns to motor 3 and secure one again with a M3x6mm horn screw.
-- Connect the forearm to motor 3 using 4 M3x6mm screws on each side.
-
-<div class="video-container">
-  <video controls width="600">
-    <source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/Joint3_v2.mp4" type="video/mp4" />
-  </video>
-</div>
-
-### Joint 4
-
-- Slide over motor holder 4.
-- Slide in motor 4.
-- Fasten motor 4 with 4 M2x6mm screws and attach its motor horns, use a M3x6mm horn screw.
-
-<div class="video-container">
-  <video controls width="600">
-    <source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/Joint4_v2.mp4" type="video/mp4" />
-  </video>
-</div>
-
-### Joint 5
-
-- Insert motor 5 into the wrist holder and secure it with 2 M2x6mm front screws.
-- Install only one motor horn on the wrist motor and secure it with a M3x6mm horn screw.
-- Secure the wrist to motor 4 using 4 M3x6mm screws on both sides.
-
-<div class="video-container">
-  <video controls width="600">
-    <source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/Joint5_v2.mp4" type="video/mp4" />
-  </video>
-</div>
-
-### Gripper / Handle
-
-<hfoptions id="assembly">
-<hfoption id="Follower">
-
-- Attach the gripper to motor 5, attach it to the motor horn on the wrist using 4 M3x6mm screws.
-- Insert the gripper motor and secure it with 2 M2x6mm screws on each side.
-- Attach the motor horns and again use a M3x6mm horn screw.
-- Install the gripper claw and secure it with 4 M3x6mm screws on both sides.
-
-<div class="video-container">
-  <video controls width="600">
-    <source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/Gripper_v2.mp4" type="video/mp4" />
-  </video>
-</div>
-
-</hfoption>
-<hfoption id="Leader">
-
-- Mount the leader holder onto the wrist and secure it with 4 M3x6mm screws.
-- Attach the handle to motor 5 using 1 M2x6mm screw.
-- Insert the gripper motor, secure it with 2 M2x6mm screws on each side, attach a motor horn using a M3x6mm horn screw.
-- Attach the follower trigger with 4 M3x6mm screws.
-
-<div class="video-container">
-  <video controls width="600">
-    <source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/Leader_v2.mp4" type="video/mp4" />
-  </video>
-</div>
-
-</hfoption>
-</hfoptions>
-
-##### Wiring
-
-- Attach the motor controller on the back.
-- Then insert all wires, use the wire guides everywhere to make sure the wires don't unplug themselves and stay in place.
-
-<div class="video-container">
-  <video controls width="600">
-    <source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/Wiring_v2.mp4" type="video/mp4" />
-  </video>
-</div>
-
-## Calibrate
-
-Next, you'll need to calibrate your SO-101 robot to ensure that the leader and follower arms have the same position values when they are in the same physical position.
-The calibration process is very important because it allows a neural network trained on one SO-101 robot to work on another.
-
-#### Manual calibration of follower arm
-
-You will need to move the follower arm to these positions sequentially, note that the rotated position is on the right side of the robot and you have to open the gripper fully.
-
-| 1. Middle position | 2. Zero position                                                                                                                                       | 3. Rotated position                                                                                                                                             | 4. Rest position                                                                                                                                       |
-| ------------ |------------------------------------------------------------------------------------------------------------------------------------------------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/follower_middle.webp?raw=true" alt="SO-101 leader arm middle position" title="SO-101 leader arm middle position" style="width:100%;"> | <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/follower_zero.webp?raw=true" alt="SO-101 leader arm zero position" title="SO-101 leader arm zero position" style="width:100%;"> | <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/follower_rotated.webp?raw=true" alt="SO-101 leader arm rotated position" title="SO-101 leader arm rotated position" style="width:100%;"> | <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/follower_rest.webp?raw=true" alt="SO-101 leader arm rest position" title="SO-101 leader arm rest position" style="width:100%;"> |
-
-Make sure both arms are connected and run this script to launch manual calibration:
-```bash
-python lerobot/scripts/control_robot.py \
-  --robot.type=so101 \
-  --robot.cameras='{}' \
-  --control.type=calibrate \
-  --control.arms='["main_follower"]'
-```
-
-#### Manual calibration of leader arm
-You will also need to move the leader arm to these positions sequentially:
-
-| 1. Middle position | 2. Zero position                                                                                                                                       | 3. Rotated position                                                                                                                                             | 4. Rest position                                                                                                                                       |
-| ------------ |------------------------------------------------------------------------------------------------------------------------------------------------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/leader_middle.webp?raw=true" alt="SO-101 leader arm middle position" title="SO-101 leader arm middle position" style="width:100%;"> | <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/leader_zero.webp?raw=true" alt="SO-101 leader arm zero position" title="SO-101 leader arm zero position" style="width:100%;"> | <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/leader_rotated.webp?raw=true" alt="SO-101 leader arm rotated position" title="SO-101 leader arm rotated position" style="width:100%;"> | <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/leader_rest.webp?raw=true" alt="SO-101 leader arm rest position" title="SO-101 leader arm rest position" style="width:100%;"> |
-
-Run this script to launch manual calibration:
-```bash
-python lerobot/scripts/control_robot.py \
-  --robot.type=so101 \
-  --robot.cameras='{}' \
-  --control.type=calibrate \
-  --control.arms='["main_leader"]'
-```
-
-Congrats 🎉, your robot is all set to learn a task on its own. Start training it by following this tutorial: [Getting started with real-world robots](./getting_started_real_world_robot)

docs/source/getting_started_real_world_robot.mdx

@@ -1,370 +0,0 @@
-# Getting Started with Real-World Robots
-
-This tutorial will explain you how to train a neural network to autonomously control a real robot.
-
-**You'll learn:**
-1. How to record and visualize your dataset.
-2. How to train a policy using your data and prepare it for evaluation.
-3. How to evaluate your policy and visualize the results.
-
-By following these steps, you'll be able to replicate tasks like picking up a Lego block and placing it in a bin with a high success rate, as demonstrated in [this video](https://x.com/RemiCadene/status/1814680760592572934).
-
-This tutorial is specifically made for the affordable [SO-101](https://github.com/TheRobotStudio/SO-ARM100) robot, but it contains additional information to be easily adapted to various types of robots like [Aloha bimanual robot](https://aloha-2.github.io) by changing some configurations. The SO-101 consists of a leader arm and a follower arm, each with 6 motors. It can work with one or several cameras to record the scene, which serve as visual sensors for the robot.
-
-During the data collection phase, you will control the follower arm by moving the leader arm. This process is known as "teleoperation." This technique is used to collect robot trajectories. Afterward, you'll train a neural network to imitate these trajectories and deploy the network to enable your robot to operate autonomously.
-
-If you encounter any issues at any step of the tutorial, feel free to seek help on [Discord](https://discord.com/invite/s3KuuzsPFb) or don't hesitate to iterate with us on the tutorial by creating issues or pull requests.
-
-## Setup and Calibrate
-
-If you haven't yet setup and calibrate the SO-101 follow these steps:
-1. [Find ports and update config file](./assemble_so101#find-the-usb-ports-associated-to-each-arm)
-2. [Calibrate](./assemble_so101#calibrate)
-
-## Teleoperate
-
-Run this simple script to teleoperate your robot (it won't connect and display the cameras):
-```bash
-python lerobot/scripts/control_robot.py \
-  --robot.type=so101 \
-  --robot.cameras='{}' \
-  --control.type=teleoperate
-```
-
-The teleoperate command will automatically:
-1. Identify any missing calibrations and initiate the calibration procedure.
-2. Connect the robot and start teleoperation.
-
-## Setup Cameras
-
-To connect a camera you have three options:
-1. OpenCVCamera which allows us to use any camera: usb, realsense, laptop webcam
-2. iPhone camera with MacOS
-3. Phone camera on Linux
-
-### Use OpenCVCamera
-
-The [`OpenCVCamera`](../lerobot/common/robot_devices/cameras/opencv.py) class allows you to efficiently record frames from most cameras using the [`opencv2`](https://docs.opencv.org) library.  For more details on compatibility, see [Video I/O with OpenCV Overview](https://docs.opencv.org/4.x/d0/da7/videoio_overview.html).
-
-To instantiate an [`OpenCVCamera`](../lerobot/common/robot_devices/cameras/opencv.py), you need a camera index (e.g. `OpenCVCamera(camera_index=0)`). When you only have one camera like a webcam of a laptop, the camera index is usually `0` but it might differ, and the camera index might change if you reboot your computer or re-plug your camera. This behavior depends on your operating system.
-
-To find the camera indices, run the following utility script, which will save a few frames from each detected camera:
-```bash
-python lerobot/common/robot_devices/cameras/opencv.py \
-    --images-dir outputs/images_from_opencv_cameras
-```
-
-The output will look something like this if you have two cameras connected:
-```
-Mac or Windows detected. Finding available camera indices through scanning all indices from 0 to 60
-[...]
-Camera found at index 0
-Camera found at index 1
-[...]
-Connecting cameras
-OpenCVCamera(0, fps=30.0, width=1920.0, height=1080.0, color_mode=rgb)
-OpenCVCamera(1, fps=24.0, width=1920.0, height=1080.0, color_mode=rgb)
-Saving images to outputs/images_from_opencv_cameras
-Frame: 0000	Latency (ms): 39.52
-[...]
-Frame: 0046	Latency (ms): 40.07
-Images have been saved to outputs/images_from_opencv_cameras
-```
-
-Check the saved images in `outputs/images_from_opencv_cameras` to identify which camera index corresponds to which physical camera (e.g. `0` for `camera_00` or `1` for `camera_01`):
-```
-camera_00_frame_000000.png
-[...]
-camera_00_frame_000047.png
-camera_01_frame_000000.png
-[...]
-camera_01_frame_000047.png
-```
-
-Note: Some cameras may take a few seconds to warm up, and the first frame might be black or green.
-
-Now that you have the camera indexes, you should specify the camera's in the config.
-
-### Use your phone
-<hfoptions id="use phone">
-<hfoption id="Mac">
-
-To use your iPhone as a camera on macOS, enable the Continuity Camera feature:
-- Ensure your Mac is running macOS 13 or later, and your iPhone is on iOS 16 or later.
-- Sign in both devices with the same Apple ID.
-- Connect your devices with a USB cable or turn on Wi-Fi and Bluetooth for a wireless connection.
-
-For more details, visit [Apple support](https://support.apple.com/en-gb/guide/mac-help/mchl77879b8a/mac).
-
-Your iPhone should be detected automatically when running the camera setup script in the next section.
-
-</hfoption>
-<hfoption id="Linux">
-
-If you want to use your phone as a camera on Linux, follow these steps to set up a virtual camera
-
-1. *Install `v4l2loopback-dkms` and `v4l-utils`*. Those packages are required to create virtual camera devices (`v4l2loopback`) and verify their settings with the `v4l2-ctl` utility from `v4l-utils`. Install them using:
-```python
-sudo apt install v4l2loopback-dkms v4l-utils
-```
-2. *Install [DroidCam](https://droidcam.app) on your phone*. This app is available for both iOS and Android.
-3. *Install [OBS Studio](https://obsproject.com)*. This software will help you manage the camera feed. Install it using [Flatpak](https://flatpak.org):
-```python
-flatpak install flathub com.obsproject.Studio
-```
-4. *Install the DroidCam OBS plugin*. This plugin integrates DroidCam with OBS Studio. Install it with:
-```python
-flatpak install flathub com.obsproject.Studio.Plugin.DroidCam
-```
-5. *Start OBS Studio*. Launch with:
-```python
-flatpak run com.obsproject.Studio
-```
-6. *Add your phone as a source*. Follow the instructions [here](https://droidcam.app/obs/usage). Be sure to set the resolution to `640x480`.
-7. *Adjust resolution settings*. In OBS Studio, go to `File > Settings > Video`. Change the `Base(Canvas) Resolution` and the `Output(Scaled) Resolution` to `640x480` by manually typing it in.
-8. *Start virtual camera*. In OBS Studio, follow the instructions [here](https://obsproject.com/kb/virtual-camera-guide).
-9. *Verify the virtual camera setup*. Use `v4l2-ctl` to list the devices:
-```python
-v4l2-ctl --list-devices
-```
-You should see an entry like:
-```
-VirtualCam (platform:v4l2loopback-000):
-/dev/video1
-```
-10. *Check the camera resolution*. Use `v4l2-ctl` to ensure that the virtual camera output resolution is `640x480`. Change `/dev/video1` to the port of your virtual camera from the output of `v4l2-ctl --list-devices`.
-```python
-v4l2-ctl -d /dev/video1 --get-fmt-video
-```
-You should see an entry like:
-```
->>> Format Video Capture:
->>>	Width/Height      : 640/480
->>>	Pixel Format      : 'YUYV' (YUYV 4:2:2)
-```
-
-Troubleshooting: If the resolution is not correct you will have to delete the Virtual Camera port and try again as it cannot be changed.
-
-If everything is set up correctly, you can proceed with the rest of the tutorial.
-
-</hfoption>
-</hfoptions>
-
-## Teleoperate with cameras
-
-We can now teleoperate again while at the same time visualizing the cameras and joint positions with `rerun`.
-
-```bash
-python lerobot/scripts/control_robot.py \
-  --robot.type=so101 \
-  --control.type=teleoperate
-  --control.display_data=true
-```
-
-## Record a dataset
-
-Once you're familiar with teleoperation, you can record your first dataset with SO-101.
-
-We use the Hugging Face hub features for uploading your dataset. If you haven't previously used the Hub, make sure you can login via the cli using a write-access token, this token can be generated from the [Hugging Face settings](https://huggingface.co/settings/tokens).
-
-Add your token to the cli by running this command:
-```bash
-huggingface-cli 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)
-echo $HF_USER
-```
-
-Now you can record a dataset, to record 2 episodes and upload your dataset to the hub execute this command:
-```bash
-python lerobot/scripts/control_robot.py \
-  --robot.type=so101 \
-  --control.type=record \
-  --control.fps=30 \
-  --control.single_task="Grasp a lego block and put it in the bin." \
-  --control.repo_id=${HF_USER}/so101_test \
-  --control.tags='["so101","tutorial"]' \
-  --control.warmup_time_s=5 \
-  --control.episode_time_s=30 \
-  --control.reset_time_s=30 \
-  --control.num_episodes=2 \
-  --control.push_to_hub=true
-```
-
-You will see a lot of lines appearing like this one:
-```
-INFO 2024-08-10 15:02:58 ol_robot.py:219 dt:33.34 (30.0hz) dtRlead: 5.06 (197.5hz) dtWfoll: 0.25 (3963.7hz) dtRfoll: 6.22 (160.7hz) dtRlaptop: 32.57 (30.7hz) dtRphone: 33.84 (29.5hz)
-```
-
-| Field | Meaning |
-|:---|:---|
-| `2024-08-10 15:02:58` | Timestamp when `print` was called. |
-| `ol_robot.py:219` | Source file and line number of the `print` call (`lerobot/scripts/control_robot.py` at line `219`). |
-| `dt: 33.34 (30.0 Hz)` | Delta time (ms) between teleop steps (target: 30.0 Hz, `--fps 30`). Yellow if step is too slow. |
-| `dtRlead: 5.06 (197.5 Hz)` | Delta time (ms) for reading present position from the **leader arm**. |
-| `dtWfoll: 0.25 (3963.7 Hz)` | Delta time (ms) for writing goal position to the **follower arm** (asynchronous). |
-| `dtRfoll: 6.22 (160.7 Hz)` | Delta time (ms) for reading present position from the **follower arm**. |
-| `dtRlaptop: 32.57 (30.7 Hz)` | Delta time (ms) for capturing an image from the **laptop camera** (async thread). |
-| `dtRphone: 33.84 (29.5 Hz)` | Delta time (ms) for capturing an image from the **phone camera** (async thread). |
-
-
-#### Dataset upload
-Locally your dataset is stored in this folder: `~/.cache/huggingface/lerobot/{repo-id}` (e.g. `data/cadene/so101_test`). At the end of data recording, your dataset will be uploaded on your Hugging Face page (e.g. https://huggingface.co/datasets/cadene/so101_test) that you can obtain by running:
-```bash
-echo https://huggingface.co/datasets/${HF_USER}/so101_test
-```
-Your dataset will be automatically tagged with `LeRobot` for the community to find it easily, and you can also add custom tags (in this case `tutorial` for example).
-
-You can look for other LeRobot datasets on the hub by searching for `LeRobot` [tags](https://huggingface.co/datasets?other=LeRobot).
-
-#### Record function
-
-The `record` function provides a suite of tools for capturing and managing data during robot operation:
-
-##### 1. Frame Capture and Video Encoding
-- Frames from cameras are saved to disk during recording.
-- At the end of each episode, frames are encoded into video files.
-
-##### 2. Data Storage
-- Data is stored using the `LeRobotDataset` format.
-- By default, the dataset is pushed to your Hugging Face page.
-  - To disable uploading, use `--control.push_to_hub=false`.
-
-##### 3. Checkpointing and Resuming
-- Checkpoints are automatically created during recording.
-- If an issue occurs, you can resume by re-running the same command with `--control.resume=true`.
-- To start recording from scratch, **manually delete** the dataset directory.
-
-##### 4. Recording Parameters
-Set the flow of data recording using command-line arguments:
-- `--control.warmup_time_s=10`
-  Number of seconds before starting data collection (default: **10 seconds**).
-  Allows devices to warm up and synchronize.
-- `--control.episode_time_s=60`
-  Duration of each data recording episode (default: **60 seconds**).
-- `--control.reset_time_s=60`
-  Duration for resetting the environment after each episode (default: **60 seconds**).
-- `--control.num_episodes=50`
-  Total number of episodes to record (default: **50**).
-
-##### 5. Keyboard Controls During Recording
-Control the data recording flow using keyboard shortcuts:
-- Press **Right Arrow (`→`)**: Early stop the current episode or reset time and move to the next.
-- Press **Left Arrow (`←`)**: Cancel the current episode and re-record it.
-- Press **Escape (`ESC`)**: Immediately stop the session, encode videos, and upload the dataset.
-
-#### Tips for gathering data
-
-Once you're comfortable with data recording, you can create a larger dataset for training. A good starting task is grasping an object at different locations and placing it in a bin. We suggest recording at least 50 episodes, with 10 episodes per location. Keep the cameras fixed and maintain consistent grasping behavior throughout the recordings. Also make sure the object you are manipulating is visible on the camera's. A good rule of thumb is you should be able to do the task yourself by only looking at the camera images.
-
-In the following sections, you’ll train your neural network. After achieving reliable grasping performance, you can start introducing more variations during data collection, such as additional grasp locations, different grasping techniques, and altering camera positions.
-
-Avoid adding too much variation too quickly, as it may hinder your results.
-
-
-#### Troubleshooting:
-- On Linux, if the left and right arrow keys and escape key don't have any effect during data recording, make sure you've set the `$DISPLAY` environment variable. See [pynput limitations](https://pynput.readthedocs.io/en/latest/limitations.html#linux).
-
-## Visualize a dataset
-
-If you uploaded your dataset to the hub with `--control.push_to_hub=true`, you can [visualize your dataset online](https://huggingface.co/spaces/lerobot/visualize_dataset) by copy pasting your repo id given by:
-```bash
-echo ${HF_USER}/so101_test
-```
-
-If you didn't upload with `--control.push_to_hub=false`, you can visualize it locally with (via a window in the browser `http://127.0.0.1:9090` with the visualization tool):
-```bash
-python lerobot/scripts/visualize_dataset_html.py \
-  --repo-id ${HF_USER}/so101_test \
-  --local-files-only 1
-```
-
-This will launch a local web server that looks like this:
-<div style="text-align:center;">
-  <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/visualize_dataset_html.webp?raw=true" alt="Koch v1.1 leader and follower arms" title="Koch v1.1 leader and follower arms" width="100%"></img>
-</div>
-
-## Replay an episode
-
-A useful feature is the `replay` function, which allows to replay on your robot any episode that you've recorded or episodes from any dataset out there. This function helps you test the repeatability of your robot's actions and assess transferability across robots of the same model.
-
-You can replay the first episode on your robot with:
-```bash
-python lerobot/scripts/control_robot.py \
-  --robot.type=so101 \
-  --control.type=replay \
-  --control.fps=30 \
-  --control.repo_id=${HF_USER}/so101_test \
-  --control.episode=0
-```
-
-Your robot should replicate movements similar to those you recorded. For example, check out [this video](https://x.com/RemiCadene/status/1793654950905680090) where we use `replay` on a Aloha robot from [Trossen Robotics](https://www.trossenrobotics.com).
-
-## Train a policy
-
-To train a policy to control your robot, use the [`python lerobot/scripts/train.py`](../lerobot/scripts/train.py) script. A few arguments are required. Here is an example command:
-```bash
-python lerobot/scripts/train.py \
-  --dataset.repo_id=${HF_USER}/so101_test \
-  --policy.type=act \
-  --output_dir=outputs/train/act_so101_test \
-  --job_name=act_so101_test \
-  --policy.device=cuda \
-  --wandb.enable=true
-```
-
-Let's explain the command:
-1. We provided the dataset as argument with `--dataset.repo_id=${HF_USER}/so101_test`.
-2. We provided the policy with `policy.type=act`. This loads configurations from [`configuration_act.py`](../lerobot/common/policies/act/configuration_act.py). Importantly, this policy will automatically adapt to the number of motor states, motor actions and cameras of your robot (e.g. `laptop` and `phone`) which have been saved in your dataset.
-4. We provided `policy.device=cuda` since we are training on a Nvidia GPU, but you could use `policy.device=mps` to train on Apple silicon.
-5. We provided `wandb.enable=true` to use [Weights and Biases](https://docs.wandb.ai/quickstart) for visualizing training plots. This is optional but if you use it, make sure you are logged in by running `wandb login`.
-
-Training should take several hours. You will find checkpoints in `outputs/train/act_so101_test/checkpoints`.
-
-To resume training from a checkpoint, below is an example command to resume from `last` checkpoint of the `act_so101_test` policy:
-```bash
-python lerobot/scripts/train.py \
-  --config_path=outputs/train/act_so101_test/checkpoints/last/pretrained_model/train_config.json \
-  --resume=true
-```
-
-#### Upload policy checkpoints
-
-Once training is done, upload the latest checkpoint with:
-```bash
-huggingface-cli upload ${HF_USER}/act_so101_test \
-  outputs/train/act_so101_test/checkpoints/last/pretrained_model
-```
-
-You can also upload intermediate checkpoints with:
-```bash
-CKPT=010000
-huggingface-cli upload ${HF_USER}/act_so101_test${CKPT} \
-  outputs/train/act_so101_test/checkpoints/${CKPT}/pretrained_model
-```
-
-## Evaluate your policy
-
-You can use the `record` function from [`lerobot/scripts/control_robot.py`](../lerobot/scripts/control_robot.py) but with a policy checkpoint as input. For instance, run this command to record 10 evaluation episodes:
-```bash
-python lerobot/scripts/control_robot.py \
-  --robot.type=so101 \
-  --control.type=record \
-  --control.fps=30 \
-  --control.single_task="Grasp a lego block and put it in the bin." \
-  --control.repo_id=${HF_USER}/eval_act_so101_test \
-  --control.tags='["tutorial"]' \
-  --control.warmup_time_s=5 \
-  --control.episode_time_s=30 \
-  --control.reset_time_s=30 \
-  --control.num_episodes=10 \
-  --control.push_to_hub=true \
-  --control.policy.path=outputs/train/act_so101_test/checkpoints/last/pretrained_model
-```
-
-As you can see, it's almost the same command as previously used to record your training dataset. Two things changed:
-1. There is an additional `--control.policy.path` argument which indicates the path to your policy checkpoint with  (e.g. `outputs/train/eval_act_so101_test/checkpoints/last/pretrained_model`). You can also use the model repository if you uploaded a model checkpoint to the hub (e.g. `${HF_USER}/act_so101_test`).
-2. The name of dataset begins by `eval` to reflect that you are running inference (e.g. `${HF_USER}/eval_act_so101_test`).

