Remove _has_gym checks

.dockerignore

@@ -1,142 +0,0 @@
-# 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/data
-htmlcov/
-.tox/
-.nox/
-.coverage
-.coverage.*
-.cache
-nosetests.xml
-coverage.xml
-*.cover
-*.py,cover
-.hypothesis/
-.pytest_cache/
-
-# 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/

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -1,54 +0,0 @@
-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

@@ -1,32 +0,0 @@
-# What does this PR do?
-
-Examples:
-- Fixes # (issue)
-- Adds new dataset
-- Optimizes something
-
-## How was it tested?
-
-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)
-
-Examples:
-```bash
-DATA_DIR=tests/data pytest -sx tests/test_stuff.py::test_something
-```
-```bash
-python lerobot/scripts/train.py --some.option=true
-```
-
-## Before submitting
-Please read the [contributor guideline](https://github.com/huggingface/lerobot/blob/main/CONTRIBUTING.md#submitting-a-pull-request-pr).
-
-
-## Who can review?
-
-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.

.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/scripts/dep_build.py

@@ -1,30 +0,0 @@
-PYPROJECT = "pyproject.toml"
-DEPS = {
-    "gym-pusht": '{ git = "git@github.com:huggingface/gym-pusht.git", optional = true}',
-    "gym-xarm": '{ git = "git@github.com:huggingface/gym-xarm.git", optional = true}',
-    "gym-aloha": '{ git = "git@github.com:huggingface/gym-aloha.git", optional = true}',
-}
-
-
-def update_envs_as_path_dependencies():
-    with open(PYPROJECT) as file:
-        lines = file.readlines()
-
-    new_lines = []
-    for line in lines:
-        if any(dep in line for dep in DEPS.values()):
-            for dep in DEPS:
-                if dep in line:
-                    new_line = f'{dep} = {{ path = "envs/{dep}/", optional = true}}\n'
-                    new_lines.append(new_line)
-                    break
-
-        else:
-            new_lines.append(line)
-
-    with open(PYPROJECT, "w") as file:
-        file.writelines(new_lines)
-
-
-if __name__ == "__main__":
-    update_envs_as_path_dependencies()

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

@@ -1,203 +0,0 @@
-# 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 * * *"
-
-env:
-  PYTHON_VERSION: "3.10"
-#   CI_SLACK_CHANNEL: ${{ secrets.CI_DOCKER_CHANNEL }}
-
-jobs:
-  latest-cpu:
-    name: "Build CPU"
-    runs-on: ubuntu-latest
-    steps:
-      - name: Cleanup disk
-        run: |
-          sudo df -h
-          # sudo ls -l /usr/local/lib/
-          # sudo ls -l /usr/share/
-          sudo du -sh /usr/local/lib/
-          sudo du -sh /usr/share/
-          sudo rm -rf /usr/local/lib/android
-          sudo rm -rf /usr/share/dotnet
-          sudo du -sh /usr/local/lib/
-          sudo du -sh /usr/share/
-          sudo df -h
-
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
-
-      - name: Check out code
-        uses: actions/checkout@v4
-
-      # HACK(aliberts): to be removed for release
-      # -----------------------------------------
-      - name: Checkout gym-aloha
-        uses: actions/checkout@v4
-        with:
-          repository: huggingface/gym-aloha
-          path: envs/gym-aloha
-          ssh-key: ${{ secrets.SSH_PRIVATE_KEY }}
-
-      - name: Checkout gym-xarm
-        uses: actions/checkout@v4
-        with:
-          repository: huggingface/gym-xarm
-          path: envs/gym-xarm
-          ssh-key: ${{ secrets.SSH_PRIVATE_KEY }}
-
-      - name: Checkout gym-pusht
-        uses: actions/checkout@v4
-        with:
-          repository: huggingface/gym-pusht
-          path: envs/gym-pusht
-          ssh-key: ${{ secrets.SSH_PRIVATE_KEY }}
-
-      - name: Set up Python 3.10
-        uses: actions/setup-python@v5
-        with:
-          python-version: "3.10"
-
-      - name: Change envs dependencies as local path
-        run: python .github/scripts/dep_build.py
-      # -----------------------------------------
-
-      - 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 }}
-
-    #   - name: Post to a Slack channel
-    #     id: slack
-    #     #uses: slackapi/slack-github-action@v1.25.0
-    #     uses: slackapi/slack-github-action@6c661ce58804a1a20f6dc5fbee7f0381b469e001
-    #     with:
-    #       # Slack channel id, channel name, or user id to post message.
-    #       # See also: https://api.slack.com/methods/chat.postMessage#channels
-    #       channel-id: ${{ env.CI_SLACK_CHANNEL }}
-    #       # For posting a rich message using Block Kit
-    #       payload: |
-    #         {
-    #           "text": "lerobot-cpu Docker Image build result: ${{ job.status }}\n${{ github.event.pull_request.html_url || github.event.head_commit.url }}",
-    #           "blocks": [
-    #             {
-    #               "type": "section",
-    #               "text": {
-    #                 "type": "mrkdwn",
-    #                 "text": "lerobot-cpu Docker Image build result: ${{ job.status }}\n${{ github.event.pull_request.html_url || github.event.head_commit.url }}"
-    #               }
-    #             }
-    #           ]
-    #         }
-    #     env:
-    #       SLACK_BOT_TOKEN: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }}
-
-  latest-cuda:
-    name: "Build GPU"
-    runs-on: ubuntu-latest
-    steps:
-      - name: Cleanup disk
-        run: |
-          sudo df -h
-          # sudo ls -l /usr/local/lib/
-          # sudo ls -l /usr/share/
-          sudo du -sh /usr/local/lib/
-          sudo du -sh /usr/share/
-          sudo rm -rf /usr/local/lib/android
-          sudo rm -rf /usr/share/dotnet
-          sudo du -sh /usr/local/lib/
-          sudo du -sh /usr/share/
-          sudo df -h
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
-
-      - name: Check out code
-        uses: actions/checkout@v4
-
-      # HACK(aliberts): to be removed for release
-      # -----------------------------------------
-      - name: Checkout gym-aloha
-        uses: actions/checkout@v4
-        with:
-          repository: huggingface/gym-aloha
-          path: envs/gym-aloha
-          ssh-key: ${{ secrets.SSH_PRIVATE_KEY }}
-
-      - name: Checkout gym-xarm
-        uses: actions/checkout@v4
-        with:
-          repository: huggingface/gym-xarm
-          path: envs/gym-xarm
-          ssh-key: ${{ secrets.SSH_PRIVATE_KEY }}
-
-      - name: Checkout gym-pusht
-        uses: actions/checkout@v4
-        with:
-          repository: huggingface/gym-pusht
-          path: envs/gym-pusht
-          ssh-key: ${{ secrets.SSH_PRIVATE_KEY }}
-
-      - name: Set up Python 3.10
-        uses: actions/setup-python@v5
-        with:
-          python-version: "3.10"
-
-      - name: Change envs dependencies as local path
-        run: python .github/scripts/dep_build.py
-      # -----------------------------------------
-
-      - 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 }}
-
-      # - name: Post to a Slack channel
-      #   id: slack
-      #   #uses: slackapi/slack-github-action@v1.25.0
-      #   uses: slackapi/slack-github-action@6c661ce58804a1a20f6dc5fbee7f0381b469e001
-      #   with:
-      #     # Slack channel id, channel name, or user id to post message.
-      #     # See also: https://api.slack.com/methods/chat.postMessage#channels
-      #     channel-id: ${{ env.CI_SLACK_CHANNEL }}
-      #     # For posting a rich message using Block Kit
-      #     payload: |
-      #       {
-      #         "text": "lerobot-gpu Docker Image build result: ${{ job.status }}\n${{ github.event.pull_request.html_url || github.event.head_commit.url }}",
-      #         "blocks": [
-      #           {
-      #             "type": "section",
-      #             "text": {
-      #               "type": "mrkdwn",
-      #               "text": "lerobot-gpu Docker Image build result: ${{ job.status }}\n${{ github.event.pull_request.html_url || github.event.head_commit.url }}"
-      #             }
-      #           }
-      #         ]
-      #       }
-      #   env:
-      #     SLACK_BOT_TOKEN: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }}

.github/workflows/nightly-tests.yml

@@ -1,79 +0,0 @@
-# Inspired by
-# https://github.com/huggingface/peft/blob/main/.github/workflows/nightly.yml
-name: Nightly
-
-on:
-  workflow_dispatch:
-  schedule:
-    - cron: "0 2 * * *"
-
-env:
-  DATA_DIR: tests/data
-  # SLACK_API_TOKEN: ${{ secrets.SLACK_API_TOKEN }}
-
-jobs:
-  run_all_tests_cpu:
-    name: "Test CPU"
-    strategy:
-      fail-fast: false
-    runs-on: ubuntu-latest
-    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
-        env:
-          DATA_DIR: tests/data
-        run: pytest -v --cov=./lerobot --disable-warnings tests
-
-      - name: Tests end-to-end
-        env:
-          DATA_DIR: tests/data
-        run: make test-end-to-end
-
-
-  run_all_tests_single_gpu:
-    name: "Test GPU"
-    strategy:
-      fail-fast: false
-    runs-on: [single-gpu, nvidia-gpu, t4, ci]
-    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
-        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/style.yml

@@ -1,38 +0,0 @@
-name: Style
-
-on:
-  workflow_dispatch:
-  workflow_call:
-  pull_request:
-    branches:
-      - main
-  push:
-    branches:
-      - main
-
-env:
-  PYTHON_VERSION: "3.10"
-
-jobs:
-  ruff_check:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout Repository
-        uses: actions/checkout@v3
-
-      - 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}" >> $GITHUB_ENV
-
-      - name: Install Ruff
-        run: python -m pip install "ruff==${{ env.RUFF_VERSION }}"
-
-      - name: Run Ruff
-        run: ruff check .

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

@@ -1,109 +0,0 @@
-# Inspired by
-# https://github.com/huggingface/peft/blob/main/.github/workflows/test-docker-build.yml
-name: Test Docker builds (PR)
-
-on:
-  pull_request:
-    branches:
-      - main
-    paths:
-      # Run only when DockerFile files are modified
-      - "docker/**"
-
-env:
-  PYTHON_VERSION: "3.10"
-
-jobs:
-  get_changed_files:
-    name: "Get all modified Dockerfiles"
-    runs-on: ubuntu-latest
-    outputs:
-      matrix: ${{ steps.set-matrix.outputs.matrix }}
-    steps:
-      - name: Check out code
-        uses: actions/checkout@v4
-
-      - name: Get changed files
-        id: changed-files
-        uses: tj-actions/changed-files@v44
-        with:
-          files: docker/**
-          json: "true"
-
-      - name: Run step if only the files listed above change
-        if: steps.changed-files.outputs.any_changed == 'true'
-        id: set-matrix
-        env:
-          ALL_CHANGED_FILES: ${{ steps.changed-files.outputs.all_changed_files }}
-        run: |
-          echo "matrix=${{ steps.changed-files.outputs.all_changed_files}}" >> $GITHUB_OUTPUT
-
-
-  build_modified_dockerfiles:
-    name: "Build all modified Docker images"
-    needs: get_changed_files
-    runs-on: ubuntu-latest
-    if: ${{ needs.get_changed_files.outputs.matrix }} != ''
-    strategy:
-      fail-fast: false
-      matrix:
-        docker-file: ${{ fromJson(needs.get_changed_files.outputs.matrix) }}
-    steps:
-      - name: Cleanup disk
-        run: |
-          sudo df -h
-          # sudo ls -l /usr/local/lib/
-          # sudo ls -l /usr/share/
-          sudo du -sh /usr/local/lib/
-          sudo du -sh /usr/share/
-          sudo rm -rf /usr/local/lib/android
-          sudo rm -rf /usr/share/dotnet
-          sudo du -sh /usr/local/lib/
-          sudo du -sh /usr/share/
-          sudo df -h
-
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
-
-      - name: Check out code
-        uses: actions/checkout@v4
-
-      # HACK(aliberts): to be removed for release
-      # -----------------------------------------
-      - name: Checkout gym-aloha
-        uses: actions/checkout@v4
-        with:
-          repository: huggingface/gym-aloha
-          path: envs/gym-aloha
-          ssh-key: ${{ secrets.SSH_PRIVATE_KEY }}
-
-      - name: Checkout gym-xarm
-        uses: actions/checkout@v4
-        with:
-          repository: huggingface/gym-xarm
-          path: envs/gym-xarm
-          ssh-key: ${{ secrets.SSH_PRIVATE_KEY }}
-
-      - name: Checkout gym-pusht
-        uses: actions/checkout@v4
-        with:
-          repository: huggingface/gym-pusht
-          path: envs/gym-pusht
-          ssh-key: ${{ secrets.SSH_PRIVATE_KEY }}
-
-      - name: Set up Python 3.10
-        uses: actions/setup-python@v5
-        with:
-          python-version: "3.10"
-
-      - name: Change envs dependencies as local path
-        run: python .github/scripts/dep_build.py
-      # -----------------------------------------
-
-      - 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

@@ -4,71 +4,231 @@ on:
   pull_request:
     branches:
       - main
-    paths:
-      - "lerobot/**"
-      - "tests/**"
-      - "examples/**"
-      - ".github/**"
-      - "poetry.lock"
+    types: [opened, synchronize, reopened, labeled]
   push:
     branches:
       - main
-    paths:
-      - "lerobot/**"
-      - "tests/**"
-      - "examples/**"
-      - ".github/**"
-      - "poetry.lock"
 
 jobs:
   tests:
-    runs-on: ${{ matrix.os }}
-    strategy:
-      matrix:
-        os: [ubuntu-latest, macos-latest, macos-latest-large]
+    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:
-      - name: Add SSH key for installing envs
-        uses: webfactory/ssh-agent@v0.9.0
+      #----------------------------------------------
+      #       check-out repo and set-up python
+      #----------------------------------------------
+      - name: Check out repository
+        uses: actions/checkout@v4
         with:
-          ssh-private-key: ${{ secrets.SSH_PRIVATE_KEY }}
+          lfs: true
 
-      - uses: actions/checkout@v4
-
-      - name: Install EGL
-        run: |
-          if [[ "${{ matrix.os }}" == 'ubuntu-latest' ]]; then
-            sudo apt-get update && sudo apt-get install -y libegl1-mesa-dev
-          elif [[ "${{ matrix.os }}" == 'macos-latest' || "${{ matrix.os }}" == 'macos-latest-large' ]]; then
-            brew install mesa
-          fi
-
-      - name: Install poetry
-        run: |
-          pipx install poetry && poetry config virtualenvs.in-project true
-          echo "${{ github.workspace }}/.venv/bin" >> $GITHUB_PATH
-
-      - name: Set up Python 3.10
+      - name: Set up python
+        id: setup-python
         uses: actions/setup-python@v5
         with:
-          python-version: "3.10"
-          cache: "poetry"
+          python-version: '3.10'
 
-      - name: Install poetry dependencies
-        run: |
-          poetry install --all-extras
+      #----------------------------------------------
+      #         install & configure poetry
+      #----------------------------------------------
+      - name: Load cached Poetry installation
+        id: restore-poetry-cache
+        uses: actions/cache/restore@v3
+        with:
+          path: ~/.local
+          key: poetry-${{ env.POETRY_VERSION }}
 
-      - name: Test with pytest
-        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
+      - name: Install Poetry
+        if: steps.restore-poetry-cache.outputs.cache-hit != 'true'
+        uses: snok/install-poetry@v1
+        with:
+          version: ${{ env.POETRY_VERSION }}
+          virtualenvs-create: true
+          installer-parallel: true
 
-      - name: Test end-to-end
+      - 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:
+          path: ~/.local
+          key: poetry-${{ env.POETRY_VERSION }}
+
+      - name: Configure Poetry
+        run: poetry config virtualenvs.in-project true
+
+      #----------------------------------------------
+      #           install dependencies
+      #----------------------------------------------
+      # TODO(aliberts): move to gpu runners
+      - name: Select cpu dependencies  # HACK
+        run: cp -t . .github/poetry/cpu/pyproject.toml .github/poetry/cpu/poetry.lock
+
+      - name: Load cached venv
+        id: restore-dependencies-cache
+        uses: actions/cache/restore@v3
+        with:
+          path: .venv
+          key: venv-${{ steps.setup-python.outputs.python-version }}-${{ env.POETRY_VERSION }}-${{ hashFiles('**/poetry.lock') }}
+
+      - name: Install dependencies
+        if: steps.restore-dependencies-cache.outputs.cache-hit != 'true'
+        env:
+          TMPDIR: ~/tmp
+          TEMP: ~/tmp
+          TMP: ~/tmp
         run: |
-          make test-end-to-end \
-            && rm -rf outputs
+          mkdir ~/tmp
+          poetry install --no-interaction --no-root --without dev --all-extras
+
+      - 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: 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: |
+          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

.gitignore

@@ -11,9 +11,6 @@ rl
 nautilus/*.yaml
 *.key
 
-# Slurm
-sbatch*.sh
-
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]

.pre-commit-config.yaml

@@ -1,9 +1,9 @@
-exclude: ^(tests/data)
+exclude: ^(data/|tests/)
 default_language_version:
     python: python3.10
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.6.0
+    rev: v4.5.0
     hooks:
       - id: check-added-large-files
       - id: debug-statements
@@ -18,7 +18,7 @@ repos:
     hooks:
     -   id: pyupgrade
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.4.2
+    rev: v0.3.4
     hooks:
       - id: ruff
         args: [--fix]

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

@@ -1,270 +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](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 use `poetry` instead of just `pip` to easily track our dependencies.
-   If you don't have it already, follow the [instructions](https://python-poetry.org/docs/#installation) to install it.
-
-   Set up a development environment with conda or miniconda:
-   ```bash
-   conda create -y -n lerobot-dev python=3.10 && conda activate lerobot-dev
-   ```
-
-   To develop on 🤗 LeRobot, you will at least need to install the `dev` and `test` extras dependencies along with the core library:
-   ```bash
-   poetry install --sync --extras "dev test"
-   ```
-
-   You can also install the project with all its dependencies (including environments):
-   ```bash
-   poetry install --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 install --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:
-   ```bash
-   poetry add some-package
-   ```
-
-   When making changes to the poetry sections of the `pyproject.toml`, you should run the following command to lock dependencies.
-   ```bash
-   poetry lock --no-update
-   ```
-
-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
-   ```
-
-   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/data](tests/data)
-```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
-DATA_DIR="tests/data" 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

@@ -1,95 +0,0 @@
-.PHONY: tests
-
-PYTHON_PATH := $(shell which python)
-
-# If Poetry is installed, redefine PYTHON_PATH to use the Poetry-managed Python
-POETRY_CHECK := $(shell command -v poetry)
-ifneq ($(POETRY_CHECK),)
-    PYTHON_PATH := $(shell poetry run which python)
-endif
-
-export PATH := $(dir $(PYTHON_PATH)):$(PATH)
-
-
-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} test-act-ete-train
-	${MAKE} test-act-ete-eval
-	${MAKE} test-diffusion-ete-train
-	${MAKE} test-diffusion-ete-eval
-	${MAKE} test-tdmpc-ete-train
-	${MAKE} test-tdmpc-ete-eval
-
-test-act-ete-train:
-	python lerobot/scripts/train.py \
-		policy=act \
-		env=aloha \
-		wandb.enable=False \
-		offline_steps=2 \
-		online_steps=0 \
-		eval_episodes=1 \
-		device=cpu \
-		save_model=true \
-		save_freq=2 \
-		policy.n_action_steps=20 \
-		policy.chunk_size=20 \
-		policy.batch_size=2 \
-		hydra.run.dir=tests/outputs/act/
-
-test-act-ete-eval:
-	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
-
-test-diffusion-ete-train:
-	python lerobot/scripts/train.py \
-		policy=diffusion \
-		env=pusht \
-		wandb.enable=False \
-		offline_steps=2 \
-		online_steps=0 \
-		eval_episodes=1 \
-		device=cpu \
-		save_model=true \
-		save_freq=2 \
-		policy.batch_size=2 \
-		hydra.run.dir=tests/outputs/diffusion/
-
-test-diffusion-ete-eval:
-	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
-
-test-tdmpc-ete-train:
-	python lerobot/scripts/train.py \
-		policy=tdmpc \
-		env=xarm \
-		wandb.enable=False \
-		offline_steps=1 \
-		online_steps=2 \
-		eval_episodes=1 \
-		env.episode_length=2 \
-		device=cpu \
-		save_model=true \
-		save_freq=2 \
-		policy.batch_size=2 \
-		hydra.run.dir=tests/outputs/tdmpc/
-
-test-tdmpc-ete-eval:
-	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

README.md

@@ -10,14 +10,13 @@
 
 <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>
@@ -63,27 +62,19 @@
 
 Download our source code:
 ```bash
-git clone https://github.com/huggingface/lerobot.git && cd lerobot
+git clone https://github.com/huggingface/lerobot.git
+cd lerobot
 ```
 
 Create a virtual environment with Python 3.10 and activate it, e.g. with [`miniconda`](https://docs.anaconda.com/free/miniconda/index.html):
 ```bash
-conda create -y -n lerobot python=3.10 && conda activate lerobot
+conda create -y -n lerobot python=3.10
+conda activate lerobot
 ```
 
-Install 🤗 LeRobot:
+Then, install 🤗 LeRobot:
 ```bash
-pip install .
-```
-
-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 ".[aloha, pusht]"
+python -m pip install .
 ```
 
 To use [Weights and Biases](https://docs.wandb.ai/quickstart) for experiments tracking, log in with
@@ -98,11 +89,11 @@ wandb login
 ├── lerobot
 |   ├── 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, xarm.yaml
+|   |   ├── 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
+|   |   ├── 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
@@ -118,19 +109,48 @@ wandb login
 
 ### Visualize datasets
 
-Check out [examples](./examples) to see how you can import our dataset class, download the data from the HuggingFace hub and use our rendering utilities.
+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
+
+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 \
-env=pusht \
+env=aloha \
+task=sim_sim_transfer_cube_human \
 hydra.run.dir=outputs/visualize_dataset/example
 # >>> ['outputs/visualize_dataset/example/episode_0.mp4']
 ```
 
 ### Evaluate a pretrained policy
 
-Check out [examples](./examples) to see how you can load a pretrained policy from HuggingFace hub, load up the corresponding environment and model, and run an evaluation.
+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
+```
 
 Or you can achieve the same result by executing our script from the command line:
 ```bash
@@ -140,7 +160,7 @@ eval_episodes=10 \
 hydra.run.dir=outputs/eval/example_hub
 ```
 
-After training your own policy, you can also re-evaluate the checkpoints with:
+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 \
@@ -153,30 +173,104 @@ See `python lerobot/scripts/eval.py --help` for more instructions.
 
 ### Train your own policy
 
-Check out [examples](./examples) to see how you can start training a model on a dataset, which will be automatically downloaded if needed.
+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
+```
 
-In general, you can use our training script to easily train any policy on any environment:
+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 \
-repo_id=lerobot/aloha_sim_insertion_scripted \
+dataset_id=aloha_sim_insertion_scripted \
 policy=act \
 hydra.run.dir=outputs/train/aloha_act
 ```
 
-After training, you may want to revisit model evaluation to change the evaluation settings. In fact, during training every checkpoint is already evaluated but on a low number of episodes for efficiency. Check out [example](./examples) to evaluate any model checkpoint on more episodes to increase statistical significance.
-
 ## 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).
+
+### TODO
+
+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
 
-```python
-# TODO(rcadene, AdilZouitine): rewrite this section
-```
-
 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
@@ -191,7 +285,7 @@ HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli upload $HF_USER/$DATASET data/$DATAS
 
 You will need to set the corresponding version as a default argument in your dataset class:
 ```python
-  version: str | None = "v1.1",
+  version: str | None = "v1.0",
 ```
 See: [`lerobot/common/datasets/pusht.py`](https://github.com/Cadene/lerobot/blob/main/lerobot/common/datasets/pusht.py)
 
@@ -238,10 +332,6 @@ python tests/scripts/mock_dataset.py --in-data-dir data/$DATASET --out-data-dir
 
 ### Add a pretrained policy
 
-```python
-# TODO(rcadene, alexander-soare): rewrite this section
-```
-
 Once you have trained a policy you may upload it to the HuggingFace hub.
 