docs/source/index.mdx

@@ -1,19 +0,0 @@
-<div class="flex justify-center">
-  <a target="_blank" href="https://huggingface.co/lerobot">
-      <img alt="HuggingFace Expert Acceleration Program" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/lerobot-logo-thumbnail.png" style="width: 100%"></img>
-  </a>
-</div>
-
-# LeRobot
-
-**State-of-the-art machine learning for real-world robotics**
-
-🤗 LeRobot aims to provide models, datasets, and tools for real-world robotics in PyTorch. The goal is to lower the barrier for entry to robotics so that everyone can contribute and benefit from sharing datasets and pretrained models.
-
-🤗 LeRobot contains state-of-the-art approaches that have been shown to transfer to the real-world with a focus on imitation learning and reinforcement learning.
-
-🤗 LeRobot already provides a set of pretrained models, datasets with human collected demonstrations, and simulated environments so that everyone can get started.
-
-🤗 LeRobot hosts pretrained models and datasets on the LeRobot HuggingFace page.
-
-Join the LeRobot community on [Discord](https://discord.gg/s3KuuzsPFb)

docs/source/installation.mdx

@@ -1,84 +0,0 @@
-# Installation
-
-## Install LeRobot
-
-Download our source code:
-```bash
-git clone https://github.com/huggingface/lerobot.git
-cd lerobot
-```
-
-Create a virtual environment with Python 3.10, using [`Miniconda`](https://docs.anaconda.com/miniconda/install/#quick-command-line-install)
-```bash
-conda create -y -n lerobot python=3.10
-```
-
-Now restart the shell by running:
-<hfoptions id="shell_restart">
-<hfoption id="Windows">
-
-```bash
-source ~/.bashrc
-```
-</hfoption>
-<hfoption id="Mac">
-
-```bash
-source ~/.bash_profile
-```
-</hfoption>
-<hfoption id="zshell">
-
-```bash
-source ~/.zshrc
-```
-</hfoption>
-</hfoptions>
-
-Then activate your conda environment, you have to do this each time you open a shell to use lerobot:
-```bash
-conda activate lerobot
-```
-
-When using `miniconda`, install `ffmpeg` in your environment:
-```bash
-conda install ffmpeg -c conda-forge
-```
-
-> [!TIP]
-> This usually installs `ffmpeg 7.X` for your platform compiled with the `libsvtav1` encoder. If `libsvtav1` is not supported (check supported encoders with `ffmpeg -encoders`), you can:
->  - _[On any platform]_ Explicitly install `ffmpeg 7.X` using:
->  ```bash
->  conda install ffmpeg=7.1.1 -c conda-forge
->  ```
->  - _[On Linux only]_ Install [ffmpeg build dependencies](https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu#GettheDependencies) and [compile ffmpeg from source with libsvtav1](https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu#libsvtav1), and make sure you use the corresponding ffmpeg binary to your install with `which ffmpeg`.
-
-Install 🤗 LeRobot:
-```bash
-cd lerobot && pip install ".[feetech]"
-```
-
-## 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:
-```bash
-sudo apt-get install cmake build-essential python-dev pkg-config libavformat-dev libavcodec-dev libavdevice-dev libavutil-dev libswscale-dev libswresample-dev libavfilter-dev pkg-config
-```
-For other systems, see: [Compiling PyAV](https://pyav.org/docs/develop/overview/installation.html#bring-your-own-ffmpeg)
-
-## Sim
-For simulations, 🤗 LeRobot comes with gymnasium environments that can be installed as extras:
-- [aloha](https://github.com/huggingface/gym-aloha)
-- [xarm](https://github.com/huggingface/gym-xarm)
-- [pusht](https://github.com/huggingface/gym-pusht)
-
-For instance, to install 🤗 LeRobot with aloha and pusht, use:
-```bash
-pip install -e ".[aloha, pusht]"
-```
-
-## W&B
-To use [Weights and Biases](https://docs.wandb.ai/quickstart) for experiment tracking, log in with
-```bash
-wandb login
-```

envs/sim_aloha/README.md

@@ -0,0 +1 @@
+# ALOHA environment for LeRobot

envs/sim_aloha/aloha/assets/bimanual_viperx_end_effector_insertion.xml

@@ -0,0 +1,59 @@
+<mujoco>
+    <include file="scene.xml"/>
+    <include file="vx300s_dependencies.xml"/>
+
+    <equality>
+        <weld body1="mocap_left" body2="vx300s_left/gripper_link" solref="0.01 1" solimp=".25 .25 0.001" />
+        <weld body1="mocap_right" body2="vx300s_right/gripper_link" solref="0.01 1" solimp=".25 .25 0.001" />
+    </equality>
+
+
+    <worldbody>
+        <include file="vx300s_left.xml" />
+        <include file="vx300s_right.xml" />
+
+        <body mocap="true" name="mocap_left" pos="0.095 0.50 0.425">
+            <site pos="0 0 0" size="0.003 0.003 0.03" type="box" name="mocap_left_site1" rgba="1 0 0 1"/>
+            <site pos="0 0 0" size="0.003 0.03 0.003" type="box" name="mocap_left_site2" rgba="1 0 0 1"/>
+            <site pos="0 0 0" size="0.03 0.003 0.003" type="box" name="mocap_left_site3" rgba="1 0 0 1"/>
+        </body>
+        <body mocap="true" name="mocap_right" pos="-0.095 0.50 0.425">
+            <site pos="0 0 0" size="0.003 0.003 0.03" type="box" name="mocap_right_site1" rgba="1 0 0 1"/>
+            <site pos="0 0 0" size="0.003 0.03 0.003" type="box" name="mocap_right_site2" rgba="1 0 0 1"/>
+            <site pos="0 0 0" size="0.03 0.003 0.003" type="box" name="mocap_right_site3" rgba="1 0 0 1"/>
+        </body>
+
+        <body name="peg" pos="0.2 0.5 0.05">
+            <joint name="red_peg_joint" type="free" frictionloss="0.01" />
+            <inertial pos="0 0 0" mass="0.05" diaginertia="0.002 0.002 0.002" />
+            <geom condim="4" solimp="2 1 0.01" solref="0.01 1" friction="1 0.005 0.0001" pos="0 0 0" size="0.06 0.01 0.01" type="box" name="red_peg" rgba="1 0 0 1" />
+        </body>
+
+        <body name="socket" pos="-0.2 0.5 0.05">
+            <joint name="blue_socket_joint" type="free" frictionloss="0.01" />
+            <inertial pos="0 0 0" mass="0.05" diaginertia="0.002 0.002 0.002" />
+<!--            <geom condim="4" solimp="2 1 0.01" solref="0.01 1" friction="1 0.005 0.0001" pos="0 0 0" size="0.06 0.01 0.01" type="box" name="red_peg_ref" rgba="1 0 0 1" />-->
+            <geom condim="4" solimp="2 1 0.01" solref="0.01 1" friction="1 0.05 0.001" pos="0 0 -0.02" size="0.06 0.018 0.002" type="box" name="socket-1" rgba="0 0 1 1" />
+            <geom condim="4" solimp="2 1 0.01" solref="0.01 1" friction="1 0.05 0.001" pos="0 0 0.02" size="0.06 0.018 0.002" type="box" name="socket-2" rgba="0 0 1 1" />
+            <geom condim="4" solimp="2 1 0.01" solref="0.01 1" friction="1 0.05 0.001" pos="0 0.02 0" size="0.06 0.002 0.018" type="box" name="socket-3" rgba="0 0 1 1" />
+            <geom condim="4" solimp="2 1 0.01" solref="0.01 1" friction="1 0.05 0.001" pos="0 -0.02 0" size="0.06 0.002 0.018" type="box" name="socket-4" rgba="0 0 1 1" />
+            <geom condim="4" solimp="2 1 0.01" solref="0.01 1" friction="1 0.005 0.0001" pos="0 0 0" size="0.04 0.01 0.01" type="box" name="pin" rgba="1 0 0 1" />
+        </body>
+
+    </worldbody>
+
+    <actuator>
+        <position ctrllimited="true" ctrlrange="0.021 0.057" joint="vx300s_left/left_finger" kp="200"  user="1"/>
+        <position ctrllimited="true" ctrlrange="-0.057 -0.021" joint="vx300s_left/right_finger" kp="200"  user="1"/>
+
+        <position ctrllimited="true" ctrlrange="0.021 0.057" joint="vx300s_right/left_finger" kp="200"  user="1"/>
+        <position ctrllimited="true" ctrlrange="-0.057 -0.021" joint="vx300s_right/right_finger" kp="200"  user="1"/>
+
+    </actuator>
+
+    <keyframe>
+        <key qpos="0 -0.96 1.16 0 -0.3 0 0.024 -0.024  0 -0.96 1.16 0 -0.3 0 0.024 -0.024  0.2 0.5 0.05 1 0 0 0  -0.2 0.5 0.05 1 0 0 0"/>
+    </keyframe>
+
+
+</mujoco>

envs/sim_aloha/aloha/assets/bimanual_viperx_end_effector_transfer_cube.xml

@@ -0,0 +1,48 @@
+<mujoco>
+    <include file="scene.xml"/>
+    <include file="vx300s_dependencies.xml"/>
+
+    <equality>
+        <weld body1="mocap_left" body2="vx300s_left/gripper_link" solref="0.01 1" solimp=".25 .25 0.001" />
+        <weld body1="mocap_right" body2="vx300s_right/gripper_link" solref="0.01 1" solimp=".25 .25 0.001" />
+    </equality>
+
+
+    <worldbody>
+        <include file="vx300s_left.xml" />
+        <include file="vx300s_right.xml" />
+
+        <body mocap="true" name="mocap_left" pos="0.095 0.50 0.425">
+            <site pos="0 0 0" size="0.003 0.003 0.03" type="box" name="mocap_left_site1" rgba="1 0 0 1"/>
+            <site pos="0 0 0" size="0.003 0.03 0.003" type="box" name="mocap_left_site2" rgba="1 0 0 1"/>
+            <site pos="0 0 0" size="0.03 0.003 0.003" type="box" name="mocap_left_site3" rgba="1 0 0 1"/>
+        </body>
+        <body mocap="true" name="mocap_right" pos="-0.095 0.50 0.425">
+            <site pos="0 0 0" size="0.003 0.003 0.03" type="box" name="mocap_right_site1" rgba="1 0 0 1"/>
+            <site pos="0 0 0" size="0.003 0.03 0.003" type="box" name="mocap_right_site2" rgba="1 0 0 1"/>
+            <site pos="0 0 0" size="0.03 0.003 0.003" type="box" name="mocap_right_site3" rgba="1 0 0 1"/>
+        </body>
+
+        <body name="box" pos="0.2 0.5 0.05">
+            <joint name="red_box_joint" type="free" frictionloss="0.01" />
+            <inertial pos="0 0 0" mass="0.05" diaginertia="0.002 0.002 0.002" />
+            <geom condim="4" solimp="2 1 0.01" solref="0.01 1" friction="1 0.005 0.0001" pos="0 0 0" size="0.02 0.02 0.02" type="box" name="red_box" rgba="1 0 0 1" />
+        </body>
+
+    </worldbody>
+
+    <actuator>
+        <position ctrllimited="true" ctrlrange="0.021 0.057" joint="vx300s_left/left_finger" kp="200"  user="1"/>
+        <position ctrllimited="true" ctrlrange="-0.057 -0.021" joint="vx300s_left/right_finger" kp="200"  user="1"/>
+
+        <position ctrllimited="true" ctrlrange="0.021 0.057" joint="vx300s_right/left_finger" kp="200"  user="1"/>
+        <position ctrllimited="true" ctrlrange="-0.057 -0.021" joint="vx300s_right/right_finger" kp="200"  user="1"/>
+
+    </actuator>
+
+    <keyframe>
+        <key qpos="0 -0.96 1.16 0 -0.3 0 0.024 -0.024  0 -0.96 1.16 0 -0.3 0 0.024 -0.024  0.2 0.5 0.05 1 0 0 0"/>
+    </keyframe>
+
+
+</mujoco>

envs/sim_aloha/aloha/assets/bimanual_viperx_insertion.xml

@@ -0,0 +1,53 @@
+<mujoco>
+    <include file="scene.xml"/>
+    <include file="vx300s_dependencies.xml"/>
+    <worldbody>
+        <include file="vx300s_left.xml" />
+        <include file="vx300s_right.xml" />
+
+        <body name="peg" pos="0.2 0.5 0.05">
+            <joint name="red_peg_joint" type="free" frictionloss="0.01" />
+            <inertial pos="0 0 0" mass="0.05" diaginertia="0.002 0.002 0.002" />
+            <geom condim="4" solimp="2 1 0.01" solref="0.01 1" friction="1 0.005 0.0001" pos="0 0 0" size="0.06 0.01 0.01" type="box" name="red_peg" rgba="1 0 0 1" />
+        </body>
+
+        <body name="socket" pos="-0.2 0.5 0.05">
+            <joint name="blue_socket_joint" type="free" frictionloss="0.01" />
+            <inertial pos="0 0 0" mass="0.05" diaginertia="0.002 0.002 0.002" />
+<!--            <geom condim="4" solimp="2 1 0.01" solref="0.01 1" friction="1 0.005 0.0001" pos="0 0 0" size="0.06 0.01 0.01" type="box" name="red_peg_ref" rgba="1 0 0 1" />-->
+            <geom condim="4" solimp="2 1 0.01" solref="0.01 1" friction="1 0.05 0.001" pos="0 0 -0.02" size="0.06 0.018 0.002" type="box" name="socket-1" rgba="0 0 1 1" />
+            <geom condim="4" solimp="2 1 0.01" solref="0.01 1" friction="1 0.05 0.001" pos="0 0 0.02" size="0.06 0.018 0.002" type="box" name="socket-2" rgba="0 0 1 1" />
+            <geom condim="4" solimp="2 1 0.01" solref="0.01 1" friction="1 0.05 0.001" pos="0 0.02 0" size="0.06 0.002 0.018" type="box" name="socket-3" rgba="0 0 1 1" />
+            <geom condim="4" solimp="2 1 0.01" solref="0.01 1" friction="1 0.05 0.001" pos="0 -0.02 0" size="0.06 0.002 0.018" type="box" name="socket-4" rgba="0 0 1 1" />
+            <geom condim="4" solimp="2 1 0.01" solref="0.01 1" friction="1 0.005 0.0001" pos="0 0 0" size="0.04 0.01 0.01" type="box" name="pin" rgba="1 0 0 1" />
+        </body>
+
+    </worldbody>
+
+    <actuator>
+        <position ctrllimited="true" ctrlrange="-3.14158 3.14158" joint="vx300s_left/waist" kp="800"  user="1" forcelimited="true" forcerange="-150 150"/>
+        <position ctrllimited="true" ctrlrange="-1.85005 1.25664" joint="vx300s_left/shoulder" kp="1600"  user="1" forcelimited="true" forcerange="-300 300"/>
+        <position ctrllimited="true" ctrlrange="-1.76278 1.6057" joint="vx300s_left/elbow" kp="800"  user="1" forcelimited="true" forcerange="-100 100"/>
+        <position ctrllimited="true" ctrlrange="-3.14158 3.14158" joint="vx300s_left/forearm_roll" kp="10"  user="1" forcelimited="true" forcerange="-100 100"/>
+        <position ctrllimited="true" ctrlrange="-1.8675 2.23402" joint="vx300s_left/wrist_angle" kp="50"  user="1"/>
+        <position ctrllimited="true" ctrlrange="-3.14158 3.14158" joint="vx300s_left/wrist_rotate" kp="20"  user="1"/>
+        <position ctrllimited="true" ctrlrange="0.021 0.057" joint="vx300s_left/left_finger" kp="200"  user="1"/>
+        <position ctrllimited="true" ctrlrange="-0.057 -0.021" joint="vx300s_left/right_finger" kp="200"  user="1"/>
+
+        <position ctrllimited="true" ctrlrange="-3.14158 3.14158" joint="vx300s_right/waist" kp="800"  user="1" forcelimited="true" forcerange="-150 150"/>
+        <position ctrllimited="true" ctrlrange="-1.85005 1.25664" joint="vx300s_right/shoulder" kp="1600"  user="1" forcelimited="true" forcerange="-300 300"/>
+        <position ctrllimited="true" ctrlrange="-1.76278 1.6057" joint="vx300s_right/elbow" kp="800"  user="1" forcelimited="true" forcerange="-100 100"/>
+        <position ctrllimited="true" ctrlrange="-3.14158 3.14158" joint="vx300s_right/forearm_roll" kp="10"  user="1" forcelimited="true" forcerange="-100 100"/>
+        <position ctrllimited="true" ctrlrange="-1.8675 2.23402" joint="vx300s_right/wrist_angle" kp="50"  user="1"/>
+        <position ctrllimited="true" ctrlrange="-3.14158 3.14158" joint="vx300s_right/wrist_rotate" kp="20"  user="1"/>
+        <position ctrllimited="true" ctrlrange="0.021 0.057" joint="vx300s_right/left_finger" kp="200"  user="1"/>
+        <position ctrllimited="true" ctrlrange="-0.057 -0.021" joint="vx300s_right/right_finger" kp="200"  user="1"/>
+
+    </actuator>
+
+    <keyframe>
+        <key qpos="0 -0.96 1.16 0 -0.3 0 0.024 -0.024  0 -0.96 1.16 0 -0.3 0 0.024 -0.024  0.2 0.5 0.05 1 0 0 0  -0.2 0.5 0.05 1 0 0 0"/>
+    </keyframe>
+
+
+</mujoco>

envs/sim_aloha/aloha/assets/bimanual_viperx_transfer_cube.xml

@@ -0,0 +1,42 @@
+<mujoco>
+    <include file="scene.xml"/>
+    <include file="vx300s_dependencies.xml"/>
+    <worldbody>
+        <include file="vx300s_left.xml" />
+        <include file="vx300s_right.xml" />
+
+        <body name="box" pos="0.2 0.5 0.05">
+            <joint name="red_box_joint" type="free" frictionloss="0.01" />
+            <inertial pos="0 0 0" mass="0.05" diaginertia="0.002 0.002 0.002" />
+            <geom condim="4" solimp="2 1 0.01" solref="0.01 1" friction="1 0.005 0.0001" pos="0 0 0" size="0.02 0.02 0.02" type="box" name="red_box" rgba="1 0 0 1" />
+        </body>
+
+    </worldbody>
+
+    <actuator>
+        <position ctrllimited="true" ctrlrange="-3.14158 3.14158" joint="vx300s_left/waist" kp="800"  user="1" forcelimited="true" forcerange="-150 150"/>
+        <position ctrllimited="true" ctrlrange="-1.85005 1.25664" joint="vx300s_left/shoulder" kp="1600"  user="1" forcelimited="true" forcerange="-300 300"/>
+        <position ctrllimited="true" ctrlrange="-1.76278 1.6057" joint="vx300s_left/elbow" kp="800"  user="1" forcelimited="true" forcerange="-100 100"/>
+        <position ctrllimited="true" ctrlrange="-3.14158 3.14158" joint="vx300s_left/forearm_roll" kp="10"  user="1" forcelimited="true" forcerange="-100 100"/>
+        <position ctrllimited="true" ctrlrange="-1.8675 2.23402" joint="vx300s_left/wrist_angle" kp="50"  user="1"/>
+        <position ctrllimited="true" ctrlrange="-3.14158 3.14158" joint="vx300s_left/wrist_rotate" kp="20"  user="1"/>
+        <position ctrllimited="true" ctrlrange="0.021 0.057" joint="vx300s_left/left_finger" kp="200"  user="1"/>
+        <position ctrllimited="true" ctrlrange="-0.057 -0.021" joint="vx300s_left/right_finger" kp="200"  user="1"/>
+
+        <position ctrllimited="true" ctrlrange="-3.14158 3.14158" joint="vx300s_right/waist" kp="800"  user="1" forcelimited="true" forcerange="-150 150"/>
+        <position ctrllimited="true" ctrlrange="-1.85005 1.25664" joint="vx300s_right/shoulder" kp="1600"  user="1" forcelimited="true" forcerange="-300 300"/>
+        <position ctrllimited="true" ctrlrange="-1.76278 1.6057" joint="vx300s_right/elbow" kp="800"  user="1" forcelimited="true" forcerange="-100 100"/>
+        <position ctrllimited="true" ctrlrange="-3.14158 3.14158" joint="vx300s_right/forearm_roll" kp="10"  user="1" forcelimited="true" forcerange="-100 100"/>
+        <position ctrllimited="true" ctrlrange="-1.8675 2.23402" joint="vx300s_right/wrist_angle" kp="50"  user="1"/>
+        <position ctrllimited="true" ctrlrange="-3.14158 3.14158" joint="vx300s_right/wrist_rotate" kp="20"  user="1"/>
+        <position ctrllimited="true" ctrlrange="0.021 0.057" joint="vx300s_right/left_finger" kp="200"  user="1"/>
+        <position ctrllimited="true" ctrlrange="-0.057 -0.021" joint="vx300s_right/right_finger" kp="200"  user="1"/>
+
+    </actuator>
+
+    <keyframe>
+        <key qpos="0 -0.96 1.16 0 -0.3 0 0.024 -0.024  0 -0.96 1.16 0 -0.3 0 0.024 -0.024  0.2 0.5 0.05 1 0 0 0"/>
+    </keyframe>
+
+
+</mujoco>

envs/sim_aloha/aloha/assets/scene.xml

@@ -0,0 +1,38 @@
+<mujocoinclude>
+<!--    <option timestep='0.0025' iterations="50" tolerance="1e-10" solver="Newton" jacobian="dense" cone="elliptic"/>-->
+
+    <asset>
+        <mesh file="tabletop.stl" name="tabletop" scale="0.001 0.001 0.001"/>
+    </asset>
+
+    <visual>
+        <map fogstart="1.5" fogend="5" force="0.1" znear="0.1"/>
+        <quality shadowsize="4096" offsamples="4"/>
+        <headlight ambient="0.4 0.4 0.4"/>
+    </visual>
+
+    <worldbody>
+        <light castshadow="false" directional='true' diffuse='.3 .3 .3' specular='0.3 0.3 0.3' pos='-1 -1 1'
+               dir='1 1 -1'/>
+        <light directional='true' diffuse='.3 .3 .3' specular='0.3 0.3 0.3' pos='1 -1 1' dir='-1 1 -1'/>
+        <light castshadow="false" directional='true' diffuse='.3 .3 .3' specular='0.3 0.3 0.3' pos='0 1 1'
+               dir='0 -1 -1'/>
+
+        <body name="table" pos="0 .6 0">
+            <geom group="1" mesh="tabletop" pos="0 0 0" type="mesh" conaffinity="1" contype="1" name="table" rgba="0.2 0.2 0.2 1" />
+        </body>
+        <body name="midair" pos="0 .6 0.2">
+            <site pos="0 0 0" size="0.01" type="sphere" name="midair" rgba="1 0 0 0"/>
+        </body>
+
+        <camera name="left_pillar" pos="-0.5 0.2 0.6" fovy="78" mode="targetbody" target="table"/>
+        <camera name="right_pillar" pos="0.5 0.2 0.6" fovy="78" mode="targetbody" target="table"/>
+        <camera name="top" pos="0 0.6 0.8" fovy="78" mode="targetbody" target="table"/>
+        <camera name="angle" pos="0 0 0.6" fovy="78" mode="targetbody" target="table"/>
+        <camera name="front_close" pos="0 0.2 0.4" fovy="78" mode="targetbody" target="vx300s_left/camera_focus"/>
+
+    </worldbody>
+
+
+
+</mujocoinclude>

envs/sim_aloha/aloha/assets/tabletop.stl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:76a1571d1aa36520f2bd81c268991b99816c2a7819464d718e0fd9976fe30dce
+size 684

envs/sim_aloha/aloha/assets/vx300s_10_custom_finger_left.stl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:df73ae5b9058e5d50a6409ac2ab687dade75053a86591bb5e23ab051dbf2d659
+size 83384

envs/sim_aloha/aloha/assets/vx300s_10_custom_finger_right.stl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:56fb3cc1236d4193106038adf8e457c7252ae9e86c7cee6dabf0578c53666358
+size 83384

envs/sim_aloha/aloha/assets/vx300s_10_gripper_finger.stl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a4baacd9a64df1be60ea5e98f50f3c660e1b7a1fe9684aace6004c5058c09483
+size 42884

envs/sim_aloha/aloha/assets/vx300s_11_ar_tag.stl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a18a1601074d29ed1d546ead70cd18fbb063f1db7b5b96b9f0365be714f3136a
+size 3884

envs/sim_aloha/aloha/assets/vx300s_1_base.stl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d100cafe656671ca8fde98fb6a4cf2d1b746995c51c61c25ad9ea2715635d146
+size 99984

envs/sim_aloha/aloha/assets/vx300s_2_shoulder.stl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:139745a74055cb0b23430bb5bc032bf68cf7bea5e4975c8f4c04107ae005f7f0
+size 63884

envs/sim_aloha/aloha/assets/vx300s_3_upper_arm.stl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:900f236320dd3d500870c5fde763b2d47502d51e043a5c377875e70237108729
+size 102984

envs/sim_aloha/aloha/assets/vx300s_4_upper_forearm.stl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4104fc54bbfb8a9b533029f1e7e3ade3d54d638372b3195daa0c98f57e0295b5
+size 49584

envs/sim_aloha/aloha/assets/vx300s_5_lower_forearm.stl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:66814e27fa728056416e25e02e89eb7d34c51d51c51e7c3df873829037ddc6b8
+size 99884

envs/sim_aloha/aloha/assets/vx300s_6_wrist.stl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:90eb145c85627968c3776ae6de23ccff7e112c9dd713c46bc9acdfdaa859a048
+size 70784

envs/sim_aloha/aloha/assets/vx300s_7_gripper.stl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:786c1077bfd226f14219581b11d5f19464ca95b17132e0bb7532503568f5af90
+size 450084

envs/sim_aloha/aloha/assets/vx300s_8_gripper_prop.stl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d1275a93fe2157c83dbc095617fb7e672888bdd48ec070a35ef4ab9ebd9755b0
+size 31684

envs/sim_aloha/aloha/assets/vx300s_9_gripper_bar.stl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a4de62c9a2ed2c78433010e4c05530a1254b1774a7651967f406120c9bf8973e
+size 379484

envs/sim_aloha/aloha/assets/vx300s_dependencies.xml

@@ -0,0 +1,17 @@
+<mujocoinclude>
+    <compiler angle="radian" inertiafromgeom="auto" inertiagrouprange="4 5"/>
+    <asset>
+        <mesh name="vx300s_1_base" file="vx300s_1_base.stl" scale="0.001 0.001 0.001" />
+        <mesh name="vx300s_2_shoulder" file="vx300s_2_shoulder.stl" scale="0.001 0.001 0.001" />
+        <mesh name="vx300s_3_upper_arm" file="vx300s_3_upper_arm.stl" scale="0.001 0.001 0.001" />
+        <mesh name="vx300s_4_upper_forearm" file="vx300s_4_upper_forearm.stl" scale="0.001 0.001 0.001" />
+        <mesh name="vx300s_5_lower_forearm" file="vx300s_5_lower_forearm.stl" scale="0.001 0.001 0.001" />
+        <mesh name="vx300s_6_wrist" file="vx300s_6_wrist.stl" scale="0.001 0.001 0.001" />
+        <mesh name="vx300s_7_gripper" file="vx300s_7_gripper.stl" scale="0.001 0.001 0.001" />
+        <mesh name="vx300s_8_gripper_prop" file="vx300s_8_gripper_prop.stl" scale="0.001 0.001 0.001" />
+        <mesh name="vx300s_9_gripper_bar" file="vx300s_9_gripper_bar.stl" scale="0.001 0.001 0.001" />
+        <mesh name="vx300s_10_gripper_finger_left" file="vx300s_10_custom_finger_left.stl" scale="0.001 0.001 0.001" />
+        <mesh name="vx300s_10_gripper_finger_right" file="vx300s_10_custom_finger_right.stl" scale="0.001 0.001 0.001" />
+    </asset>
+
+</mujocoinclude>