 Firstly, make sure you have a model repository set up on the hub. The hub ID looks like HF_USER/REPO_NAME.
@@ -250,13 +340,15 @@ 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
+    ├── model.pt
+    └── stats.pth
 ```
 
 With the folder prepared, run the following with a desired revision ID.

docker/lerobot-cpu/Dockerfile

@@ -1,31 +0,0 @@
-# Configure image
-ARG PYTHON_VERSION=3.10
-
-FROM python:${PYTHON_VERSION}-slim
-ARG PYTHON_VERSION
-ARG DEBIAN_FRONTEND=noninteractive
-
-# Install apt dependencies
-RUN apt-get update && apt-get install -y --no-install-recommends \
-    build-essential cmake \
-    libglib2.0-0 libgl1-mesa-glx libegl1-mesa \
-    && apt-get clean && rm -rf /var/lib/apt/lists/*
-
-# Create virtual environment
-RUN ln -s /usr/bin/python${PYTHON_VERSION} /usr/bin/python
-RUN python -m venv /opt/venv
-ENV PATH="/opt/venv/bin:$PATH"
-RUN echo "source /opt/venv/bin/activate" >> /root/.bashrc
-
-# Install LeRobot
-COPY . /lerobot
-WORKDIR /lerobot
-RUN pip install --upgrade --no-cache-dir pip
-RUN pip install --no-cache-dir ".[test, aloha, xarm, pusht]" \
-    --extra-index-url https://download.pytorch.org/whl/cpu
-
-# Set EGL as the rendering backend for MuJoCo
-ENV MUJOCO_GL="egl"
-
-# Execute in bash shell rather than python
-CMD ["/bin/bash"]

docker/lerobot-gpu/Dockerfile

@@ -1,27 +0,0 @@
-FROM nvidia/cuda:12.4.1-base-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 \
-    libglib2.0-0 libgl1-mesa-glx libegl1-mesa \
-    python${PYTHON_VERSION} python${PYTHON_VERSION}-venv \
-    && apt-get clean && rm -rf /var/lib/apt/lists/*
-
-# Create virtual environment
-RUN ln -s /usr/bin/python${PYTHON_VERSION} /usr/bin/python
-RUN python -m venv /opt/venv
-ENV PATH="/opt/venv/bin:$PATH"
-RUN echo "source /opt/venv/bin/activate" >> /root/.bashrc
-
-# Install LeRobot
-COPY . /lerobot
-WORKDIR /lerobot
-RUN pip install --upgrade --no-cache-dir pip
-RUN pip install --no-cache-dir ".[test, aloha, xarm, pusht]"
-
-# Set EGL as the rendering backend for MuJoCo
-ENV MUJOCO_GL="egl"

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

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d100cafe656671ca8fde98fb6a4cf2d1b746995c51c61c25ad9ea2715635d146
+size 99984

envs/sim_aloha/aloha/assets/vx300s_2_shoulder.stl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:139745a74055cb0b23430bb5bc032bf68cf7bea5e4975c8f4c04107ae005f7f0
+size 63884

envs/sim_aloha/aloha/assets/vx300s_3_upper_arm.stl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:900f236320dd3d500870c5fde763b2d47502d51e043a5c377875e70237108729
+size 102984

envs/sim_aloha/aloha/assets/vx300s_4_upper_forearm.stl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4104fc54bbfb8a9b533029f1e7e3ade3d54d638372b3195daa0c98f57e0295b5
+size 49584

envs/sim_aloha/aloha/assets/vx300s_5_lower_forearm.stl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:66814e27fa728056416e25e02e89eb7d34c51d51c51e7c3df873829037ddc6b8
+size 99884

envs/sim_aloha/aloha/assets/vx300s_6_wrist.stl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:90eb145c85627968c3776ae6de23ccff7e112c9dd713c46bc9acdfdaa859a048
+size 70784

envs/sim_aloha/aloha/assets/vx300s_7_gripper.stl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:786c1077bfd226f14219581b11d5f19464ca95b17132e0bb7532503568f5af90
+size 450084

envs/sim_aloha/aloha/assets/vx300s_8_gripper_prop.stl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d1275a93fe2157c83dbc095617fb7e672888bdd48ec070a35ef4ab9ebd9755b0
+size 31684

envs/sim_aloha/aloha/assets/vx300s_9_gripper_bar.stl

@@ -0,0 +1,3 @@
+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

@@ -0,0 +1,766 @@
+# 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 = "certifi"
+version = "2024.2.2"
+description = "Python package for providing Mozilla's CA Bundle."
+optional = false
+python-versions = ">=3.6"
+files = [
+    {file = "certifi-2024.2.2-py3-none-any.whl", hash = "sha256:dc383c07b76109f368f6106eee2b593b04a011ea4d55f652c6ca24a754d1cdd1"},
+    {file = "certifi-2024.2.2.tar.gz", hash = "sha256:0569859f95fc761b18b45ef421b1290a0f65f147e92a1e5eb3e635f9a5e4e66f"},
+]
+
+[[package]]
+name = "charset-normalizer"
+version = "3.3.2"
+description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
+optional = false
+python-versions = ">=3.7.0"
+files = [
+    {file = "charset-normalizer-3.3.2.tar.gz", hash = "sha256:f30c3cb33b24454a82faecaf01b19c18562b1e89558fb6c56de4d9118a032fd5"},
+    {file = "charset_normalizer-3.3.2-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:25baf083bf6f6b341f4121c2f3c548875ee6f5339300e08be3f2b2ba1721cdd3"},
+    {file = "charset_normalizer-3.3.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:06435b539f889b1f6f4ac1758871aae42dc3a8c0e24ac9e60c2384973ad73027"},
+    {file = "charset_normalizer-3.3.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:9063e24fdb1e498ab71cb7419e24622516c4a04476b17a2dab57e8baa30d6e03"},
+    {file = "charset_normalizer-3.3.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6897af51655e3691ff853668779c7bad41579facacf5fd7253b0133308cf000d"},
+    {file = "charset_normalizer-3.3.2-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:1d3193f4a680c64b4b6a9115943538edb896edc190f0b222e73761716519268e"},
+    {file = "charset_normalizer-3.3.2-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:cd70574b12bb8a4d2aaa0094515df2463cb429d8536cfb6c7ce983246983e5a6"},
+    {file = "charset_normalizer-3.3.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8465322196c8b4d7ab6d1e049e4c5cb460d0394da4a27d23cc242fbf0034b6b5"},
+    {file = "charset_normalizer-3.3.2-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:a9a8e9031d613fd2009c182b69c7b2c1ef8239a0efb1df3f7c8da66d5dd3d537"},
+    {file = "charset_normalizer-3.3.2-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:beb58fe5cdb101e3a055192ac291b7a21e3b7ef4f67fa1d74e331a7f2124341c"},
+    {file = "charset_normalizer-3.3.2-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:e06ed3eb3218bc64786f7db41917d4e686cc4856944f53d5bdf83a6884432e12"},
+    {file = "charset_normalizer-3.3.2-cp310-cp310-musllinux_1_1_ppc64le.whl", hash = "sha256:2e81c7b9c8979ce92ed306c249d46894776a909505d8f5a4ba55b14206e3222f"},
+    {file = "charset_normalizer-3.3.2-cp310-cp310-musllinux_1_1_s390x.whl", hash = "sha256:572c3763a264ba47b3cf708a44ce965d98555f618ca42c926a9c1616d8f34269"},
+    {file = "charset_normalizer-3.3.2-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:fd1abc0d89e30cc4e02e4064dc67fcc51bd941eb395c502aac3ec19fab46b519"},
+    {file = "charset_normalizer-3.3.2-cp310-cp310-win32.whl", hash = "sha256:3d47fa203a7bd9c5b6cee4736ee84ca03b8ef23193c0d1ca99b5089f72645c73"},
+    {file = "charset_normalizer-3.3.2-cp310-cp310-win_amd64.whl", hash = "sha256:10955842570876604d404661fbccbc9c7e684caf432c09c715ec38fbae45ae09"},
+    {file = "charset_normalizer-3.3.2-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:802fe99cca7457642125a8a88a084cef28ff0cf9407060f7b93dca5aa25480db"},
+    {file = "charset_normalizer-3.3.2-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:573f6eac48f4769d667c4442081b1794f52919e7edada77495aaed9236d13a96"},
+    {file = "charset_normalizer-3.3.2-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:549a3a73da901d5bc3ce8d24e0600d1fa85524c10287f6004fbab87672bf3e1e"},
+    {file = "charset_normalizer-3.3.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f27273b60488abe721a075bcca6d7f3964f9f6f067c8c4c605743023d7d3944f"},
+    {file = "charset_normalizer-3.3.2-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:1ceae2f17a9c33cb48e3263960dc5fc8005351ee19db217e9b1bb15d28c02574"},
+    {file = "charset_normalizer-3.3.2-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:65f6f63034100ead094b8744b3b97965785388f308a64cf8d7c34f2f2e5be0c4"},
+    {file = "charset_normalizer-3.3.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:753f10e867343b4511128c6ed8c82f7bec3bd026875576dfd88483c5c73b2fd8"},
+    {file = "charset_normalizer-3.3.2-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:4a78b2b446bd7c934f5dcedc588903fb2f5eec172f3d29e52a9096a43722adfc"},
+    {file = "charset_normalizer-3.3.2-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:e537484df0d8f426ce2afb2d0f8e1c3d0b114b83f8850e5f2fbea0e797bd82ae"},
+    {file = "charset_normalizer-3.3.2-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:eb6904c354526e758fda7167b33005998fb68c46fbc10e013ca97f21ca5c8887"},
+    {file = "charset_normalizer-3.3.2-cp311-cp311-musllinux_1_1_ppc64le.whl", hash = "sha256:deb6be0ac38ece9ba87dea880e438f25ca3eddfac8b002a2ec3d9183a454e8ae"},
+    {file = "charset_normalizer-3.3.2-cp311-cp311-musllinux_1_1_s390x.whl", hash = "sha256:4ab2fe47fae9e0f9dee8c04187ce5d09f48eabe611be8259444906793ab7cbce"},
+    {file = "charset_normalizer-3.3.2-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:80402cd6ee291dcb72644d6eac93785fe2c8b9cb30893c1af5b8fdd753b9d40f"},
+    {file = "charset_normalizer-3.3.2-cp311-cp311-win32.whl", hash = "sha256:7cd13a2e3ddeed6913a65e66e94b51d80a041145a026c27e6bb76c31a853c6ab"},
+    {file = "charset_normalizer-3.3.2-cp311-cp311-win_amd64.whl", hash = "sha256:663946639d296df6a2bb2aa51b60a2454ca1cb29835324c640dafb5ff2131a77"},
+    {file = "charset_normalizer-3.3.2-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:0b2b64d2bb6d3fb9112bafa732def486049e63de9618b5843bcdd081d8144cd8"},
+    {file = "charset_normalizer-3.3.2-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:ddbb2551d7e0102e7252db79ba445cdab71b26640817ab1e3e3648dad515003b"},
+    {file = "charset_normalizer-3.3.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:55086ee1064215781fff39a1af09518bc9255b50d6333f2e4c74ca09fac6a8f6"},
+    {file = "charset_normalizer-3.3.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:8f4a014bc36d3c57402e2977dada34f9c12300af536839dc38c0beab8878f38a"},
+    {file = "charset_normalizer-3.3.2-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:a10af20b82360ab00827f916a6058451b723b4e65030c5a18577c8b2de5b3389"},
+    {file = "charset_normalizer-3.3.2-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:8d756e44e94489e49571086ef83b2bb8ce311e730092d2c34ca8f7d925cb20aa"},
+    {file = "charset_normalizer-3.3.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:90d558489962fd4918143277a773316e56c72da56ec7aa3dc3dbbe20fdfed15b"},
+    {file = "charset_normalizer-3.3.2-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:6ac7ffc7ad6d040517be39eb591cac5ff87416c2537df6ba3cba3bae290c0fed"},
+    {file = "charset_normalizer-3.3.2-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:7ed9e526742851e8d5cc9e6cf41427dfc6068d4f5a3bb03659444b4cabf6bc26"},
+    {file = "charset_normalizer-3.3.2-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:8bdb58ff7ba23002a4c5808d608e4e6c687175724f54a5dade5fa8c67b604e4d"},
+    {file = "charset_normalizer-3.3.2-cp312-cp312-musllinux_1_1_ppc64le.whl", hash = "sha256:6b3251890fff30ee142c44144871185dbe13b11bab478a88887a639655be1068"},
+    {file = "charset_normalizer-3.3.2-cp312-cp312-musllinux_1_1_s390x.whl", hash = "sha256:b4a23f61ce87adf89be746c8a8974fe1c823c891d8f86eb218bb957c924bb143"},
+    {file = "charset_normalizer-3.3.2-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:efcb3f6676480691518c177e3b465bcddf57cea040302f9f4e6e191af91174d4"},
+    {file = "charset_normalizer-3.3.2-cp312-cp312-win32.whl", hash = "sha256:d965bba47ddeec8cd560687584e88cf699fd28f192ceb452d1d7ee807c5597b7"},
+    {file = "charset_normalizer-3.3.2-cp312-cp312-win_amd64.whl", hash = "sha256:96b02a3dc4381e5494fad39be677abcb5e6634bf7b4fa83a6dd3112607547001"},
+    {file = "charset_normalizer-3.3.2-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:95f2a5796329323b8f0512e09dbb7a1860c46a39da62ecb2324f116fa8fdc85c"},
+    {file = "charset_normalizer-3.3.2-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c002b4ffc0be611f0d9da932eb0f704fe2602a9a949d1f738e4c34c75b0863d5"},
+    {file = "charset_normalizer-3.3.2-cp37-cp37m-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:a981a536974bbc7a512cf44ed14938cf01030a99e9b3a06dd59578882f06f985"},
+    {file = "charset_normalizer-3.3.2-cp37-cp37m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:3287761bc4ee9e33561a7e058c72ac0938c4f57fe49a09eae428fd88aafe7bb6"},
+    {file = "charset_normalizer-3.3.2-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:42cb296636fcc8b0644486d15c12376cb9fa75443e00fb25de0b8602e64c1714"},
+    {file = "charset_normalizer-3.3.2-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:0a55554a2fa0d408816b3b5cedf0045f4b8e1a6065aec45849de2d6f3f8e9786"},
+    {file = "charset_normalizer-3.3.2-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:c083af607d2515612056a31f0a8d9e0fcb5876b7bfc0abad3ecd275bc4ebc2d5"},
+    {file = "charset_normalizer-3.3.2-cp37-cp37m-musllinux_1_1_i686.whl", hash = "sha256:87d1351268731db79e0f8e745d92493ee2841c974128ef629dc518b937d9194c"},
+    {file = "charset_normalizer-3.3.2-cp37-cp37m-musllinux_1_1_ppc64le.whl", hash = "sha256:bd8f7df7d12c2db9fab40bdd87a7c09b1530128315d047a086fa3ae3435cb3a8"},
+    {file = "charset_normalizer-3.3.2-cp37-cp37m-musllinux_1_1_s390x.whl", hash = "sha256:c180f51afb394e165eafe4ac2936a14bee3eb10debc9d9e4db8958fe36afe711"},
+    {file = "charset_normalizer-3.3.2-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:8c622a5fe39a48f78944a87d4fb8a53ee07344641b0562c540d840748571b811"},
+    {file = "charset_normalizer-3.3.2-cp37-cp37m-win32.whl", hash = "sha256:db364eca23f876da6f9e16c9da0df51aa4f104a972735574842618b8c6d999d4"},
+    {file = "charset_normalizer-3.3.2-cp37-cp37m-win_amd64.whl", hash = "sha256:86216b5cee4b06df986d214f664305142d9c76df9b6512be2738aa72a2048f99"},
+    {file = "charset_normalizer-3.3.2-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:6463effa3186ea09411d50efc7d85360b38d5f09b870c48e4600f63af490e56a"},
+    {file = "charset_normalizer-3.3.2-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:6c4caeef8fa63d06bd437cd4bdcf3ffefe6738fb1b25951440d80dc7df8c03ac"},
+    {file = "charset_normalizer-3.3.2-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:37e55c8e51c236f95b033f6fb391d7d7970ba5fe7ff453dad675e88cf303377a"},
+    {file = "charset_normalizer-3.3.2-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:fb69256e180cb6c8a894fee62b3afebae785babc1ee98b81cdf68bbca1987f33"},
+    {file = "charset_normalizer-3.3.2-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:ae5f4161f18c61806f411a13b0310bea87f987c7d2ecdbdaad0e94eb2e404238"},
+    {file = "charset_normalizer-3.3.2-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:b2b0a0c0517616b6869869f8c581d4eb2dd83a4d79e0ebcb7d373ef9956aeb0a"},
+    {file = "charset_normalizer-3.3.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:45485e01ff4d3630ec0d9617310448a8702f70e9c01906b0d0118bdf9d124cf2"},
+    {file = "charset_normalizer-3.3.2-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:eb00ed941194665c332bf8e078baf037d6c35d7c4f3102ea2d4f16ca94a26dc8"},
+    {file = "charset_normalizer-3.3.2-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:2127566c664442652f024c837091890cb1942c30937add288223dc895793f898"},
+    {file = "charset_normalizer-3.3.2-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:a50aebfa173e157099939b17f18600f72f84eed3049e743b68ad15bd69b6bf99"},
+    {file = "charset_normalizer-3.3.2-cp38-cp38-musllinux_1_1_ppc64le.whl", hash = "sha256:4d0d1650369165a14e14e1e47b372cfcb31d6ab44e6e33cb2d4e57265290044d"},
+    {file = "charset_normalizer-3.3.2-cp38-cp38-musllinux_1_1_s390x.whl", hash = "sha256:923c0c831b7cfcb071580d3f46c4baf50f174be571576556269530f4bbd79d04"},
+    {file = "charset_normalizer-3.3.2-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:06a81e93cd441c56a9b65d8e1d043daeb97a3d0856d177d5c90ba85acb3db087"},
+    {file = "charset_normalizer-3.3.2-cp38-cp38-win32.whl", hash = "sha256:6ef1d82a3af9d3eecdba2321dc1b3c238245d890843e040e41e470ffa64c3e25"},
+    {file = "charset_normalizer-3.3.2-cp38-cp38-win_amd64.whl", hash = "sha256:eb8821e09e916165e160797a6c17edda0679379a4be5c716c260e836e122f54b"},
+    {file = "charset_normalizer-3.3.2-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:c235ebd9baae02f1b77bcea61bce332cb4331dc3617d254df3323aa01ab47bd4"},
+    {file = "charset_normalizer-3.3.2-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:5b4c145409bef602a690e7cfad0a15a55c13320ff7a3ad7ca59c13bb8ba4d45d"},
+    {file = "charset_normalizer-3.3.2-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:68d1f8a9e9e37c1223b656399be5d6b448dea850bed7d0f87a8311f1ff3dabb0"},
+    {file = "charset_normalizer-3.3.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:22afcb9f253dac0696b5a4be4a1c0f8762f8239e21b99680099abd9b2b1b2269"},
+    {file = "charset_normalizer-3.3.2-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:e27ad930a842b4c5eb8ac0016b0a54f5aebbe679340c26101df33424142c143c"},
+    {file = "charset_normalizer-3.3.2-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:1f79682fbe303db92bc2b1136016a38a42e835d932bab5b3b1bfcfbf0640e519"},
+    {file = "charset_normalizer-3.3.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b261ccdec7821281dade748d088bb6e9b69e6d15b30652b74cbbac25e280b796"},
+    {file = "charset_normalizer-3.3.2-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:122c7fa62b130ed55f8f285bfd56d5f4b4a5b503609d181f9ad85e55c89f4185"},
+    {file = "charset_normalizer-3.3.2-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:d0eccceffcb53201b5bfebb52600a5fb483a20b61da9dbc885f8b103cbe7598c"},
+    {file = "charset_normalizer-3.3.2-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:9f96df6923e21816da7e0ad3fd47dd8f94b2a5ce594e00677c0013018b813458"},
+    {file = "charset_normalizer-3.3.2-cp39-cp39-musllinux_1_1_ppc64le.whl", hash = "sha256:7f04c839ed0b6b98b1a7501a002144b76c18fb1c1850c8b98d458ac269e26ed2"},
+    {file = "charset_normalizer-3.3.2-cp39-cp39-musllinux_1_1_s390x.whl", hash = "sha256:34d1c8da1e78d2e001f363791c98a272bb734000fcef47a491c1e3b0505657a8"},
+    {file = "charset_normalizer-3.3.2-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:ff8fa367d09b717b2a17a052544193ad76cd49979c805768879cb63d9ca50561"},
+    {file = "charset_normalizer-3.3.2-cp39-cp39-win32.whl", hash = "sha256:aed38f6e4fb3f5d6bf81bfa990a07806be9d83cf7bacef998ab1a9bd660a581f"},
+    {file = "charset_normalizer-3.3.2-cp39-cp39-win_amd64.whl", hash = "sha256:b01b88d45a6fcb69667cd6d2f7a9aeb4bf53760d7fc536bf679ec94fe9f3ff3d"},
+    {file = "charset_normalizer-3.3.2-py3-none-any.whl", hash = "sha256:3e4d1f6587322d2788836a99c69062fbb091331ec940e02d12d179c1d53e25fc"},
+]
+
+[[package]]
+name = "colorama"
+version = "0.4.6"
+description = "Cross-platform colored terminal text."
+optional = false
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7"
+files = [
+    {file = "colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6"},
+    {file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"},
+]
+
+[[package]]
+name = "dm-control"
+version = "1.0.14"
+description = "Continuous control environments and MuJoCo Python bindings."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "dm_control-1.0.14-py3-none-any.whl", hash = "sha256:883c63244a7ebf598700a97564ed19fffd3479ca79efd090aed881609cdb9fc6"},
+    {file = "dm_control-1.0.14.tar.gz", hash = "sha256:def1ece747b6f175c581150826b50f1a6134086dab34f8f3fd2d088ea035cf3d"},
+]
+
+[package.dependencies]
+absl-py = ">=0.7.0"
+dm-env = "*"
+dm-tree = "!=0.1.2"
+glfw = "*"
+labmaze = "*"
+lxml = "*"
+mujoco = ">=2.3.7"
+numpy = ">=1.9.0"
+protobuf = ">=3.19.4"
+pyopengl = ">=3.1.4"
+pyparsing = ">=3.0.0"
+requests = "*"
+scipy = "*"
+setuptools = "!=50.0.0"
+tqdm = "*"
+
+[package.extras]
+hdf5 = ["h5py"]
+
+[[package]]
+name = "dm-env"
+version = "1.6"
+description = "A Python interface for Reinforcement Learning environments."
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "dm-env-1.6.tar.gz", hash = "sha256:a436eb1c654c39e0c986a516cee218bea7140b510fceff63f97eb4fcff3d93de"},
+    {file = "dm_env-1.6-py3-none-any.whl", hash = "sha256:0eabb6759dd453b625e041032f7ae0c1e87d4eb61b6a96b9ca586483837abf29"},
+]
+
+[package.dependencies]
+absl-py = "*"
+dm-tree = "*"
+numpy = "*"
+
+[[package]]
+name = "dm-tree"
+version = "0.1.8"
+description = "Tree is a library for working with nested data structures."
+optional = false
+python-versions = "*"
+files = [
+    {file = "dm-tree-0.1.8.tar.gz", hash = "sha256:0fcaabbb14e7980377439e7140bd05552739ca5e515ecb3119f234acee4b9430"},
+    {file = "dm_tree-0.1.8-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:35cc164a79336bfcfafb47e5f297898359123bbd3330c1967f0c4994f9cf9f60"},
+    {file = "dm_tree-0.1.8-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:39070ba268c0491af9fe7a58644d99e8b4f2cde6e5884ba3380bddc84ed43d5f"},
+    {file = "dm_tree-0.1.8-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:2869228d9c619074de501a3c10dc7f07c75422f8fab36ecdcb859b6f1b1ec3ef"},
+    {file = "dm_tree-0.1.8-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d20f2faa3672b52e5013f4077117bfb99c4cfc0b445d3bde1584c34032b57436"},
+    {file = "dm_tree-0.1.8-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:5483dca4d7eb1a0d65fe86d3b6a53ae717face83c1f17e0887b1a4a64ae5c410"},
+    {file = "dm_tree-0.1.8-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:1d7c26e431fc93cc7e0cba867eb000db6a05f6f2b25af11ac4e9dada88fc5bca"},
+    {file = "dm_tree-0.1.8-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e4d714371bb08839e4e5e29024fc95832d9affe129825ef38836b143028bd144"},
+    {file = "dm_tree-0.1.8-cp310-cp310-win_amd64.whl", hash = "sha256:d40fa4106ca6edc66760246a08f500ec0c85ef55c762fb4a363f6ee739ba02ee"},
+    {file = "dm_tree-0.1.8-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:ad16ceba90a56ec47cf45b21856d14962ac314787975ef786efb5e6e9ca75ec7"},
+    {file = "dm_tree-0.1.8-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:803bfc53b4659f447ac694dbd04235f94a73ef7c1fd1e0df7c84ac41e0bc963b"},
+    {file = "dm_tree-0.1.8-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:378cc8ad93c5fe3590f405a309980721f021c790ca1bdf9b15bb1d59daec57f5"},
+    {file = "dm_tree-0.1.8-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1607ce49aa42f010d1e5e616d92ce899d66835d4d8bea49679582435285515de"},
+    {file = "dm_tree-0.1.8-cp311-cp311-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:343a4a4ebaa127451ff971254a4be4084eb4bdc0b2513c32b46f6f728fd03f9e"},
+    {file = "dm_tree-0.1.8-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:fa42a605d099ee7d41ba2b5fb75e21423951fd26e5d50583a00471238fb3021d"},
+    {file = "dm_tree-0.1.8-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:83b7764de0d855338abefc6e3ee9fe40d301668310aa3baea3f778ff051f4393"},
+    {file = "dm_tree-0.1.8-cp311-cp311-win_amd64.whl", hash = "sha256:a5d819c38c03f0bb5b3b3703c60e4b170355a0fc6b5819325bf3d4ceb3ae7e80"},
+    {file = "dm_tree-0.1.8-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:ea9e59e0451e7d29aece402d9f908f2e2a80922bcde2ebfd5dcb07750fcbfee8"},
+    {file = "dm_tree-0.1.8-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:94d3f0826311f45ee19b75f5b48c99466e4218a0489e81c0f0167bda50cacf22"},
+    {file = "dm_tree-0.1.8-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:435227cf3c5dc63f4de054cf3d00183790bd9ead4c3623138c74dde7f67f521b"},
+    {file = "dm_tree-0.1.8-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:09964470f76a5201aff2e8f9b26842976de7889300676f927930f6285e256760"},
+    {file = "dm_tree-0.1.8-cp312-cp312-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:75c5d528bb992981c20793b6b453e91560784215dffb8a5440ba999753c14ceb"},
+    {file = "dm_tree-0.1.8-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c0a94aba18a35457a1b5cd716fd7b46c5dafdc4cf7869b4bae665b91c4682a8e"},
+    {file = "dm_tree-0.1.8-cp312-cp312-win_amd64.whl", hash = "sha256:96a548a406a6fb15fe58f6a30a57ff2f2aafbf25f05afab00c8f5e5977b6c715"},
+    {file = "dm_tree-0.1.8-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:8c60a7eadab64c2278861f56bca320b2720f163dca9d7558103c3b77f2416571"},
+    {file = "dm_tree-0.1.8-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:af4b3d372f2477dcd89a6e717e4a575ca35ccc20cc4454a8a4b6f8838a00672d"},
+    {file = "dm_tree-0.1.8-cp37-cp37m-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:de287fabc464b8734be251e46e06aa9aa1001f34198da2b6ce07bd197172b9cb"},
+    {file = "dm_tree-0.1.8-cp37-cp37m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:054b461f8176f4bce7a21f7b1870f873a1ced3bdbe1282c816c550bb43c71fa6"},
+    {file = "dm_tree-0.1.8-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2f7915660f59c09068e428613c480150180df1060561fd0d1470684ae7007bd1"},
+    {file = "dm_tree-0.1.8-cp37-cp37m-win_amd64.whl", hash = "sha256:b9f89a454e98806b44fe9d40ec9eee61f848388f7e79ac2371a55679bd5a3ac6"},
+    {file = "dm_tree-0.1.8-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:0e9620ccf06393eb6b613b5e366469304622d4ea96ae6540b28a33840e6c89cf"},
+    {file = "dm_tree-0.1.8-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:b095ba4f8ca1ba19350fd53cf1f8f3eb0bd406aa28af64a6dfc86707b32a810a"},
+    {file = "dm_tree-0.1.8-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:b9bd9b9ccb59409d33d51d84b7668010c04c2af7d4a371632874c1ca356cff3d"},
+    {file = "dm_tree-0.1.8-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0d3172394079a86c3a759179c65f64c48d1a42b89495fcf38976d11cc3bb952c"},
+    {file = "dm_tree-0.1.8-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:d1612fcaecd79023dbc6a6ae48d51a80beb5c385d6f3f6d71688e57bc8d07de8"},
+    {file = "dm_tree-0.1.8-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:c5c8c12e3fda754ef6af94161bacdaeda816d941995fac415d6855c6c386af68"},
+    {file = "dm_tree-0.1.8-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:694c3654cfd2a81552c08ec66bb5c4a3d48fa292b9a181880fb081c36c5b9134"},
+    {file = "dm_tree-0.1.8-cp38-cp38-win_amd64.whl", hash = "sha256:bb2d109f42190225112da899b9f3d46d0d5f26aef501c61e43529fe9322530b5"},
+    {file = "dm_tree-0.1.8-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:d16e1f2a073604cfcc09f7131ae8d534674f43c3aef4c25742eae295bc60d04f"},
+    {file = "dm_tree-0.1.8-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:250b692fb75f45f02e2f58fbef9ab338904ef334b90557565621fa251df267cf"},
+    {file = "dm_tree-0.1.8-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:81fce77f22a302d7a5968aebdf4efafef4def7ce96528719a354e6990dcd49c7"},
+    {file = "dm_tree-0.1.8-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f7ac31b9aecccb2c6e1ab29706f6ded3eba0c2c69c770322c9c685929c3d6afb"},
+    {file = "dm_tree-0.1.8-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:1fe962015b2fe1282892b28ebe962faed53c7f98d942da9a4625cbf27baef913"},
+    {file = "dm_tree-0.1.8-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:28c52cbf4f8b3dbd0beaedf44f69fa85eec5e9dede612e08035e06ada6ec9426"},
+    {file = "dm_tree-0.1.8-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:181c35521d480d0365f39300542cb6cd7fd2b77351bb43d7acfda15aef63b317"},
+    {file = "dm_tree-0.1.8-cp39-cp39-win_amd64.whl", hash = "sha256:8ed3564abed97c806db122c2d3e1a2b64c74a63debe9903aad795167cc301368"},
+]
+
+[[package]]
+name = "etils"
+version = "1.7.0"
+description = "Collection of common python utils"
+optional = false
+python-versions = ">=3.10"
+files = [
+    {file = "etils-1.7.0-py3-none-any.whl", hash = "sha256:61af8f7c242171de15e22e5da02d527cb9e677d11f8bcafe18fcc3548eee3e60"},
+    {file = "etils-1.7.0.tar.gz", hash = "sha256:97b68fd25e185683215286ef3a54e38199b6245f5fe8be6bedc1189be4256350"},
+]
+
+[package.dependencies]
+fsspec = {version = "*", optional = true, markers = "extra == \"epath\""}
+importlib_resources = {version = "*", optional = true, markers = "extra == \"epath\""}
+typing_extensions = {version = "*", optional = true, markers = "extra == \"epy\""}
+zipp = {version = "*", optional = true, markers = "extra == \"epath\""}
+
+[package.extras]
+all = ["etils[array-types]", "etils[eapp]", "etils[ecolab]", "etils[edc]", "etils[enp]", "etils[epath-gcs]", "etils[epath-s3]", "etils[epath]", "etils[epy]", "etils[etqdm]", "etils[etree-dm]", "etils[etree-jax]", "etils[etree-tf]", "etils[etree]"]
+array-types = ["etils[enp]"]
+dev = ["chex", "dataclass_array", "optree", "pyink", "pylint (>=2.6.0)", "pytest", "pytest-subtests", "pytest-xdist", "torch"]
+docs = ["etils[all,dev]", "sphinx-apitree[ext]"]
+eapp = ["absl-py", "etils[epy]", "simple_parsing"]
+ecolab = ["etils[enp]", "etils[epy]", "etils[etree]", "jupyter", "mediapy", "numpy", "packaging", "protobuf"]
+edc = ["etils[epy]"]
+enp = ["etils[epy]", "numpy"]
+epath = ["etils[epy]", "fsspec", "importlib_resources", "typing_extensions", "zipp"]
+epath-gcs = ["etils[epath]", "gcsfs"]
+epath-s3 = ["etils[epath]", "s3fs"]
+epy = ["typing_extensions"]
+etqdm = ["absl-py", "etils[epy]", "tqdm"]
+etree = ["etils[array-types]", "etils[enp]", "etils[epy]", "etils[etqdm]"]
+etree-dm = ["dm-tree", "etils[etree]"]
+etree-jax = ["etils[etree]", "jax[cpu]"]
+etree-tf = ["etils[etree]", "tensorflow"]
+lazy-imports = ["etils[ecolab]"]
+
+[[package]]
+name = "fsspec"
+version = "2024.3.1"
+description = "File-system specification"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "fsspec-2024.3.1-py3-none-any.whl", hash = "sha256:918d18d41bf73f0e2b261824baeb1b124bcf771767e3a26425cd7dec3332f512"},
+    {file = "fsspec-2024.3.1.tar.gz", hash = "sha256:f39780e282d7d117ffb42bb96992f8a90795e4d0fb0f661a70ca39fe9c43ded9"},
+]
+
+[package.extras]
+abfs = ["adlfs"]
+adl = ["adlfs"]
+arrow = ["pyarrow (>=1)"]
+dask = ["dask", "distributed"]
+devel = ["pytest", "pytest-cov"]
+dropbox = ["dropbox", "dropboxdrivefs", "requests"]
+full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "dask", "distributed", "dropbox", "dropboxdrivefs", "fusepy", "gcsfs", "libarchive-c", "ocifs", "panel", "paramiko", "pyarrow (>=1)", "pygit2", "requests", "s3fs", "smbprotocol", "tqdm"]
+fuse = ["fusepy"]
+gcs = ["gcsfs"]
+git = ["pygit2"]
+github = ["requests"]
+gs = ["gcsfs"]
+gui = ["panel"]
+hdfs = ["pyarrow (>=1)"]
+http = ["aiohttp (!=4.0.0a0,!=4.0.0a1)"]
+libarchive = ["libarchive-c"]
+oci = ["ocifs"]
+s3 = ["s3fs"]
+sftp = ["paramiko"]
+smb = ["smbprotocol"]
+ssh = ["paramiko"]
+tqdm = ["tqdm"]
+
+[[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 = "idna"
+version = "3.6"
+description = "Internationalized Domain Names in Applications (IDNA)"
+optional = false
+python-versions = ">=3.5"
+files = [
+    {file = "idna-3.6-py3-none-any.whl", hash = "sha256:c05567e9c24a6b9faaa835c4821bad0590fbb9d5779e7caa6e1cc4978e7eb24f"},
+    {file = "idna-3.6.tar.gz", hash = "sha256:9ecdbbd083b06798ae1e86adcbfe8ab1479cf864e4ee30fe4e46a003d12491ca"},
+]
+
+[[package]]
+name = "importlib-resources"
+version = "6.4.0"
+description = "Read resources from Python packages"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "importlib_resources-6.4.0-py3-none-any.whl", hash = "sha256:50d10f043df931902d4194ea07ec57960f66a80449ff867bfe782b4c486ba78c"},
+    {file = "importlib_resources-6.4.0.tar.gz", hash = "sha256:cdb2b453b8046ca4e3798eb1d84f3cce1446a0e8e7b5ef4efb600f19fc398145"},
+]
+
+[package.extras]
+docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"]
+testing = ["jaraco.test (>=5.4)", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-ruff (>=0.2.1)", "zipp (>=3.17)"]
+
+[[package]]
+name = "labmaze"
+version = "1.0.6"
+description = "LabMaze: DeepMind Lab's text maze generator."
+optional = false
+python-versions = "*"
+files = [
+    {file = "labmaze-1.0.6-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:b2ddef976dfd8d992b19cfa6c633f2eba7576d759c2082da534e3f727479a84a"},
+    {file = "labmaze-1.0.6-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:157efaa93228c8ccce5cae337902dd652093e0fba9d3a0f6506e4bee272bb66f"},
+    {file = "labmaze-1.0.6-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b3ce98b9541c5fe6a306e411e7d018121dd646f2c9978d763fad86f9f30c5f57"},
+    {file = "labmaze-1.0.6-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4e6433bd49bc541791de8191040526fddfebb77151620eb04203453f43ee486a"},
+    {file = "labmaze-1.0.6-cp310-cp310-win_amd64.whl", hash = "sha256:6a507fc35961f1b1479708e2716f65e0d0611cefb55f31a77be29ce2339b6fef"},
+    {file = "labmaze-1.0.6-cp311-cp311-macosx_10_12_x86_64.whl", hash = "sha256:a0c2cb9dec971814ea9c5d7150af15fa3964482131fa969e0afb94bd224348af"},
+    {file = "labmaze-1.0.6-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:2c6ba9538d819543f4be448d36b4926a3881e53646a2b331ebb5a1f353047d05"},
+    {file = "labmaze-1.0.6-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:70635d1cdb0147a02efb6b3f607a52cdc51723bc3dcc42717a0d4ef55fa0a987"},
+    {file = "labmaze-1.0.6-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ff472793238bd9b6dabea8094594d6074ad3c111455de3afcae72f6c40c6817e"},
+    {file = "labmaze-1.0.6-cp311-cp311-win_amd64.whl", hash = "sha256:2317e65e12fa3d1abecda7e0488dab15456cee8a2e717a586bfc8f02a91579e7"},
+    {file = "labmaze-1.0.6-cp312-cp312-macosx_10_12_x86_64.whl", hash = "sha256:e36b6fadcd78f22057b597c1c77823e806a0987b3bdfbf850e14b6b5b502075e"},
+    {file = "labmaze-1.0.6-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:d1a4f8de29c2c3d7f14163759b69cd3f237093b85334c983619c1db5403a223b"},
+    {file = "labmaze-1.0.6-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a394f8bb857fcaa2884b809d63e750841c2662a106cfe8c045f2112d201ac7d5"},
+    {file = "labmaze-1.0.6-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0d17abb69d4dfc56183afb5c317e8b2eaca0587abb3aabd2326efd3143c81f4e"},
+    {file = "labmaze-1.0.6-cp312-cp312-win_amd64.whl", hash = "sha256:5af997598cc46b1929d1c5a1febc32fd56c75874fe481a2a5982c65cee8450c9"},
+    {file = "labmaze-1.0.6-cp37-cp37m-macosx_10_12_x86_64.whl", hash = "sha256:a4c5bc6e56baa55ce63b97569afec2f80cab0f6b952752a131e1f83eed190a53"},
+    {file = "labmaze-1.0.6-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3955f24fe5f708e1e97495b4cfe284b70ae4fd51be5e17b75a6fc04ffbd67bca"},
+    {file = "labmaze-1.0.6-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ed96ddc0bb8d66df36428c94db83949fd84a15867e8250763a4c5e3d82104c54"},
+    {file = "labmaze-1.0.6-cp37-cp37m-win_amd64.whl", hash = "sha256:3bd0458a29e55aa09f146e28a168d2e00b8ccf19e2259a3f71154cfff3536b1d"},
+    {file = "labmaze-1.0.6-cp38-cp38-macosx_10_12_x86_64.whl", hash = "sha256:33f5154edc83dff55a150e54b60c8582fdafc7ec45195049809cbcc01f5e8f34"},
+    {file = "labmaze-1.0.6-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:0971055ef2a5f7d8517fdc42b67c057093698f1eb911f46faa7018867b73fcc9"},
+    {file = "labmaze-1.0.6-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:de18d09680007302abf49111f3fe822d8435e4fbc4468b9ec07d50a78e267865"},
+    {file = "labmaze-1.0.6-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f18126066db2218a52853c7dd490b4c3d8129fc22eb3a47eb23007524b911d53"},
+    {file = "labmaze-1.0.6-cp38-cp38-win_amd64.whl", hash = "sha256:f9aef09a76877342bb4d634b7e05f43b038a49c4f34adfb8f1b8ac57c29472f2"},
+    {file = "labmaze-1.0.6-cp39-cp39-macosx_10_12_x86_64.whl", hash = "sha256:5dd28899418f1b8b1c7d1e1b40a4593150a7cfa95ca91e23860b9785b82cc0ee"},
+    {file = "labmaze-1.0.6-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:965569f37ee33090b4d4b3aa5aa7c9dcc4f62e2ae5d761e7f73ec76fc9d8aa96"},
+    {file = "labmaze-1.0.6-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:05eccfa98c0e781bc9f939076ae600b2e25ca736e123f2a530606aedec3b531c"},
+    {file = "labmaze-1.0.6-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:bee8c94e0fb3fc2d8180214947245c1d74a3489349a9da90b868296e77a521e9"},
+    {file = "labmaze-1.0.6-cp39-cp39-win_amd64.whl", hash = "sha256:d486e9ca3a335ad628e3bd48a09c42f1aa5f51040952ef0fe32507afedcd694b"},
+    {file = "labmaze-1.0.6.tar.gz", hash = "sha256:2e8de7094042a77d6972f1965cf5c9e8f971f1b34d225752f343190a825ebe73"},
+]
+
+[package.dependencies]
+absl-py = "*"
+numpy = ">=1.8.0"
+setuptools = "!=50.0.0"
+
+[[package]]
+name = "lxml"
+version = "5.1.1"
+description = "Powerful and Pythonic XML processing library combining libxml2/libxslt with the ElementTree API."
+optional = false
+python-versions = ">=3.6"
+files = [
+    {file = "lxml-5.1.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:9cc30dc3c49ea914fa62ea73b57198b541cf2cd522fcf2b9559f99a24df769bb"},
+    {file = "lxml-5.1.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:f1d0824e3ddb969fe1337b1bc45cf0bec8095b342f36903f41a74b7769cc8c73"},
+    {file = "lxml-5.1.1-cp310-cp310-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:4958c378d9387c45ef8c4859495cf6be76f863e4e3b31494f6ec7f2c48d3b8e3"},
+    {file = "lxml-5.1.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:aff34295a6c87638a1f1905355cf3a97e4026c45c0cf3bb6ed6bc35b885b4a33"},
+    {file = "lxml-5.1.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b174885fd2cabd1ad48585296f495e25d607f02db99668c08b2afaceb668e21b"},
+    {file = "lxml-5.1.1-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:1b0611bba10d6f5467b86673e8f6bba4de0d00f7d111eea843bc872abfe11b5c"},
+    {file = "lxml-5.1.1-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:adff469b7dbfe9f3babc9e4479449ee97085ba70ac492fbe5f0f7217940c6731"},
+    {file = "lxml-5.1.1-cp310-cp310-win32.whl", hash = "sha256:99bcdf665576a26b44c7ce767d76b769a4418b0a13cda8300b26fb7b2647bd5b"},
+    {file = "lxml-5.1.1-cp310-cp310-win_amd64.whl", hash = "sha256:3da8db291568c27b2bb248dcfc8838ca3149f373a24e204bcd1c2c89e2813d14"},
+    {file = "lxml-5.1.1-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:906966babd374fdfe46e130fc656488003f0d0d63b7cba612aa5a796c8804283"},
+    {file = "lxml-5.1.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:9c03f3715c68fc707d9383d56e482d95d198ba07cb3dad4aee9e5a5ca06b2536"},
+    {file = "lxml-5.1.1-cp311-cp311-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:d26243d994d4077a50056e9008848e5b421be0c6f0fd4e932a9463e1d89fc42b"},
+    {file = "lxml-5.1.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2de00750318ae6869b9dfa6429a4f82b8ecad043049414547474d09db549c2ee"},
+    {file = "lxml-5.1.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:29b2771b4eec4e85063f10294facdd9829d010e6cc9668040d0cf936dc56733a"},
+    {file = "lxml-5.1.1-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:d9358f7268c161dc0a1c3216018f26c04954b5dd47ba6dead79da6598f4725d4"},
+    {file = "lxml-5.1.1-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:8a943826e7a9254eed661a7134fcde3c832a9fecd989d0f47c6e08c7b769cb2c"},
+    {file = "lxml-5.1.1-cp311-cp311-win32.whl", hash = "sha256:74d0967c6f91eec6fe91159f9e8ccb3720fa0fbf9f462109c7bef62550df397c"},
+    {file = "lxml-5.1.1-cp311-cp311-win_amd64.whl", hash = "sha256:26974096654241df08a30dc2eb0e139c1ad5653660aa4b2ced66000230e96c14"},
+    {file = "lxml-5.1.1-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:55e13a19829dcdbf0c5233062977aeb6daf72e65124909128045976f659164e8"},
+    {file = "lxml-5.1.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:adedfb61be862f48907218e3a24bf051fd2ecca53358f3958b0bdb17d7881c20"},
+    {file = "lxml-5.1.1-cp312-cp312-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:77425482e4311d1cff119a2b5ab26c52ec209d2a3d728a54db3223ab91995e20"},
+    {file = "lxml-5.1.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1d380f183bd03ab827899753ea96dabe27d2025eb0bfd4f2ac0eee4afa0f351d"},
+    {file = "lxml-5.1.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f8682af96b5ad5093aab9eee5e4ff24cb7a9796c78699d914dd456ebfe7484a6"},
+    {file = "lxml-5.1.1-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:68eed33377a9925aed7ba56c8611d50aaa1e45638c07a92b4b4b0a0436cc2dd2"},
+    {file = "lxml-5.1.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:c7c1d2f6e9c7a1c4478146ee38d16dbe0eb3be998424bc0f01346c671c38b86d"},
+    {file = "lxml-5.1.1-cp312-cp312-win32.whl", hash = "sha256:81107c8de3e463052ae8fd05fd31b97c371c7a9ce4a189b8bb5f45b0b3545fb9"},
+    {file = "lxml-5.1.1-cp312-cp312-win_amd64.whl", hash = "sha256:0e46181d15fae102c53621bed9356b7a599a1e837b978c934a350dd00842b1d9"},
+    {file = "lxml-5.1.1-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:48dd28b9f410329de709a4bb6169c58f2cd8bff25f5a48d647678ec9b8a40c65"},
+    {file = "lxml-5.1.1-cp36-cp36m-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:bf7e57dbe7b3c605e63849d9c8dae246a6ab9002223c57cd3f3dec7c3a0a8e6d"},
+    {file = "lxml-5.1.1-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5020b3081030b5cfc8149eee231167aea4ff68df73a610e1d542809e1f11fde7"},
+    {file = "lxml-5.1.1-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:77842b79b63c83c04dcfe2f045c78e15e4d97c86838eabd2e6518c1ed97e3900"},
+    {file = "lxml-5.1.1-cp36-cp36m-musllinux_1_1_aarch64.whl", hash = "sha256:666432274881cb2535e71dbe745e08ef10fe25c81fbb1a6b1e3c973177823b0c"},
+    {file = "lxml-5.1.1-cp36-cp36m-musllinux_1_1_x86_64.whl", hash = "sha256:a103426e809640a2d985062d2f4b28db2f0fe4469ff72a67cb31fa70613158f1"},
+    {file = "lxml-5.1.1-cp36-cp36m-win32.whl", hash = "sha256:95a51324a55000c55f4ab79e1f7f1e0bc42b7a24e39633f79542753023a9d4b7"},
+    {file = "lxml-5.1.1-cp36-cp36m-win_amd64.whl", hash = "sha256:bd46b5b19ac969de8e87fb3d04414641d12ee489e2ea6cc75344087829b31c63"},
+    {file = "lxml-5.1.1-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:59ca75cfcf646ff64aa19ca4e7fd2a0fde77268d5a87856525d9e0b69b77d0c4"},
+    {file = "lxml-5.1.1-cp37-cp37m-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:d55ddc73dec971277b181a6d1a6abdd34f50e4511e1e60f6b4ebe22cbaad05bb"},
+    {file = "lxml-5.1.1-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:56f1e813ff660d031c77edba90a068d57e47ae93a9e811330fc88946fa68af9a"},
+    {file = "lxml-5.1.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:43f21b5929185fa4560836942020bb00a0fcdec9f67be98cac1a4b99501757c1"},
+    {file = "lxml-5.1.1-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:1528b37e83c3aeecb438e76e5be6279b353275560125a9c3f4d74642c5f110f9"},
+    {file = "lxml-5.1.1-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:2992480a25434d2df31413136ef87effab14d43b07f1f54c5012c4f6c7530144"},
+    {file = "lxml-5.1.1-cp37-cp37m-win32.whl", hash = "sha256:1d0270d33fbde6e1c6758ff58e2e284144f5331aa05dfe7f44ceafdf4e9d31aa"},
+    {file = "lxml-5.1.1-cp37-cp37m-win_amd64.whl", hash = "sha256:dec3491aa69a91ed07f5e6bc033e2b1a9424447ad5312ee69ac973e94d79083a"},
+    {file = "lxml-5.1.1-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:87b67d8620c2725d666e5d88ddba56bcdb1f52211a2e7d22f951b67c35f7f627"},
+    {file = "lxml-5.1.1-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:5bd2595ebe95214446e00a1ab94571f778b126e17736ea222c07505c4e092289"},
+    {file = "lxml-5.1.1-cp38-cp38-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:bfbdadc3cfe552331ecb0bbdcabf148d1697c73aa4321151e0e6c1704eeb76a7"},
+    {file = "lxml-5.1.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:52358249292bc155af681a9240ec3d944c1195f0124aa10ec4e3635adc1e10a1"},
+    {file = "lxml-5.1.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:036b36c48cd775e4fd2084b34ae62ffeefa7a01f955f5a5b816f9257c308cfc0"},
+    {file = "lxml-5.1.1-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:f05ab8cea65363d0cc7ce818f42407504b6d94ca885b4cde0270f021e2f4ef61"},
+    {file = "lxml-5.1.1-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:d94a28c16cc430b68c374b37b8bb536ba5f0a4a080be0e1daa8310c44a00a75c"},
+    {file = "lxml-5.1.1-cp38-cp38-win32.whl", hash = "sha256:9113fe65a62f834b8e994c8f48e7b2179bf81878c0ec80ad7feba51ab9417663"},
+    {file = "lxml-5.1.1-cp38-cp38-win_amd64.whl", hash = "sha256:acff17e0cd5344677757a152631d8411efac6a84e4476d60123a9b33f5d6c511"},
+    {file = "lxml-5.1.1-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:a94a97380ad689d751eb0a1e1ccd2a0622c5141771a31abe9a16075f80027e95"},
+    {file = "lxml-5.1.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:d1f4d37b3f8d2d44493edce3d65ac987127bababd8ae208a6f0d7d260852346e"},
+    {file = "lxml-5.1.1-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:3c5940f188189956ccb3d1adb413001ada79f2d2b81087d2612a0cc4a1197eed"},
+    {file = "lxml-5.1.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:50007f4e94dc4e38030487a8b6c4af87a2d51ed059c7b74b29e3dd937cb1dfe1"},
+    {file = "lxml-5.1.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a305d0469177fd78a0a9aa2231c60218266bb85d4b7955f9b67dab628c9267fd"},
+    {file = "lxml-5.1.1-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:13b73d78a8023203722cf98e9ea0b222da83110d1d5ef437ef8782a7755b4586"},
+    {file = "lxml-5.1.1-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:bc6904519dd1f92eb82f7d49814a33bbc444d0b66b1438e76daf3f79ef4aa38f"},
+    {file = "lxml-5.1.1-cp39-cp39-win32.whl", hash = "sha256:04ef231dde88294a5499f61a74cdc42af97d8d5ecec1b0a645d1c7d436942789"},
+    {file = "lxml-5.1.1-cp39-cp39-win_amd64.whl", hash = "sha256:071e5123d1eca861708c4be5b54e4d88923fa33fab3aa02722e907518b07071c"},
+    {file = "lxml-5.1.1-pp310-pypy310_pp73-macosx_10_9_x86_64.whl", hash = "sha256:19c6bc7476eeac4598ff925ae98597610109e21af4cd7ab1e060efcfc4b1c6e2"},
+    {file = "lxml-5.1.1-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:20cd17eb21f5ae54da96791c49e1fbd3327bf66b2c00556cdf8d0552c2270f92"},
+    {file = "lxml-5.1.1-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:a02ed1ebc469734dbfed5b688f709334de19e7a333cba7ae187b17d2b2c1d4ff"},
+    {file = "lxml-5.1.1-pp37-pypy37_pp73-macosx_10_9_x86_64.whl", hash = "sha256:161838cb95c97e8d76d01e544a3570b52ab6b863f4897a90e1f073bb110a75ba"},
+    {file = "lxml-5.1.1-pp37-pypy37_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d1abbf2249467a37da45fb2d7ff37e578dfc9813f142800e58db9da761cb7899"},
+    {file = "lxml-5.1.1-pp37-pypy37_pp73-win_amd64.whl", hash = "sha256:6c49eb5deaed1990fde5b5d80d6800aec1b5fd6113346b5f11068d988f68f2c4"},
+    {file = "lxml-5.1.1-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:60ceffdca5d637fe8ee95c7f06733a6c9646e07da80997efe3af2d4b4f366e36"},
+    {file = "lxml-5.1.1-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a76a7b35e7660c74eb3f943c19f5f78c882dceab890cf8017027b6100b79ad8e"},
+    {file = "lxml-5.1.1-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:5dcb373720b70aa05419e508265dd86f06886ca0388967f6f024fbc4d551379f"},
+    {file = "lxml-5.1.1-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:3641bc124b037921de4220538a5ebb52354fd2799fc2bbfb335d28096063c7d6"},
+    {file = "lxml-5.1.1-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3a6e9b34f59c9755aa279c652e1c48c333c665d05a88afcd8e5ff0bde86f3b14"},
+    {file = "lxml-5.1.1-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:318847c165063549c8fda6b162a0d068689b10deb825cb3859caef69fddaaaff"},
+    {file = "lxml-5.1.1.tar.gz", hash = "sha256:42a8aa957e98bd8b884a8142175ec24ce4ef0a57760e8879f193bfe64b757ca9"},
+]
+
+[package.extras]
+cssselect = ["cssselect (>=0.7)"]
+html5 = ["html5lib"]
+htmlsoup = ["BeautifulSoup4"]
+source = ["Cython (>=3.0.9)"]
+
+[[package]]
+name = "mujoco"
+version = "3.1.3"
+description = "MuJoCo Physics Simulator"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "mujoco-3.1.3-cp310-cp310-macosx_10_16_x86_64.whl", hash = "sha256:1a07e33443ca88c77128336e550502c58721e37b3830af29f0118311c17d826e"},
+    {file = "mujoco-3.1.3-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:da442c45fa08cf7f307a6f2484ff382b90714b9f52aaceffd5fcb8536dbdc11c"},
+    {file = "mujoco-3.1.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ec08dbfddef6e4c6d7b03685b929ed134e8eb9d0dbc788752ff54216b7b3544e"},
+    {file = "mujoco-3.1.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f1578279ff581ed1c70893cc16ecf48a048a14568e9e64b446a2d32c22b1154c"},
+    {file = "mujoco-3.1.3-cp310-cp310-win_amd64.whl", hash = "sha256:9a359e7787e1d0bbdb9fafeb31df61261a4cdc42d0a5d77c91fbe57c63e4c6fd"},
+    {file = "mujoco-3.1.3-cp311-cp311-macosx_10_16_x86_64.whl", hash = "sha256:b070805d65ee6b708ddf1a16a16fc2073ce2d1eea8ea26352b8aee4071de274c"},
+    {file = "mujoco-3.1.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:d789a95150cf1bef21e3a3431c26263730b0437ec3b4794b2eed0f900185746e"},
+    {file = "mujoco-3.1.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6661aa27c81be338ce0973ba6e83f655ff3cc023ea9d62398f130b46478f708a"},
+    {file = "mujoco-3.1.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f79dc6134c90a7274d2663c07bea6d45629ea52ce40bf6722c5d506df909b4b9"},
+    {file = "mujoco-3.1.3-cp311-cp311-win_amd64.whl", hash = "sha256:b1df674d9486e1bd2e93fb69009d8db4adcf4b3b7edc92da5c98d1c6a2ea7a28"},
+    {file = "mujoco-3.1.3-cp312-cp312-macosx_10_16_x86_64.whl", hash = "sha256:51841750310a1c4b5e7c7f19d28fe5e3deea0e2c7cc60ebab33c2f07360b1700"},
+    {file = "mujoco-3.1.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:fe4318af5b14ea39bc5b8892c69797a1a9deb02199178814be16abb5611308fb"},
+    {file = "mujoco-3.1.3-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:079f293a56c2b3aa6b4101c3822ee5587b5cc9bf35028afdd1f2128db102ad20"},
+    {file = "mujoco-3.1.3-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d4c120414bf89a11538e3f5eb1de6bcd6c4aeade9775ecad3e4eea27d88e1492"},
+    {file = "mujoco-3.1.3-cp312-cp312-win_amd64.whl", hash = "sha256:7fc9d69383dc0f7c4b775b2be829a065fb78dca743a25f9d864d52174c916b2b"},