envs/sim_aloha/aloha/assets/vx300s_left.xml

@@ -0,0 +1,59 @@
+
+<mujocoinclude>
+    <body name="vx300s_left" pos="-0.469 0.5 0">
+        <geom quat="0.707107 0 0 0.707107" type="mesh" mesh="vx300s_1_base" name="vx300s_left/1_base" contype="0" conaffinity="0"/>
+        <body name="vx300s_left/shoulder_link" pos="0 0 0.079">
+            <inertial pos="0.000259233 -3.3552e-06 0.0116129" quat="-0.476119 0.476083 0.52279 0.522826" mass="0.798614" diaginertia="0.00120156 0.00113744 0.0009388" />
+            <joint name="vx300s_left/waist" pos="0 0 0" axis="0 0 1" limited="true" range="-3.14158 3.14158" frictionloss="50" />
+            <geom pos="0 0 -0.003" quat="0.707107 0 0 0.707107" type="mesh" mesh="vx300s_2_shoulder" name="vx300s_left/2_shoulder" />
+            <body name="vx300s_left/upper_arm_link" pos="0 0 0.04805">
+                <inertial pos="0.0206949 4e-10 0.226459" quat="0 0.0728458 0 0.997343" mass="0.792592" diaginertia="0.00911338 0.008925 0.000759317" />
+                <joint name="vx300s_left/shoulder" pos="0 0 0" axis="0 1 0" limited="true" range="-1.85005 1.25664" frictionloss="60" />
+                <geom quat="0.707107 0 0 0.707107" type="mesh" mesh="vx300s_3_upper_arm" name="vx300s_left/3_upper_arm"/>
+                <body name="vx300s_left/upper_forearm_link" pos="0.05955 0 0.3">
+                    <inertial pos="0.105723 0 0" quat="-0.000621631 0.704724 0.0105292 0.709403" mass="0.322228" diaginertia="0.00144107 0.00134228 0.000152047" />
+                    <joint name="vx300s_left/elbow" pos="0 0 0" axis="0 1 0" limited="true" range="-1.76278 1.6057" frictionloss="60" />
+                    <geom type="mesh" mesh="vx300s_4_upper_forearm" name="vx300s_left/4_upper_forearm" />
+                    <body name="vx300s_left/lower_forearm_link" pos="0.2 0 0">
+                        <inertial pos="0.0513477 0.00680462 0" quat="-0.702604 -0.0796724 -0.702604 0.0796724" mass="0.414823" diaginertia="0.0005911 0.000546493 0.000155707" />
+                        <joint name="vx300s_left/forearm_roll" pos="0 0 0" axis="1 0 0" limited="true" range="-3.14158 3.14158" frictionloss="30" />
+                        <geom quat="0 1 0 0" type="mesh" mesh="vx300s_5_lower_forearm" name="vx300s_left/5_lower_forearm"/>
+                        <body name="vx300s_left/wrist_link" pos="0.1 0 0">
+                            <inertial pos="0.046743 -7.6652e-06 0.010565" quat="-0.00100191 0.544586 0.0026583 0.8387" mass="0.115395" diaginertia="5.45707e-05 4.63101e-05 4.32692e-05" />
+                            <joint name="vx300s_left/wrist_angle" pos="0 0 0" axis="0 1 0" limited="true" range="-1.8675 2.23402" frictionloss="30" />
+                            <geom quat="0.707107 0 0 0.707107" type="mesh" mesh="vx300s_6_wrist" name="vx300s_left/6_wrist" />
+                            <body name="vx300s_left/gripper_link" pos="0.069744 0 0">
+                                <body name="vx300s_left/camera_focus" pos="0.15 0 0.01">
+                                    <site pos="0 0 0" size="0.01" type="sphere" name="left_cam_focus" rgba="0 0 1 0"/>
+                                </body>
+                                <site pos="0.15 0 0" size="0.003 0.003 0.03" type="box" name="cali_left_site1" rgba="0 0 1 0"/>
+                                <site pos="0.15 0 0" size="0.003 0.03 0.003" type="box" name="cali_left_site2" rgba="0 0 1 0"/>
+                                <site pos="0.15 0 0" size="0.03 0.003 0.003" type="box" name="cali_left_site3" rgba="0 0 1 0"/>
+                                <camera name="left_wrist" pos="-0.1 0 0.16" fovy="20" mode="targetbody" target="vx300s_left/camera_focus"/>
+                                <inertial pos="0.0395662 -2.56311e-07 0.00400649" quat="0.62033 0.619916 -0.339682 0.339869" mass="0.251652" diaginertia="0.000689546 0.000650316 0.000468142" />
+                                <joint name="vx300s_left/wrist_rotate" pos="0 0 0" axis="1 0 0" limited="true" range="-3.14158 3.14158" frictionloss="30" />
+                                <geom pos="-0.02 0 0" quat="0.707107 0 0 0.707107" type="mesh" mesh="vx300s_7_gripper" name="vx300s_left/7_gripper" />
+                                <geom pos="-0.020175 0 0" quat="0.707107 0 0 0.707107" type="mesh" mesh="vx300s_9_gripper_bar" name="vx300s_left/9_gripper_bar" />
+                                <body name="vx300s_left/gripper_prop_link" pos="0.0485 0 0">
+                                    <inertial pos="0.002378 2.85e-08 0" quat="0 0 0.897698 0.440611" mass="0.008009" diaginertia="4.2979e-06 2.8868e-06 1.5314e-06" />
+<!--                                    <joint name="vx300s_left/gripper" pos="0 0 0" axis="1 0 0" frictionloss="30" />-->
+                                    <geom pos="-0.0685 0 0" quat="0.707107 0 0 0.707107" type="mesh" mesh="vx300s_8_gripper_prop" name="vx300s_left/8_gripper_prop" />
+                                </body>
+                                <body name="vx300s_left/left_finger_link" pos="0.0687 0 0">
+                                    <inertial pos="0.017344 -0.0060692 0" quat="0.449364 0.449364 -0.54596 -0.54596" mass="0.034796" diaginertia="2.48003e-05 1.417e-05 1.20797e-05" />
+                                    <joint name="vx300s_left/left_finger" pos="0 0 0" axis="0 1 0" type="slide" limited="true" range="0.021 0.057" frictionloss="30" />
+                                    <geom condim="4" solimp="2 1 0.01" solref="0.01 1" friction="1 0.005 0.0001" pos="0.005 -0.052 0" euler="3.14 1.57 0" type="mesh" mesh="vx300s_10_gripper_finger_left" name="vx300s_left/10_left_gripper_finger"/>
+                                </body>
+                                <body name="vx300s_left/right_finger_link" pos="0.0687 0 0">
+                                    <inertial pos="0.017344 0.0060692 0" quat="0.44937 -0.44937 0.545955 -0.545955" mass="0.034796" diaginertia="2.48002e-05 1.417e-05 1.20798e-05" />
+                                    <joint name="vx300s_left/right_finger" pos="0 0 0" axis="0 1 0" type="slide" limited="true" range="-0.057 -0.021" frictionloss="30" />
+                                    <geom condim="4" solimp="2 1 0.01" solref="0.01 1" friction="1 0.005 0.0001" pos="0.005 0.052 0" euler="3.14 1.57 0" type="mesh" mesh="vx300s_10_gripper_finger_right" name="vx300s_left/10_right_gripper_finger"/>
+                                </body>
+                            </body>
+                        </body>
+                    </body>
+                </body>
+            </body>
+        </body>
+    </body>
+</mujocoinclude>

envs/sim_aloha/aloha/assets/vx300s_right.xml

@@ -0,0 +1,59 @@
+
+<mujocoinclude>
+    <body name="vx300s_right" pos="0.469 0.5 0" euler="0 0 3.1416">
+        <geom quat="0.707107 0 0 0.707107" type="mesh" mesh="vx300s_1_base" name="vx300s_right/1_base" contype="0" conaffinity="0"/>
+        <body name="vx300s_right/shoulder_link" pos="0 0 0.079">
+            <inertial pos="0.000259233 -3.3552e-06 0.0116129" quat="-0.476119 0.476083 0.52279 0.522826" mass="0.798614" diaginertia="0.00120156 0.00113744 0.0009388" />
+            <joint name="vx300s_right/waist" pos="0 0 0" axis="0 0 1" limited="true" range="-3.14158 3.14158" frictionloss="50" />
+            <geom pos="0 0 -0.003" quat="0.707107 0 0 0.707107" type="mesh" mesh="vx300s_2_shoulder" name="vx300s_right/2_shoulder" />
+            <body name="vx300s_right/upper_arm_link" pos="0 0 0.04805">
+                <inertial pos="0.0206949 4e-10 0.226459" quat="0 0.0728458 0 0.997343" mass="0.792592" diaginertia="0.00911338 0.008925 0.000759317" />
+                <joint name="vx300s_right/shoulder" pos="0 0 0" axis="0 1 0" limited="true" range="-1.85005 1.25664" frictionloss="60" />
+                <geom quat="0.707107 0 0 0.707107" type="mesh" mesh="vx300s_3_upper_arm" name="vx300s_right/3_upper_arm"/>
+                <body name="vx300s_right/upper_forearm_link" pos="0.05955 0 0.3">
+                    <inertial pos="0.105723 0 0" quat="-0.000621631 0.704724 0.0105292 0.709403" mass="0.322228" diaginertia="0.00144107 0.00134228 0.000152047" />
+                    <joint name="vx300s_right/elbow" pos="0 0 0" axis="0 1 0" limited="true" range="-1.76278 1.6057" frictionloss="60" />
+                    <geom type="mesh" mesh="vx300s_4_upper_forearm" name="vx300s_right/4_upper_forearm" />
+                    <body name="vx300s_right/lower_forearm_link" pos="0.2 0 0">
+                        <inertial pos="0.0513477 0.00680462 0" quat="-0.702604 -0.0796724 -0.702604 0.0796724" mass="0.414823" diaginertia="0.0005911 0.000546493 0.000155707" />
+                        <joint name="vx300s_right/forearm_roll" pos="0 0 0" axis="1 0 0" limited="true" range="-3.14158 3.14158" frictionloss="30" />
+                        <geom quat="0 1 0 0" type="mesh" mesh="vx300s_5_lower_forearm" name="vx300s_right/5_lower_forearm"/>
+                        <body name="vx300s_right/wrist_link" pos="0.1 0 0">
+                            <inertial pos="0.046743 -7.6652e-06 0.010565" quat="-0.00100191 0.544586 0.0026583 0.8387" mass="0.115395" diaginertia="5.45707e-05 4.63101e-05 4.32692e-05" />
+                            <joint name="vx300s_right/wrist_angle" pos="0 0 0" axis="0 1 0" limited="true" range="-1.8675 2.23402" frictionloss="30" />
+                            <geom quat="0.707107 0 0 0.707107" type="mesh" mesh="vx300s_6_wrist" name="vx300s_right/6_wrist" />
+                            <body name="vx300s_right/gripper_link" pos="0.069744 0 0">
+                                <body name="vx300s_right/camera_focus" pos="0.15 0 0.01">
+                                    <site pos="0 0 0" size="0.01" type="sphere" name="right_cam_focus" rgba="0 0 1 0"/>
+                                </body>
+                                <site pos="0.15 0 0" size="0.003 0.003 0.03" type="box" name="cali_right_site1" rgba="0 0 1 0"/>
+                                <site pos="0.15 0 0" size="0.003 0.03 0.003" type="box" name="cali_right_site2" rgba="0 0 1 0"/>
+                                <site pos="0.15 0 0" size="0.03 0.003 0.003" type="box" name="cali_right_site3" rgba="0 0 1 0"/>
+                                <camera name="right_wrist" pos="-0.1 0 0.16" fovy="20" mode="targetbody" target="vx300s_right/camera_focus"/>
+                                <inertial pos="0.0395662 -2.56311e-07 0.00400649" quat="0.62033 0.619916 -0.339682 0.339869" mass="0.251652" diaginertia="0.000689546 0.000650316 0.000468142" />
+                                <joint name="vx300s_right/wrist_rotate" pos="0 0 0" axis="1 0 0" limited="true" range="-3.14158 3.14158" frictionloss="30" />
+                                <geom pos="-0.02 0 0" quat="0.707107 0 0 0.707107" type="mesh" mesh="vx300s_7_gripper" name="vx300s_right/7_gripper" />
+                                <geom pos="-0.020175 0 0" quat="0.707107 0 0 0.707107" type="mesh" mesh="vx300s_9_gripper_bar" name="vx300s_right/9_gripper_bar" />
+                                <body name="vx300s_right/gripper_prop_link" pos="0.0485 0 0">
+                                    <inertial pos="0.002378 2.85e-08 0" quat="0 0 0.897698 0.440611" mass="0.008009" diaginertia="4.2979e-06 2.8868e-06 1.5314e-06" />
+<!--                                    <joint name="vx300s_right/gripper" pos="0 0 0" axis="1 0 0" frictionloss="30" />-->
+                                    <geom pos="-0.0685 0 0" quat="0.707107 0 0 0.707107" type="mesh" mesh="vx300s_8_gripper_prop" name="vx300s_right/8_gripper_prop" />
+                                </body>
+                                <body name="vx300s_right/left_finger_link" pos="0.0687 0 0">
+                                    <inertial pos="0.017344 -0.0060692 0" quat="0.449364 0.449364 -0.54596 -0.54596" mass="0.034796" diaginertia="2.48003e-05 1.417e-05 1.20797e-05" />
+                                    <joint name="vx300s_right/left_finger" pos="0 0 0" axis="0 1 0" type="slide" limited="true" range="0.021 0.057" frictionloss="30" />
+                                    <geom condim="4" solimp="2 1 0.01" solref="0.01 1" friction="1 0.005 0.0001" pos="0.005 -0.052 0" euler="3.14 1.57 0" type="mesh" mesh="vx300s_10_gripper_finger_left" name="vx300s_right/10_left_gripper_finger"/>
+                                </body>
+                                <body name="vx300s_right/right_finger_link" pos="0.0687 0 0">
+                                    <inertial pos="0.017344 0.0060692 0" quat="0.44937 -0.44937 0.545955 -0.545955" mass="0.034796" diaginertia="2.48002e-05 1.417e-05 1.20798e-05" />
+                                    <joint name="vx300s_right/right_finger" pos="0 0 0" axis="0 1 0" type="slide" limited="true" range="-0.057 -0.021" frictionloss="30" />
+                                    <geom condim="4" solimp="2 1 0.01" solref="0.01 1" friction="1 0.005 0.0001" pos="0.005 0.052 0" euler="3.14 1.57 0" type="mesh" mesh="vx300s_10_gripper_finger_right" name="vx300s_right/10_right_gripper_finger"/>
+                                </body>
+                            </body>
+                        </body>
+                    </body>
+                </body>
+            </body>
+        </body>
+    </body>
+</mujocoinclude>

envs/sim_aloha/aloha/constants.py

@@ -0,0 +1,163 @@
+from pathlib import Path
+
+### Simulation envs fixed constants
+DT = 0.02  # 0.02 ms -> 1/0.2 = 50 hz
+FPS = 50
+
+
+JOINTS = [
+    # absolute joint position
+    "left_arm_waist",
+    "left_arm_shoulder",
+    "left_arm_elbow",
+    "left_arm_forearm_roll",
+    "left_arm_wrist_angle",
+    "left_arm_wrist_rotate",
+    # normalized gripper position 0: close, 1: open
+    "left_arm_gripper",
+    # absolute joint position
+    "right_arm_waist",
+    "right_arm_shoulder",
+    "right_arm_elbow",
+    "right_arm_forearm_roll",
+    "right_arm_wrist_angle",
+    "right_arm_wrist_rotate",
+    # normalized gripper position 0: close, 1: open
+    "right_arm_gripper",
+]
+
+ACTIONS = [
+    # position and quaternion for end effector
+    "left_arm_waist",
+    "left_arm_shoulder",
+    "left_arm_elbow",
+    "left_arm_forearm_roll",
+    "left_arm_wrist_angle",
+    "left_arm_wrist_rotate",
+    # normalized gripper position (0: close, 1: open)
+    "left_arm_gripper",
+    "right_arm_waist",
+    "right_arm_shoulder",
+    "right_arm_elbow",
+    "right_arm_forearm_roll",
+    "right_arm_wrist_angle",
+    "right_arm_wrist_rotate",
+    # normalized gripper position (0: close, 1: open)
+    "right_arm_gripper",
+]
+
+
+START_ARM_POSE = [
+    0,
+    -0.96,
+    1.16,
+    0,
+    -0.3,
+    0,
+    0.02239,
+    -0.02239,
+    0,
+    -0.96,
+    1.16,
+    0,
+    -0.3,
+    0,
+    0.02239,
+    -0.02239,
+]
+
+ASSETS_DIR = Path(__file__).parent.resolve() / "assets"  # note: absolute path
+
+# Left finger position limits (qpos[7]), right_finger = -1 * left_finger
+MASTER_GRIPPER_POSITION_OPEN = 0.02417
+MASTER_GRIPPER_POSITION_CLOSE = 0.01244
+PUPPET_GRIPPER_POSITION_OPEN = 0.05800
+PUPPET_GRIPPER_POSITION_CLOSE = 0.01844
+
+# Gripper joint limits (qpos[6])
+MASTER_GRIPPER_JOINT_OPEN = 0.3083
+MASTER_GRIPPER_JOINT_CLOSE = -0.6842
+PUPPET_GRIPPER_JOINT_OPEN = 1.4910
+PUPPET_GRIPPER_JOINT_CLOSE = -0.6213
+
+MASTER_GRIPPER_JOINT_MID = (MASTER_GRIPPER_JOINT_OPEN + MASTER_GRIPPER_JOINT_CLOSE) / 2
+
+############################ Helper functions ############################
+
+
+def normalize_master_gripper_position(x):
+    return (x - MASTER_GRIPPER_POSITION_CLOSE) / (
+        MASTER_GRIPPER_POSITION_OPEN - MASTER_GRIPPER_POSITION_CLOSE
+    )
+
+
+def normalize_puppet_gripper_position(x):
+    return (x - PUPPET_GRIPPER_POSITION_CLOSE) / (
+        PUPPET_GRIPPER_POSITION_OPEN - PUPPET_GRIPPER_POSITION_CLOSE
+    )
+
+
+def unnormalize_master_gripper_position(x):
+    return x * (MASTER_GRIPPER_POSITION_OPEN - MASTER_GRIPPER_POSITION_CLOSE) + MASTER_GRIPPER_POSITION_CLOSE
+
+
+def unnormalize_puppet_gripper_position(x):
+    return x * (PUPPET_GRIPPER_POSITION_OPEN - PUPPET_GRIPPER_POSITION_CLOSE) + PUPPET_GRIPPER_POSITION_CLOSE
+
+
+def convert_position_from_master_to_puppet(x):
+    return unnormalize_puppet_gripper_position(normalize_master_gripper_position(x))
+
+
+def normalizer_master_gripper_joint(x):
+    return (x - MASTER_GRIPPER_JOINT_CLOSE) / (MASTER_GRIPPER_JOINT_OPEN - MASTER_GRIPPER_JOINT_CLOSE)
+
+
+def normalize_puppet_gripper_joint(x):
+    return (x - PUPPET_GRIPPER_JOINT_CLOSE) / (PUPPET_GRIPPER_JOINT_OPEN - PUPPET_GRIPPER_JOINT_CLOSE)
+
+
+def unnormalize_master_gripper_joint(x):
+    return x * (MASTER_GRIPPER_JOINT_OPEN - MASTER_GRIPPER_JOINT_CLOSE) + MASTER_GRIPPER_JOINT_CLOSE
+
+
+def unnormalize_puppet_gripper_joint(x):
+    return x * (PUPPET_GRIPPER_JOINT_OPEN - PUPPET_GRIPPER_JOINT_CLOSE) + PUPPET_GRIPPER_JOINT_CLOSE
+
+
+def convert_join_from_master_to_puppet(x):
+    return unnormalize_puppet_gripper_joint(normalizer_master_gripper_joint(x))
+
+
+def normalize_master_gripper_velocity(x):
+    return x / (MASTER_GRIPPER_POSITION_OPEN - MASTER_GRIPPER_POSITION_CLOSE)
+
+
+def normalize_puppet_gripper_velocity(x):
+    return x / (PUPPET_GRIPPER_POSITION_OPEN - PUPPET_GRIPPER_POSITION_CLOSE)
+
+
+def convert_master_from_position_to_joint(x):
+    return (
+        normalize_master_gripper_position(x) * (MASTER_GRIPPER_JOINT_OPEN - MASTER_GRIPPER_JOINT_CLOSE)
+        + MASTER_GRIPPER_JOINT_CLOSE
+    )
+
+
+def convert_master_from_joint_to_position(x):
+    return unnormalize_master_gripper_position(
+        (x - MASTER_GRIPPER_JOINT_CLOSE) / (MASTER_GRIPPER_JOINT_OPEN - MASTER_GRIPPER_JOINT_CLOSE)
+    )
+
+
+def convert_puppet_from_position_to_join(x):
+    return (
+        normalize_puppet_gripper_position(x) * (PUPPET_GRIPPER_JOINT_OPEN - PUPPET_GRIPPER_JOINT_CLOSE)
+        + PUPPET_GRIPPER_JOINT_CLOSE
+    )
+
+
+def convert_puppet_from_joint_to_position(x):
+    return unnormalize_puppet_gripper_position(
+        (x - PUPPET_GRIPPER_JOINT_CLOSE) / (PUPPET_GRIPPER_JOINT_OPEN - PUPPET_GRIPPER_JOINT_CLOSE)
+    )