+    {file = "mujoco-3.1.3-cp38-cp38-macosx_10_16_x86_64.whl", hash = "sha256:5a29004079a40d23836228647bae9ea41f77fd7e407e8ad642dc72054e5a099e"},
+    {file = "mujoco-3.1.3-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:c1d1e083d8825faf9e2609d4e749cc5629ed7735374ed68eb3dde63dd0e4fe73"},
+    {file = "mujoco-3.1.3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9cc267cf0de8de3c8b317f7c12b2d7a484a7f462263f8ce4c8ae18e9d6817897"},
+    {file = "mujoco-3.1.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d67a2aae8d5d58f7ac41d0bcffcd745955415887843bc34da8e3d794b46afbae"},
+    {file = "mujoco-3.1.3-cp38-cp38-win_amd64.whl", hash = "sha256:8ddb9a07d5ad59c67f2d7e79568cba27ad68cf2284a68370f2054dce2e6e4128"},
+    {file = "mujoco-3.1.3-cp39-cp39-macosx_10_16_x86_64.whl", hash = "sha256:acdc761e8fa7d4bfb9f262b8886dbb3dd41a957c3ef7ec126aae3342f68b1293"},
+    {file = "mujoco-3.1.3-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:49bad0da02ebf67ab37a6f6fe435dfc6339f0b46b51b452ee79aaffa5b73659b"},
+    {file = "mujoco-3.1.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:daa6a3ca50a3769ebfd59274651d2edc76b177cd950560022120fb77cd51f607"},
+    {file = "mujoco-3.1.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:80f3e99d1f2bb02bf7903ba4fef9c31e05fba439b7292ed751390ec78e1eb890"},
+    {file = "mujoco-3.1.3-cp39-cp39-win_amd64.whl", hash = "sha256:2d2fe38b1a7f64e708e8b9a96cf7677027b33fb6e059184163976c6c03fef4cc"},
+    {file = "mujoco-3.1.3.tar.gz", hash = "sha256:f700d074031060b46111ddb60432d00425f821eeeaf0ccc76ed95d47861bd4de"},
+]
+
+[package.dependencies]
+absl-py = "*"
+etils = {version = "*", extras = ["epath"]}
+glfw = "*"
+numpy = "*"
+pyopengl = "*"
+
+[[package]]
+name = "numpy"
+version = "1.26.4"
+description = "Fundamental package for array computing in Python"
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "numpy-1.26.4-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:9ff0f4f29c51e2803569d7a51c2304de5554655a60c5d776e35b4a41413830d0"},
+    {file = "numpy-1.26.4-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:2e4ee3380d6de9c9ec04745830fd9e2eccb3e6cf790d39d7b98ffd19b0dd754a"},
+    {file = "numpy-1.26.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d209d8969599b27ad20994c8e41936ee0964e6da07478d6c35016bc386b66ad4"},
+    {file = "numpy-1.26.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ffa75af20b44f8dba823498024771d5ac50620e6915abac414251bd971b4529f"},
+    {file = "numpy-1.26.4-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:62b8e4b1e28009ef2846b4c7852046736bab361f7aeadeb6a5b89ebec3c7055a"},
+    {file = "numpy-1.26.4-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:a4abb4f9001ad2858e7ac189089c42178fcce737e4169dc61321660f1a96c7d2"},
+    {file = "numpy-1.26.4-cp310-cp310-win32.whl", hash = "sha256:bfe25acf8b437eb2a8b2d49d443800a5f18508cd811fea3181723922a8a82b07"},
+    {file = "numpy-1.26.4-cp310-cp310-win_amd64.whl", hash = "sha256:b97fe8060236edf3662adfc2c633f56a08ae30560c56310562cb4f95500022d5"},
+    {file = "numpy-1.26.4-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:4c66707fabe114439db9068ee468c26bbdf909cac0fb58686a42a24de1760c71"},
+    {file = "numpy-1.26.4-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:edd8b5fe47dab091176d21bb6de568acdd906d1887a4584a15a9a96a1dca06ef"},
+    {file = "numpy-1.26.4-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7ab55401287bfec946ced39700c053796e7cc0e3acbef09993a9ad2adba6ca6e"},
+    {file = "numpy-1.26.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:666dbfb6ec68962c033a450943ded891bed2d54e6755e35e5835d63f4f6931d5"},
+    {file = "numpy-1.26.4-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:96ff0b2ad353d8f990b63294c8986f1ec3cb19d749234014f4e7eb0112ceba5a"},
+    {file = "numpy-1.26.4-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:60dedbb91afcbfdc9bc0b1f3f402804070deed7392c23eb7a7f07fa857868e8a"},
+    {file = "numpy-1.26.4-cp311-cp311-win32.whl", hash = "sha256:1af303d6b2210eb850fcf03064d364652b7120803a0b872f5211f5234b399f20"},
+    {file = "numpy-1.26.4-cp311-cp311-win_amd64.whl", hash = "sha256:cd25bcecc4974d09257ffcd1f098ee778f7834c3ad767fe5db785be9a4aa9cb2"},
+    {file = "numpy-1.26.4-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:b3ce300f3644fb06443ee2222c2201dd3a89ea6040541412b8fa189341847218"},
+    {file = "numpy-1.26.4-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:03a8c78d01d9781b28a6989f6fa1bb2c4f2d51201cf99d3dd875df6fbd96b23b"},
+    {file = "numpy-1.26.4-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9fad7dcb1aac3c7f0584a5a8133e3a43eeb2fe127f47e3632d43d677c66c102b"},
+    {file = "numpy-1.26.4-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:675d61ffbfa78604709862923189bad94014bef562cc35cf61d3a07bba02a7ed"},
+    {file = "numpy-1.26.4-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:ab47dbe5cc8210f55aa58e4805fe224dac469cde56b9f731a4c098b91917159a"},
+    {file = "numpy-1.26.4-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:1dda2e7b4ec9dd512f84935c5f126c8bd8b9f2fc001e9f54af255e8c5f16b0e0"},
+    {file = "numpy-1.26.4-cp312-cp312-win32.whl", hash = "sha256:50193e430acfc1346175fcbdaa28ffec49947a06918b7b92130744e81e640110"},
+    {file = "numpy-1.26.4-cp312-cp312-win_amd64.whl", hash = "sha256:08beddf13648eb95f8d867350f6a018a4be2e5ad54c8d8caed89ebca558b2818"},
+    {file = "numpy-1.26.4-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:7349ab0fa0c429c82442a27a9673fc802ffdb7c7775fad780226cb234965e53c"},
+    {file = "numpy-1.26.4-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:52b8b60467cd7dd1e9ed082188b4e6bb35aa5cdd01777621a1658910745b90be"},
+    {file = "numpy-1.26.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d5241e0a80d808d70546c697135da2c613f30e28251ff8307eb72ba696945764"},
+    {file = "numpy-1.26.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f870204a840a60da0b12273ef34f7051e98c3b5961b61b0c2c1be6dfd64fbcd3"},
+    {file = "numpy-1.26.4-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:679b0076f67ecc0138fd2ede3a8fd196dddc2ad3254069bcb9faf9a79b1cebcd"},
+    {file = "numpy-1.26.4-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:47711010ad8555514b434df65f7d7b076bb8261df1ca9bb78f53d3b2db02e95c"},
+    {file = "numpy-1.26.4-cp39-cp39-win32.whl", hash = "sha256:a354325ee03388678242a4d7ebcd08b5c727033fcff3b2f536aea978e15ee9e6"},
+    {file = "numpy-1.26.4-cp39-cp39-win_amd64.whl", hash = "sha256:3373d5d70a5fe74a2c1bb6d2cfd9609ecf686d47a2d7b1d37a8f3b6bf6003aea"},
+    {file = "numpy-1.26.4-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:afedb719a9dcfc7eaf2287b839d8198e06dcd4cb5d276a3df279231138e83d30"},
+    {file = "numpy-1.26.4-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:95a7476c59002f2f6c590b9b7b998306fba6a5aa646b1e22ddfeaf8f78c3a29c"},
+    {file = "numpy-1.26.4-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:7e50d0a0cc3189f9cb0aeb3a6a6af18c16f59f004b866cd2be1c14b36134a4a0"},
+    {file = "numpy-1.26.4.tar.gz", hash = "sha256:2a02aba9ed12e4ac4eb3ea9421c420301a0c6460d9830d74a9df87efa4912010"},
+]
+
+[[package]]
+name = "protobuf"
+version = "5.26.1"
+description = ""
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "protobuf-5.26.1-cp310-abi3-win32.whl", hash = "sha256:3c388ea6ddfe735f8cf69e3f7dc7611e73107b60bdfcf5d0f024c3ccd3794e23"},
+    {file = "protobuf-5.26.1-cp310-abi3-win_amd64.whl", hash = "sha256:e6039957449cb918f331d32ffafa8eb9255769c96aa0560d9a5bf0b4e00a2a33"},
+    {file = "protobuf-5.26.1-cp37-abi3-macosx_10_9_universal2.whl", hash = "sha256:38aa5f535721d5bb99861166c445c4105c4e285c765fbb2ac10f116e32dcd46d"},
+    {file = "protobuf-5.26.1-cp37-abi3-manylinux2014_aarch64.whl", hash = "sha256:fbfe61e7ee8c1860855696e3ac6cfd1b01af5498facc6834fcc345c9684fb2ca"},
+    {file = "protobuf-5.26.1-cp37-abi3-manylinux2014_x86_64.whl", hash = "sha256:f7417703f841167e5a27d48be13389d52ad705ec09eade63dfc3180a959215d7"},
+    {file = "protobuf-5.26.1-cp38-cp38-win32.whl", hash = "sha256:d693d2504ca96750d92d9de8a103102dd648fda04540495535f0fec7577ed8fc"},
+    {file = "protobuf-5.26.1-cp38-cp38-win_amd64.whl", hash = "sha256:9b557c317ebe6836835ec4ef74ec3e994ad0894ea424314ad3552bc6e8835b4e"},
+    {file = "protobuf-5.26.1-cp39-cp39-win32.whl", hash = "sha256:b9ba3ca83c2e31219ffbeb9d76b63aad35a3eb1544170c55336993d7a18ae72c"},
+    {file = "protobuf-5.26.1-cp39-cp39-win_amd64.whl", hash = "sha256:7ee014c2c87582e101d6b54260af03b6596728505c79f17c8586e7523aaa8f8c"},
+    {file = "protobuf-5.26.1-py3-none-any.whl", hash = "sha256:da612f2720c0183417194eeaa2523215c4fcc1a1949772dc65f05047e08d5932"},
+    {file = "protobuf-5.26.1.tar.gz", hash = "sha256:8ca2a1d97c290ec7b16e4e5dff2e5ae150cc1582f55b5ab300d45cb0dfa90e51"},
+]
+
+[[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"},
+    {file = "PyOpenGL-3.1.7.tar.gz", hash = "sha256:eef31a3888e6984fd4d8e6c9961b184c9813ca82604d37fe3da80eb000a76c86"},
+]
+
+[[package]]
+name = "pyparsing"
+version = "3.1.2"
+description = "pyparsing module - Classes and methods to define and execute parsing grammars"
+optional = false
+python-versions = ">=3.6.8"
+files = [
+    {file = "pyparsing-3.1.2-py3-none-any.whl", hash = "sha256:f9db75911801ed778fe61bb643079ff86601aca99fcae6345aa67292038fb742"},
+    {file = "pyparsing-3.1.2.tar.gz", hash = "sha256:a1bac0ce561155ecc3ed78ca94d3c9378656ad4c94c1270de543f621420f94ad"},
+]
+
+[package.extras]
+diagrams = ["jinja2", "railroad-diagrams"]
+
+[[package]]
+name = "requests"
+version = "2.31.0"
+description = "Python HTTP for Humans."
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "requests-2.31.0-py3-none-any.whl", hash = "sha256:58cd2187c01e70e6e26505bca751777aa9f2ee0b7f4300988b709f44e013003f"},
+    {file = "requests-2.31.0.tar.gz", hash = "sha256:942c5a758f98d790eaed1a29cb6eefc7ffb0d1cf7af05c3d2791656dbd6ad1e1"},
+]
+
+[package.dependencies]
+certifi = ">=2017.4.17"
+charset-normalizer = ">=2,<4"
+idna = ">=2.5,<4"
+urllib3 = ">=1.21.1,<3"
+
+[package.extras]
+socks = ["PySocks (>=1.5.6,!=1.5.7)"]
+use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
+
+[[package]]
+name = "scipy"
+version = "1.12.0"
+description = "Fundamental algorithms for scientific computing in Python"
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "scipy-1.12.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:78e4402e140879387187f7f25d91cc592b3501a2e51dfb320f48dfb73565f10b"},
+    {file = "scipy-1.12.0-cp310-cp310-macosx_12_0_arm64.whl", hash = "sha256:f5f00ebaf8de24d14b8449981a2842d404152774c1a1d880c901bf454cb8e2a1"},
+    {file = "scipy-1.12.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e53958531a7c695ff66c2e7bb7b79560ffdc562e2051644c5576c39ff8efb563"},
+    {file = "scipy-1.12.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5e32847e08da8d895ce09d108a494d9eb78974cf6de23063f93306a3e419960c"},
+    {file = "scipy-1.12.0-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:4c1020cad92772bf44b8e4cdabc1df5d87376cb219742549ef69fc9fd86282dd"},
+    {file = "scipy-1.12.0-cp310-cp310-win_amd64.whl", hash = "sha256:75ea2a144096b5e39402e2ff53a36fecfd3b960d786b7efd3c180e29c39e53f2"},
+    {file = "scipy-1.12.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:408c68423f9de16cb9e602528be4ce0d6312b05001f3de61fe9ec8b1263cad08"},
+    {file = "scipy-1.12.0-cp311-cp311-macosx_12_0_arm64.whl", hash = "sha256:5adfad5dbf0163397beb4aca679187d24aec085343755fcdbdeb32b3679f254c"},
+    {file = "scipy-1.12.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c3003652496f6e7c387b1cf63f4bb720951cfa18907e998ea551e6de51a04467"},
+    {file = "scipy-1.12.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8b8066bce124ee5531d12a74b617d9ac0ea59245246410e19bca549656d9a40a"},
+    {file = "scipy-1.12.0-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:8bee4993817e204d761dba10dbab0774ba5a8612e57e81319ea04d84945375ba"},
+    {file = "scipy-1.12.0-cp311-cp311-win_amd64.whl", hash = "sha256:a24024d45ce9a675c1fb8494e8e5244efea1c7a09c60beb1eeb80373d0fecc70"},
+    {file = "scipy-1.12.0-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:e7e76cc48638228212c747ada851ef355c2bb5e7f939e10952bc504c11f4e372"},
+    {file = "scipy-1.12.0-cp312-cp312-macosx_12_0_arm64.whl", hash = "sha256:f7ce148dffcd64ade37b2df9315541f9adad6efcaa86866ee7dd5db0c8f041c3"},
+    {file = "scipy-1.12.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9c39f92041f490422924dfdb782527a4abddf4707616e07b021de33467f917bc"},
+    {file = "scipy-1.12.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a7ebda398f86e56178c2fa94cad15bf457a218a54a35c2a7b4490b9f9cb2676c"},
+    {file = "scipy-1.12.0-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:95e5c750d55cf518c398a8240571b0e0782c2d5a703250872f36eaf737751338"},
+    {file = "scipy-1.12.0-cp312-cp312-win_amd64.whl", hash = "sha256:e646d8571804a304e1da01040d21577685ce8e2db08ac58e543eaca063453e1c"},
+    {file = "scipy-1.12.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:913d6e7956c3a671de3b05ccb66b11bc293f56bfdef040583a7221d9e22a2e35"},
+    {file = "scipy-1.12.0-cp39-cp39-macosx_12_0_arm64.whl", hash = "sha256:bba1b0c7256ad75401c73e4b3cf09d1f176e9bd4248f0d3112170fb2ec4db067"},
+    {file = "scipy-1.12.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:730badef9b827b368f351eacae2e82da414e13cf8bd5051b4bdfd720271a5371"},
+    {file = "scipy-1.12.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6546dc2c11a9df6926afcbdd8a3edec28566e4e785b915e849348c6dd9f3f490"},
+    {file = "scipy-1.12.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:196ebad3a4882081f62a5bf4aeb7326aa34b110e533aab23e4374fcccb0890dc"},
+    {file = "scipy-1.12.0-cp39-cp39-win_amd64.whl", hash = "sha256:b360f1b6b2f742781299514e99ff560d1fe9bd1bff2712894b52abe528d1fd1e"},
+    {file = "scipy-1.12.0.tar.gz", hash = "sha256:4bf5abab8a36d20193c698b0f1fc282c1d083c94723902c447e5d2f1780936a3"},
+]
+
+[package.dependencies]
+numpy = ">=1.22.4,<1.29.0"
+
+[package.extras]
+dev = ["click", "cython-lint (>=0.12.2)", "doit (>=0.36.0)", "mypy", "pycodestyle", "pydevtool", "rich-click", "ruff", "types-psutil", "typing_extensions"]
+doc = ["jupytext", "matplotlib (>2)", "myst-nb", "numpydoc", "pooch", "pydata-sphinx-theme (==0.9.0)", "sphinx (!=4.1.0)", "sphinx-design (>=0.2.0)"]
+test = ["asv", "gmpy2", "hypothesis", "mpmath", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
+
+[[package]]
+name = "setuptools"
+version = "69.2.0"
+description = "Easily download, build, install, upgrade, and uninstall Python packages"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "setuptools-69.2.0-py3-none-any.whl", hash = "sha256:c21c49fb1042386df081cb5d86759792ab89efca84cf114889191cd09aacc80c"},
+    {file = "setuptools-69.2.0.tar.gz", hash = "sha256:0ff4183f8f42cd8fa3acea16c45205521a4ef28f73c6391d8a25e92893134f2e"},
+]
+
+[package.extras]
+docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier"]
+testing = ["build[virtualenv]", "filelock (>=3.4.0)", "importlib-metadata", "ini2toml[lite] (>=0.9)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "mypy (==1.9)", "packaging (>=23.2)", "pip (>=19.1)", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-home (>=0.5)", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-ruff (>=0.2.1)", "pytest-timeout", "pytest-xdist (>=3)", "tomli", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
+testing-integration = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "packaging (>=23.2)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"]
+
+[[package]]
+name = "tqdm"
+version = "4.66.2"
+description = "Fast, Extensible Progress Meter"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "tqdm-4.66.2-py3-none-any.whl", hash = "sha256:1ee4f8a893eb9bef51c6e35730cebf234d5d0b6bd112b0271e10ed7c24a02bd9"},
+    {file = "tqdm-4.66.2.tar.gz", hash = "sha256:6cd52cdf0fef0e0f543299cfc96fec90d7b8a7e88745f411ec33eb44d5ed3531"},
+]
+
+[package.dependencies]
+colorama = {version = "*", markers = "platform_system == \"Windows\""}
+
+[package.extras]
+dev = ["pytest (>=6)", "pytest-cov", "pytest-timeout", "pytest-xdist"]
+notebook = ["ipywidgets (>=6)"]
+slack = ["slack-sdk"]
+telegram = ["requests"]
+
+[[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"},
+    {file = "typing_extensions-4.10.0.tar.gz", hash = "sha256:b0abd7c89e8fb96f98db18d86106ff1d90ab692004eb746cf6eda2682f91b3cb"},
+]
+
+[[package]]
+name = "urllib3"
+version = "2.2.1"
+description = "HTTP library with thread-safe connection pooling, file post, and more."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "urllib3-2.2.1-py3-none-any.whl", hash = "sha256:450b20ec296a467077128bff42b73080516e71b56ff59a60a02bef2232c4fa9d"},
+    {file = "urllib3-2.2.1.tar.gz", hash = "sha256:d0570876c61ab9e520d776c38acbbb5b05a776d3f9ff98a5c8fd5162a444cf19"},
+]
+
+[package.extras]
+brotli = ["brotli (>=1.0.9)", "brotlicffi (>=0.8.0)"]
+h2 = ["h2 (>=4,<5)"]
+socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
+zstd = ["zstandard (>=0.18.0)"]
+
+[[package]]
+name = "zipp"
+version = "3.18.1"
+description = "Backport of pathlib-compatible object wrapper for zip files"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "zipp-3.18.1-py3-none-any.whl", hash = "sha256:206f5a15f2af3dbaee80769fb7dc6f249695e940acca08dfb2a4769fe61e538b"},
+    {file = "zipp-3.18.1.tar.gz", hash = "sha256:2884ed22e7d8961de1c9a05142eb69a247f120291bc0206a00a7642f09b5b715"},
+]
+
+[package.extras]
+docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
+
+[metadata]
+lock-version = "2.0"
+python-versions = "^3.10"
+content-hash = "07c474dba5df862978c1e7f32a95edf4636ed9ba459c6f3e8c013ad1007a2884"

envs/sim_aloha/pyproject.toml

@@ -0,0 +1,32 @@
+[tool.poetry]
+name = "sim_aloha"
+version = "0.1.2"
+description = "ALOHA 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 = "aloha"}]
+
+
+[tool.poetry.dependencies]
+python = "^3.10"
+dm-control = "1.0.14"
+
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"

envs/sim_pusht/README.md

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

envs/sim_pusht/poetry.lock

@@ -0,0 +1,675 @@
+# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
+
+[[package]]
+name = "cffi"
+version = "1.16.0"
+description = "Foreign Function Interface for Python calling C code."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "cffi-1.16.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:6b3d6606d369fc1da4fd8c357d026317fbb9c9b75d36dc16e90e84c26854b088"},
+    {file = "cffi-1.16.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:ac0f5edd2360eea2f1daa9e26a41db02dd4b0451b48f7c318e217ee092a213e9"},
+    {file = "cffi-1.16.0-cp310-cp310-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:7e61e3e4fa664a8588aa25c883eab612a188c725755afff6289454d6362b9673"},
+    {file = "cffi-1.16.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a72e8961a86d19bdb45851d8f1f08b041ea37d2bd8d4fd19903bc3083d80c896"},
+    {file = "cffi-1.16.0-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:5b50bf3f55561dac5438f8e70bfcdfd74543fd60df5fa5f62d94e5867deca684"},
+    {file = "cffi-1.16.0-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:7651c50c8c5ef7bdb41108b7b8c5a83013bfaa8a935590c5d74627c047a583c7"},
+    {file = "cffi-1.16.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e4108df7fe9b707191e55f33efbcb2d81928e10cea45527879a4749cbe472614"},
+    {file = "cffi-1.16.0-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:32c68ef735dbe5857c810328cb2481e24722a59a2003018885514d4c09af9743"},
+    {file = "cffi-1.16.0-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:673739cb539f8cdaa07d92d02efa93c9ccf87e345b9a0b556e3ecc666718468d"},
+    {file = "cffi-1.16.0-cp310-cp310-win32.whl", hash = "sha256:9f90389693731ff1f659e55c7d1640e2ec43ff725cc61b04b2f9c6d8d017df6a"},
+    {file = "cffi-1.16.0-cp310-cp310-win_amd64.whl", hash = "sha256:e6024675e67af929088fda399b2094574609396b1decb609c55fa58b028a32a1"},
+    {file = "cffi-1.16.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:b84834d0cf97e7d27dd5b7f3aca7b6e9263c56308ab9dc8aae9784abb774d404"},
+    {file = "cffi-1.16.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:1b8ebc27c014c59692bb2664c7d13ce7a6e9a629be20e54e7271fa696ff2b417"},
+    {file = "cffi-1.16.0-cp311-cp311-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ee07e47c12890ef248766a6e55bd38ebfb2bb8edd4142d56db91b21ea68b7627"},
+    {file = "cffi-1.16.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d8a9d3ebe49f084ad71f9269834ceccbf398253c9fac910c4fd7053ff1386936"},
+    {file = "cffi-1.16.0-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:e70f54f1796669ef691ca07d046cd81a29cb4deb1e5f942003f401c0c4a2695d"},
+    {file = "cffi-1.16.0-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:5bf44d66cdf9e893637896c7faa22298baebcd18d1ddb6d2626a6e39793a1d56"},
+    {file = "cffi-1.16.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7b78010e7b97fef4bee1e896df8a4bbb6712b7f05b7ef630f9d1da00f6444d2e"},
+    {file = "cffi-1.16.0-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:c6a164aa47843fb1b01e941d385aab7215563bb8816d80ff3a363a9f8448a8dc"},
+    {file = "cffi-1.16.0-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:e09f3ff613345df5e8c3667da1d918f9149bd623cd9070c983c013792a9a62eb"},
+    {file = "cffi-1.16.0-cp311-cp311-win32.whl", hash = "sha256:2c56b361916f390cd758a57f2e16233eb4f64bcbeee88a4881ea90fca14dc6ab"},
+    {file = "cffi-1.16.0-cp311-cp311-win_amd64.whl", hash = "sha256:db8e577c19c0fda0beb7e0d4e09e0ba74b1e4c092e0e40bfa12fe05b6f6d75ba"},
+    {file = "cffi-1.16.0-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:fa3a0128b152627161ce47201262d3140edb5a5c3da88d73a1b790a959126956"},
+    {file = "cffi-1.16.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:68e7c44931cc171c54ccb702482e9fc723192e88d25a0e133edd7aff8fcd1f6e"},
+    {file = "cffi-1.16.0-cp312-cp312-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:abd808f9c129ba2beda4cfc53bde801e5bcf9d6e0f22f095e45327c038bfe68e"},
+    {file = "cffi-1.16.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:88e2b3c14bdb32e440be531ade29d3c50a1a59cd4e51b1dd8b0865c54ea5d2e2"},
+    {file = "cffi-1.16.0-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:fcc8eb6d5902bb1cf6dc4f187ee3ea80a1eba0a89aba40a5cb20a5087d961357"},
+    {file = "cffi-1.16.0-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:b7be2d771cdba2942e13215c4e340bfd76398e9227ad10402a8767ab1865d2e6"},
+    {file = "cffi-1.16.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e715596e683d2ce000574bae5d07bd522c781a822866c20495e52520564f0969"},
+    {file = "cffi-1.16.0-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:2d92b25dbf6cae33f65005baf472d2c245c050b1ce709cc4588cdcdd5495b520"},
+    {file = "cffi-1.16.0-cp312-cp312-win32.whl", hash = "sha256:b2ca4e77f9f47c55c194982e10f058db063937845bb2b7a86c84a6cfe0aefa8b"},
+    {file = "cffi-1.16.0-cp312-cp312-win_amd64.whl", hash = "sha256:68678abf380b42ce21a5f2abde8efee05c114c2fdb2e9eef2efdb0257fba1235"},
+    {file = "cffi-1.16.0-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:0c9ef6ff37e974b73c25eecc13952c55bceed9112be2d9d938ded8e856138bcc"},
+    {file = "cffi-1.16.0-cp38-cp38-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:a09582f178759ee8128d9270cd1344154fd473bb77d94ce0aeb2a93ebf0feaf0"},
+    {file = "cffi-1.16.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e760191dd42581e023a68b758769e2da259b5d52e3103c6060ddc02c9edb8d7b"},
+    {file = "cffi-1.16.0-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:80876338e19c951fdfed6198e70bc88f1c9758b94578d5a7c4c91a87af3cf31c"},
+    {file = "cffi-1.16.0-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:a6a14b17d7e17fa0d207ac08642c8820f84f25ce17a442fd15e27ea18d67c59b"},
+    {file = "cffi-1.16.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6602bc8dc6f3a9e02b6c22c4fc1e47aa50f8f8e6d3f78a5e16ac33ef5fefa324"},
+    {file = "cffi-1.16.0-cp38-cp38-win32.whl", hash = "sha256:131fd094d1065b19540c3d72594260f118b231090295d8c34e19a7bbcf2e860a"},
+    {file = "cffi-1.16.0-cp38-cp38-win_amd64.whl", hash = "sha256:31d13b0f99e0836b7ff893d37af07366ebc90b678b6664c955b54561fc36ef36"},
+    {file = "cffi-1.16.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:582215a0e9adbe0e379761260553ba11c58943e4bbe9c36430c4ca6ac74b15ed"},
+    {file = "cffi-1.16.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:b29ebffcf550f9da55bec9e02ad430c992a87e5f512cd63388abb76f1036d8d2"},
+    {file = "cffi-1.16.0-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:dc9b18bf40cc75f66f40a7379f6a9513244fe33c0e8aa72e2d56b0196a7ef872"},
+    {file = "cffi-1.16.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9cb4a35b3642fc5c005a6755a5d17c6c8b6bcb6981baf81cea8bfbc8903e8ba8"},
+    {file = "cffi-1.16.0-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:b86851a328eedc692acf81fb05444bdf1891747c25af7529e39ddafaf68a4f3f"},
+    {file = "cffi-1.16.0-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:c0f31130ebc2d37cdd8e44605fb5fa7ad59049298b3f745c74fa74c62fbfcfc4"},
+    {file = "cffi-1.16.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8f8e709127c6c77446a8c0a8c8bf3c8ee706a06cd44b1e827c3e6a2ee6b8c098"},
+    {file = "cffi-1.16.0-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:748dcd1e3d3d7cd5443ef03ce8685043294ad6bd7c02a38d1bd367cfd968e000"},
+    {file = "cffi-1.16.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:8895613bcc094d4a1b2dbe179d88d7fb4a15cee43c052e8885783fac397d91fe"},
+    {file = "cffi-1.16.0-cp39-cp39-win32.whl", hash = "sha256:ed86a35631f7bfbb28e108dd96773b9d5a6ce4811cf6ea468bb6a359b256b1e4"},
+    {file = "cffi-1.16.0-cp39-cp39-win_amd64.whl", hash = "sha256:3686dffb02459559c74dd3d81748269ffb0eb027c39a6fc99502de37d501faa8"},
+    {file = "cffi-1.16.0.tar.gz", hash = "sha256:bcb3ef43e58665bbda2fb198698fcae6776483e0c4a631aa5647806c25e02cc0"},
+]
+
+[package.dependencies]
+pycparser = "*"
+
+[[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 = "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"},
+]
+
+[package.dependencies]
+cloudpickle = ">=1.2.0"
+farama-notifications = ">=0.0.1"
+numpy = ">=1.21.0"
+typing-extensions = ">=4.3.0"
+
+[package.extras]
+accept-rom-license = ["autorom[accept-rom-license] (>=0.4.2,<0.5.0)"]
+all = ["box2d-py (==2.3.5)", "cython (<3)", "imageio (>=2.14.1)", "jax (>=0.4.0)", "jaxlib (>=0.4.0)", "lz4 (>=3.1.0)", "matplotlib (>=3.0)", "moviepy (>=1.0.0)", "mujoco (>=2.3.3)", "mujoco-py (>=2.1,<2.2)", "opencv-python (>=3.0)", "pygame (>=2.1.3)", "shimmy[atari] (>=0.1.0,<1.0)", "swig (==4.*)", "torch (>=1.0.0)"]
+atari = ["shimmy[atari] (>=0.1.0,<1.0)"]
+box2d = ["box2d-py (==2.3.5)", "pygame (>=2.1.3)", "swig (==4.*)"]
+classic-control = ["pygame (>=2.1.3)", "pygame (>=2.1.3)"]
+jax = ["jax (>=0.4.0)", "jaxlib (>=0.4.0)"]
+mujoco = ["imageio (>=2.14.1)", "mujoco (>=2.3.3)"]
+mujoco-py = ["cython (<3)", "cython (<3)", "mujoco-py (>=2.1,<2.2)", "mujoco-py (>=2.1,<2.2)"]
+other = ["lz4 (>=3.1.0)", "matplotlib (>=3.0)", "moviepy (>=1.0.0)", "opencv-python (>=3.0)", "torch (>=1.0.0)"]
+testing = ["pytest (==7.1.3)", "scipy (>=1.7.3)"]
+toy-text = ["pygame (>=2.1.3)", "pygame (>=2.1.3)"]
+
+[[package]]
+name = "imageio"
+version = "2.34.0"
+description = "Library for reading and writing a wide range of image, video, scientific, and volumetric data formats."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "imageio-2.34.0-py3-none-any.whl", hash = "sha256:08082bf47ccb54843d9c73fe9fc8f3a88c72452ab676b58aca74f36167e8ccba"},
+    {file = "imageio-2.34.0.tar.gz", hash = "sha256:ae9732e10acf807a22c389aef193f42215718e16bd06eed0c5bb57e1034a4d53"},
+]
+
+[package.dependencies]
+numpy = "*"
+pillow = ">=8.3.2"
+
+[package.extras]
+all-plugins = ["astropy", "av", "imageio-ffmpeg", "pillow-heif", "psutil", "tifffile"]
+all-plugins-pypy = ["av", "imageio-ffmpeg", "pillow-heif", "psutil", "tifffile"]
+build = ["wheel"]
+dev = ["black", "flake8", "fsspec[github]", "pytest", "pytest-cov"]
+docs = ["numpydoc", "pydata-sphinx-theme", "sphinx (<6)"]
+ffmpeg = ["imageio-ffmpeg", "psutil"]
+fits = ["astropy"]
+full = ["astropy", "av", "black", "flake8", "fsspec[github]", "gdal", "imageio-ffmpeg", "itk", "numpydoc", "pillow-heif", "psutil", "pydata-sphinx-theme", "pytest", "pytest-cov", "sphinx (<6)", "tifffile", "wheel"]
+gdal = ["gdal"]
+itk = ["itk"]
+linting = ["black", "flake8"]
+pillow-heif = ["pillow-heif"]
+pyav = ["av"]
+test = ["fsspec[github]", "pytest", "pytest-cov"]
+tifffile = ["tifffile"]
+
+[[package]]
+name = "lazy-loader"
+version = "0.3"
+description = "lazy_loader"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "lazy_loader-0.3-py3-none-any.whl", hash = "sha256:1e9e76ee8631e264c62ce10006718e80b2cfc74340d17d1031e0f84af7478554"},
+    {file = "lazy_loader-0.3.tar.gz", hash = "sha256:3b68898e34f5b2a29daaaac172c6555512d0f32074f147e2254e4a6d9d838f37"},
+]
+
+[package.extras]
+lint = ["pre-commit (>=3.3)"]
+test = ["pytest (>=7.4)", "pytest-cov (>=4.1)"]
+
+[[package]]
+name = "networkx"
+version = "3.2.1"
+description = "Python package for creating and manipulating graphs and networks"
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "networkx-3.2.1-py3-none-any.whl", hash = "sha256:f18c69adc97877c42332c170849c96cefa91881c99a7cb3e95b7c659ebdc1ec2"},
+    {file = "networkx-3.2.1.tar.gz", hash = "sha256:9f1bb5cf3409bf324e0a722c20bdb4c20ee39bf1c30ce8ae499c8502b0b5e0c6"},
+]
+
+[package.extras]
+default = ["matplotlib (>=3.5)", "numpy (>=1.22)", "pandas (>=1.4)", "scipy (>=1.9,!=1.11.0,!=1.11.1)"]
+developer = ["changelist (==0.4)", "mypy (>=1.1)", "pre-commit (>=3.2)", "rtoml"]
+doc = ["nb2plots (>=0.7)", "nbconvert (<7.9)", "numpydoc (>=1.6)", "pillow (>=9.4)", "pydata-sphinx-theme (>=0.14)", "sphinx (>=7)", "sphinx-gallery (>=0.14)", "texext (>=0.6.7)"]
+extra = ["lxml (>=4.6)", "pydot (>=1.4.2)", "pygraphviz (>=1.11)", "sympy (>=1.10)"]
+test = ["pytest (>=7.2)", "pytest-cov (>=4.0)"]
+
+[[package]]
+name = "numpy"
+version = "1.26.4"
+description = "Fundamental package for array computing in Python"
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "numpy-1.26.4-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:9ff0f4f29c51e2803569d7a51c2304de5554655a60c5d776e35b4a41413830d0"},
+    {file = "numpy-1.26.4-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:2e4ee3380d6de9c9ec04745830fd9e2eccb3e6cf790d39d7b98ffd19b0dd754a"},
+    {file = "numpy-1.26.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d209d8969599b27ad20994c8e41936ee0964e6da07478d6c35016bc386b66ad4"},
+    {file = "numpy-1.26.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ffa75af20b44f8dba823498024771d5ac50620e6915abac414251bd971b4529f"},
+    {file = "numpy-1.26.4-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:62b8e4b1e28009ef2846b4c7852046736bab361f7aeadeb6a5b89ebec3c7055a"},
+    {file = "numpy-1.26.4-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:a4abb4f9001ad2858e7ac189089c42178fcce737e4169dc61321660f1a96c7d2"},
+    {file = "numpy-1.26.4-cp310-cp310-win32.whl", hash = "sha256:bfe25acf8b437eb2a8b2d49d443800a5f18508cd811fea3181723922a8a82b07"},
+    {file = "numpy-1.26.4-cp310-cp310-win_amd64.whl", hash = "sha256:b97fe8060236edf3662adfc2c633f56a08ae30560c56310562cb4f95500022d5"},
+    {file = "numpy-1.26.4-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:4c66707fabe114439db9068ee468c26bbdf909cac0fb58686a42a24de1760c71"},
+    {file = "numpy-1.26.4-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:edd8b5fe47dab091176d21bb6de568acdd906d1887a4584a15a9a96a1dca06ef"},
+    {file = "numpy-1.26.4-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7ab55401287bfec946ced39700c053796e7cc0e3acbef09993a9ad2adba6ca6e"},
+    {file = "numpy-1.26.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:666dbfb6ec68962c033a450943ded891bed2d54e6755e35e5835d63f4f6931d5"},
+    {file = "numpy-1.26.4-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:96ff0b2ad353d8f990b63294c8986f1ec3cb19d749234014f4e7eb0112ceba5a"},
+    {file = "numpy-1.26.4-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:60dedbb91afcbfdc9bc0b1f3f402804070deed7392c23eb7a7f07fa857868e8a"},
+    {file = "numpy-1.26.4-cp311-cp311-win32.whl", hash = "sha256:1af303d6b2210eb850fcf03064d364652b7120803a0b872f5211f5234b399f20"},
+    {file = "numpy-1.26.4-cp311-cp311-win_amd64.whl", hash = "sha256:cd25bcecc4974d09257ffcd1f098ee778f7834c3ad767fe5db785be9a4aa9cb2"},
+    {file = "numpy-1.26.4-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:b3ce300f3644fb06443ee2222c2201dd3a89ea6040541412b8fa189341847218"},
+    {file = "numpy-1.26.4-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:03a8c78d01d9781b28a6989f6fa1bb2c4f2d51201cf99d3dd875df6fbd96b23b"},
+    {file = "numpy-1.26.4-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9fad7dcb1aac3c7f0584a5a8133e3a43eeb2fe127f47e3632d43d677c66c102b"},
+    {file = "numpy-1.26.4-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:675d61ffbfa78604709862923189bad94014bef562cc35cf61d3a07bba02a7ed"},
+    {file = "numpy-1.26.4-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:ab47dbe5cc8210f55aa58e4805fe224dac469cde56b9f731a4c098b91917159a"},
+    {file = "numpy-1.26.4-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:1dda2e7b4ec9dd512f84935c5f126c8bd8b9f2fc001e9f54af255e8c5f16b0e0"},
+    {file = "numpy-1.26.4-cp312-cp312-win32.whl", hash = "sha256:50193e430acfc1346175fcbdaa28ffec49947a06918b7b92130744e81e640110"},
+    {file = "numpy-1.26.4-cp312-cp312-win_amd64.whl", hash = "sha256:08beddf13648eb95f8d867350f6a018a4be2e5ad54c8d8caed89ebca558b2818"},
+    {file = "numpy-1.26.4-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:7349ab0fa0c429c82442a27a9673fc802ffdb7c7775fad780226cb234965e53c"},
+    {file = "numpy-1.26.4-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:52b8b60467cd7dd1e9ed082188b4e6bb35aa5cdd01777621a1658910745b90be"},
+    {file = "numpy-1.26.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d5241e0a80d808d70546c697135da2c613f30e28251ff8307eb72ba696945764"},
+    {file = "numpy-1.26.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f870204a840a60da0b12273ef34f7051e98c3b5961b61b0c2c1be6dfd64fbcd3"},
+    {file = "numpy-1.26.4-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:679b0076f67ecc0138fd2ede3a8fd196dddc2ad3254069bcb9faf9a79b1cebcd"},
+    {file = "numpy-1.26.4-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:47711010ad8555514b434df65f7d7b076bb8261df1ca9bb78f53d3b2db02e95c"},
+    {file = "numpy-1.26.4-cp39-cp39-win32.whl", hash = "sha256:a354325ee03388678242a4d7ebcd08b5c727033fcff3b2f536aea978e15ee9e6"},
+    {file = "numpy-1.26.4-cp39-cp39-win_amd64.whl", hash = "sha256:3373d5d70a5fe74a2c1bb6d2cfd9609ecf686d47a2d7b1d37a8f3b6bf6003aea"},
+    {file = "numpy-1.26.4-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:afedb719a9dcfc7eaf2287b839d8198e06dcd4cb5d276a3df279231138e83d30"},
+    {file = "numpy-1.26.4-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:95a7476c59002f2f6c590b9b7b998306fba6a5aa646b1e22ddfeaf8f78c3a29c"},
+    {file = "numpy-1.26.4-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:7e50d0a0cc3189f9cb0aeb3a6a6af18c16f59f004b866cd2be1c14b36134a4a0"},
+    {file = "numpy-1.26.4.tar.gz", hash = "sha256:2a02aba9ed12e4ac4eb3ea9421c420301a0c6460d9830d74a9df87efa4912010"},
+]
+
+[[package]]
+name = "opencv-python"
+version = "4.9.0.80"
+description = "Wrapper package for OpenCV python bindings."
+optional = false
+python-versions = ">=3.6"
+files = [
+    {file = "opencv-python-4.9.0.80.tar.gz", hash = "sha256:1a9f0e6267de3a1a1db0c54213d022c7c8b5b9ca4b580e80bdc58516c922c9e1"},
+    {file = "opencv_python-4.9.0.80-cp37-abi3-macosx_10_16_x86_64.whl", hash = "sha256:7e5f7aa4486651a6ebfa8ed4b594b65bd2d2f41beeb4241a3e4b1b85acbbbadb"},
+    {file = "opencv_python-4.9.0.80-cp37-abi3-macosx_11_0_arm64.whl", hash = "sha256:71dfb9555ccccdd77305fc3dcca5897fbf0cf28b297c51ee55e079c065d812a3"},
+    {file = "opencv_python-4.9.0.80-cp37-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7b34a52e9da36dda8c151c6394aed602e4b17fa041df0b9f5b93ae10b0fcca2a"},
+    {file = "opencv_python-4.9.0.80-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e4088cab82b66a3b37ffc452976b14a3c599269c247895ae9ceb4066d8188a57"},
+    {file = "opencv_python-4.9.0.80-cp37-abi3-win32.whl", hash = "sha256:dcf000c36dd1651118a2462257e3a9e76db789a78432e1f303c7bac54f63ef6c"},
+    {file = "opencv_python-4.9.0.80-cp37-abi3-win_amd64.whl", hash = "sha256:3f16f08e02b2a2da44259c7cc712e779eff1dd8b55fdb0323e8cab09548086c0"},
+]
+
+[package.dependencies]
+numpy = [
+    {version = ">=1.26.0", markers = "python_version >= \"3.12\""},
+    {version = ">=1.23.5", markers = "python_version >= \"3.11\" and python_version < \"3.12\""},
+    {version = ">=1.21.4", markers = "python_version >= \"3.10\" and platform_system == \"Darwin\" and python_version < \"3.11\""},
+    {version = ">=1.21.2", markers = "platform_system != \"Darwin\" and python_version >= \"3.10\" and python_version < \"3.11\""},
+]
+
+[[package]]
+name = "packaging"
+version = "24.0"
+description = "Core utilities for Python packages"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "packaging-24.0-py3-none-any.whl", hash = "sha256:2ddfb553fdf02fb784c234c7ba6ccc288296ceabec964ad2eae3777778130bc5"},
+    {file = "packaging-24.0.tar.gz", hash = "sha256:eb82c5e3e56209074766e6885bb04b8c38a0c015d0a30036ebe7ece34c9989e9"},
+]
+
+[[package]]
+name = "pillow"
+version = "10.2.0"
+description = "Python Imaging Library (Fork)"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "pillow-10.2.0-cp310-cp310-macosx_10_10_x86_64.whl", hash = "sha256:7823bdd049099efa16e4246bdf15e5a13dbb18a51b68fa06d6c1d4d8b99a796e"},
+    {file = "pillow-10.2.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:83b2021f2ade7d1ed556bc50a399127d7fb245e725aa0113ebd05cfe88aaf588"},
+    {file = "pillow-10.2.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6fad5ff2f13d69b7e74ce5b4ecd12cc0ec530fcee76356cac6742785ff71c452"},
+    {file = "pillow-10.2.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:da2b52b37dad6d9ec64e653637a096905b258d2fc2b984c41ae7d08b938a67e4"},
+    {file = "pillow-10.2.0-cp310-cp310-manylinux_2_28_aarch64.whl", hash = "sha256:47c0995fc4e7f79b5cfcab1fc437ff2890b770440f7696a3ba065ee0fd496563"},
+    {file = "pillow-10.2.0-cp310-cp310-manylinux_2_28_x86_64.whl", hash = "sha256:322bdf3c9b556e9ffb18f93462e5f749d3444ce081290352c6070d014c93feb2"},
+    {file = "pillow-10.2.0-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:51f1a1bffc50e2e9492e87d8e09a17c5eea8409cda8d3f277eb6edc82813c17c"},
+    {file = "pillow-10.2.0-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:69ffdd6120a4737710a9eee73e1d2e37db89b620f702754b8f6e62594471dee0"},
+    {file = "pillow-10.2.0-cp310-cp310-win32.whl", hash = "sha256:c6dafac9e0f2b3c78df97e79af707cdc5ef8e88208d686a4847bab8266870023"},
+    {file = "pillow-10.2.0-cp310-cp310-win_amd64.whl", hash = "sha256:aebb6044806f2e16ecc07b2a2637ee1ef67a11840a66752751714a0d924adf72"},
+    {file = "pillow-10.2.0-cp310-cp310-win_arm64.whl", hash = "sha256:7049e301399273a0136ff39b84c3678e314f2158f50f517bc50285fb5ec847ad"},
+    {file = "pillow-10.2.0-cp311-cp311-macosx_10_10_x86_64.whl", hash = "sha256:35bb52c37f256f662abdfa49d2dfa6ce5d93281d323a9af377a120e89a9eafb5"},
+    {file = "pillow-10.2.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:9c23f307202661071d94b5e384e1e1dc7dfb972a28a2310e4ee16103e66ddb67"},
+    {file = "pillow-10.2.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:773efe0603db30c281521a7c0214cad7836c03b8ccff897beae9b47c0b657d61"},
+    {file = "pillow-10.2.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:11fa2e5984b949b0dd6d7a94d967743d87c577ff0b83392f17cb3990d0d2fd6e"},
+    {file = "pillow-10.2.0-cp311-cp311-manylinux_2_28_aarch64.whl", hash = "sha256:716d30ed977be8b37d3ef185fecb9e5a1d62d110dfbdcd1e2a122ab46fddb03f"},
+    {file = "pillow-10.2.0-cp311-cp311-manylinux_2_28_x86_64.whl", hash = "sha256:a086c2af425c5f62a65e12fbf385f7c9fcb8f107d0849dba5839461a129cf311"},
+    {file = "pillow-10.2.0-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:c8de2789052ed501dd829e9cae8d3dcce7acb4777ea4a479c14521c942d395b1"},
+    {file = "pillow-10.2.0-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:609448742444d9290fd687940ac0b57fb35e6fd92bdb65386e08e99af60bf757"},
+    {file = "pillow-10.2.0-cp311-cp311-win32.whl", hash = "sha256:823ef7a27cf86df6597fa0671066c1b596f69eba53efa3d1e1cb8b30f3533068"},
+    {file = "pillow-10.2.0-cp311-cp311-win_amd64.whl", hash = "sha256:1da3b2703afd040cf65ec97efea81cfba59cdbed9c11d8efc5ab09df9509fc56"},
+    {file = "pillow-10.2.0-cp311-cp311-win_arm64.whl", hash = "sha256:edca80cbfb2b68d7b56930b84a0e45ae1694aeba0541f798e908a49d66b837f1"},
+    {file = "pillow-10.2.0-cp312-cp312-macosx_10_10_x86_64.whl", hash = "sha256:1b5e1b74d1bd1b78bc3477528919414874748dd363e6272efd5abf7654e68bef"},
+    {file = "pillow-10.2.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:0eae2073305f451d8ecacb5474997c08569fb4eb4ac231ffa4ad7d342fdc25ac"},
+    {file = "pillow-10.2.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b7c2286c23cd350b80d2fc9d424fc797575fb16f854b831d16fd47ceec078f2c"},
+    {file = "pillow-10.2.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1e23412b5c41e58cec602f1135c57dfcf15482013ce6e5f093a86db69646a5aa"},
+    {file = "pillow-10.2.0-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:52a50aa3fb3acb9cf7213573ef55d31d6eca37f5709c69e6858fe3bc04a5c2a2"},
+    {file = "pillow-10.2.0-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:127cee571038f252a552760076407f9cff79761c3d436a12af6000cd182a9d04"},
+    {file = "pillow-10.2.0-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:8d12251f02d69d8310b046e82572ed486685c38f02176bd08baf216746eb947f"},
+    {file = "pillow-10.2.0-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:54f1852cd531aa981bc0965b7d609f5f6cc8ce8c41b1139f6ed6b3c54ab82bfb"},
+    {file = "pillow-10.2.0-cp312-cp312-win32.whl", hash = "sha256:257d8788df5ca62c980314053197f4d46eefedf4e6175bc9412f14412ec4ea2f"},
+    {file = "pillow-10.2.0-cp312-cp312-win_amd64.whl", hash = "sha256:154e939c5f0053a383de4fd3d3da48d9427a7e985f58af8e94d0b3c9fcfcf4f9"},
+    {file = "pillow-10.2.0-cp312-cp312-win_arm64.whl", hash = "sha256:f379abd2f1e3dddb2b61bc67977a6b5a0a3f7485538bcc6f39ec76163891ee48"},
+    {file = "pillow-10.2.0-cp38-cp38-macosx_10_10_x86_64.whl", hash = "sha256:8373c6c251f7ef8bda6675dd6d2b3a0fcc31edf1201266b5cf608b62a37407f9"},
+    {file = "pillow-10.2.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:870ea1ada0899fd0b79643990809323b389d4d1d46c192f97342eeb6ee0b8483"},
+    {file = "pillow-10.2.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b4b6b1e20608493548b1f32bce8cca185bf0480983890403d3b8753e44077129"},
+    {file = "pillow-10.2.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3031709084b6e7852d00479fd1d310b07d0ba82765f973b543c8af5061cf990e"},
+    {file = "pillow-10.2.0-cp38-cp38-manylinux_2_28_aarch64.whl", hash = "sha256:3ff074fc97dd4e80543a3e91f69d58889baf2002b6be64347ea8cf5533188213"},
+    {file = "pillow-10.2.0-cp38-cp38-manylinux_2_28_x86_64.whl", hash = "sha256:cb4c38abeef13c61d6916f264d4845fab99d7b711be96c326b84df9e3e0ff62d"},
+    {file = "pillow-10.2.0-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:b1b3020d90c2d8e1dae29cf3ce54f8094f7938460fb5ce8bc5c01450b01fbaf6"},
+    {file = "pillow-10.2.0-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:170aeb00224ab3dc54230c797f8404507240dd868cf52066f66a41b33169bdbe"},
+    {file = "pillow-10.2.0-cp38-cp38-win32.whl", hash = "sha256:c4225f5220f46b2fde568c74fca27ae9771536c2e29d7c04f4fb62c83275ac4e"},
+    {file = "pillow-10.2.0-cp38-cp38-win_amd64.whl", hash = "sha256:0689b5a8c5288bc0504d9fcee48f61a6a586b9b98514d7d29b840143d6734f39"},
+    {file = "pillow-10.2.0-cp39-cp39-macosx_10_10_x86_64.whl", hash = "sha256:b792a349405fbc0163190fde0dc7b3fef3c9268292586cf5645598b48e63dc67"},
+    {file = "pillow-10.2.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:c570f24be1e468e3f0ce7ef56a89a60f0e05b30a3669a459e419c6eac2c35364"},
+    {file = "pillow-10.2.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d8ecd059fdaf60c1963c58ceb8997b32e9dc1b911f5da5307aab614f1ce5c2fb"},
+    {file = "pillow-10.2.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c365fd1703040de1ec284b176d6af5abe21b427cb3a5ff68e0759e1e313a5e7e"},
+    {file = "pillow-10.2.0-cp39-cp39-manylinux_2_28_aarch64.whl", hash = "sha256:70c61d4c475835a19b3a5aa42492409878bbca7438554a1f89d20d58a7c75c01"},
+    {file = "pillow-10.2.0-cp39-cp39-manylinux_2_28_x86_64.whl", hash = "sha256:b6f491cdf80ae540738859d9766783e3b3c8e5bd37f5dfa0b76abdecc5081f13"},
+    {file = "pillow-10.2.0-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:9d189550615b4948f45252d7f005e53c2040cea1af5b60d6f79491a6e147eef7"},
+    {file = "pillow-10.2.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:49d9ba1ed0ef3e061088cd1e7538a0759aab559e2e0a80a36f9fd9d8c0c21591"},
+    {file = "pillow-10.2.0-cp39-cp39-win32.whl", hash = "sha256:babf5acfede515f176833ed6028754cbcd0d206f7f614ea3447d67c33be12516"},
+    {file = "pillow-10.2.0-cp39-cp39-win_amd64.whl", hash = "sha256:0304004f8067386b477d20a518b50f3fa658a28d44e4116970abfcd94fac34a8"},
+    {file = "pillow-10.2.0-cp39-cp39-win_arm64.whl", hash = "sha256:0fb3e7fc88a14eacd303e90481ad983fd5b69c761e9e6ef94c983f91025da869"},
+    {file = "pillow-10.2.0-pp310-pypy310_pp73-macosx_10_10_x86_64.whl", hash = "sha256:322209c642aabdd6207517e9739c704dc9f9db943015535783239022002f054a"},
+    {file = "pillow-10.2.0-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3eedd52442c0a5ff4f887fab0c1c0bb164d8635b32c894bc1faf4c618dd89df2"},
+    {file = "pillow-10.2.0-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:cb28c753fd5eb3dd859b4ee95de66cc62af91bcff5db5f2571d32a520baf1f04"},
+    {file = "pillow-10.2.0-pp310-pypy310_pp73-manylinux_2_28_aarch64.whl", hash = "sha256:33870dc4653c5017bf4c8873e5488d8f8d5f8935e2f1fb9a2208c47cdd66efd2"},
+    {file = "pillow-10.2.0-pp310-pypy310_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:3c31822339516fb3c82d03f30e22b1d038da87ef27b6a78c9549888f8ceda39a"},
+    {file = "pillow-10.2.0-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:a2b56ba36e05f973d450582fb015594aaa78834fefe8dfb8fcd79b93e64ba4c6"},
+    {file = "pillow-10.2.0-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:d8e6aeb9201e655354b3ad049cb77d19813ad4ece0df1249d3c793de3774f8c7"},
+    {file = "pillow-10.2.0-pp39-pypy39_pp73-macosx_10_10_x86_64.whl", hash = "sha256:2247178effb34a77c11c0e8ac355c7a741ceca0a732b27bf11e747bbc950722f"},
+    {file = "pillow-10.2.0-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:15587643b9e5eb26c48e49a7b33659790d28f190fc514a322d55da2fb5c2950e"},
+    {file = "pillow-10.2.0-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:753cd8f2086b2b80180d9b3010dd4ed147efc167c90d3bf593fe2af21265e5a5"},
+    {file = "pillow-10.2.0-pp39-pypy39_pp73-manylinux_2_28_aarch64.whl", hash = "sha256:7c8f97e8e7a9009bcacbe3766a36175056c12f9a44e6e6f2d5caad06dcfbf03b"},
+    {file = "pillow-10.2.0-pp39-pypy39_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:d1b35bcd6c5543b9cb547dee3150c93008f8dd0f1fef78fc0cd2b141c5baf58a"},
+    {file = "pillow-10.2.0-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:fe4c15f6c9285dc54ce6553a3ce908ed37c8f3825b5a51a15c91442bb955b868"},
+    {file = "pillow-10.2.0.tar.gz", hash = "sha256:e87f0b2c78157e12d7686b27d63c070fd65d994e8ddae6f328e0dcf4a0cd007e"},
+]
+
+[package.extras]
+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 = "pycparser"
+version = "2.21"
+description = "C parser in Python"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+files = [
+    {file = "pycparser-2.21-py2.py3-none-any.whl", hash = "sha256:8ee45429555515e1f6b185e78100aea234072576aa43ab53aefcae078162fca9"},
+    {file = "pycparser-2.21.tar.gz", hash = "sha256:e644fdec12f7872f86c58ff790da456218b10f863970249516d60a5eaca77206"},
+]
+
+[[package]]
+name = "pygame"
+version = "2.5.2"
+description = "Python Game Development"
+optional = false
+python-versions = ">=3.6"
+files = [
+    {file = "pygame-2.5.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:a0769eb628c818761755eb0a0ca8216b95270ea8cbcbc82227e39ac9644643da"},
+    {file = "pygame-2.5.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:ed9a3d98adafa0805ccbaaff5d2996a2b5795381285d8437a4a5d248dbd12b4a"},
+    {file = "pygame-2.5.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f30d1618672a55e8c6669281ba264464b3ab563158e40d89e8c8b3faa0febebd"},
+    {file = "pygame-2.5.2-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:39690e9be9baf58b7359d1f3b2336e1fd6f92fedbbce42987be5df27f8d30718"},
+    {file = "pygame-2.5.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:03879ec299c9f4ba23901b2649a96b2143f0a5d787f0b6c39469989e2320caf1"},
+    {file = "pygame-2.5.2-cp310-cp310-win32.whl", hash = "sha256:74e1d6284100e294f445832e6f6343be4fe4748decc4f8a51131ae197dae8584"},
+    {file = "pygame-2.5.2-cp310-cp310-win_amd64.whl", hash = "sha256:485239c7d32265fd35b76ae8f64f34b0637ae11e69d76de15710c4b9edcc7c8d"},
+    {file = "pygame-2.5.2-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:34646ca20e163dc6f6cf8170f1e12a2e41726780112594ac061fa448cf7ccd75"},
+    {file = "pygame-2.5.2-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:3b8a6e351665ed26ea791f0e1fd649d3f483e8681892caef9d471f488f9ea5ee"},
+    {file = "pygame-2.5.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:dc346965847aef00013fa2364f41a64f068cd096dcc7778fc306ca3735f0eedf"},