envs/sim_aloha/aloha/env.py

@@ -0,0 +1,40 @@
+from dm_control import mujoco
+from dm_control.rl import control
+
+from aloha.constants import ASSETS_DIR, DT
+from aloha.tasks.sim import InsertionTask, TransferCubeTask
+from aloha.tasks.sim_end_effector import (
+    InsertionEndEffectorTask,
+    TransferCubeEndEffectorTask,
+)
+
+
+def make_env_task(task_name):
+    # time limit is controlled by StepCounter in env factory
+    time_limit = float("inf")
+
+    if "sim_transfer_cube" in task_name:
+        xml_path = ASSETS_DIR / "bimanual_viperx_transfer_cube.xml"
+        physics = mujoco.Physics.from_xml_path(str(xml_path))
+        task = TransferCubeTask(random=False)
+    elif "sim_insertion" in task_name:
+        xml_path = ASSETS_DIR / "bimanual_viperx_insertion.xml"
+        physics = mujoco.Physics.from_xml_path(str(xml_path))
+        task = InsertionTask(random=False)
+    elif "sim_end_effector_transfer_cube" in task_name:
+        raise NotImplementedError()
+        xml_path = ASSETS_DIR / "bimanual_viperx_end_effector_transfer_cube.xml"
+        physics = mujoco.Physics.from_xml_path(str(xml_path))
+        task = TransferCubeEndEffectorTask(random=False)
+    elif "sim_end_effector_insertion" in task_name:
+        raise NotImplementedError()
+        xml_path = ASSETS_DIR / "bimanual_viperx_end_effector_insertion.xml"
+        physics = mujoco.Physics.from_xml_path(str(xml_path))
+        task = InsertionEndEffectorTask(random=False)
+    else:
+        raise NotImplementedError(task_name)
+
+    env = control.Environment(
+        physics, task, time_limit, control_timestep=DT, n_sub_steps=None, flat_observation=False
+    )
+    return env

envs/sim_aloha/aloha/tasks/sim.py

@@ -0,0 +1,218 @@
+import collections
+
+import numpy as np
+from aloha.constants import (
+    START_ARM_POSE,
+    normalize_puppet_gripper_position,
+    normalize_puppet_gripper_velocity,
+    unnormalize_puppet_gripper_position,
+)
+from dm_control.suite import base
+
+BOX_POSE = [None]  # to be changed from outside
+
+"""
+Environment for simulated robot bi-manual manipulation, with joint position control
+Action space:      [left_arm_qpos (6),             # absolute joint position
+                    left_gripper_positions (1),    # normalized gripper position (0: close, 1: open)
+                    right_arm_qpos (6),            # absolute joint position
+                    right_gripper_positions (1),]  # normalized gripper position (0: close, 1: open)
+
+Observation space: {"qpos": Concat[ left_arm_qpos (6),         # absolute joint position
+                                    left_gripper_position (1),  # normalized gripper position (0: close, 1: open)
+                                    right_arm_qpos (6),         # absolute joint position
+                                    right_gripper_qpos (1)]     # normalized gripper position (0: close, 1: open)
+                    "qvel": Concat[ left_arm_qvel (6),         # absolute joint velocity (rad)
+                                    left_gripper_velocity (1),  # normalized gripper velocity (pos: opening, neg: closing)
+                                    right_arm_qvel (6),         # absolute joint velocity (rad)
+                                    right_gripper_qvel (1)]     # normalized gripper velocity (pos: opening, neg: closing)
+                    "images": {"main": (480x640x3)}        # h, w, c, dtype='uint8'
+"""
+
+
+class BimanualViperXTask(base.Task):
+    def __init__(self, random=None):
+        super().__init__(random=random)
+
+    def before_step(self, action, physics):
+        left_arm_action = action[:6]
+        right_arm_action = action[7 : 7 + 6]
+        normalized_left_gripper_action = action[6]
+        normalized_right_gripper_action = action[7 + 6]
+
+        left_gripper_action = unnormalize_puppet_gripper_position(normalized_left_gripper_action)
+        right_gripper_action = unnormalize_puppet_gripper_position(normalized_right_gripper_action)
+
+        full_left_gripper_action = [left_gripper_action, -left_gripper_action]
+        full_right_gripper_action = [right_gripper_action, -right_gripper_action]
+
+        env_action = np.concatenate(
+            [left_arm_action, full_left_gripper_action, right_arm_action, full_right_gripper_action]
+        )
+        super().before_step(env_action, physics)
+        return
+
+    def initialize_episode(self, physics):
+        """Sets the state of the environment at the start of each episode."""
+        super().initialize_episode(physics)
+
+    @staticmethod
+    def get_qpos(physics):
+        qpos_raw = physics.data.qpos.copy()
+        left_qpos_raw = qpos_raw[:8]
+        right_qpos_raw = qpos_raw[8:16]
+        left_arm_qpos = left_qpos_raw[:6]
+        right_arm_qpos = right_qpos_raw[:6]
+        left_gripper_qpos = [normalize_puppet_gripper_position(left_qpos_raw[6])]
+        right_gripper_qpos = [normalize_puppet_gripper_position(right_qpos_raw[6])]
+        return np.concatenate([left_arm_qpos, left_gripper_qpos, right_arm_qpos, right_gripper_qpos])
+
+    @staticmethod
+    def get_qvel(physics):
+        qvel_raw = physics.data.qvel.copy()
+        left_qvel_raw = qvel_raw[:8]
+        right_qvel_raw = qvel_raw[8:16]
+        left_arm_qvel = left_qvel_raw[:6]
+        right_arm_qvel = right_qvel_raw[:6]
+        left_gripper_qvel = [normalize_puppet_gripper_velocity(left_qvel_raw[6])]
+        right_gripper_qvel = [normalize_puppet_gripper_velocity(right_qvel_raw[6])]
+        return np.concatenate([left_arm_qvel, left_gripper_qvel, right_arm_qvel, right_gripper_qvel])
+
+    @staticmethod
+    def get_env_state(physics):
+        raise NotImplementedError
+
+    def get_observation(self, physics):
+        obs = collections.OrderedDict()
+        obs["qpos"] = self.get_qpos(physics)
+        obs["qvel"] = self.get_qvel(physics)
+        obs["env_state"] = self.get_env_state(physics)
+        obs["images"] = {}
+        obs["images"]["top"] = physics.render(height=480, width=640, camera_id="top")
+        obs["images"]["angle"] = physics.render(height=480, width=640, camera_id="angle")
+        obs["images"]["vis"] = physics.render(height=480, width=640, camera_id="front_close")
+
+        return obs
+
+    def get_reward(self, physics):
+        # return whether left gripper is holding the box
+        raise NotImplementedError
+
+
+class TransferCubeTask(BimanualViperXTask):
+    def __init__(self, random=None):
+        super().__init__(random=random)
+        self.max_reward = 4
+
+    def initialize_episode(self, physics):
+        """Sets the state of the environment at the start of each episode."""
+        # TODO Notice: this function does not randomize the env configuration. Instead, set BOX_POSE from outside
+        # reset qpos, control and box position
+        with physics.reset_context():
+            physics.named.data.qpos[:16] = START_ARM_POSE
+            np.copyto(physics.data.ctrl, START_ARM_POSE)
+            assert BOX_POSE[0] is not None
+            physics.named.data.qpos[-7:] = BOX_POSE[0]
+            # print(f"{BOX_POSE=}")
+        super().initialize_episode(physics)
+
+    @staticmethod
+    def get_env_state(physics):
+        env_state = physics.data.qpos.copy()[16:]
+        return env_state
+
+    def get_reward(self, physics):
+        # return whether left gripper is holding the box
+        all_contact_pairs = []
+        for i_contact in range(physics.data.ncon):
+            id_geom_1 = physics.data.contact[i_contact].geom1
+            id_geom_2 = physics.data.contact[i_contact].geom2
+            name_geom_1 = physics.model.id2name(id_geom_1, "geom")
+            name_geom_2 = physics.model.id2name(id_geom_2, "geom")
+            contact_pair = (name_geom_1, name_geom_2)
+            all_contact_pairs.append(contact_pair)
+
+        touch_left_gripper = ("red_box", "vx300s_left/10_left_gripper_finger") in all_contact_pairs
+        touch_right_gripper = ("red_box", "vx300s_right/10_right_gripper_finger") in all_contact_pairs
+        touch_table = ("red_box", "table") in all_contact_pairs
+
+        reward = 0
+        if touch_right_gripper:
+            reward = 1
+        if touch_right_gripper and not touch_table:  # lifted
+            reward = 2
+        if touch_left_gripper:  # attempted transfer
+            reward = 3
+        if touch_left_gripper and not touch_table:  # successful transfer
+            reward = 4
+        return reward
+
+
+class InsertionTask(BimanualViperXTask):
+    def __init__(self, random=None):
+        super().__init__(random=random)
+        self.max_reward = 4
+
+    def initialize_episode(self, physics):
+        """Sets the state of the environment at the start of each episode."""
+        # TODO Notice: this function does not randomize the env configuration. Instead, set BOX_POSE from outside
+        # reset qpos, control and box position
+        with physics.reset_context():
+            physics.named.data.qpos[:16] = START_ARM_POSE
+            np.copyto(physics.data.ctrl, START_ARM_POSE)
+            assert BOX_POSE[0] is not None
+            physics.named.data.qpos[-7 * 2 :] = BOX_POSE[0]  # two objects
+            # print(f"{BOX_POSE=}")
+        super().initialize_episode(physics)
+
+    @staticmethod
+    def get_env_state(physics):
+        env_state = physics.data.qpos.copy()[16:]
+        return env_state
+
+    def get_reward(self, physics):
+        # return whether peg touches the pin
+        all_contact_pairs = []
+        for i_contact in range(physics.data.ncon):
+            id_geom_1 = physics.data.contact[i_contact].geom1
+            id_geom_2 = physics.data.contact[i_contact].geom2
+            name_geom_1 = physics.model.id2name(id_geom_1, "geom")
+            name_geom_2 = physics.model.id2name(id_geom_2, "geom")
+            contact_pair = (name_geom_1, name_geom_2)
+            all_contact_pairs.append(contact_pair)
+
+        touch_right_gripper = ("red_peg", "vx300s_right/10_right_gripper_finger") in all_contact_pairs
+        touch_left_gripper = (
+            ("socket-1", "vx300s_left/10_left_gripper_finger") in all_contact_pairs
+            or ("socket-2", "vx300s_left/10_left_gripper_finger") in all_contact_pairs
+            or ("socket-3", "vx300s_left/10_left_gripper_finger") in all_contact_pairs
+            or ("socket-4", "vx300s_left/10_left_gripper_finger") in all_contact_pairs
+        )
+
+        peg_touch_table = ("red_peg", "table") in all_contact_pairs
+        socket_touch_table = (
+            ("socket-1", "table") in all_contact_pairs
+            or ("socket-2", "table") in all_contact_pairs
+            or ("socket-3", "table") in all_contact_pairs
+            or ("socket-4", "table") in all_contact_pairs
+        )
+        peg_touch_socket = (
+            ("red_peg", "socket-1") in all_contact_pairs
+            or ("red_peg", "socket-2") in all_contact_pairs
+            or ("red_peg", "socket-3") in all_contact_pairs
+            or ("red_peg", "socket-4") in all_contact_pairs
+        )
+        pin_touched = ("red_peg", "pin") in all_contact_pairs
+
+        reward = 0
+        if touch_left_gripper and touch_right_gripper:  # touch both
+            reward = 1
+        if (
+            touch_left_gripper and touch_right_gripper and (not peg_touch_table) and (not socket_touch_table)
+        ):  # grasp both
+            reward = 2
+        if peg_touch_socket and (not peg_touch_table) and (not socket_touch_table):  # peg and socket touching
+            reward = 3
+        if pin_touched:  # successful insertion
+            reward = 4
+        return reward

envs/sim_aloha/aloha/tasks/sim_end_effector.py

@@ -0,0 +1,262 @@
+import collections
+
+import numpy as np
+from aloha.constants import (
+    PUPPET_GRIPPER_POSITION_CLOSE,
+    START_ARM_POSE,
+    normalize_puppet_gripper_position,
+    normalize_puppet_gripper_velocity,
+    unnormalize_puppet_gripper_position,
+)
+from aloha.utils import sample_box_pose, sample_insertion_pose
+from dm_control.suite import base
+
+"""
+Environment for simulated robot bi-manual manipulation, with end-effector control.
+Action space:      [left_arm_pose (7),             # position and quaternion for end effector
+                    left_gripper_positions (1),    # normalized gripper position (0: close, 1: open)
+                    right_arm_pose (7),            # position and quaternion for end effector
+                    right_gripper_positions (1),]  # normalized gripper position (0: close, 1: open)
+
+Observation space: {"qpos": Concat[ left_arm_qpos (6),         # absolute joint position
+                                    left_gripper_position (1),  # normalized gripper position (0: close, 1: open)
+                                    right_arm_qpos (6),         # absolute joint position
+                                    right_gripper_qpos (1)]     # normalized gripper position (0: close, 1: open)
+                    "qvel": Concat[ left_arm_qvel (6),         # absolute joint velocity (rad)
+                                    left_gripper_velocity (1),  # normalized gripper velocity (pos: opening, neg: closing)
+                                    right_arm_qvel (6),         # absolute joint velocity (rad)
+                                    right_gripper_qvel (1)]     # normalized gripper velocity (pos: opening, neg: closing)
+                    "images": {"main": (480x640x3)}        # h, w, c, dtype='uint8'
+"""
+
+
+class BimanualViperXEndEffectorTask(base.Task):
+    def __init__(self, random=None):
+        super().__init__(random=random)
+
+    def before_step(self, action, physics):
+        a_len = len(action) // 2
+        action_left = action[:a_len]
+        action_right = action[a_len:]
+
+        # set mocap position and quat
+        # left
+        np.copyto(physics.data.mocap_pos[0], action_left[:3])
+        np.copyto(physics.data.mocap_quat[0], action_left[3:7])
+        # right
+        np.copyto(physics.data.mocap_pos[1], action_right[:3])
+        np.copyto(physics.data.mocap_quat[1], action_right[3:7])
+
+        # set gripper
+        g_left_ctrl = unnormalize_puppet_gripper_position(action_left[7])
+        g_right_ctrl = unnormalize_puppet_gripper_position(action_right[7])
+        np.copyto(physics.data.ctrl, np.array([g_left_ctrl, -g_left_ctrl, g_right_ctrl, -g_right_ctrl]))
+
+    def initialize_robots(self, physics):
+        # reset joint position
+        physics.named.data.qpos[:16] = START_ARM_POSE
+
+        # reset mocap to align with end effector
+        # to obtain these numbers:
+        # (1) make an ee_sim env and reset to the same start_pose
+        # (2) get env._physics.named.data.xpos['vx300s_left/gripper_link']
+        #     get env._physics.named.data.xquat['vx300s_left/gripper_link']
+        #     repeat the same for right side
+        np.copyto(physics.data.mocap_pos[0], [-0.31718881, 0.5, 0.29525084])
+        np.copyto(physics.data.mocap_quat[0], [1, 0, 0, 0])
+        # right
+        np.copyto(physics.data.mocap_pos[1], np.array([0.31718881, 0.49999888, 0.29525084]))
+        np.copyto(physics.data.mocap_quat[1], [1, 0, 0, 0])
+
+        # reset gripper control
+        close_gripper_control = np.array(
+            [
+                PUPPET_GRIPPER_POSITION_CLOSE,
+                -PUPPET_GRIPPER_POSITION_CLOSE,
+                PUPPET_GRIPPER_POSITION_CLOSE,
+                -PUPPET_GRIPPER_POSITION_CLOSE,
+            ]
+        )
+        np.copyto(physics.data.ctrl, close_gripper_control)
+
+    def initialize_episode(self, physics):
+        """Sets the state of the environment at the start of each episode."""
+        super().initialize_episode(physics)
+
+    @staticmethod
+    def get_qpos(physics):
+        qpos_raw = physics.data.qpos.copy()
+        left_qpos_raw = qpos_raw[:8]
+        right_qpos_raw = qpos_raw[8:16]
+        left_arm_qpos = left_qpos_raw[:6]
+        right_arm_qpos = right_qpos_raw[:6]
+        left_gripper_qpos = [normalize_puppet_gripper_position(left_qpos_raw[6])]
+        right_gripper_qpos = [normalize_puppet_gripper_position(right_qpos_raw[6])]
+        return np.concatenate([left_arm_qpos, left_gripper_qpos, right_arm_qpos, right_gripper_qpos])
+
+    @staticmethod
+    def get_qvel(physics):
+        qvel_raw = physics.data.qvel.copy()
+        left_qvel_raw = qvel_raw[:8]
+        right_qvel_raw = qvel_raw[8:16]
+        left_arm_qvel = left_qvel_raw[:6]
+        right_arm_qvel = right_qvel_raw[:6]
+        left_gripper_qvel = [normalize_puppet_gripper_velocity(left_qvel_raw[6])]
+        right_gripper_qvel = [normalize_puppet_gripper_velocity(right_qvel_raw[6])]
+        return np.concatenate([left_arm_qvel, left_gripper_qvel, right_arm_qvel, right_gripper_qvel])
+
+    @staticmethod
+    def get_env_state(physics):
+        raise NotImplementedError
+
+    def get_observation(self, physics):
+        # note: it is important to do .copy()
+        obs = collections.OrderedDict()
+        obs["qpos"] = self.get_qpos(physics)
+        obs["qvel"] = self.get_qvel(physics)
+        obs["env_state"] = self.get_env_state(physics)
+        obs["images"] = {}
+        obs["images"]["top"] = physics.render(height=480, width=640, camera_id="top")
+        obs["images"]["angle"] = physics.render(height=480, width=640, camera_id="angle")
+        obs["images"]["vis"] = physics.render(height=480, width=640, camera_id="front_close")
+        # used in scripted policy to obtain starting pose
+        obs["mocap_pose_left"] = np.concatenate(
+            [physics.data.mocap_pos[0], physics.data.mocap_quat[0]]
+        ).copy()
+        obs["mocap_pose_right"] = np.concatenate(
+            [physics.data.mocap_pos[1], physics.data.mocap_quat[1]]
+        ).copy()
+
+        # used when replaying joint trajectory
+        obs["gripper_ctrl"] = physics.data.ctrl.copy()
+        return obs
+
+    def get_reward(self, physics):
+        raise NotImplementedError
+
+
+class TransferCubeEndEffectorTask(BimanualViperXEndEffectorTask):
+    def __init__(self, random=None):
+        super().__init__(random=random)
+        self.max_reward = 4
+
+    def initialize_episode(self, physics):
+        """Sets the state of the environment at the start of each episode."""
+        self.initialize_robots(physics)
+        # randomize box position
+        cube_pose = sample_box_pose()
+        box_start_idx = physics.model.name2id("red_box_joint", "joint")
+        np.copyto(physics.data.qpos[box_start_idx : box_start_idx + 7], cube_pose)
+        # print(f"randomized cube position to {cube_position}")
+
+        super().initialize_episode(physics)
+
+    @staticmethod
+    def get_env_state(physics):
+        env_state = physics.data.qpos.copy()[16:]
+        return env_state
+
+    def get_reward(self, physics):
+        # return whether left gripper is holding the box
+        all_contact_pairs = []
+        for i_contact in range(physics.data.ncon):
+            id_geom_1 = physics.data.contact[i_contact].geom1
+            id_geom_2 = physics.data.contact[i_contact].geom2
+            name_geom_1 = physics.model.id2name(id_geom_1, "geom")
+            name_geom_2 = physics.model.id2name(id_geom_2, "geom")
+            contact_pair = (name_geom_1, name_geom_2)
+            all_contact_pairs.append(contact_pair)
+
+        touch_left_gripper = ("red_box", "vx300s_left/10_left_gripper_finger") in all_contact_pairs
+        touch_right_gripper = ("red_box", "vx300s_right/10_right_gripper_finger") in all_contact_pairs
+        touch_table = ("red_box", "table") in all_contact_pairs
+
+        reward = 0
+        if touch_right_gripper:
+            reward = 1
+        if touch_right_gripper and not touch_table:  # lifted
+            reward = 2
+        if touch_left_gripper:  # attempted transfer
+            reward = 3
+        if touch_left_gripper and not touch_table:  # successful transfer
+            reward = 4
+        return reward
+
+
+class InsertionEndEffectorTask(BimanualViperXEndEffectorTask):
+    def __init__(self, random=None):
+        super().__init__(random=random)
+        self.max_reward = 4
+
+    def initialize_episode(self, physics):
+        """Sets the state of the environment at the start of each episode."""
+        self.initialize_robots(physics)
+        # randomize peg and socket position
+        peg_pose, socket_pose = sample_insertion_pose()
+
+        def id2index(j_id):
+            return 16 + (j_id - 16) * 7  # first 16 is robot qpos, 7 is pose dim # hacky
+
+        peg_start_id = physics.model.name2id("red_peg_joint", "joint")
+        peg_start_idx = id2index(peg_start_id)
+        np.copyto(physics.data.qpos[peg_start_idx : peg_start_idx + 7], peg_pose)
+        # print(f"randomized cube position to {cube_position}")
+
+        socket_start_id = physics.model.name2id("blue_socket_joint", "joint")
+        socket_start_idx = id2index(socket_start_id)
+        np.copyto(physics.data.qpos[socket_start_idx : socket_start_idx + 7], socket_pose)
+        # print(f"randomized cube position to {cube_position}")
+
+        super().initialize_episode(physics)
+
+    @staticmethod
+    def get_env_state(physics):
+        env_state = physics.data.qpos.copy()[16:]
+        return env_state
+
+    def get_reward(self, physics):
+        # return whether peg touches the pin
+        all_contact_pairs = []
+        for i_contact in range(physics.data.ncon):
+            id_geom_1 = physics.data.contact[i_contact].geom1
+            id_geom_2 = physics.data.contact[i_contact].geom2
+            name_geom_1 = physics.model.id2name(id_geom_1, "geom")
+            name_geom_2 = physics.model.id2name(id_geom_2, "geom")
+            contact_pair = (name_geom_1, name_geom_2)
+            all_contact_pairs.append(contact_pair)
+
+        touch_right_gripper = ("red_peg", "vx300s_right/10_right_gripper_finger") in all_contact_pairs
+        touch_left_gripper = (
+            ("socket-1", "vx300s_left/10_left_gripper_finger") in all_contact_pairs
+            or ("socket-2", "vx300s_left/10_left_gripper_finger") in all_contact_pairs
+            or ("socket-3", "vx300s_left/10_left_gripper_finger") in all_contact_pairs
+            or ("socket-4", "vx300s_left/10_left_gripper_finger") in all_contact_pairs
+        )
+
+        peg_touch_table = ("red_peg", "table") in all_contact_pairs
+        socket_touch_table = (
+            ("socket-1", "table") in all_contact_pairs
+            or ("socket-2", "table") in all_contact_pairs
+            or ("socket-3", "table") in all_contact_pairs
+            or ("socket-4", "table") in all_contact_pairs
+        )
+        peg_touch_socket = (
+            ("red_peg", "socket-1") in all_contact_pairs
+            or ("red_peg", "socket-2") in all_contact_pairs
+            or ("red_peg", "socket-3") in all_contact_pairs
+            or ("red_peg", "socket-4") in all_contact_pairs
+        )
+        pin_touched = ("red_peg", "pin") in all_contact_pairs
+
+        reward = 0
+        if touch_left_gripper and touch_right_gripper:  # touch both
+            reward = 1
+        if (
+            touch_left_gripper and touch_right_gripper and (not peg_touch_table) and (not socket_touch_table)
+        ):  # grasp both
+            reward = 2
+        if peg_touch_socket and (not peg_touch_table) and (not socket_touch_table):  # peg and socket touching
+            reward = 3
+        if pin_touched:  # successful insertion
+            reward = 4
+        return reward