+    {file = "pygame-2.5.2-cp311-cp311-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:35632035fd81261f2d797fa810ea8c46111bd78ceb6089d52b61ed7dc3c5d05f"},
+    {file = "pygame-2.5.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0e24d05184e4195fe5ebcdce8b18ecb086f00182b9ae460a86682d312ce8d31f"},
+    {file = "pygame-2.5.2-cp311-cp311-win32.whl", hash = "sha256:f02c1c7505af18d426d355ac9872bd5c916b27f7b0fe224749930662bea47a50"},
+    {file = "pygame-2.5.2-cp311-cp311-win_amd64.whl", hash = "sha256:6d58c8cf937815d3b7cdc0fa9590c5129cb2c9658b72d00e8a4568dea2ff1d42"},
+    {file = "pygame-2.5.2-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:1a2a43802bb5e89ce2b3b775744e78db4f9a201bf8d059b946c61722840ceea8"},
+    {file = "pygame-2.5.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:1c289f2613c44fe70a1e40769de4a49c5ab5a29b9376f1692bb1a15c9c1c9bfa"},
+    {file = "pygame-2.5.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:074aa6c6e110c925f7f27f00c7733c6303407edc61d738882985091d1eb2ef17"},
+    {file = "pygame-2.5.2-cp312-cp312-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:fe0228501ec616779a0b9c4299e837877783e18df294dd690b9ab0eed3d8aaab"},
+    {file = "pygame-2.5.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:31648d38ecdc2335ffc0e38fb18a84b3339730521505dac68514f83a1092e3f4"},
+    {file = "pygame-2.5.2-cp312-cp312-win32.whl", hash = "sha256:224c308856334bc792f696e9278e50d099a87c116f7fc314cd6aa3ff99d21592"},
+    {file = "pygame-2.5.2-cp312-cp312-win_amd64.whl", hash = "sha256:dd2d2650faf54f9a0f5bd0db8409f79609319725f8f08af6507a0609deadcad4"},
+    {file = "pygame-2.5.2-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:9b30bc1220c457169571aac998e54b013aaeb732d2fd8744966cb1cfab1f61d1"},
+    {file = "pygame-2.5.2-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:78fcd7643358b886a44127ff7dec9041c056c212b3a98977674f83f99e9b12d3"},
+    {file = "pygame-2.5.2-cp36-cp36m-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:35cf093a51cb294ede56c29d4acf41538c00f297fcf78a9b186fb7d23c0577b6"},
+    {file = "pygame-2.5.2-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6fe323acbf53a0195c8c98b1b941eba7ac24e3e2b28ae48e8cda566f15fc4945"},
+    {file = "pygame-2.5.2-cp36-cp36m-win32.whl", hash = "sha256:5697528266b4716d9cdd44a5a1d210f4d86ef801d0f64ca5da5d0816704009d9"},
+    {file = "pygame-2.5.2-cp36-cp36m-win_amd64.whl", hash = "sha256:edda1f7cff4806a4fa39e0e8ccd75f38d1d340fa5fc52d8582ade87aca247d92"},
+    {file = "pygame-2.5.2-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:9bd738fd4ecc224769d0b4a719f96900a86578e26e0105193658a32966df2aae"},
+    {file = "pygame-2.5.2-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:30a8d7cf12363b4140bf2f93b5eec4028376ca1d0fe4b550588f836279485308"},
+    {file = "pygame-2.5.2-cp37-cp37m-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:bc12e4dea3e88ea8a553de6d56a37b704dbe2aed95105889f6afeb4b96e62097"},
+    {file = "pygame-2.5.2-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2b34c73cb328024f8db3cb6487a37e54000148988275d8d6e5adf99d9323c937"},
+    {file = "pygame-2.5.2-cp37-cp37m-win32.whl", hash = "sha256:7d0a2794649defa57ef50b096a99f7113d3d0c2e32d1426cafa7d618eadce4c7"},
+    {file = "pygame-2.5.2-cp37-cp37m-win_amd64.whl", hash = "sha256:41f8779f52e0f6e6e6ccb8f0b5536e432bf386ee29c721a1c22cada7767b0cef"},
+    {file = "pygame-2.5.2-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:677e37bc0ea7afd89dde5a88ced4458aa8656159c70a576eea68b5622ee1997b"},
+    {file = "pygame-2.5.2-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:47a8415d2bd60e6909823b5643a1d4ef5cc29417d817f2a214b255f6fa3a1e4c"},
+    {file = "pygame-2.5.2-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4ff21201df6278b8ca2e948fb148ffe88f5481fd03760f381dd61e45954c7dff"},
+    {file = "pygame-2.5.2-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:d29a84b2e02814b9ba925357fd2e1df78efe5e1aa64dc3051eaed95d2b96eafd"},
+    {file = "pygame-2.5.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d78485c4d21133d6b2fbb504cd544ca655e50b6eb551d2995b3aa6035928adda"},
+    {file = "pygame-2.5.2-cp38-cp38-win32.whl", hash = "sha256:d851247239548aa357c4a6840fb67adc2d570ce7cb56988d036a723d26b48bff"},
+    {file = "pygame-2.5.2-cp38-cp38-win_amd64.whl", hash = "sha256:88d1cdacc2d3471eceab98bf0c93c14d3a8461f93e58e3d926f20d4de3a75554"},
+    {file = "pygame-2.5.2-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:4f1559e7efe4efb9dc19d2d811d702f325d9605f9f6f9ececa39ee6890c798f5"},
+    {file = "pygame-2.5.2-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:cf2191b756ceb0e8458a761d0c665b0c70b538570449e0d39b75a5ba94ac5cf0"},
+    {file = "pygame-2.5.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6cf2257447ce7f2d6de37e5fb019d2bbe32ed05a5721ace8bc78c2d9beaf3aee"},
+    {file = "pygame-2.5.2-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:d75cbbfaba2b81434d62631d0b08b85fab16cf4a36e40b80298d3868927e1299"},
+    {file = "pygame-2.5.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:daca456d5b9f52e088e06a127dec182b3638a775684fb2260f25d664351cf1ae"},
+    {file = "pygame-2.5.2-cp39-cp39-win32.whl", hash = "sha256:3b3e619e33d11c297d7a57a82db40681f9c2c3ae1d5bf06003520b4fe30c435d"},
+    {file = "pygame-2.5.2-cp39-cp39-win_amd64.whl", hash = "sha256:1822d534bb7fe756804647b6da2c9ea5d7a62d8796b2e15d172d3be085de28c6"},
+    {file = "pygame-2.5.2-pp36-pypy36_pp73-win32.whl", hash = "sha256:e708fc8f709a0fe1d1876489345f2e443d47f3976d33455e2e1e937f972f8677"},
+    {file = "pygame-2.5.2-pp37-pypy37_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:c13edebc43c240fb0532969e914f0ccefff5ae7e50b0b788d08ad2c15ef793e4"},
+    {file = "pygame-2.5.2-pp37-pypy37_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:263b4a7cbfc9fe2055abc21b0251cc17dea6dff750f0e1c598919ff350cdbffe"},
+    {file = "pygame-2.5.2-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:e58e2b0c791041e4bccafa5bd7650623ba1592b8fe62ae0a276b7d0ecb314b6c"},
+    {file = "pygame-2.5.2-pp38-pypy38_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:a0bd67426c02ffe6c9827fc4bcbda9442fbc451d29b17c83a3c088c56fef2c90"},
+    {file = "pygame-2.5.2-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9dcff6cbba1584cf7732ce1dbdd044406cd4f6e296d13bcb7fba963fb4aeefc9"},
+    {file = "pygame-2.5.2-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:ce4b6c0bfe44d00bb0998a6517bd0cf9455f642f30f91bc671ad41c05bf6f6ae"},
+    {file = "pygame-2.5.2-pp39-pypy39_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:68c4e8e60b725ffc7a6c6ecd9bb5fcc5ed2d6e0e2a2c4a29a8454856ef16ad63"},
+    {file = "pygame-2.5.2-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1f3849f97372a3381c66955f99a0d58485ccd513c3d00c030b869094ce6997a6"},
+    {file = "pygame-2.5.2.tar.gz", hash = "sha256:c1b89eb5d539e7ac5cf75513125fb5f2f0a2d918b1fd6e981f23bf0ac1b1c24a"},
+]
+
+[[package]]
+name = "pymunk"
+version = "6.6.0"
+description = "Pymunk is a easy-to-use pythonic 2d physics library"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "pymunk-6.6.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:6da50dd97683337a290110d594fad07a75153d2d837b570ef972478d739c33f8"},
+    {file = "pymunk-6.6.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:bcd7d16a2b4d51d45d6780a701f65c8d5b36fdf545c3f4738910da41e2a9c4ee"},
+    {file = "pymunk-6.6.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:32c91a783b645267518588515acdc3ff315135297eef39386d488c4ff2a7c139"},
+    {file = "pymunk-6.6.0-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:74694f92f46fe54e2c033b598b2c38185f456711888955aa3f67003692a3ef91"},
+    {file = "pymunk-6.6.0-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:fe011afb3f7594a679ba35dc7a44e12c8c8aacb55e58d54f14bfe8b82959695c"},
+    {file = "pymunk-6.6.0-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:60e5cc6e33f7e880418f75a7d6b5ac3eed47396bbe7c68ca47c389de3b5d1d3a"},
+    {file = "pymunk-6.6.0-cp310-cp310-win32.whl", hash = "sha256:10518074e33d4fe723bce795f705ad3e850ecec9987559ec3fa072a6539c47ad"},
+    {file = "pymunk-6.6.0-cp310-cp310-win_amd64.whl", hash = "sha256:5b163b28f9500df1bb5e123e2dba2d1f255e63be6ca098544936a93c05022a43"},
+    {file = "pymunk-6.6.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:8322594fc68858bfc0142f2f7a100cfb4edb85678a75983ce2fc58ed763afb96"},
+    {file = "pymunk-6.6.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:4c1d0be60b781d1b8bb11303b25936d01cdef7ccfcc3a68b0c2fd689f63ac11c"},
+    {file = "pymunk-6.6.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9462200c47f3eb344373077dc01384cb16355a982ce0e33571201f3b7ee44487"},
+    {file = "pymunk-6.6.0-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ede46cc44432b1316a402129fc225743f7e9f502d0d055790eab877627ddfd98"},
+    {file = "pymunk-6.6.0-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:3582cd67d6ac16f122d2b7100e0b00d9b55f97a0a7e21336df885166e2bffdc3"},
+    {file = "pymunk-6.6.0-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:e4247ede965df39d2fd7ae25e9360762cce61f4d39b95af91d29c1c556c80777"},
+    {file = "pymunk-6.6.0-cp311-cp311-win32.whl", hash = "sha256:a77f9bb634ab216ac8991f73aa68b4dadfd6690e8cb17627a6646dc8fecd6126"},
+    {file = "pymunk-6.6.0-cp311-cp311-win_amd64.whl", hash = "sha256:2f579e8c5498b3e8c0686841f1f5e3adf1bdd32b339ee36001ebae19bbafc008"},
+    {file = "pymunk-6.6.0-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:ad6ca584a9ea1d6a1536ae158350d73dbbdc637f302a86019b7fb299120439c4"},
+    {file = "pymunk-6.6.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:b86be4ecfb86d4af26c3dd2e390884305c3b8604e5df8550fbb2968d3ac78411"},
+    {file = "pymunk-6.6.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:68006cfb71351b6f23a81f541a2eca56596e69977e051e46cfe93a5ffdc410ef"},
+    {file = "pymunk-6.6.0-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:361d2fa43e65aa5e47dcb50e6b058b3814e19cbdb5bf062d2da78c2b3bdba192"},
+    {file = "pymunk-6.6.0-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:a3c35975f4172b024e0bb1be6f57f1048dcb469a8cf257c30123d11a9fe57e2a"},
+    {file = "pymunk-6.6.0-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:35a57546294656b5bb989e08426a4926e26a17726aef35daf34c2703ee54c0e9"},
+    {file = "pymunk-6.6.0-cp312-cp312-win32.whl", hash = "sha256:a68480440b60bf5acf3a7a8db1eb571e13ed425d5b693a20020f2efa9cc09592"},
+    {file = "pymunk-6.6.0-cp312-cp312-win_amd64.whl", hash = "sha256:f7ed11a1e2a306e4213d88a1879ae0fb7c2c983a890fa1b35ed26b9392213c02"},
+    {file = "pymunk-6.6.0-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:49961e339571d14afa9ebc815190ebfdda69e6ffd433536451bb07d6bc55e430"},
+    {file = "pymunk-6.6.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:70d9d5edcb2e90eeea0afb322c82d75a02e6bb77a9ff08b86daa2245a2c2a4ef"},
+    {file = "pymunk-6.6.0-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:622746251dda14507d3655b64c93a4509125c0a651265c473945f227ba5763ec"},
+    {file = "pymunk-6.6.0-cp37-cp37m-musllinux_1_1_i686.whl", hash = "sha256:35277485eb69cc5dda3f15b139723c77d69b9271f9fedf4264d08e8afdea67d0"},
+    {file = "pymunk-6.6.0-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:ceadcf03988c51697a3357d6dd3c96dd60e48b993734346edf8955fcd3770466"},
+    {file = "pymunk-6.6.0-cp37-cp37m-win32.whl", hash = "sha256:0d8e0e79135e86b6e0e686fd287f297488e728cb8276fc713cb33fdd7ce4f5f2"},
+    {file = "pymunk-6.6.0-cp37-cp37m-win_amd64.whl", hash = "sha256:259a371150a9e264851d0a9caa85b5a19ba661f364da630a231a3eb326d49ca1"},
+    {file = "pymunk-6.6.0-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:e97ad1ce7fa3e9ea15622d1e0c45e2757f02e1c947a354888c2014799575c100"},
+    {file = "pymunk-6.6.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:a4cd70ffc259b8069eabb54ed5c7cbc39d0f5158610791c14ad0437f6cb6d18d"},
+    {file = "pymunk-6.6.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1cf85e95774a89b2adf084c0129d62f69eaa23b97b800892ddcfa7862b931bbb"},
+    {file = "pymunk-6.6.0-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:b8d60b9fcde952d6e25c740a1ced5612ace59fa85e578986f7f053a538a681ed"},
+    {file = "pymunk-6.6.0-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:5652c423ea2769b1d44e33fd2b19f2a6f7f4a34acacec9a86b63c780ac611552"},
+    {file = "pymunk-6.6.0-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:939e46d9021cb5bc6ac4dbecafe89245af2b8325787869983b0a99181e37fd39"},
+    {file = "pymunk-6.6.0-cp38-cp38-win32.whl", hash = "sha256:7785f5ac0597be5693dd2da819233297984d324b6470bf31b76c71399f25a18e"},
+    {file = "pymunk-6.6.0-cp38-cp38-win_amd64.whl", hash = "sha256:26a0834207785878ba2bb244ab5616d9b6e09d01c2f19641f10247ca22d3c10e"},
+    {file = "pymunk-6.6.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:ed23f05a65687750cba4d6cde045147d28eee84e44cd33829b79601dc655adf3"},
+    {file = "pymunk-6.6.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:b2c23f2f182f91944c4ba5cfd6f652e873e6e8b113506c3eca255df5e6c79b6e"},
+    {file = "pymunk-6.6.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c51b6de5869dcb103467d8ac75f62a1a9f43faa18bf12e37e89247b2d5554a61"},
+    {file = "pymunk-6.6.0-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:5b44c4420b43cfdfedd2278e3beb60970a9a9564f1272c7cc74090931268ab43"},
+    {file = "pymunk-6.6.0-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:4b8d9c14275fd4853ae863e38bec8a7ae4c7aef4417550ff74fc9f68f120fa00"},
+    {file = "pymunk-6.6.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:31631a91bf29dde9c4a5f1283056cb91d451fe352f35a440c5cb668b0de19ad5"},
+    {file = "pymunk-6.6.0-cp39-cp39-win32.whl", hash = "sha256:832d83570d0781e2bcba555b0974e9a5f9ee592079dfd3b183a493cf0ceaac7f"},
+    {file = "pymunk-6.6.0-cp39-cp39-win_amd64.whl", hash = "sha256:88625cca15c90dc8c0c1b55113f0ff19a8e6601ac0981804d317660c0afde9e2"},
+    {file = "pymunk-6.6.0-pp310-pypy310_pp73-macosx_10_9_x86_64.whl", hash = "sha256:8e27a8c7b762d43e91f18c320ad849c113dead500184d151aa14bd11a62c2c47"},
+    {file = "pymunk-6.6.0-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:aad898ca45546f084b0d88f73c771e3de0d19acc65f1171a9dbdba171945a915"},
+    {file = "pymunk-6.6.0-pp310-pypy310_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:45f537c79e817330753e6ed220b3ff46b5b983266d5b85ce7c1381a77b33d1f3"},
+    {file = "pymunk-6.6.0-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:609341ff1329e59ee7a67b622973064c213111e87916981bc45838f38981ba47"},
+    {file = "pymunk-6.6.0-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:345b99d19cb848359fbefcaba54a5f1bcc8dd05b084563d693ca4d0622aa1079"},
+    {file = "pymunk-6.6.0-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f33c418b758e06960fa28e0434c14818c0d9755f431045db05cc93e646df9b22"},
+    {file = "pymunk-6.6.0-pp39-pypy39_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:59991310cb1a6f201878e9519cbb36ff746f825c9fac49fa76cf8c85b64bf7ad"},
+    {file = "pymunk-6.6.0-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:c7513caf1add221cfa1228c12e14e0997a7212e583a59f517b68e72b1f02e08f"},
+    {file = "pymunk-6.6.0.tar.gz", hash = "sha256:89be7b6ba237e313c440edfb99612de59bf119e43976d5c76802907cb7a3911c"},
+]
+
+[package.dependencies]
+cffi = ">=1.15.0"
+
+[package.extras]
+dev = ["aafigure", "matplotlib", "pygame", "pyglet (<2.0.0)", "sphinx", "wheel"]
+
+[[package]]
+name = "scikit-image"
+version = "0.22.0"
+description = "Image processing in Python"
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "scikit_image-0.22.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:74ec5c1d4693506842cc7c9487c89d8fc32aed064e9363def7af08b8f8cbb31d"},
+    {file = "scikit_image-0.22.0-cp310-cp310-macosx_12_0_arm64.whl", hash = "sha256:a05ae4fe03d802587ed8974e900b943275548cde6a6807b785039d63e9a7a5ff"},
+    {file = "scikit_image-0.22.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6a92dca3d95b1301442af055e196a54b5a5128c6768b79fc0a4098f1d662dee6"},
+    {file = "scikit_image-0.22.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3663d063d8bf2fb9bdfb0ca967b9ee3b6593139c860c7abc2d2351a8a8863938"},
+    {file = "scikit_image-0.22.0-cp310-cp310-win_amd64.whl", hash = "sha256:ebdbdc901bae14dab637f8d5c99f6d5cc7aaf4a3b6f4003194e003e9f688a6fc"},
+    {file = "scikit_image-0.22.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:95d6da2d8a44a36ae04437c76d32deb4e3c993ffc846b394b9949fd8ded73cb2"},
+    {file = "scikit_image-0.22.0-cp311-cp311-macosx_12_0_arm64.whl", hash = "sha256:2c6ef454a85f569659b813ac2a93948022b0298516b757c9c6c904132be327e2"},
+    {file = "scikit_image-0.22.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e87872f067444ee90a00dd49ca897208308645382e8a24bd3e76f301af2352cd"},
+    {file = "scikit_image-0.22.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c5c378db54e61b491b9edeefff87e49fcf7fdf729bb93c777d7a5f15d36f743e"},
+    {file = "scikit_image-0.22.0-cp311-cp311-win_amd64.whl", hash = "sha256:2bcb74adb0634258a67f66c2bb29978c9a3e222463e003b67ba12056c003971b"},
+    {file = "scikit_image-0.22.0-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:003ca2274ac0fac252280e7179ff986ff783407001459ddea443fe7916e38cff"},
+    {file = "scikit_image-0.22.0-cp312-cp312-macosx_12_0_arm64.whl", hash = "sha256:cf3c0c15b60ae3e557a0c7575fbd352f0c3ce0afca562febfe3ab80efbeec0e9"},
+    {file = "scikit_image-0.22.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f5b23908dd4d120e6aecb1ed0277563e8cbc8d6c0565bdc4c4c6475d53608452"},
+    {file = "scikit_image-0.22.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:be79d7493f320a964f8fcf603121595ba82f84720de999db0fcca002266a549a"},
+    {file = "scikit_image-0.22.0-cp312-cp312-win_amd64.whl", hash = "sha256:722b970aa5da725dca55252c373b18bbea7858c1cdb406e19f9b01a4a73b30b2"},
+    {file = "scikit_image-0.22.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:22318b35044cfeeb63ee60c56fc62450e5fe516228138f1d06c7a26378248a86"},
+    {file = "scikit_image-0.22.0-cp39-cp39-macosx_12_0_arm64.whl", hash = "sha256:9e801c44a814afdadeabf4dffdffc23733e393767958b82319706f5fa3e1eaa9"},
+    {file = "scikit_image-0.22.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c472a1fb3665ec5c00423684590631d95f9afcbc97f01407d348b821880b2cb3"},
+    {file = "scikit_image-0.22.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3b7a6c89e8d6252332121b58f50e1625c35f7d6a85489c0b6b7ee4f5155d547a"},
+    {file = "scikit_image-0.22.0-cp39-cp39-win_amd64.whl", hash = "sha256:5071b8f6341bfb0737ab05c8ab4ac0261f9e25dbcc7b5d31e5ed230fd24a7929"},
+    {file = "scikit_image-0.22.0.tar.gz", hash = "sha256:018d734df1d2da2719087d15f679d19285fce97cd37695103deadfaef2873236"},
+]
+
+[package.dependencies]
+imageio = ">=2.27"
+lazy_loader = ">=0.3"
+networkx = ">=2.8"
+numpy = ">=1.22"
+packaging = ">=21"
+pillow = ">=9.0.1"
+scipy = ">=1.8"
+tifffile = ">=2022.8.12"
+
+[package.extras]
+build = ["Cython (>=0.29.32)", "build", "meson-python (>=0.14)", "ninja", "numpy (>=1.22)", "packaging (>=21)", "pythran", "setuptools (>=67)", "spin (==0.6)", "wheel"]
+data = ["pooch (>=1.6.0)"]
+developer = ["pre-commit", "tomli"]
+docs = ["PyWavelets (>=1.1.1)", "dask[array] (>=2022.9.2)", "ipykernel", "ipywidgets", "kaleido", "matplotlib (>=3.5)", "myst-parser", "numpydoc (>=1.6)", "pandas (>=1.5)", "plotly (>=5.10)", "pooch (>=1.6)", "pydata-sphinx-theme (>=0.14.1)", "pytest-runner", "scikit-learn (>=1.1)", "seaborn (>=0.11)", "sphinx (>=7.2)", "sphinx-copybutton", "sphinx-gallery (>=0.14)", "sphinx_design (>=0.5)", "tifffile (>=2022.8.12)"]
+optional = ["PyWavelets (>=1.1.1)", "SimpleITK", "astropy (>=5.0)", "cloudpickle (>=0.2.1)", "dask[array] (>=2021.1.0)", "matplotlib (>=3.5)", "pooch (>=1.6.0)", "pyamg", "scikit-learn (>=1.1)"]
+test = ["asv", "matplotlib (>=3.5)", "numpydoc (>=1.5)", "pooch (>=1.6.0)", "pytest (>=7.0)", "pytest-cov (>=2.11.0)", "pytest-faulthandler", "pytest-localserver"]
+
+[[package]]
+name = "scipy"
+version = "1.12.0"
+description = "Fundamental algorithms for scientific computing in Python"
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "scipy-1.12.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:78e4402e140879387187f7f25d91cc592b3501a2e51dfb320f48dfb73565f10b"},
+    {file = "scipy-1.12.0-cp310-cp310-macosx_12_0_arm64.whl", hash = "sha256:f5f00ebaf8de24d14b8449981a2842d404152774c1a1d880c901bf454cb8e2a1"},
+    {file = "scipy-1.12.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e53958531a7c695ff66c2e7bb7b79560ffdc562e2051644c5576c39ff8efb563"},
+    {file = "scipy-1.12.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5e32847e08da8d895ce09d108a494d9eb78974cf6de23063f93306a3e419960c"},
+    {file = "scipy-1.12.0-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:4c1020cad92772bf44b8e4cdabc1df5d87376cb219742549ef69fc9fd86282dd"},
+    {file = "scipy-1.12.0-cp310-cp310-win_amd64.whl", hash = "sha256:75ea2a144096b5e39402e2ff53a36fecfd3b960d786b7efd3c180e29c39e53f2"},
+    {file = "scipy-1.12.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:408c68423f9de16cb9e602528be4ce0d6312b05001f3de61fe9ec8b1263cad08"},
+    {file = "scipy-1.12.0-cp311-cp311-macosx_12_0_arm64.whl", hash = "sha256:5adfad5dbf0163397beb4aca679187d24aec085343755fcdbdeb32b3679f254c"},
+    {file = "scipy-1.12.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c3003652496f6e7c387b1cf63f4bb720951cfa18907e998ea551e6de51a04467"},
+    {file = "scipy-1.12.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8b8066bce124ee5531d12a74b617d9ac0ea59245246410e19bca549656d9a40a"},
+    {file = "scipy-1.12.0-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:8bee4993817e204d761dba10dbab0774ba5a8612e57e81319ea04d84945375ba"},
+    {file = "scipy-1.12.0-cp311-cp311-win_amd64.whl", hash = "sha256:a24024d45ce9a675c1fb8494e8e5244efea1c7a09c60beb1eeb80373d0fecc70"},
+    {file = "scipy-1.12.0-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:e7e76cc48638228212c747ada851ef355c2bb5e7f939e10952bc504c11f4e372"},
+    {file = "scipy-1.12.0-cp312-cp312-macosx_12_0_arm64.whl", hash = "sha256:f7ce148dffcd64ade37b2df9315541f9adad6efcaa86866ee7dd5db0c8f041c3"},
+    {file = "scipy-1.12.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9c39f92041f490422924dfdb782527a4abddf4707616e07b021de33467f917bc"},
+    {file = "scipy-1.12.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a7ebda398f86e56178c2fa94cad15bf457a218a54a35c2a7b4490b9f9cb2676c"},
+    {file = "scipy-1.12.0-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:95e5c750d55cf518c398a8240571b0e0782c2d5a703250872f36eaf737751338"},
+    {file = "scipy-1.12.0-cp312-cp312-win_amd64.whl", hash = "sha256:e646d8571804a304e1da01040d21577685ce8e2db08ac58e543eaca063453e1c"},
+    {file = "scipy-1.12.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:913d6e7956c3a671de3b05ccb66b11bc293f56bfdef040583a7221d9e22a2e35"},
+    {file = "scipy-1.12.0-cp39-cp39-macosx_12_0_arm64.whl", hash = "sha256:bba1b0c7256ad75401c73e4b3cf09d1f176e9bd4248f0d3112170fb2ec4db067"},
+    {file = "scipy-1.12.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:730badef9b827b368f351eacae2e82da414e13cf8bd5051b4bdfd720271a5371"},
+    {file = "scipy-1.12.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6546dc2c11a9df6926afcbdd8a3edec28566e4e785b915e849348c6dd9f3f490"},
+    {file = "scipy-1.12.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:196ebad3a4882081f62a5bf4aeb7326aa34b110e533aab23e4374fcccb0890dc"},
+    {file = "scipy-1.12.0-cp39-cp39-win_amd64.whl", hash = "sha256:b360f1b6b2f742781299514e99ff560d1fe9bd1bff2712894b52abe528d1fd1e"},
+    {file = "scipy-1.12.0.tar.gz", hash = "sha256:4bf5abab8a36d20193c698b0f1fc282c1d083c94723902c447e5d2f1780936a3"},
+]
+
+[package.dependencies]
+numpy = ">=1.22.4,<1.29.0"
+
+[package.extras]
+dev = ["click", "cython-lint (>=0.12.2)", "doit (>=0.36.0)", "mypy", "pycodestyle", "pydevtool", "rich-click", "ruff", "types-psutil", "typing_extensions"]
+doc = ["jupytext", "matplotlib (>2)", "myst-nb", "numpydoc", "pooch", "pydata-sphinx-theme (==0.9.0)", "sphinx (!=4.1.0)", "sphinx-design (>=0.2.0)"]
+test = ["asv", "gmpy2", "hypothesis", "mpmath", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
+
+[[package]]
+name = "shapely"
+version = "2.0.3"
+description = "Manipulation and analysis of geometric objects"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "shapely-2.0.3-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:af7e9abe180b189431b0f490638281b43b84a33a960620e6b2e8d3e3458b61a1"},
+    {file = "shapely-2.0.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:98040462b36ced9671e266b95c326b97f41290d9d17504a1ee4dc313a7667b9c"},
+    {file = "shapely-2.0.3-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:71eb736ef2843f23473c6e37f6180f90f0a35d740ab284321548edf4e55d9a52"},
+    {file = "shapely-2.0.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:881eb9dbbb4a6419667e91fcb20313bfc1e67f53dbb392c6840ff04793571ed1"},
+    {file = "shapely-2.0.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f10d2ccf0554fc0e39fad5886c839e47e207f99fdf09547bc687a2330efda35b"},
+    {file = "shapely-2.0.3-cp310-cp310-win32.whl", hash = "sha256:6dfdc077a6fcaf74d3eab23a1ace5abc50c8bce56ac7747d25eab582c5a2990e"},
+    {file = "shapely-2.0.3-cp310-cp310-win_amd64.whl", hash = "sha256:64c5013dacd2d81b3bb12672098a0b2795c1bf8190cfc2980e380f5ef9d9e4d9"},
+    {file = "shapely-2.0.3-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:56cee3e4e8159d6f2ce32e421445b8e23154fd02a0ac271d6a6c0b266a8e3cce"},
+    {file = "shapely-2.0.3-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:619232c8276fded09527d2a9fd91a7885ff95c0ff9ecd5e3cb1e34fbb676e2ae"},
+    {file = "shapely-2.0.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:b2a7d256db6f5b4b407dc0c98dd1b2fcf1c9c5814af9416e5498d0a2e4307a4b"},
+    {file = "shapely-2.0.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e45f0c8cd4583647db3216d965d49363e6548c300c23fd7e57ce17a03f824034"},
+    {file = "shapely-2.0.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:13cb37d3826972a82748a450328fe02a931dcaed10e69a4d83cc20ba021bc85f"},
+    {file = "shapely-2.0.3-cp311-cp311-win32.whl", hash = "sha256:9302d7011e3e376d25acd30d2d9e70d315d93f03cc748784af19b00988fc30b1"},
+    {file = "shapely-2.0.3-cp311-cp311-win_amd64.whl", hash = "sha256:6b464f2666b13902835f201f50e835f2f153f37741db88f68c7f3b932d3505fa"},
+    {file = "shapely-2.0.3-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:e86e7cb8e331a4850e0c2a8b2d66dc08d7a7b301b8d1d34a13060e3a5b4b3b55"},
+    {file = "shapely-2.0.3-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:c91981c99ade980fc49e41a544629751a0ccd769f39794ae913e53b07b2f78b9"},
+    {file = "shapely-2.0.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:bd45d456983dc60a42c4db437496d3f08a4201fbf662b69779f535eb969660af"},
+    {file = "shapely-2.0.3-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:882fb1ffc7577e88c1194f4f1757e277dc484ba096a3b94844319873d14b0f2d"},
+    {file = "shapely-2.0.3-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b9f2d93bff2ea52fa93245798cddb479766a18510ea9b93a4fb9755c79474889"},
+    {file = "shapely-2.0.3-cp312-cp312-win32.whl", hash = "sha256:99abad1fd1303b35d991703432c9481e3242b7b3a393c186cfb02373bf604004"},
+    {file = "shapely-2.0.3-cp312-cp312-win_amd64.whl", hash = "sha256:6f555fe3304a1f40398977789bc4fe3c28a11173196df9ece1e15c5bc75a48db"},
+    {file = "shapely-2.0.3-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:a983cc418c1fa160b7d797cfef0e0c9f8c6d5871e83eae2c5793fce6a837fad9"},
+    {file = "shapely-2.0.3-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:18bddb8c327f392189a8d5d6b9a858945722d0bb95ccbd6a077b8e8fc4c7890d"},
+    {file = "shapely-2.0.3-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:442f4dcf1eb58c5a4e3428d88e988ae153f97ab69a9f24e07bf4af8038536325"},
+    {file = "shapely-2.0.3-cp37-cp37m-win32.whl", hash = "sha256:31a40b6e3ab00a4fd3a1d44efb2482278642572b8e0451abdc8e0634b787173e"},
+    {file = "shapely-2.0.3-cp37-cp37m-win_amd64.whl", hash = "sha256:59b16976c2473fec85ce65cc9239bef97d4205ab3acead4e6cdcc72aee535679"},
+    {file = "shapely-2.0.3-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:705efbce1950a31a55b1daa9c6ae1c34f1296de71ca8427974ec2f27d57554e3"},
+    {file = "shapely-2.0.3-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:601c5c0058a6192df704cb889439f64994708563f57f99574798721e9777a44b"},
+    {file = "shapely-2.0.3-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:f24ecbb90a45c962b3b60d8d9a387272ed50dc010bfe605f1d16dfc94772d8a1"},
+    {file = "shapely-2.0.3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d8c2a2989222c6062f7a0656e16276c01bb308bc7e5d999e54bf4e294ce62e76"},
+    {file = "shapely-2.0.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:42bceb9bceb3710a774ce04908fda0f28b291323da2688f928b3f213373b5aee"},
+    {file = "shapely-2.0.3-cp38-cp38-win32.whl", hash = "sha256:54d925c9a311e4d109ec25f6a54a8bd92cc03481a34ae1a6a92c1fe6729b7e01"},
+    {file = "shapely-2.0.3-cp38-cp38-win_amd64.whl", hash = "sha256:300d203b480a4589adefff4c4af0b13919cd6d760ba3cbb1e56275210f96f654"},
+    {file = "shapely-2.0.3-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:083d026e97b6c1f4a9bd2a9171c7692461092ed5375218170d91705550eecfd5"},
+    {file = "shapely-2.0.3-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:27b6e1910094d93e9627f2664121e0e35613262fc037051680a08270f6058daf"},
+    {file = "shapely-2.0.3-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:71b2de56a9e8c0e5920ae5ddb23b923490557ac50cb0b7fa752761bf4851acde"},
+    {file = "shapely-2.0.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4d279e56bbb68d218d63f3efc80c819cedcceef0e64efbf058a1df89dc57201b"},
+    {file = "shapely-2.0.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:88566d01a30f0453f7d038db46bc83ce125e38e47c5f6bfd4c9c287010e9bf74"},
+    {file = "shapely-2.0.3-cp39-cp39-win32.whl", hash = "sha256:58afbba12c42c6ed44c4270bc0e22f3dadff5656d711b0ad335c315e02d04707"},
+    {file = "shapely-2.0.3-cp39-cp39-win_amd64.whl", hash = "sha256:5026b30433a70911979d390009261b8c4021ff87c7c3cbd825e62bb2ffa181bc"},
+    {file = "shapely-2.0.3.tar.gz", hash = "sha256:4d65d0aa7910af71efa72fd6447e02a8e5dd44da81a983de9d736d6e6ccbe674"},
+]
+
+[package.dependencies]
+numpy = ">=1.14,<2"
+
+[package.extras]
+docs = ["matplotlib", "numpydoc (==1.1.*)", "sphinx", "sphinx-book-theme", "sphinx-remove-toctrees"]
+test = ["pytest", "pytest-cov"]
+
+[[package]]
+name = "tifffile"
+version = "2024.2.12"
+description = "Read and write TIFF files"
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "tifffile-2024.2.12-py3-none-any.whl", hash = "sha256:870998f82fbc94ff7c3528884c1b0ae54863504ff51dbebea431ac3fa8fb7c21"},
+    {file = "tifffile-2024.2.12.tar.gz", hash = "sha256:4920a3ec8e8e003e673d3c6531863c99eedd570d1b8b7e141c072ed78ff8030d"},
+]
+
+[package.dependencies]
+numpy = "*"
+
+[package.extras]
+all = ["defusedxml", "fsspec", "imagecodecs (>=2023.8.12)", "lxml", "matplotlib", "zarr"]
+
+[[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"},
+    {file = "typing_extensions-4.10.0.tar.gz", hash = "sha256:b0abd7c89e8fb96f98db18d86106ff1d90ab692004eb746cf6eda2682f91b3cb"},
+]
+
+[metadata]
+lock-version = "2.0"
+python-versions = "^3.10"
+content-hash = "bedbec07c63d805de4503e1158d9f656e675831e9dd69a1e79f993dcf6da0295"

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"},
+]
+
+[package.dependencies]
+cloudpickle = ">=1.2.0"
+farama-notifications = ">=0.0.1"
+numpy = ">=1.21.0"
+typing-extensions = ">=4.3.0"
+
+[package.extras]
+accept-rom-license = ["autorom[accept-rom-license] (>=0.4.2,<0.5.0)"]
+all = ["box2d-py (==2.3.5)", "cython (<3)", "imageio (>=2.14.1)", "jax (>=0.4.0)", "jaxlib (>=0.4.0)", "lz4 (>=3.1.0)", "matplotlib (>=3.0)", "moviepy (>=1.0.0)", "mujoco (>=2.3.3)", "mujoco-py (>=2.1,<2.2)", "opencv-python (>=3.0)", "pygame (>=2.1.3)", "shimmy[atari] (>=0.1.0,<1.0)", "swig (==4.*)", "torch (>=1.0.0)"]
+atari = ["shimmy[atari] (>=0.1.0,<1.0)"]
+box2d = ["box2d-py (==2.3.5)", "pygame (>=2.1.3)", "swig (==4.*)"]
+classic-control = ["pygame (>=2.1.3)", "pygame (>=2.1.3)"]
+jax = ["jax (>=0.4.0)", "jaxlib (>=0.4.0)"]
+mujoco = ["imageio (>=2.14.1)", "mujoco (>=2.3.3)"]
+mujoco-py = ["cython (<3)", "cython (<3)", "mujoco-py (>=2.1,<2.2)", "mujoco-py (>=2.1,<2.2)"]
+other = ["lz4 (>=3.1.0)", "matplotlib (>=3.0)", "moviepy (>=1.0.0)", "opencv-python (>=3.0)", "torch (>=1.0.0)"]
+testing = ["pytest (==7.1.3)", "scipy (>=1.7.3)"]
+toy-text = ["pygame (>=2.1.3)", "pygame (>=2.1.3)"]
+
+[[package]]
+name = "gymnasium-robotics"
+version = "1.2.4"
+description = "Robotics environments for the Gymnasium repo."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "gymnasium-robotics-1.2.4.tar.gz", hash = "sha256:d304192b066f8b800599dfbe3d9d90bba9b761ee884472bdc4d05968a8bc61cb"},
+    {file = "gymnasium_robotics-1.2.4-py3-none-any.whl", hash = "sha256:c2cb23e087ca0280ae6802837eb7b3a6d14e5bd24c00803ab09f015fcff3eef5"},
+]
+
+[package.dependencies]
+gymnasium = ">=0.26"
+imageio = "*"
+Jinja2 = ">=3.0.3"
+mujoco = ">=2.3.3,<3.0"
+numpy = ">=1.21.0"
+PettingZoo = ">=1.23.0"
+
+[package.extras]
+mujoco-py = ["cython (<3)", "mujoco-py (>=2.1,<2.2)"]
+testing = ["Jinja2 (>=3.0.3)", "PettingZoo (>=1.23.0)", "cython (<3)", "mujoco-py (>=2.1,<2.2)", "pytest (==7.0.1)"]
+
+[[package]]
+name = "imageio"
+version = "2.34.0"
+description = "Library for reading and writing a wide range of image, video, scientific, and volumetric data formats."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "imageio-2.34.0-py3-none-any.whl", hash = "sha256:08082bf47ccb54843d9c73fe9fc8f3a88c72452ab676b58aca74f36167e8ccba"},
+    {file = "imageio-2.34.0.tar.gz", hash = "sha256:ae9732e10acf807a22c389aef193f42215718e16bd06eed0c5bb57e1034a4d53"},
+]
+
+[package.dependencies]
+numpy = "*"
+pillow = ">=8.3.2"
+
+[package.extras]
+all-plugins = ["astropy", "av", "imageio-ffmpeg", "pillow-heif", "psutil", "tifffile"]
+all-plugins-pypy = ["av", "imageio-ffmpeg", "pillow-heif", "psutil", "tifffile"]
+build = ["wheel"]
+dev = ["black", "flake8", "fsspec[github]", "pytest", "pytest-cov"]
+docs = ["numpydoc", "pydata-sphinx-theme", "sphinx (<6)"]
+ffmpeg = ["imageio-ffmpeg", "psutil"]
+fits = ["astropy"]
+full = ["astropy", "av", "black", "flake8", "fsspec[github]", "gdal", "imageio-ffmpeg", "itk", "numpydoc", "pillow-heif", "psutil", "pydata-sphinx-theme", "pytest", "pytest-cov", "sphinx (<6)", "tifffile", "wheel"]
+gdal = ["gdal"]
+itk = ["itk"]
+linting = ["black", "flake8"]
+pillow-heif = ["pillow-heif"]
+pyav = ["av"]
+test = ["fsspec[github]", "pytest", "pytest-cov"]
+tifffile = ["tifffile"]
+
+[[package]]
+name = "jinja2"
+version = "3.1.3"
+description = "A very fast and expressive template engine."
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "Jinja2-3.1.3-py3-none-any.whl", hash = "sha256:7d6d50dd97d52cbc355597bd845fabfbac3f551e1f99619e39a35ce8c370b5fa"},
+    {file = "Jinja2-3.1.3.tar.gz", hash = "sha256:ac8bd6544d4bb2c9792bf3a159e80bba8fda7f07e81bc3aed565432d5925ba90"},
+]
+
+[package.dependencies]
+MarkupSafe = ">=2.0"
+
+[package.extras]
+i18n = ["Babel (>=2.7)"]
+
+[[package]]
+name = "markupsafe"
+version = "2.1.5"
+description = "Safely add untrusted strings to HTML/XML markup."
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "MarkupSafe-2.1.5-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:a17a92de5231666cfbe003f0e4b9b3a7ae3afb1ec2845aadc2bacc93ff85febc"},
+    {file = "MarkupSafe-2.1.5-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:72b6be590cc35924b02c78ef34b467da4ba07e4e0f0454a2c5907f473fc50ce5"},
+    {file = "MarkupSafe-2.1.5-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e61659ba32cf2cf1481e575d0462554625196a1f2fc06a1c777d3f48e8865d46"},
+    {file = "MarkupSafe-2.1.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2174c595a0d73a3080ca3257b40096db99799265e1c27cc5a610743acd86d62f"},
+    {file = "MarkupSafe-2.1.5-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ae2ad8ae6ebee9d2d94b17fb62763125f3f374c25618198f40cbb8b525411900"},
+    {file = "MarkupSafe-2.1.5-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:075202fa5b72c86ad32dc7d0b56024ebdbcf2048c0ba09f1cde31bfdd57bcfff"},
+    {file = "MarkupSafe-2.1.5-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:598e3276b64aff0e7b3451b72e94fa3c238d452e7ddcd893c3ab324717456bad"},
+    {file = "MarkupSafe-2.1.5-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:fce659a462a1be54d2ffcacea5e3ba2d74daa74f30f5f143fe0c58636e355fdd"},
+    {file = "MarkupSafe-2.1.5-cp310-cp310-win32.whl", hash = "sha256:d9fad5155d72433c921b782e58892377c44bd6252b5af2f67f16b194987338a4"},
+    {file = "MarkupSafe-2.1.5-cp310-cp310-win_amd64.whl", hash = "sha256:bf50cd79a75d181c9181df03572cdce0fbb75cc353bc350712073108cba98de5"},
+    {file = "MarkupSafe-2.1.5-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:629ddd2ca402ae6dbedfceeba9c46d5f7b2a61d9749597d4307f943ef198fc1f"},
+    {file = "MarkupSafe-2.1.5-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:5b7b716f97b52c5a14bffdf688f971b2d5ef4029127f1ad7a513973cfd818df2"},
+    {file = "MarkupSafe-2.1.5-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6ec585f69cec0aa07d945b20805be741395e28ac1627333b1c5b0105962ffced"},
+    {file = "MarkupSafe-2.1.5-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b91c037585eba9095565a3556f611e3cbfaa42ca1e865f7b8015fe5c7336d5a5"},
+    {file = "MarkupSafe-2.1.5-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:7502934a33b54030eaf1194c21c692a534196063db72176b0c4028e140f8f32c"},
+    {file = "MarkupSafe-2.1.5-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:0e397ac966fdf721b2c528cf028494e86172b4feba51d65f81ffd65c63798f3f"},
+    {file = "MarkupSafe-2.1.5-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:c061bb86a71b42465156a3ee7bd58c8c2ceacdbeb95d05a99893e08b8467359a"},
+    {file = "MarkupSafe-2.1.5-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:3a57fdd7ce31c7ff06cdfbf31dafa96cc533c21e443d57f5b1ecc6cdc668ec7f"},
+    {file = "MarkupSafe-2.1.5-cp311-cp311-win32.whl", hash = "sha256:397081c1a0bfb5124355710fe79478cdbeb39626492b15d399526ae53422b906"},
+    {file = "MarkupSafe-2.1.5-cp311-cp311-win_amd64.whl", hash = "sha256:2b7c57a4dfc4f16f7142221afe5ba4e093e09e728ca65c51f5620c9aaeb9a617"},
+    {file = "MarkupSafe-2.1.5-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:8dec4936e9c3100156f8a2dc89c4b88d5c435175ff03413b443469c7c8c5f4d1"},
+    {file = "MarkupSafe-2.1.5-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:3c6b973f22eb18a789b1460b4b91bf04ae3f0c4234a0a6aa6b0a92f6f7b951d4"},
+    {file = "MarkupSafe-2.1.5-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ac07bad82163452a6884fe8fa0963fb98c2346ba78d779ec06bd7a6262132aee"},
+    {file = "MarkupSafe-2.1.5-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f5dfb42c4604dddc8e4305050aa6deb084540643ed5804d7455b5df8fe16f5e5"},
+    {file = "MarkupSafe-2.1.5-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ea3d8a3d18833cf4304cd2fc9cbb1efe188ca9b5efef2bdac7adc20594a0e46b"},
+    {file = "MarkupSafe-2.1.5-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:d050b3361367a06d752db6ead6e7edeb0009be66bc3bae0ee9d97fb326badc2a"},
+    {file = "MarkupSafe-2.1.5-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:bec0a414d016ac1a18862a519e54b2fd0fc8bbfd6890376898a6c0891dd82e9f"},
+    {file = "MarkupSafe-2.1.5-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:58c98fee265677f63a4385256a6d7683ab1832f3ddd1e66fe948d5880c21a169"},
+    {file = "MarkupSafe-2.1.5-cp312-cp312-win32.whl", hash = "sha256:8590b4ae07a35970728874632fed7bd57b26b0102df2d2b233b6d9d82f6c62ad"},
+    {file = "MarkupSafe-2.1.5-cp312-cp312-win_amd64.whl", hash = "sha256:823b65d8706e32ad2df51ed89496147a42a2a6e01c13cfb6ffb8b1e92bc910bb"},
+    {file = "MarkupSafe-2.1.5-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:c8b29db45f8fe46ad280a7294f5c3ec36dbac9491f2d1c17345be8e69cc5928f"},
+    {file = "MarkupSafe-2.1.5-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ec6a563cff360b50eed26f13adc43e61bc0c04d94b8be985e6fb24b81f6dcfdf"},
+    {file = "MarkupSafe-2.1.5-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a549b9c31bec33820e885335b451286e2969a2d9e24879f83fe904a5ce59d70a"},
+    {file = "MarkupSafe-2.1.5-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:4f11aa001c540f62c6166c7726f71f7573b52c68c31f014c25cc7901deea0b52"},
+    {file = "MarkupSafe-2.1.5-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:7b2e5a267c855eea6b4283940daa6e88a285f5f2a67f2220203786dfa59b37e9"},
+    {file = "MarkupSafe-2.1.5-cp37-cp37m-musllinux_1_1_i686.whl", hash = "sha256:2d2d793e36e230fd32babe143b04cec8a8b3eb8a3122d2aceb4a371e6b09b8df"},
+    {file = "MarkupSafe-2.1.5-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:ce409136744f6521e39fd8e2a24c53fa18ad67aa5bc7c2cf83645cce5b5c4e50"},
+    {file = "MarkupSafe-2.1.5-cp37-cp37m-win32.whl", hash = "sha256:4096e9de5c6fdf43fb4f04c26fb114f61ef0bf2e5604b6ee3019d51b69e8c371"},
+    {file = "MarkupSafe-2.1.5-cp37-cp37m-win_amd64.whl", hash = "sha256:4275d846e41ecefa46e2015117a9f491e57a71ddd59bbead77e904dc02b1bed2"},
+    {file = "MarkupSafe-2.1.5-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:656f7526c69fac7f600bd1f400991cc282b417d17539a1b228617081106feb4a"},
+    {file = "MarkupSafe-2.1.5-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:97cafb1f3cbcd3fd2b6fbfb99ae11cdb14deea0736fc2b0952ee177f2b813a46"},
+    {file = "MarkupSafe-2.1.5-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1f3fbcb7ef1f16e48246f704ab79d79da8a46891e2da03f8783a5b6fa41a9532"},
+    {file = "MarkupSafe-2.1.5-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fa9db3f79de01457b03d4f01b34cf91bc0048eb2c3846ff26f66687c2f6d16ab"},
+    {file = "MarkupSafe-2.1.5-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ffee1f21e5ef0d712f9033568f8344d5da8cc2869dbd08d87c84656e6a2d2f68"},
+    {file = "MarkupSafe-2.1.5-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:5dedb4db619ba5a2787a94d877bc8ffc0566f92a01c0ef214865e54ecc9ee5e0"},
+    {file = "MarkupSafe-2.1.5-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:30b600cf0a7ac9234b2638fbc0fb6158ba5bdcdf46aeb631ead21248b9affbc4"},
+    {file = "MarkupSafe-2.1.5-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:8dd717634f5a044f860435c1d8c16a270ddf0ef8588d4887037c5028b859b0c3"},
+    {file = "MarkupSafe-2.1.5-cp38-cp38-win32.whl", hash = "sha256:daa4ee5a243f0f20d528d939d06670a298dd39b1ad5f8a72a4275124a7819eff"},
+    {file = "MarkupSafe-2.1.5-cp38-cp38-win_amd64.whl", hash = "sha256:619bc166c4f2de5caa5a633b8b7326fbe98e0ccbfacabd87268a2b15ff73a029"},
+    {file = "MarkupSafe-2.1.5-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:7a68b554d356a91cce1236aa7682dc01df0edba8d043fd1ce607c49dd3c1edcf"},
+    {file = "MarkupSafe-2.1.5-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:db0b55e0f3cc0be60c1f19efdde9a637c32740486004f20d1cff53c3c0ece4d2"},
+    {file = "MarkupSafe-2.1.5-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3e53af139f8579a6d5f7b76549125f0d94d7e630761a2111bc431fd820e163b8"},
+    {file = "MarkupSafe-2.1.5-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:17b950fccb810b3293638215058e432159d2b71005c74371d784862b7e4683f3"},
+    {file = "MarkupSafe-2.1.5-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:4c31f53cdae6ecfa91a77820e8b151dba54ab528ba65dfd235c80b086d68a465"},
+    {file = "MarkupSafe-2.1.5-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:bff1b4290a66b490a2f4719358c0cdcd9bafb6b8f061e45c7a2460866bf50c2e"},
+    {file = "MarkupSafe-2.1.5-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:bc1667f8b83f48511b94671e0e441401371dfd0f0a795c7daa4a3cd1dde55bea"},
+    {file = "MarkupSafe-2.1.5-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:5049256f536511ee3f7e1b3f87d1d1209d327e818e6ae1365e8653d7e3abb6a6"},
+    {file = "MarkupSafe-2.1.5-cp39-cp39-win32.whl", hash = "sha256:00e046b6dd71aa03a41079792f8473dc494d564611a8f89bbbd7cb93295ebdcf"},
+    {file = "MarkupSafe-2.1.5-cp39-cp39-win_amd64.whl", hash = "sha256:fa173ec60341d6bb97a89f5ea19c85c5643c1e7dedebc22f5181eb73573142c5"},
+    {file = "MarkupSafe-2.1.5.tar.gz", hash = "sha256:d283d37a890ba4c1ae73ffadf8046435c76e7bc2247bbb63c00bd1a709c6544b"},
+]
+
+[[package]]
+name = "mujoco"
+version = "2.3.7"
+description = "MuJoCo Physics Simulator"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "mujoco-2.3.7-cp310-cp310-macosx_10_16_x86_64.whl", hash = "sha256:e8714a5ff6a1561b364b7b4648d4c0c8d13e751874cf7401c309b9d23fa9598b"},
+    {file = "mujoco-2.3.7-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:a934315f858a4e0c4b90a682fde519471cfdd7baa64435179da8cd20d4ae3f99"},
+    {file = "mujoco-2.3.7-cp310-cp310-macosx_11_0_x86_64.whl", hash = "sha256:36513024330f88b5f9a43558efef5692b33599bffd5141029b690a27918ffcbe"},
+    {file = "mujoco-2.3.7-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6d4eede8ba8210fbd3d3cd1dbf69e24dd1541aa74c5af5b8adbbbf65504b6dba"},
+    {file = "mujoco-2.3.7-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ab85fafc9d5a091c712947573b7e694512d283876bf7f33ae3f8daad3a20c0db"},
+    {file = "mujoco-2.3.7-cp310-cp310-win_amd64.whl", hash = "sha256:f8b7e13fef8c813d91b78f975ed0815157692777907ffa4b4be53a4edb75019b"},
+    {file = "mujoco-2.3.7-cp311-cp311-macosx_10_16_x86_64.whl", hash = "sha256:779520216f72a8e370e3f0cdd71b45c3b7384c63331a3189194c930a3e7cff5c"},
+    {file = "mujoco-2.3.7-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:9d4018053879016282d27ab7a91e292c72d44efb5a88553feacfe5b843dde103"},
+    {file = "mujoco-2.3.7-cp311-cp311-macosx_11_0_x86_64.whl", hash = "sha256:3149b16b8122ee62642474bfd2871064e8edc40235471cf5d84be3569afc0312"},
+    {file = "mujoco-2.3.7-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c08660a8d52ef3efde76095f0991e807703a950c1e882d2bcd984b9a846626f7"},
+    {file = "mujoco-2.3.7-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:426af8965f8636d94a0f75740c3024a62b3e585020ee817ef5208ec844a1ad94"},
+    {file = "mujoco-2.3.7-cp311-cp311-win_amd64.whl", hash = "sha256:215415a8e98a4b50625beae859079d5e0810b2039e50420f0ba81763c34abb59"},
+    {file = "mujoco-2.3.7-cp38-cp38-macosx_10_16_x86_64.whl", hash = "sha256:8b78d14f4c60cea3c58e046bd4de453fb5b9b33aca6a25fc91d39a53f3a5342a"},
+    {file = "mujoco-2.3.7-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:5c6f5a51d6f537a4bf294cf73816f3a6384573f8f10a5452b044df2771412a96"},
+    {file = "mujoco-2.3.7-cp38-cp38-macosx_11_0_x86_64.whl", hash = "sha256:ea8911e6047f92d7d775701f37e4c093971b6def3160f01d0b6926e29a7e962e"},
+    {file = "mujoco-2.3.7-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7473a3de4dd1a8762d569ffb139196b4c5e7eca27d256df97b6cd4c66d2a09b2"},
+    {file = "mujoco-2.3.7-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:40e7e2d8f93d2495ec74efec84e5118ecc6e1d85157a844789c73c9ac9a4e28e"},
+    {file = "mujoco-2.3.7-cp38-cp38-win_amd64.whl", hash = "sha256:720bc228a2023b3b0ed6af78f5b0f8ea36867be321d473321555c57dbf6e4e5b"},
+    {file = "mujoco-2.3.7-cp39-cp39-macosx_10_16_x86_64.whl", hash = "sha256:855e79686366442aa410246043b44f7d842d3900d68fe7e37feb42147db9d707"},
+    {file = "mujoco-2.3.7-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:98947f4a742d34d36f3c3f83e9167025bb0414bbaa4bd859b0673bdab9959963"},
+    {file = "mujoco-2.3.7-cp39-cp39-macosx_11_0_x86_64.whl", hash = "sha256:d42818f2ee5d1632dbce31d136ed5ff868db54b04e4e9aca0c5a3ac329f8a90f"},
+    {file = "mujoco-2.3.7-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9237e1ba14bced9449c31199e6d5be49547f3a4c99bc83b196af7ca45fd73b83"},
+    {file = "mujoco-2.3.7-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:39b728ea638245b150e2650c5433e6952e0ed3798c63e47e264574270caea2a3"},
+    {file = "mujoco-2.3.7-cp39-cp39-win_amd64.whl", hash = "sha256:9c721a5042b99d948d5f0296a534bcce3f142c777c4d7642f503a539513f3912"},
+    {file = "mujoco-2.3.7.tar.gz", hash = "sha256:422041f1ce37c6d151fbced1048df626837e94fe3cd9f813585907046336a7d0"},
+]
+
+[package.dependencies]
+absl-py = "*"
+glfw = "*"
+numpy = "*"
+pyopengl = "*"
+
+[[package]]
+name = "numpy"
+version = "1.26.4"
+description = "Fundamental package for array computing in Python"
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "numpy-1.26.4-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:9ff0f4f29c51e2803569d7a51c2304de5554655a60c5d776e35b4a41413830d0"},
+    {file = "numpy-1.26.4-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:2e4ee3380d6de9c9ec04745830fd9e2eccb3e6cf790d39d7b98ffd19b0dd754a"},
+    {file = "numpy-1.26.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d209d8969599b27ad20994c8e41936ee0964e6da07478d6c35016bc386b66ad4"},
+    {file = "numpy-1.26.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ffa75af20b44f8dba823498024771d5ac50620e6915abac414251bd971b4529f"},
+    {file = "numpy-1.26.4-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:62b8e4b1e28009ef2846b4c7852046736bab361f7aeadeb6a5b89ebec3c7055a"},
+    {file = "numpy-1.26.4-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:a4abb4f9001ad2858e7ac189089c42178fcce737e4169dc61321660f1a96c7d2"},
+    {file = "numpy-1.26.4-cp310-cp310-win32.whl", hash = "sha256:bfe25acf8b437eb2a8b2d49d443800a5f18508cd811fea3181723922a8a82b07"},
+    {file = "numpy-1.26.4-cp310-cp310-win_amd64.whl", hash = "sha256:b97fe8060236edf3662adfc2c633f56a08ae30560c56310562cb4f95500022d5"},