envs/sim_aloha/aloha/utils.py

@@ -0,0 +1,39 @@
+import numpy as np
+
+
+def sample_box_pose():
+    x_range = [0.0, 0.2]
+    y_range = [0.4, 0.6]
+    z_range = [0.05, 0.05]
+
+    ranges = np.vstack([x_range, y_range, z_range])
+    cube_position = np.random.uniform(ranges[:, 0], ranges[:, 1])
+
+    cube_quat = np.array([1, 0, 0, 0])
+    return np.concatenate([cube_position, cube_quat])
+
+
+def sample_insertion_pose():
+    # Peg
+    x_range = [0.1, 0.2]
+    y_range = [0.4, 0.6]
+    z_range = [0.05, 0.05]
+
+    ranges = np.vstack([x_range, y_range, z_range])
+    peg_position = np.random.uniform(ranges[:, 0], ranges[:, 1])
+
+    peg_quat = np.array([1, 0, 0, 0])
+    peg_pose = np.concatenate([peg_position, peg_quat])
+
+    # Socket
+    x_range = [-0.2, -0.1]
+    y_range = [0.4, 0.6]
+    z_range = [0.05, 0.05]
+
+    ranges = np.vstack([x_range, y_range, z_range])
+    socket_position = np.random.uniform(ranges[:, 0], ranges[:, 1])
+
+    socket_quat = np.array([1, 0, 0, 0])
+    socket_pose = np.concatenate([socket_position, socket_quat])
+
+    return peg_pose, socket_pose

envs/sim_aloha/poetry.lock

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+
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+]
+
+[[package]]
+name = "certifi"
+version = "2024.2.2"
+description = "Python package for providing Mozilla's CA Bundle."
+optional = false
+python-versions = ">=3.6"
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envs/sim_aloha/pyproject.toml

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+[tool.poetry]
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+version = "0.1.2"
+description = "ALOHA environment for LeRobot"
+authors = [
+    "Rémi Cadène <re.cadene@gmail.com>",
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+maintainers = [
+    "Alexander Soare <alexander.soare159@gmail.com>",
+    "Quentin Gallouédec <quentin.gallouedec@ec-lyon.fr>",
+    "Simon Alibert <alibert.sim@gmail.com>",
+]
+readme = "README.md"
+license = "Apache-2.0"
+classifiers=[
+    "Development Status :: 3 - Alpha",
+    "Intended Audience :: Developers",
+    "Topic :: Software Development :: Build Tools",
+    "License :: OSI Approved :: Apache Software License",
+    "Programming Language :: Python :: 3.10",
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+
+
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+
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envs/sim_pusht/README.md

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+# PushT environment for LeRobot

envs/sim_pusht/poetry.lock

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+
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+
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envs/sim_pusht/pusht/pusht_env.py

@@ -0,0 +1,378 @@
+import collections
+
+import cv2
+import gymnasium as gym
+import numpy as np
+import pygame
+import pymunk
+import pymunk.pygame_util
+import shapely.geometry as sg
+import skimage.transform as st
+from gymnasium import spaces
+from pymunk.vec2d import Vec2d
+
+from pusht.pymunk_override import DrawOptions
+
+
+def pymunk_to_shapely(body, shapes):
+    geoms = []
+    for shape in shapes:
+        if isinstance(shape, pymunk.shapes.Poly):
+            verts = [body.local_to_world(v) for v in shape.get_vertices()]
+            verts += [verts[0]]
+            geoms.append(sg.Polygon(verts))
+        else:
+            raise RuntimeError(f"Unsupported shape type {type(shape)}")
+    geom = sg.MultiPolygon(geoms)
+    return geom
+
+
+class PushTEnv(gym.Env):
+    metadata = {"render.modes": ["human", "rgb_array"], "video.frames_per_second": 10}
+    reward_range = (0.0, 1.0)
+
+    def __init__(
+        self,
+        legacy=True,  # compatibility with original
+        block_cog=None,
+        damping=None,
+        render_action=True,
+        render_size=96,
+        reset_to_state=None,
+    ):
+        self._seed = None
+        self.seed()
+        self.window_size = ws = 512  # The size of the PyGame window
+        self.render_size = render_size
+        self.sim_hz = 100
+        # Local controller params.
+        self.k_p, self.k_v = 100, 20  # PD control.z
+        self.control_hz = self.metadata["video.frames_per_second"]
+        # legcay set_state for data compatibility
+        self.legacy = legacy
+
+        # agent_pos, block_pos, block_angle
+        self.observation_space = spaces.Box(
+            low=np.array([0, 0, 0, 0, 0], dtype=np.float64),
+            high=np.array([ws, ws, ws, ws, np.pi * 2], dtype=np.float64),
+            shape=(5,),
+            dtype=np.float64,
+        )
+
+        # positional goal for agent
+        self.action_space = spaces.Box(
+            low=np.array([0, 0], dtype=np.float64),
+            high=np.array([ws, ws], dtype=np.float64),
+            shape=(2,),
+            dtype=np.float64,
+        )
+
+        self.block_cog = block_cog
+        self.damping = damping
+        self.render_action = render_action
+
+        """
+        If human-rendering is used, `self.window` will be a reference
+        to the window that we draw to. `self.clock` will be a clock that is used
+        to ensure that the environment is rendered at the correct framerate in
+        human-mode. They will remain `None` until human-mode is used for the
+        first time.
+        """
+        self.window = None
+        self.clock = None
+        self.screen = None
+
+        self.space = None
+        self.teleop = None
+        self.render_buffer = None
+        self.latest_action = None
+        self.reset_to_state = reset_to_state
+
+    def reset(self):
+        seed = self._seed
+        self._setup()
+        if self.block_cog is not None:
+            self.block.center_of_gravity = self.block_cog
+        if self.damping is not None:
+            self.space.damping = self.damping
+
+        # use legacy RandomState for compatibility
+        state = self.reset_to_state
+        if state is None:
+            rs = np.random.RandomState(seed=seed)
+            state = np.array(
+                [
+                    rs.randint(50, 450),
+                    rs.randint(50, 450),
+                    rs.randint(100, 400),
+                    rs.randint(100, 400),
+                    rs.randn() * 2 * np.pi - np.pi,
+                ]
+            )
+        self._set_state(state)
+
+        observation = self._get_obs()
+        return observation
+
+    def step(self, action):
+        dt = 1.0 / self.sim_hz
+        self.n_contact_points = 0
+        n_steps = self.sim_hz // self.control_hz
+        if action is not None:
+            self.latest_action = action
+            for _ in range(n_steps):
+                # Step PD control.
+                # self.agent.velocity = self.k_p * (act - self.agent.position)    # P control works too.
+                acceleration = self.k_p * (action - self.agent.position) + self.k_v * (
+                    Vec2d(0, 0) - self.agent.velocity
+                )
+                self.agent.velocity += acceleration * dt
+
+                # Step physics.
+                self.space.step(dt)
+
+        # compute reward
+        goal_body = self._get_goal_pose_body(self.goal_pose)
+        goal_geom = pymunk_to_shapely(goal_body, self.block.shapes)
+        block_geom = pymunk_to_shapely(self.block, self.block.shapes)
+
+        intersection_area = goal_geom.intersection(block_geom).area
+        goal_area = goal_geom.area
+        coverage = intersection_area / goal_area
+        reward = np.clip(coverage / self.success_threshold, 0, 1)
+        done = coverage > self.success_threshold
+
+        observation = self._get_obs()
+        info = self._get_info()
+
+        return observation, reward, done, info
+
+    def render(self, mode):
+        return self._render_frame(mode)
+
+    def teleop_agent(self):
+        TeleopAgent = collections.namedtuple("TeleopAgent", ["act"])
+
+        def act(obs):
+            act = None
+            mouse_position = pymunk.pygame_util.from_pygame(Vec2d(*pygame.mouse.get_pos()), self.screen)
+            if self.teleop or (mouse_position - self.agent.position).length < 30:
+                self.teleop = True
+                act = mouse_position
+            return act
+
+        return TeleopAgent(act)
+
+    def _get_obs(self):
+        obs = np.array(
+            tuple(self.agent.position) + tuple(self.block.position) + (self.block.angle % (2 * np.pi),)
+        )
+        return obs
+
+    def _get_goal_pose_body(self, pose):
+        mass = 1
+        inertia = pymunk.moment_for_box(mass, (50, 100))
+        body = pymunk.Body(mass, inertia)
+        # preserving the legacy assignment order for compatibility
+        # the order here doesn't matter somehow, maybe because CoM is aligned with body origin
+        body.position = pose[:2].tolist()
+        body.angle = pose[2]
+        return body
+
+    def _get_info(self):
+        n_steps = self.sim_hz // self.control_hz
+        n_contact_points_per_step = int(np.ceil(self.n_contact_points / n_steps))
+        info = {
+            "pos_agent": np.array(self.agent.position),
+            "vel_agent": np.array(self.agent.velocity),
+            "block_pose": np.array(list(self.block.position) + [self.block.angle]),
+            "goal_pose": self.goal_pose,
+            "n_contacts": n_contact_points_per_step,
+        }
+        return info
+
+    def _render_frame(self, mode):
+        if self.window is None and mode == "human":
+            pygame.init()
+            pygame.display.init()
+            self.window = pygame.display.set_mode((self.window_size, self.window_size))
+        if self.clock is None and mode == "human":
+            self.clock = pygame.time.Clock()
+
+        canvas = pygame.Surface((self.window_size, self.window_size))
+        canvas.fill((255, 255, 255))
+        self.screen = canvas
+
+        draw_options = DrawOptions(canvas)
+
+        # Draw goal pose.
+        goal_body = self._get_goal_pose_body(self.goal_pose)
+        for shape in self.block.shapes:
+            goal_points = [
+                pymunk.pygame_util.to_pygame(goal_body.local_to_world(v), draw_options.surface)
+                for v in shape.get_vertices()
+            ]
+            goal_points += [goal_points[0]]
+            pygame.draw.polygon(canvas, self.goal_color, goal_points)
+
+        # Draw agent and block.
+        self.space.debug_draw(draw_options)
+
+        if mode == "human":
+            # The following line copies our drawings from `canvas` to the visible window
+            self.window.blit(canvas, canvas.get_rect())
+            pygame.event.pump()
+            pygame.display.update()
+
+            # the clock is already ticked during in step for "human"
+
+        img = np.transpose(np.array(pygame.surfarray.pixels3d(canvas)), axes=(1, 0, 2))
+        img = cv2.resize(img, (self.render_size, self.render_size))
+        if self.render_action and self.latest_action is not None:
+            action = np.array(self.latest_action)
+            coord = (action / 512 * 96).astype(np.int32)
+            marker_size = int(8 / 96 * self.render_size)
+            thickness = int(1 / 96 * self.render_size)
+            cv2.drawMarker(
+                img,
+                coord,
+                color=(255, 0, 0),
+                markerType=cv2.MARKER_CROSS,
+                markerSize=marker_size,
+                thickness=thickness,
+            )
+        return img
+
+    def close(self):
+        if self.window is not None:
+            pygame.display.quit()
+            pygame.quit()
+
+    def seed(self, seed=None):
+        if seed is None:
+            seed = np.random.randint(0, 25536)
+        self._seed = seed
+        self.np_random = np.random.default_rng(seed)
+
+    def _handle_collision(self, arbiter, space, data):
+        self.n_contact_points += len(arbiter.contact_point_set.points)
+
+    def _set_state(self, state):
+        if isinstance(state, np.ndarray):
+            state = state.tolist()
+        pos_agent = state[:2]
+        pos_block = state[2:4]
+        rot_block = state[4]
+        self.agent.position = pos_agent
+        # setting angle rotates with respect to center of mass
+        # therefore will modify the geometric position
+        # if not the same as CoM
+        # therefore should be modified first.
+        if self.legacy:
+            # for compatibility with legacy data
+            self.block.position = pos_block
+            self.block.angle = rot_block
+        else:
+            self.block.angle = rot_block
+            self.block.position = pos_block
+
+        # Run physics to take effect
+        self.space.step(1.0 / self.sim_hz)
+
+    def _set_state_local(self, state_local):
+        agent_pos_local = state_local[:2]
+        block_pose_local = state_local[2:]
+        tf_img_obj = st.AffineTransform(translation=self.goal_pose[:2], rotation=self.goal_pose[2])
+        tf_obj_new = st.AffineTransform(translation=block_pose_local[:2], rotation=block_pose_local[2])
+        tf_img_new = st.AffineTransform(matrix=tf_img_obj.params @ tf_obj_new.params)
+        agent_pos_new = tf_img_new(agent_pos_local)
+        new_state = np.array(list(agent_pos_new[0]) + list(tf_img_new.translation) + [tf_img_new.rotation])
+        self._set_state(new_state)
+        return new_state
+
+    def _setup(self):
+        self.space = pymunk.Space()
+        self.space.gravity = 0, 0
+        self.space.damping = 0
+        self.teleop = False
+        self.render_buffer = []
+
+        # Add walls.
+        walls = [
+            self._add_segment((5, 506), (5, 5), 2),
+            self._add_segment((5, 5), (506, 5), 2),
+            self._add_segment((506, 5), (506, 506), 2),
+            self._add_segment((5, 506), (506, 506), 2),
+        ]
+        self.space.add(*walls)
+
+        # Add agent, block, and goal zone.
+        self.agent = self.add_circle((256, 400), 15)
+        self.block = self.add_tee((256, 300), 0)
+        self.goal_color = pygame.Color("LightGreen")
+        self.goal_pose = np.array([256, 256, np.pi / 4])  # x, y, theta (in radians)
+
+        # Add collision handling
+        self.collision_handeler = self.space.add_collision_handler(0, 0)
+        self.collision_handeler.post_solve = self._handle_collision
+        self.n_contact_points = 0
+
+        self.max_score = 50 * 100
+        self.success_threshold = 0.95  # 95% coverage.
+
+    def _add_segment(self, a, b, radius):
+        shape = pymunk.Segment(self.space.static_body, a, b, radius)
+        shape.color = pygame.Color("LightGray")  # https://htmlcolorcodes.com/color-names
+        return shape
+
+    def add_circle(self, position, radius):
+        body = pymunk.Body(body_type=pymunk.Body.KINEMATIC)
+        body.position = position
+        body.friction = 1
+        shape = pymunk.Circle(body, radius)
+        shape.color = pygame.Color("RoyalBlue")
+        self.space.add(body, shape)
+        return body
+
+    def add_box(self, position, height, width):
+        mass = 1
+        inertia = pymunk.moment_for_box(mass, (height, width))
+        body = pymunk.Body(mass, inertia)
+        body.position = position
+        shape = pymunk.Poly.create_box(body, (height, width))
+        shape.color = pygame.Color("LightSlateGray")
+        self.space.add(body, shape)
+        return body
+
+    def add_tee(self, position, angle, scale=30, color="LightSlateGray", mask=None):
+        if mask is None:
+            mask = pymunk.ShapeFilter.ALL_MASKS()
+        mass = 1
+        length = 4
+        vertices1 = [
+            (-length * scale / 2, scale),
+            (length * scale / 2, scale),
+            (length * scale / 2, 0),
+            (-length * scale / 2, 0),
+        ]
+        inertia1 = pymunk.moment_for_poly(mass, vertices=vertices1)
+        vertices2 = [
+            (-scale / 2, scale),
+            (-scale / 2, length * scale),
+            (scale / 2, length * scale),
+            (scale / 2, scale),
+        ]
+        inertia2 = pymunk.moment_for_poly(mass, vertices=vertices1)
+        body = pymunk.Body(mass, inertia1 + inertia2)
+        shape1 = pymunk.Poly(body, vertices1)
+        shape2 = pymunk.Poly(body, vertices2)
+        shape1.color = pygame.Color(color)
+        shape2.color = pygame.Color(color)
+        shape1.filter = pymunk.ShapeFilter(mask=mask)
+        shape2.filter = pymunk.ShapeFilter(mask=mask)
+        body.center_of_gravity = (shape1.center_of_gravity + shape2.center_of_gravity) / 2
+        body.position = position
+        body.angle = angle
+        body.friction = 1
+        self.space.add(body, shape1, shape2)
+        return body

envs/sim_pusht/pusht/pusht_image_env.py

@@ -0,0 +1,41 @@
+import numpy as np
+from gymnasium import spaces
+
+from pusht.pusht_env import PushTEnv
+
+
+class PushTImageEnv(PushTEnv):
+    metadata = {"render.modes": ["rgb_array"], "video.frames_per_second": 10}
+
+    # Note: legacy defaults to True for compatibility with original
+    def __init__(self, legacy=True, block_cog=None, damping=None, render_size=96):
+        super().__init__(
+            legacy=legacy, block_cog=block_cog, damping=damping, render_size=render_size, render_action=False
+        )
+        ws = self.window_size
+        self.observation_space = spaces.Dict(
+            {
+                "image": spaces.Box(low=0, high=1, shape=(3, render_size, render_size), dtype=np.float32),
+                "agent_pos": spaces.Box(low=0, high=ws, shape=(2,), dtype=np.float32),
+            }
+        )
+        self.render_cache = None
+
+    def _get_obs(self):
+        img = super()._render_frame(mode="rgb_array")
+
+        agent_pos = np.array(self.agent.position)
+        img_obs = np.moveaxis(img, -1, 0)
+        obs = {"image": img_obs, "agent_pos": agent_pos}
+
+        self.render_cache = img
+
+        return obs
+
+    def render(self, mode):
+        assert mode == "rgb_array"
+
+        if self.render_cache is None:
+            self._get_obs()
+
+        return self.render_cache

envs/sim_pusht/pusht/pymunk_override.py

@@ -0,0 +1,244 @@
+# ----------------------------------------------------------------------------
+# pymunk
+# Copyright (c) 2007-2016 Victor Blomqvist
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in
+# all copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+# ----------------------------------------------------------------------------
+
+"""This submodule contains helper functions to help with quick prototyping
+using pymunk together with pygame.
+
+Intended to help with debugging and prototyping, not for actual production use
+in a full application. The methods contained in this module is opinionated
+about your coordinate system and not in any way optimized.
+"""
+
+__docformat__ = "reStructuredText"
+
+__all__ = [
+    "DrawOptions",
+    "get_mouse_pos",
+    "to_pygame",
+    "from_pygame",
+    # "lighten",
+    "positive_y_is_up",
+]
+
+from typing import Sequence, Tuple
+
+import numpy as np
+import pygame
+import pymunk
+from pymunk.space_debug_draw_options import SpaceDebugColor
+from pymunk.vec2d import Vec2d
+
+positive_y_is_up: bool = False
+"""Make increasing values of y point upwards.
+
+When True::
+
+    y
+    ^
+    |      . (3, 3)
+    |
+    |   . (2, 2)
+    |
+    +------ > x
+
+When False::
+
+    +------ > x
+    |
+    |   . (2, 2)
+    |
+    |      . (3, 3)
+    v
+    y
+
+"""
+
+
+class DrawOptions(pymunk.SpaceDebugDrawOptions):
+    def __init__(self, surface: pygame.Surface) -> None:
+        """Draw a pymunk.Space on a pygame.Surface object.
+
+        Typical usage::
+
+        >>> import pymunk
+        >>> surface = pygame.Surface((10,10))
+        >>> space = pymunk.Space()
+        >>> options = pymunk.pygame_util.DrawOptions(surface)
+        >>> space.debug_draw(options)
+
+        You can control the color of a shape by setting shape.color to the color
+        you want it drawn in::
+
+        >>> c = pymunk.Circle(None, 10)
+        >>> c.color = pygame.Color("pink")
+
+        See pygame_util.demo.py for a full example
+
+        Since pygame uses a coordinate system where y points down (in contrast
+        to many other cases), you either have to make the physics simulation
+        with Pymunk also behave in that way, or flip everything when you draw.
+
+        The easiest is probably to just make the simulation behave the same
+        way as Pygame does. In that way all coordinates used are in the same
+        orientation and easy to reason about::
+
+        >>> space = pymunk.Space()
+        >>> space.gravity = (0, -1000)
+        >>> body = pymunk.Body()
+        >>> body.position = (0, 0) # will be positioned in the top left corner
+        >>> space.debug_draw(options)
+
+        To flip the drawing its possible to set the module property
+        :py:data:`positive_y_is_up` to True. Then the pygame drawing will flip
+        the simulation upside down before drawing::
+
+        >>> positive_y_is_up = True
+        >>> body = pymunk.Body()
+        >>> body.position = (0, 0)
+        >>> # Body will be position in bottom left corner
+
+        :Parameters:
+                surface : pygame.Surface
+                    Surface that the objects will be drawn on
+        """
+        self.surface = surface
+        super().__init__()
+
+    def draw_circle(
+        self,
+        pos: Vec2d,
+        angle: float,
+        radius: float,
+        outline_color: SpaceDebugColor,
+        fill_color: SpaceDebugColor,
+    ) -> None:
+        p = to_pygame(pos, self.surface)
+
+        pygame.draw.circle(self.surface, fill_color.as_int(), p, round(radius), 0)
+        pygame.draw.circle(self.surface, light_color(fill_color).as_int(), p, round(radius - 4), 0)
+
+        # circle_edge = pos + Vec2d(radius, 0).rotated(angle)
+        # p2 = to_pygame(circle_edge, self.surface)
+        # line_r = 2 if radius > 20 else 1
+        # pygame.draw.lines(self.surface, outline_color.as_int(), False, [p, p2], line_r)
+
+    def draw_segment(self, a: Vec2d, b: Vec2d, color: SpaceDebugColor) -> None:
+        p1 = to_pygame(a, self.surface)
+        p2 = to_pygame(b, self.surface)
+
+        pygame.draw.aalines(self.surface, color.as_int(), False, [p1, p2])
+
+    def draw_fat_segment(
+        self,
+        a: Tuple[float, float],
+        b: Tuple[float, float],
+        radius: float,
+        outline_color: SpaceDebugColor,
+        fill_color: SpaceDebugColor,
+    ) -> None:
+        p1 = to_pygame(a, self.surface)
+        p2 = to_pygame(b, self.surface)
+
+        r = round(max(1, radius * 2))
+        pygame.draw.lines(self.surface, fill_color.as_int(), False, [p1, p2], r)
+        if r > 2:
+            orthog = [abs(p2[1] - p1[1]), abs(p2[0] - p1[0])]
+            if orthog[0] == 0 and orthog[1] == 0:
+                return
+            scale = radius / (orthog[0] * orthog[0] + orthog[1] * orthog[1]) ** 0.5
+            orthog[0] = round(orthog[0] * scale)
+            orthog[1] = round(orthog[1] * scale)
+            points = [
+                (p1[0] - orthog[0], p1[1] - orthog[1]),
+                (p1[0] + orthog[0], p1[1] + orthog[1]),
+                (p2[0] + orthog[0], p2[1] + orthog[1]),
+                (p2[0] - orthog[0], p2[1] - orthog[1]),
+            ]
+            pygame.draw.polygon(self.surface, fill_color.as_int(), points)
+            pygame.draw.circle(
+                self.surface,
+                fill_color.as_int(),
+                (round(p1[0]), round(p1[1])),
+                round(radius),
+            )
+            pygame.draw.circle(
+                self.surface,
+                fill_color.as_int(),
+                (round(p2[0]), round(p2[1])),
+                round(radius),
+            )
+
+    def draw_polygon(
+        self,
+        verts: Sequence[Tuple[float, float]],
+        radius: float,
+        outline_color: SpaceDebugColor,
+        fill_color: SpaceDebugColor,
+    ) -> None:
+        ps = [to_pygame(v, self.surface) for v in verts]
+        ps += [ps[0]]
+
+        radius = 2
+        pygame.draw.polygon(self.surface, light_color(fill_color).as_int(), ps)
+
+        if radius > 0:
+            for i in range(len(verts)):
+                a = verts[i]
+                b = verts[(i + 1) % len(verts)]
+                self.draw_fat_segment(a, b, radius, fill_color, fill_color)
+
+    def draw_dot(self, size: float, pos: Tuple[float, float], color: SpaceDebugColor) -> None:
+        p = to_pygame(pos, self.surface)
+        pygame.draw.circle(self.surface, color.as_int(), p, round(size), 0)
+
+
+def get_mouse_pos(surface: pygame.Surface) -> Tuple[int, int]:
+    """Get position of the mouse pointer in pymunk coordinates."""
+    p = pygame.mouse.get_pos()
+    return from_pygame(p, surface)
+
+
+def to_pygame(p: Tuple[float, float], surface: pygame.Surface) -> Tuple[int, int]:
+    """Convenience method to convert pymunk coordinates to pygame surface
+    local coordinates.
+
+    Note that in case positive_y_is_up is False, this function won't actually do
+    anything except converting the point to integers.
+    """
+    if positive_y_is_up:
+        return round(p[0]), surface.get_height() - round(p[1])
+    else:
+        return round(p[0]), round(p[1])
+
+
+def from_pygame(p: Tuple[float, float], surface: pygame.Surface) -> Tuple[int, int]:
+    """Convenience method to convert pygame surface local coordinates to
+    pymunk coordinates
+    """
+    return to_pygame(p, surface)
+
+
+def light_color(color: SpaceDebugColor):
+    color = np.minimum(1.2 * np.float32([color.r, color.g, color.b, color.a]), np.float32([255]))
+    color = SpaceDebugColor(r=color[0], g=color[1], b=color[2], a=color[3])
+    return color

envs/sim_pusht/pyproject.toml

@@ -0,0 +1,37 @@
+[tool.poetry]
+name = "sim_pusht"
+version = "0.1.0"
+description = "PushT environment for LeRobot"
+authors = [
+    "Rémi Cadène <re.cadene@gmail.com>",
+]
+maintainers = [
+    "Alexander Soare <alexander.soare159@gmail.com>",
+    "Quentin Gallouédec <quentin.gallouedec@ec-lyon.fr>",
+    "Simon Alibert <alibert.sim@gmail.com>",
+]
+readme = "README.md"
+license = "Apache-2.0"
+classifiers=[
+    "Development Status :: 3 - Alpha",
+    "Intended Audience :: Developers",
+    "Topic :: Software Development :: Build Tools",
+    "License :: OSI Approved :: Apache Software License",
+    "Programming Language :: Python :: 3.10",
+]
+packages = [{include = "pusht"}]
+
+
+[tool.poetry.dependencies]
+python = "^3.10"
+gymnasium = "^0.29.1"
+opencv-python = "^4.9.0.80"
+pygame = "^2.5.2"
+pymunk = "^6.6.0"
+shapely = "^2.0.3"
+scikit-image = "^0.22.0"
+
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"

envs/sim_xarm/README.md

@@ -0,0 +1 @@
+# xArm environment for LeRobot

envs/sim_xarm/poetry.lock

@@ -0,0 +1,448 @@
+# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
+
+[[package]]
+name = "absl-py"
+version = "2.1.0"
+description = "Abseil Python Common Libraries, see https://github.com/abseil/abseil-py."
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "absl-py-2.1.0.tar.gz", hash = "sha256:7820790efbb316739cde8b4e19357243fc3608a152024288513dd968d7d959ff"},
+    {file = "absl_py-2.1.0-py3-none-any.whl", hash = "sha256:526a04eadab8b4ee719ce68f204172ead1027549089702d99b9059f129ff1308"},
+]
+
+[[package]]
+name = "cloudpickle"
+version = "3.0.0"
+description = "Pickler class to extend the standard pickle.Pickler functionality"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "cloudpickle-3.0.0-py3-none-any.whl", hash = "sha256:246ee7d0c295602a036e86369c77fecda4ab17b506496730f2f576d9016fd9c7"},
+    {file = "cloudpickle-3.0.0.tar.gz", hash = "sha256:996d9a482c6fb4f33c1a35335cf8afd065d2a56e973270364840712d9131a882"},
+]
+
+[[package]]
+name = "farama-notifications"
+version = "0.0.4"
+description = "Notifications for all Farama Foundation maintained libraries."
+optional = false
+python-versions = "*"
+files = [
+    {file = "Farama-Notifications-0.0.4.tar.gz", hash = "sha256:13fceff2d14314cf80703c8266462ebf3733c7d165336eee998fc58e545efd18"},
+    {file = "Farama_Notifications-0.0.4-py3-none-any.whl", hash = "sha256:14de931035a41961f7c056361dc7f980762a143d05791ef5794a751a2caf05ae"},
+]
+
+[[package]]
+name = "glfw"
+version = "2.7.0"
+description = "A ctypes-based wrapper for GLFW3."
+optional = false
+python-versions = "*"
+files = [
+    {file = "glfw-2.7.0-py2.py27.py3.py30.py31.py32.py33.py34.py35.py36.py37.py38-none-macosx_10_6_intel.whl", hash = "sha256:bd82849edcceda4e262bd1227afaa74b94f9f0731c1197863cd25c15bfc613fc"},
+    {file = "glfw-2.7.0-py2.py27.py3.py30.py31.py32.py33.py34.py35.py36.py37.py38-none-macosx_11_0_arm64.whl", hash = "sha256:56ea163c964bb0bc336def2d6a6a1bd42f9db4b870ef834ac77d7b7ee68b8dfc"},
+    {file = "glfw-2.7.0-py2.py27.py3.py30.py31.py32.py33.py34.py35.py36.py37.py38-none-manylinux2010_i686.whl", hash = "sha256:463aab9e5567c83d8120556b3a845807c60950ed0218fc1283368f46f5ece331"},
+    {file = "glfw-2.7.0-py2.py27.py3.py30.py31.py32.py33.py34.py35.py36.py37.py38-none-manylinux2010_x86_64.whl", hash = "sha256:a6f54188dfc349e5426b0ada84843f6eb35a3811d8dbf57ae49c448e7d683bb4"},
+    {file = "glfw-2.7.0-py2.py27.py3.py30.py31.py32.py33.py34.py35.py36.py37.py38-none-manylinux2014_aarch64.whl", hash = "sha256:e33568b0aba2045a3d7555f22fcf83fafcacc7c2fc4cb995741894ea51e43ab6"},
+    {file = "glfw-2.7.0-py2.py27.py3.py30.py31.py32.py33.py34.py35.py36.py37.py38-none-manylinux2014_x86_64.whl", hash = "sha256:d8630dd9673860c427abde5b79bbc348e02eccde8a3f2a802c5a2a4fb5d79fb8"},
+    {file = "glfw-2.7.0-py2.py27.py3.py30.py31.py32.py33.py34.py35.py36.py37.py38-none-win32.whl", hash = "sha256:ff92d14ac1c7afa9c5deb495c335b485868709880e6e080e99ace7026d74c756"},
+    {file = "glfw-2.7.0-py2.py27.py3.py30.py31.py32.py33.py34.py35.py36.py37.py38-none-win_amd64.whl", hash = "sha256:20d4b31a5a6a61fb787b25f8408204e0e248313cc500953071d13d30a2e5cc9d"},
+    {file = "glfw-2.7.0.tar.gz", hash = "sha256:0e209ad38fa8c5be67ca590d7b17533d95ad1eb57d0a3f07b98131db69b79000"},
+]
+
+[package.extras]
+preview = ["glfw-preview"]
+
+[[package]]
+name = "gymnasium"
+version = "0.29.1"
+description = "A standard API for reinforcement learning and a diverse set of reference environments (formerly Gym)."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "gymnasium-0.29.1-py3-none-any.whl", hash = "sha256:61c3384b5575985bb7f85e43213bcb40f36fcdff388cae6bc229304c71f2843e"},
+    {file = "gymnasium-0.29.1.tar.gz", hash = "sha256:1a532752efcb7590478b1cc7aa04f608eb7a2fdad5570cd217b66b6a35274bb1"},
+]
+
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+docs = ["furo", "olefile", "sphinx (>=2.4)", "sphinx-copybutton", "sphinx-inline-tabs", "sphinx-removed-in", "sphinxext-opengraph"]
+fpx = ["olefile"]
+mic = ["olefile"]
+tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout"]
+typing = ["typing-extensions"]
+xmp = ["defusedxml"]
+
+[[package]]
+name = "pyopengl"
+version = "3.1.7"
+description = "Standard OpenGL bindings for Python"
+optional = false
+python-versions = "*"
+files = [
+    {file = "PyOpenGL-3.1.7-py3-none-any.whl", hash = "sha256:a6ab19cf290df6101aaf7470843a9c46207789855746399d0af92521a0a92b7a"},
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+]
+
+[[package]]
+name = "typing-extensions"
+version = "4.10.0"
+description = "Backported and Experimental Type Hints for Python 3.8+"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "typing_extensions-4.10.0-py3-none-any.whl", hash = "sha256:69b1a937c3a517342112fb4c6df7e72fc39a38e7891a5730ed4985b5214b5475"},
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+]
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+[metadata]
+lock-version = "2.0"
+python-versions = "^3.10"
+content-hash = "165d82035aade2abad497b32e156ec18d8ebc6c57a36376c3351b593c6889f22"

envs/sim_xarm/pyproject.toml

@@ -0,0 +1,34 @@
+[tool.poetry]
+name = "sim_xarm"
+version = "0.1.0"
+description = "xArm environment for LeRobot"
+authors = [
+    "Rémi Cadène <re.cadene@gmail.com>",
+]
+maintainers = [
+    "Alexander Soare <alexander.soare159@gmail.com>",
+    "Quentin Gallouédec <quentin.gallouedec@ec-lyon.fr>",
+    "Simon Alibert <alibert.sim@gmail.com>",
+]
+readme = "README.md"
+license = "Apache-2.0"
+classifiers=[
+    "Development Status :: 3 - Alpha",
+    "Intended Audience :: Developers",
+    "Topic :: Software Development :: Build Tools",
+    "License :: OSI Approved :: Apache Software License",
+    "Programming Language :: Python :: 3.10",
+]
+packages = [{include = "xarm"}]
+
+
+[tool.poetry.dependencies]
+python = "^3.10"
+mujoco = "^2.3.7"
+gymnasium = "^0.29.1"
+gymnasium-robotics = "^1.2.4"
+
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"

envs/sim_xarm/xarm/__init__.py

@@ -0,0 +1,166 @@
+from collections import OrderedDict, deque
+
+import gymnasium as gym
+import numpy as np
+from gymnasium.wrappers import TimeLimit
+
+from xarm.tasks.base import Base as Base
+from xarm.tasks.lift import Lift
+from xarm.tasks.peg_in_box import PegInBox
+from xarm.tasks.push import Push
+from xarm.tasks.reach import Reach
+
+TASKS = OrderedDict(
+    (
+        (
+            "reach",
+            {
+                "env": Reach,
+                "action_space": "xyz",
+                "episode_length": 50,
+                "description": "Reach a target location with the end effector",
+            },
+        ),
+        (
+            "push",
+            {
+                "env": Push,
+                "action_space": "xyz",
+                "episode_length": 50,
+                "description": "Push a cube to a target location",
+            },
+        ),
+        (
+            "peg_in_box",
+            {
+                "env": PegInBox,
+                "action_space": "xyz",
+                "episode_length": 50,
+                "description": "Insert a peg into a box",
+            },
+        ),
+        (
+            "lift",
+            {
+                "env": Lift,
+                "action_space": "xyzw",
+                "episode_length": 50,
+                "description": "Lift a cube above a height threshold",
+            },
+        ),
+    )
+)
+
+
+class SimXarmWrapper(gym.Wrapper):
+    """
+    A wrapper for the SimXarm environments. This wrapper is used to
+    convert the action and observation spaces to the correct format.
+    """
+
+    def __init__(self, env, task, obs_mode, image_size, action_repeat, frame_stack=1, channel_last=False):
+        super().__init__(env)
+        self._env = env
+        self.obs_mode = obs_mode
+        self.image_size = image_size
+        self.action_repeat = action_repeat
+        self.frame_stack = frame_stack
+        self._frames = deque([], maxlen=frame_stack)
+        self.channel_last = channel_last
+        self._max_episode_steps = task["episode_length"] // action_repeat
+
+        image_shape = (
+            (image_size, image_size, 3 * frame_stack)
+            if channel_last
+            else (3 * frame_stack, image_size, image_size)
+        )
+        if obs_mode == "state":
+            self.observation_space = env.observation_space["observation"]
+        elif obs_mode == "rgb":
+            self.observation_space = gym.spaces.Box(low=0, high=255, shape=image_shape, dtype=np.uint8)
+        elif obs_mode == "all":
+            self.observation_space = gym.spaces.Dict(
+                state=gym.spaces.Box(low=-np.inf, high=np.inf, shape=(4,), dtype=np.float32),
+                rgb=gym.spaces.Box(low=0, high=255, shape=image_shape, dtype=np.uint8),
+            )
+        else:
+            raise ValueError(f"Unknown obs_mode {obs_mode}. Must be one of [rgb, all, state]")
+        self.action_space = gym.spaces.Box(low=-1.0, high=1.0, shape=(len(task["action_space"]),))
+        self.action_padding = np.zeros(4 - len(task["action_space"]), dtype=np.float32)
+        if "w" not in task["action_space"]:
+            self.action_padding[-1] = 1.0
+
+    def _render_obs(self):
+        obs = self.render(mode="rgb_array", width=self.image_size, height=self.image_size)
+        if not self.channel_last:
+            obs = obs.transpose(2, 0, 1)
+        return obs.copy()
+
+    def _update_frames(self, reset=False):
+        pixels = self._render_obs()
+        self._frames.append(pixels)
+        if reset:
+            for _ in range(1, self.frame_stack):
+                self._frames.append(pixels)
+        assert len(self._frames) == self.frame_stack
+
+    def transform_obs(self, obs, reset=False):
+        if self.obs_mode == "state":
+            return obs["observation"]
+        elif self.obs_mode == "rgb":
+            self._update_frames(reset=reset)
+            rgb_obs = np.concatenate(list(self._frames), axis=-1 if self.channel_last else 0)
+            return rgb_obs
+        elif self.obs_mode == "all":
+            self._update_frames(reset=reset)
+            rgb_obs = np.concatenate(list(self._frames), axis=-1 if self.channel_last else 0)
+            return OrderedDict((("rgb", rgb_obs), ("state", self.robot_state)))
+        else:
+            raise ValueError(f"Unknown obs_mode {self.obs_mode}. Must be one of [rgb, all, state]")
+
+    def reset(self):
+        return self.transform_obs(self._env.reset(), reset=True)
+
+    def step(self, action):
+        action = np.concatenate([action, self.action_padding])
+        reward = 0.0
+        for _ in range(self.action_repeat):
+            obs, r, done, info = self._env.step(action)
+            reward += r
+        return self.transform_obs(obs), reward, done, info
+
+    def render(self, mode="rgb_array", width=384, height=384, **kwargs):
+        return self._env.render(mode, width=width, height=height)
+
+    @property
+    def state(self):
+        return self._env.robot_state
+
+
+def make(task, obs_mode="state", image_size=84, action_repeat=1, frame_stack=1, channel_last=False, seed=0):
+    """
+    Create a new environment.
+    Args:
+            task (str): The task to create an environment for. Must be one of:
+                    - 'reach'
+                    - 'push'
+                    - 'peg-in-box'
+                    - 'lift'
+            obs_mode (str): The observation mode to use. Must be one of:
+                    - 'state': Only state observations
+                    - 'rgb': RGB images
+                    - 'all': RGB images and state observations
+            image_size (int): The size of the image observations
+            action_repeat (int): The number of times to repeat the action
+            seed (int): The random seed to use
+    Returns:
+            gym.Env: The environment
+    """
+    if task not in TASKS:
+        raise ValueError(f"Unknown task {task}. Must be one of {list(TASKS.keys())}")
+    env = TASKS[task]["env"]()
+    env = TimeLimit(env, TASKS[task]["episode_length"])
+    env = SimXarmWrapper(env, TASKS[task], obs_mode, image_size, action_repeat, frame_stack, channel_last)
+    env.seed(seed)
+
+    return env

envs/sim_xarm/xarm/tasks/assets/lift.xml

@@ -0,0 +1,53 @@
+<?xml version="1.0" encoding="utf-8"?>
+
+<mujoco>
+	<compiler angle="radian" coordinate="local" meshdir="mesh" texturedir="texture"></compiler>
+	<size nconmax="2000" njmax="500"/>
+
+	<option timestep="0.002">
+		<flag warmstart="enable"></flag>
+	</option>
+
+	<include file="shared.xml"></include>
+
+	<worldbody>
+		<body name="floor0" pos="0 0 0">
+			<geom name="floorgeom0" pos="1.2 -2.0 0" size="20.0 20.0 1" type="plane" condim="3" material="floor_mat"></geom>
+		</body>
+
+		<include file="xarm.xml"></include>
+
+		<body pos="0.75 0 0.6325" name="pedestal0">
+			<geom name="pedestalgeom0" size="0.1 0.1 0.01" pos="0.32 0.27 0" type="box" mass="2000" material="pedestal_mat"></geom>
+			<site pos="0.30 0.30 0" size="0.075 0.075 0.002" type="box" name="robotmountsite0" rgba="0.55 0.54 0.53 1" />
+		</body>
+
+		<body pos="1.5 0.075 0.3425" name="table0">
+			<geom name="tablegeom0" size="0.3 0.6 0.2" pos="0 0 0" type="box" material="table_mat" density="2000" friction="1 1 1"></geom>
+		</body>
+
+		<body name="object" pos="1.405 0.3 0.58625">
+			<joint name="object_joint0" type="free" limited="false"></joint>
+			<geom size="0.035 0.035 0.035" type="box" name="object0" material="block_mat" density="50000" condim="4" friction="1 1 1" solimp="1 1 1" solref="0.02 1"></geom>
+			<site name="object_site" pos="0 0 0" size="0.035 0.035 0.035" rgba="1 0 0 0" type="box"></site>
+		</body>
+
+		<light directional="true" ambient="0.1 0.1 0.1" diffuse="0 0 0" specular="0 0 0" castshadow="false" pos="1.65 0 10" dir="-0.57 -0.57 -0.57" name="light0"></light>
+        <light directional="true" ambient="0.1 0.1 0.1" diffuse="0 0 0" specular="0 0 0" castshadow="false" pos="0 -4 4" dir="0 1 -0.1" name="light1"></light>
+        <light directional="true" ambient="0.05 0.05 0.05" diffuse="0 0 0" specular="0 0 0" castshadow="false" pos="2.13 1.6 2.5" name="light2"></light>
+        <light pos="0 0 2" dir="0.2 0.2 -0.8" directional="true"  diffuse="0.3 0.3 0.3"  castshadow="false" name="light3"></light>
+
+		<camera fovy="50" name="camera0" pos="0.9559 1.0 1.1" euler="-1.1 -0.6 3.4" />
+	</worldbody>
+
+	<equality>
+    	<connect body2="left_finger" body1="left_inner_knuckle" anchor="0.0 0.035 0.042" solimp="0.9 0.95 0.001 0.5 2" solref="0.0002 1.0" ></connect>
+    	<connect body2="right_finger" body1="right_inner_knuckle" anchor="0.0 -0.035 0.042" solimp="0.9 0.95 0.001 0.5 2" solref="0.0002 1.0" ></connect>
+        <joint joint1="left_inner_knuckle_joint" joint2="right_inner_knuckle_joint"></joint>
+    </equality>
+
+    <actuator>
+		<motor ctrllimited="true" ctrlrange="-1.0 1.0" joint="left_inner_knuckle_joint" gear="200.0"/>
+        <motor ctrllimited="true" ctrlrange="-1.0 1.0" joint="right_inner_knuckle_joint" gear="200.0"/>
+    </actuator>
+</mujoco>

envs/sim_xarm/xarm/tasks/assets/mesh/base_link.stl

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+oid sha256:21fb81ae7fba19e3c6b2d2ca60c8051712ba273357287eb5a397d92d61c7a736
+size 1211434

envs/sim_xarm/xarm/tasks/assets/mesh/block_inner.stl

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+size 3284

envs/sim_xarm/xarm/tasks/assets/mesh/block_inner2.stl

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+oid sha256:2c6448552bf6b1c4f17334d686a5320ce051bcdfe31431edf69303d8a570d1de
+size 3284

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envs/sim_xarm/xarm/tasks/assets/peg_in_box.xml

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+<?xml version="1.0" encoding="utf-8"?>
+
+<mujoco>
+	<compiler angle="radian" coordinate="local" meshdir="mesh" texturedir="texture"></compiler>
+	<size nconmax="2000" njmax="500"/>
+
+	<option timestep="0.001">
+		<flag warmstart="enable"></flag>
+	</option>
+
+	<include file="shared.xml"></include>
+
+	<worldbody>
+		<body name="floor0" pos="0 0 0">
+			<geom name="floorgeom0" pos="1.2 -2.0 0" size="1.0 10.0 1" type="plane" condim="3" material="floor_mat"></geom>
+		</body>
+
+		<include file="xarm.xml"></include>
+
+		<body pos="0.75 0 0.6325" name="pedestal0">
+			<geom name="pedestalgeom0" size="0.1 0.1 0.01" pos="0.32 0.27 0" type="box" mass="2000" material="pedestal_mat"></geom>
+			<site pos="0.30 0.30 0" size="0.075 0.075 0.002" type="box" name="robotmountsite0" rgba="0.55 0.54 0.53 1" />
+		</body>
+
+		<body pos="1.5 0.075 0.3425" name="table0">
+			<geom name="tablegeom0" size="0.3 0.6 0.2" pos="0 0 0" type="box" material="table_mat" density="2000" friction="1 0.005 0.0002"></geom>
+		</body>
+
+		<body name="box0" pos="1.605 0.25 0.55">
+			<joint name="box_joint0" type="free" limited="false"></joint>
+			<site name="box_site" pos="0 0.075 -0.01" size="0.02" rgba="0 0 0 0" type="sphere"></site>
+			<geom name="box_side0" pos="0 0 0" size="0.065 0.002 0.04" type= "box" rgba="0.8 0.1 0.1 1" mass ="1" condim="4" />
+			<geom name="box_side1" pos="0 0.149 0" size="0.065 0.002 0.04" type="box" rgba="0.9 0.2 0.2 1" mass ="2" condim="4" />
+			<geom name="box_side2" pos="0.064 0.074 0" size="0.002 0.075 0.04" type="box" rgba="0.8 0.1 0.1 1" mass ="2" condim="4" />
+			<geom name="box_side3" pos="-0.064 0.074 0" size="0.002 0.075 0.04" type="box" rgba="0.9 0.2 0.2 1" mass ="2" condim="4" />
+			<geom name="box_side4" pos="-0 0.074 -0.038" size="0.065 0.075 0.002" type="box" rgba="0.5 0 0 1" mass ="2" condim="4"/>
+		</body>
+
+		<body name="object0" pos="1.4 0.25 0.65">
+			<joint name="object_joint0" type="free" limited="false"></joint>
+			<geom name="object_target0" type="cylinder" pos="0 0 -0.05" size="0.03 0.035" rgba="0.6 0.8 0.5 1" mass ="0.1" condim="3"  />
+			<site name="object_site" pos="0 0 -0.05" size="0.0325 0.0375" rgba="0 0 0 0" type="cylinder"></site>
+			<body name="B0" pos="0 0 0" euler="0 0 0 ">
+				<joint name="B0:joint" type="slide" limited="true" axis="0 0 1" damping="0.05" range="0.0001 0.0001001" solimpfriction="0.98 0.98 0.95" frictionloss="1"></joint>
+				<geom type="capsule" size="0.002 0.03" rgba="0 0 0 1" mass="0.001" condim="4"/>
+				<body name="B1" pos="0 0 0.04" euler="0 3.14 0 ">
+					<joint name="B1:joint1" type="hinge" axis="1 0 0" range="-0.1 0.1"  frictionloss="1"></joint>
+					<joint name="B1:joint2" type="hinge" axis="0 1 0" range="-0.1 0.1"  frictionloss="1"></joint>
+					<joint name="B1:joint3" type="hinge" axis="0 0 1" range="-0.1 0.1"  frictionloss="1"></joint>
+					<geom type="capsule" size="0.002 0.004" rgba="1 0 0 0" mass="0.001" condim="4"/>
+				</body>
+			</body>
+		</body>
+
+		<light directional="true" ambient="0.1 0.1 0.1" diffuse="0 0 0" specular="0 0 0" castshadow="false" pos="1.65 0 10" dir="-0.57 -0.57 -0.57" name="light0"></light>
+		<light directional="true" ambient="0.1 0.1 0.1" diffuse="0 0 0" specular="0 0 0" castshadow="false" pos="0 -4 4" dir="0 1 -0.1" name="light1"></light>
+		<light directional="true" ambient="0.05 0.05 0.05" diffuse="0 0 0" specular="0 0 0" castshadow="false" pos="2.13 1.6 2.5" name="light2"></light>
+		<light pos="0 0 2" dir="0.2 0.2 -0.8" directional="true"  diffuse="0.3 0.3 0.3"  castshadow="false" name="light3"></light>
+
+		<camera fovy="50" name="camera0" pos="0.9559 1.0 1.1" euler="-1.1 -0.6 3.4" />
+	</worldbody>
+
+	<equality>
+		<connect body2="left_finger" body1="left_inner_knuckle" anchor="0.0 0.035 0.042" solimp="0.9 0.95 0.001 0.5 2" solref="0.0002 1.0" ></connect>
+		<connect body2="right_finger" body1="right_inner_knuckle" anchor="0.0 -0.035 0.042" solimp="0.9 0.95 0.001 0.5 2" solref="0.0002 1.0" ></connect>
+		<weld body1="right_hand" body2="B1" solimp="0.99 0.99 0.99" solref="0.02 1"></weld>
+		<joint joint1="left_inner_knuckle_joint" joint2="right_inner_knuckle_joint"></joint>
+	</equality>
+
+	<actuator>
+		<motor ctrllimited="true" ctrlrange="-1.0 1.0" joint="left_inner_knuckle_joint" gear="200.0"/>
+		<motor ctrllimited="true" ctrlrange="-1.0 1.0" joint="right_inner_knuckle_joint" gear="200.0"/>
+	</actuator>
+</mujoco>