+    {file = "numpy-1.26.4-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:4c66707fabe114439db9068ee468c26bbdf909cac0fb58686a42a24de1760c71"},
+    {file = "numpy-1.26.4-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:edd8b5fe47dab091176d21bb6de568acdd906d1887a4584a15a9a96a1dca06ef"},
+    {file = "numpy-1.26.4-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7ab55401287bfec946ced39700c053796e7cc0e3acbef09993a9ad2adba6ca6e"},
+    {file = "numpy-1.26.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:666dbfb6ec68962c033a450943ded891bed2d54e6755e35e5835d63f4f6931d5"},
+    {file = "numpy-1.26.4-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:96ff0b2ad353d8f990b63294c8986f1ec3cb19d749234014f4e7eb0112ceba5a"},
+    {file = "numpy-1.26.4-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:60dedbb91afcbfdc9bc0b1f3f402804070deed7392c23eb7a7f07fa857868e8a"},
+    {file = "numpy-1.26.4-cp311-cp311-win32.whl", hash = "sha256:1af303d6b2210eb850fcf03064d364652b7120803a0b872f5211f5234b399f20"},
+    {file = "numpy-1.26.4-cp311-cp311-win_amd64.whl", hash = "sha256:cd25bcecc4974d09257ffcd1f098ee778f7834c3ad767fe5db785be9a4aa9cb2"},
+    {file = "numpy-1.26.4-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:b3ce300f3644fb06443ee2222c2201dd3a89ea6040541412b8fa189341847218"},
+    {file = "numpy-1.26.4-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:03a8c78d01d9781b28a6989f6fa1bb2c4f2d51201cf99d3dd875df6fbd96b23b"},
+    {file = "numpy-1.26.4-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9fad7dcb1aac3c7f0584a5a8133e3a43eeb2fe127f47e3632d43d677c66c102b"},
+    {file = "numpy-1.26.4-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:675d61ffbfa78604709862923189bad94014bef562cc35cf61d3a07bba02a7ed"},
+    {file = "numpy-1.26.4-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:ab47dbe5cc8210f55aa58e4805fe224dac469cde56b9f731a4c098b91917159a"},
+    {file = "numpy-1.26.4-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:1dda2e7b4ec9dd512f84935c5f126c8bd8b9f2fc001e9f54af255e8c5f16b0e0"},
+    {file = "numpy-1.26.4-cp312-cp312-win32.whl", hash = "sha256:50193e430acfc1346175fcbdaa28ffec49947a06918b7b92130744e81e640110"},
+    {file = "numpy-1.26.4-cp312-cp312-win_amd64.whl", hash = "sha256:08beddf13648eb95f8d867350f6a018a4be2e5ad54c8d8caed89ebca558b2818"},
+    {file = "numpy-1.26.4-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:7349ab0fa0c429c82442a27a9673fc802ffdb7c7775fad780226cb234965e53c"},
+    {file = "numpy-1.26.4-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:52b8b60467cd7dd1e9ed082188b4e6bb35aa5cdd01777621a1658910745b90be"},
+    {file = "numpy-1.26.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d5241e0a80d808d70546c697135da2c613f30e28251ff8307eb72ba696945764"},
+    {file = "numpy-1.26.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f870204a840a60da0b12273ef34f7051e98c3b5961b61b0c2c1be6dfd64fbcd3"},
+    {file = "numpy-1.26.4-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:679b0076f67ecc0138fd2ede3a8fd196dddc2ad3254069bcb9faf9a79b1cebcd"},
+    {file = "numpy-1.26.4-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:47711010ad8555514b434df65f7d7b076bb8261df1ca9bb78f53d3b2db02e95c"},
+    {file = "numpy-1.26.4-cp39-cp39-win32.whl", hash = "sha256:a354325ee03388678242a4d7ebcd08b5c727033fcff3b2f536aea978e15ee9e6"},
+    {file = "numpy-1.26.4-cp39-cp39-win_amd64.whl", hash = "sha256:3373d5d70a5fe74a2c1bb6d2cfd9609ecf686d47a2d7b1d37a8f3b6bf6003aea"},
+    {file = "numpy-1.26.4-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:afedb719a9dcfc7eaf2287b839d8198e06dcd4cb5d276a3df279231138e83d30"},
+    {file = "numpy-1.26.4-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:95a7476c59002f2f6c590b9b7b998306fba6a5aa646b1e22ddfeaf8f78c3a29c"},
+    {file = "numpy-1.26.4-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:7e50d0a0cc3189f9cb0aeb3a6a6af18c16f59f004b866cd2be1c14b36134a4a0"},
+    {file = "numpy-1.26.4.tar.gz", hash = "sha256:2a02aba9ed12e4ac4eb3ea9421c420301a0c6460d9830d74a9df87efa4912010"},
+]
+
+[[package]]
+name = "pettingzoo"
+version = "1.24.3"
+description = "Gymnasium for multi-agent reinforcement learning."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "pettingzoo-1.24.3-py3-none-any.whl", hash = "sha256:23ed90517d2e8a7098bdaf5e31234b3a7f7b73ca578d70d1ca7b9d0cb0e37982"},
+    {file = "pettingzoo-1.24.3.tar.gz", hash = "sha256:91f9094f18e06fb74b98f4099cd22e8ae4396125e51719d50b30c9f1c7ab07e6"},
+]
+
+[package.dependencies]
+gymnasium = ">=0.28.0"
+numpy = ">=1.21.0"
+
+[package.extras]
+all = ["box2d-py (==2.3.5)", "chess (==1.9.4)", "multi-agent-ale-py (==0.1.11)", "pillow (>=8.0.1)", "pygame (==2.3.0)", "pymunk (==6.2.0)", "rlcard (==1.0.5)", "scipy (>=1.4.1)", "shimmy[openspiel] (>=1.2.0)"]
+atari = ["multi-agent-ale-py (==0.1.11)", "pygame (==2.3.0)"]
+butterfly = ["pygame (==2.3.0)", "pymunk (==6.2.0)"]
+classic = ["chess (==1.9.4)", "pygame (==2.3.0)", "rlcard (==1.0.5)", "shimmy[openspiel] (>=1.2.0)"]
+mpe = ["pygame (==2.3.0)"]
+other = ["pillow (>=8.0.1)"]
+sisl = ["box2d-py (==2.3.5)", "pygame (==2.3.0)", "pymunk (==6.2.0)", "scipy (>=1.4.1)"]
+testing = ["AutoROM", "pre-commit", "pynput", "pytest", "pytest-cov", "pytest-markdown-docs", "pytest-xdist"]
+
+[[package]]
+name = "pillow"
+version = "10.2.0"
+description = "Python Imaging Library (Fork)"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "pillow-10.2.0-cp310-cp310-macosx_10_10_x86_64.whl", hash = "sha256:7823bdd049099efa16e4246bdf15e5a13dbb18a51b68fa06d6c1d4d8b99a796e"},
+    {file = "pillow-10.2.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:83b2021f2ade7d1ed556bc50a399127d7fb245e725aa0113ebd05cfe88aaf588"},
+    {file = "pillow-10.2.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6fad5ff2f13d69b7e74ce5b4ecd12cc0ec530fcee76356cac6742785ff71c452"},
+    {file = "pillow-10.2.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:da2b52b37dad6d9ec64e653637a096905b258d2fc2b984c41ae7d08b938a67e4"},
+    {file = "pillow-10.2.0-cp310-cp310-manylinux_2_28_aarch64.whl", hash = "sha256:47c0995fc4e7f79b5cfcab1fc437ff2890b770440f7696a3ba065ee0fd496563"},
+    {file = "pillow-10.2.0-cp310-cp310-manylinux_2_28_x86_64.whl", hash = "sha256:322bdf3c9b556e9ffb18f93462e5f749d3444ce081290352c6070d014c93feb2"},
+    {file = "pillow-10.2.0-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:51f1a1bffc50e2e9492e87d8e09a17c5eea8409cda8d3f277eb6edc82813c17c"},
+    {file = "pillow-10.2.0-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:69ffdd6120a4737710a9eee73e1d2e37db89b620f702754b8f6e62594471dee0"},
+    {file = "pillow-10.2.0-cp310-cp310-win32.whl", hash = "sha256:c6dafac9e0f2b3c78df97e79af707cdc5ef8e88208d686a4847bab8266870023"},
+    {file = "pillow-10.2.0-cp310-cp310-win_amd64.whl", hash = "sha256:aebb6044806f2e16ecc07b2a2637ee1ef67a11840a66752751714a0d924adf72"},
+    {file = "pillow-10.2.0-cp310-cp310-win_arm64.whl", hash = "sha256:7049e301399273a0136ff39b84c3678e314f2158f50f517bc50285fb5ec847ad"},
+    {file = "pillow-10.2.0-cp311-cp311-macosx_10_10_x86_64.whl", hash = "sha256:35bb52c37f256f662abdfa49d2dfa6ce5d93281d323a9af377a120e89a9eafb5"},
+    {file = "pillow-10.2.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:9c23f307202661071d94b5e384e1e1dc7dfb972a28a2310e4ee16103e66ddb67"},
+    {file = "pillow-10.2.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:773efe0603db30c281521a7c0214cad7836c03b8ccff897beae9b47c0b657d61"},
+    {file = "pillow-10.2.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:11fa2e5984b949b0dd6d7a94d967743d87c577ff0b83392f17cb3990d0d2fd6e"},
+    {file = "pillow-10.2.0-cp311-cp311-manylinux_2_28_aarch64.whl", hash = "sha256:716d30ed977be8b37d3ef185fecb9e5a1d62d110dfbdcd1e2a122ab46fddb03f"},
+    {file = "pillow-10.2.0-cp311-cp311-manylinux_2_28_x86_64.whl", hash = "sha256:a086c2af425c5f62a65e12fbf385f7c9fcb8f107d0849dba5839461a129cf311"},
+    {file = "pillow-10.2.0-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:c8de2789052ed501dd829e9cae8d3dcce7acb4777ea4a479c14521c942d395b1"},
+    {file = "pillow-10.2.0-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:609448742444d9290fd687940ac0b57fb35e6fd92bdb65386e08e99af60bf757"},
+    {file = "pillow-10.2.0-cp311-cp311-win32.whl", hash = "sha256:823ef7a27cf86df6597fa0671066c1b596f69eba53efa3d1e1cb8b30f3533068"},
+    {file = "pillow-10.2.0-cp311-cp311-win_amd64.whl", hash = "sha256:1da3b2703afd040cf65ec97efea81cfba59cdbed9c11d8efc5ab09df9509fc56"},
+    {file = "pillow-10.2.0-cp311-cp311-win_arm64.whl", hash = "sha256:edca80cbfb2b68d7b56930b84a0e45ae1694aeba0541f798e908a49d66b837f1"},
+    {file = "pillow-10.2.0-cp312-cp312-macosx_10_10_x86_64.whl", hash = "sha256:1b5e1b74d1bd1b78bc3477528919414874748dd363e6272efd5abf7654e68bef"},
+    {file = "pillow-10.2.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:0eae2073305f451d8ecacb5474997c08569fb4eb4ac231ffa4ad7d342fdc25ac"},
+    {file = "pillow-10.2.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b7c2286c23cd350b80d2fc9d424fc797575fb16f854b831d16fd47ceec078f2c"},
+    {file = "pillow-10.2.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1e23412b5c41e58cec602f1135c57dfcf15482013ce6e5f093a86db69646a5aa"},
+    {file = "pillow-10.2.0-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:52a50aa3fb3acb9cf7213573ef55d31d6eca37f5709c69e6858fe3bc04a5c2a2"},
+    {file = "pillow-10.2.0-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:127cee571038f252a552760076407f9cff79761c3d436a12af6000cd182a9d04"},
+    {file = "pillow-10.2.0-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:8d12251f02d69d8310b046e82572ed486685c38f02176bd08baf216746eb947f"},
+    {file = "pillow-10.2.0-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:54f1852cd531aa981bc0965b7d609f5f6cc8ce8c41b1139f6ed6b3c54ab82bfb"},
+    {file = "pillow-10.2.0-cp312-cp312-win32.whl", hash = "sha256:257d8788df5ca62c980314053197f4d46eefedf4e6175bc9412f14412ec4ea2f"},
+    {file = "pillow-10.2.0-cp312-cp312-win_amd64.whl", hash = "sha256:154e939c5f0053a383de4fd3d3da48d9427a7e985f58af8e94d0b3c9fcfcf4f9"},
+    {file = "pillow-10.2.0-cp312-cp312-win_arm64.whl", hash = "sha256:f379abd2f1e3dddb2b61bc67977a6b5a0a3f7485538bcc6f39ec76163891ee48"},
+    {file = "pillow-10.2.0-cp38-cp38-macosx_10_10_x86_64.whl", hash = "sha256:8373c6c251f7ef8bda6675dd6d2b3a0fcc31edf1201266b5cf608b62a37407f9"},
+    {file = "pillow-10.2.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:870ea1ada0899fd0b79643990809323b389d4d1d46c192f97342eeb6ee0b8483"},
+    {file = "pillow-10.2.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b4b6b1e20608493548b1f32bce8cca185bf0480983890403d3b8753e44077129"},
+    {file = "pillow-10.2.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3031709084b6e7852d00479fd1d310b07d0ba82765f973b543c8af5061cf990e"},
+    {file = "pillow-10.2.0-cp38-cp38-manylinux_2_28_aarch64.whl", hash = "sha256:3ff074fc97dd4e80543a3e91f69d58889baf2002b6be64347ea8cf5533188213"},
+    {file = "pillow-10.2.0-cp38-cp38-manylinux_2_28_x86_64.whl", hash = "sha256:cb4c38abeef13c61d6916f264d4845fab99d7b711be96c326b84df9e3e0ff62d"},
+    {file = "pillow-10.2.0-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:b1b3020d90c2d8e1dae29cf3ce54f8094f7938460fb5ce8bc5c01450b01fbaf6"},
+    {file = "pillow-10.2.0-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:170aeb00224ab3dc54230c797f8404507240dd868cf52066f66a41b33169bdbe"},
+    {file = "pillow-10.2.0-cp38-cp38-win32.whl", hash = "sha256:c4225f5220f46b2fde568c74fca27ae9771536c2e29d7c04f4fb62c83275ac4e"},
+    {file = "pillow-10.2.0-cp38-cp38-win_amd64.whl", hash = "sha256:0689b5a8c5288bc0504d9fcee48f61a6a586b9b98514d7d29b840143d6734f39"},
+    {file = "pillow-10.2.0-cp39-cp39-macosx_10_10_x86_64.whl", hash = "sha256:b792a349405fbc0163190fde0dc7b3fef3c9268292586cf5645598b48e63dc67"},
+    {file = "pillow-10.2.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:c570f24be1e468e3f0ce7ef56a89a60f0e05b30a3669a459e419c6eac2c35364"},
+    {file = "pillow-10.2.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d8ecd059fdaf60c1963c58ceb8997b32e9dc1b911f5da5307aab614f1ce5c2fb"},
+    {file = "pillow-10.2.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c365fd1703040de1ec284b176d6af5abe21b427cb3a5ff68e0759e1e313a5e7e"},
+    {file = "pillow-10.2.0-cp39-cp39-manylinux_2_28_aarch64.whl", hash = "sha256:70c61d4c475835a19b3a5aa42492409878bbca7438554a1f89d20d58a7c75c01"},
+    {file = "pillow-10.2.0-cp39-cp39-manylinux_2_28_x86_64.whl", hash = "sha256:b6f491cdf80ae540738859d9766783e3b3c8e5bd37f5dfa0b76abdecc5081f13"},
+    {file = "pillow-10.2.0-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:9d189550615b4948f45252d7f005e53c2040cea1af5b60d6f79491a6e147eef7"},
+    {file = "pillow-10.2.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:49d9ba1ed0ef3e061088cd1e7538a0759aab559e2e0a80a36f9fd9d8c0c21591"},
+    {file = "pillow-10.2.0-cp39-cp39-win32.whl", hash = "sha256:babf5acfede515f176833ed6028754cbcd0d206f7f614ea3447d67c33be12516"},
+    {file = "pillow-10.2.0-cp39-cp39-win_amd64.whl", hash = "sha256:0304004f8067386b477d20a518b50f3fa658a28d44e4116970abfcd94fac34a8"},
+    {file = "pillow-10.2.0-cp39-cp39-win_arm64.whl", hash = "sha256:0fb3e7fc88a14eacd303e90481ad983fd5b69c761e9e6ef94c983f91025da869"},
+    {file = "pillow-10.2.0-pp310-pypy310_pp73-macosx_10_10_x86_64.whl", hash = "sha256:322209c642aabdd6207517e9739c704dc9f9db943015535783239022002f054a"},
+    {file = "pillow-10.2.0-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3eedd52442c0a5ff4f887fab0c1c0bb164d8635b32c894bc1faf4c618dd89df2"},
+    {file = "pillow-10.2.0-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:cb28c753fd5eb3dd859b4ee95de66cc62af91bcff5db5f2571d32a520baf1f04"},
+    {file = "pillow-10.2.0-pp310-pypy310_pp73-manylinux_2_28_aarch64.whl", hash = "sha256:33870dc4653c5017bf4c8873e5488d8f8d5f8935e2f1fb9a2208c47cdd66efd2"},
+    {file = "pillow-10.2.0-pp310-pypy310_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:3c31822339516fb3c82d03f30e22b1d038da87ef27b6a78c9549888f8ceda39a"},
+    {file = "pillow-10.2.0-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:a2b56ba36e05f973d450582fb015594aaa78834fefe8dfb8fcd79b93e64ba4c6"},
+    {file = "pillow-10.2.0-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:d8e6aeb9201e655354b3ad049cb77d19813ad4ece0df1249d3c793de3774f8c7"},
+    {file = "pillow-10.2.0-pp39-pypy39_pp73-macosx_10_10_x86_64.whl", hash = "sha256:2247178effb34a77c11c0e8ac355c7a741ceca0a732b27bf11e747bbc950722f"},
+    {file = "pillow-10.2.0-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:15587643b9e5eb26c48e49a7b33659790d28f190fc514a322d55da2fb5c2950e"},
+    {file = "pillow-10.2.0-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:753cd8f2086b2b80180d9b3010dd4ed147efc167c90d3bf593fe2af21265e5a5"},
+    {file = "pillow-10.2.0-pp39-pypy39_pp73-manylinux_2_28_aarch64.whl", hash = "sha256:7c8f97e8e7a9009bcacbe3766a36175056c12f9a44e6e6f2d5caad06dcfbf03b"},
+    {file = "pillow-10.2.0-pp39-pypy39_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:d1b35bcd6c5543b9cb547dee3150c93008f8dd0f1fef78fc0cd2b141c5baf58a"},
+    {file = "pillow-10.2.0-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:fe4c15f6c9285dc54ce6553a3ce908ed37c8f3825b5a51a15c91442bb955b868"},
+    {file = "pillow-10.2.0.tar.gz", hash = "sha256:e87f0b2c78157e12d7686b27d63c070fd65d994e8ddae6f328e0dcf4a0cd007e"},
+]
+
+[package.extras]
+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"},
+    {file = "PyOpenGL-3.1.7.tar.gz", hash = "sha256:eef31a3888e6984fd4d8e6c9961b184c9813ca82604d37fe3da80eb000a76c86"},
+]
+
+[[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"},
+    {file = "typing_extensions-4.10.0.tar.gz", hash = "sha256:b0abd7c89e8fb96f98db18d86106ff1d90ab692004eb746cf6eda2682f91b3cb"},
+]
+
+[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

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:21fb81ae7fba19e3c6b2d2ca60c8051712ba273357287eb5a397d92d61c7a736
+size 1211434

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

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:be68ce180d11630a667a5f37f4dffcc3feebe4217d4bb3912c813b6d9ca3ec66
+size 3284

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

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2c6448552bf6b1c4f17334d686a5320ce051bcdfe31431edf69303d8a570d1de
+size 3284

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

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:748b9e197e6521914f18d1f6383a36f211136b3f33f2ad2a8c11b9f921c2cf86
+size 6284

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

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a44756eb72f9c214cb37e61dc209cd7073fdff3e4271a7423476ef6fd090d2d4
+size 242684

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

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e8e48692ad26837bb3d6a97582c89784d09948fc09bfe4e5a59017859ff04dac
+size 366284

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

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:501665812b08d67e764390db781e839adc6896a9540301d60adf606f57648921
+size 22284

envs/sim_xarm/xarm/tasks/assets/mesh/link1.stl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:34b541122df84d2ef5fcb91b715eb19659dc15ad8d44a191dde481f780265636
+size 184184

envs/sim_xarm/xarm/tasks/assets/mesh/link2.stl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:61e641cd47c169ecef779683332e00e4914db729bf02dfb61bfbe69351827455
+size 225584

envs/sim_xarm/xarm/tasks/assets/mesh/link3.stl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9e2798e7946dd70046c95455d5ba96392d0b54a6069caba91dc4ca66e1379b42
+size 237084

envs/sim_xarm/xarm/tasks/assets/mesh/link4.stl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c757fee95f873191a0633c355c07a360032960771cabbd7593a6cdb0f1ffb089
+size 243684

envs/sim_xarm/xarm/tasks/assets/mesh/link5.stl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:715ad5787c5dab57589937fd47289882707b5e1eb997e340d567785b02f4ec90
+size 229084

envs/sim_xarm/xarm/tasks/assets/mesh/link6.stl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:85b320aa420497827223d16d492bba8de091173374e361396fc7a5dad7bdb0cb
+size 399384

envs/sim_xarm/xarm/tasks/assets/mesh/link7.stl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:97115d848fbf802cb770cd9be639ae2af993103b9d9bbb0c50c943c738a36f18
+size 231684

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

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f6fcbc18258090eb56c21cfb17baa5ae43abc98b1958cd366f3a73b9898fc7f0
+size 2106184

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

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c5dee87c7f37baf554b8456ebfe0b3e8ed0b22b8938bd1add6505c2ad6d32c7d
+size 242684

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

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b41dd2c2c550281bf78d7cc6fa117b14786700e5c453560a0cb5fd6dfa0ffb3e
+size 366284

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

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:75ca1107d0a42a0f03802a9a49cab48419b31851ee8935f8f1ca06be1c1c91e8
+size 22284

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

@@ -0,0 +1,74 @@
+<?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>

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

@@ -0,0 +1,54 @@
+<?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="1.0 10.0 1" type="plane" condim="3" material="floor_mat"></geom>
+			<site name="target0" pos="1.565 0.3 0.545" size="0.0475 0.001" rgba="1 0 0 1" type="cylinder"></site>
+		</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="object" pos="1.655 0.3 0.68">
+			<joint name="object_joint0" type="free" limited="false"></joint>
+			<geom size="0.024 0.024 0.024" type="box" name="object" 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="0.024 0.024 0.024" rgba="0 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/reach.xml

@@ -0,0 +1,48 @@
+<?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="1.0 10.0 1" type="plane" condim="3" material="floor_mat"></geom>
+			<site name="target0" pos="1.605 0.3 0.58" size="0.0475 0.001" rgba="1 0 0 1" type="cylinder"></site>
+		</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>
+
+		<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/shared.xml

@@ -0,0 +1,51 @@
+<mujoco>
+    <asset>
+        <texture type="skybox" builtin="gradient" rgb1="0.0 0.0 0.0" rgb2="0.0 0.0 0.0" width="32" height="32"></texture>
+        <material name="floor_mat" specular="0" shininess="0.0" reflectance="0" rgba="0.043 0.055 0.051 1"></material>
+
+        <material name="table_mat" specular="0.2" shininess="0.2" reflectance="0" rgba="1 1 1 1"></material>
+        <material name="pedestal_mat" specular="0.35" shininess="0.5" reflectance="0" rgba="0.705 0.585 0.405 1"></material>
+        <material name="block_mat" specular="0.5" shininess="0.9" reflectance="0.05" rgba="0.373 0.678 0.627 1"></material>
+
+        <material name="robot0:geomMat" shininess="0.03" specular="0.4"></material>
+        <material name="robot0:gripper_finger_mat" shininess="0.03" specular="0.4" reflectance="0"></material>
+        <material name="robot0:gripper_mat" shininess="0.03" specular="0.4" reflectance="0"></material>
+        <material name="background:gripper_mat" shininess="0.03" specular="0.4" reflectance="0"></material>
+        <material name="robot0:arm_mat" shininess="0.03" specular="0.4" reflectance="0"></material>
+        <material name="robot0:head_mat" shininess="0.03" specular="0.4" reflectance="0"></material>
+        <material name="robot0:torso_mat" shininess="0.03" specular="0.4" reflectance="0"></material>
+        <material name="robot0:base_mat" shininess="0.03" specular="0.4" reflectance="0"></material>
+
+        <mesh name="link_base" file="link_base.stl" />
+        <mesh name="link1" file="link1.stl" />
+        <mesh name="link2" file="link2.stl" />
+        <mesh name="link3" file="link3.stl" />
+        <mesh name="link4" file="link4.stl" />
+        <mesh name="link5" file="link5.stl" />
+        <mesh name="link6" file="link6.stl" />
+        <mesh name="link7" file="link7.stl" />
+        <mesh name="base_link" file="base_link.stl" />
+        <mesh name="left_outer_knuckle" file="left_outer_knuckle.stl" />
+        <mesh name="left_finger" file="left_finger.stl" />
+        <mesh name="left_inner_knuckle" file="left_inner_knuckle.stl" />
+        <mesh name="right_outer_knuckle" file="right_outer_knuckle.stl" />
+        <mesh name="right_finger" file="right_finger.stl" />
+        <mesh name="right_inner_knuckle" file="right_inner_knuckle.stl" />
+    </asset>
+
+    <equality>
+        <weld body1="robot0:mocap2" body2="link7" solimp="0.9 0.95 0.001" solref="0.02 1"></weld>
+    </equality>
+
+    <default>
+        <joint armature="1" damping="0.1" limited="true"/>
+         <default class="robot0:blue">
+            <geom rgba="0.086 0.506 0.767 1.0"></geom>
+        </default>
+
+        <default class="robot0:grey">
+            <geom rgba="0.356 0.361 0.376 1.0"></geom>
+        </default>
+    </default>
+
+</mujoco>

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

@@ -0,0 +1,88 @@
+<mujoco model="xarm7">
+    <body mocap="true" name="robot0:mocap2" pos="0 0 0">
+        <geom conaffinity="0" contype="0" pos="0 0 0" rgba="0 0.5 0 0" size="0.005 0.005 0.005" type="box"></geom>
+        <geom conaffinity="0" contype="0" pos="0 0 0" rgba="0.5 0 0 0" size="1 0.005 0.005" type="box"></geom>
+        <geom conaffinity="0" contype="0" pos="0 0 0" rgba="0 0 0.5 0" size="0.005 1 0.001" type="box"></geom>
+        <geom conaffinity="0" contype="0" pos="0 0 0" rgba="0.5 0.5 0 0" size="0.005 0.005 1" type="box"></geom>
+    </body>
+
+    <body name="link0" pos="1.09 0.28 0.655">
+        <geom name="bb" type="mesh" mesh="link_base" material="robot0:base_mat" rgba="1 1 1 1"/>
+        <body name="link1" pos="0 0 0.267">
+            <inertial pos="-0.0042142 0.02821 -0.0087788" quat="0.917781 -0.277115 0.0606681 0.277858" mass="0.42603" diaginertia="0.00144551 0.00137757 0.000823511" />
+            <joint name="joint1" pos="0 0 0" axis="0 0 1" limited="true" range="-6.28319 6.28319" damping="10" frictionloss="1" />
+            <geom name="j1" type="mesh" mesh="link1" material="robot0:arm_mat" rgba="1 1 1 1"/>
+            <body name="link2" pos="0 0 0" quat="0.707105 -0.707108 0 0">
+                <inertial pos="-3.3178e-05 -0.12849 0.026337" quat="0.447793 0.894132 -0.00224061 0.00218314" mass="0.56095" diaginertia="0.00319151 0.00311598 0.000980804" />
+                <joint name="joint2" pos="0 0 0" axis="0 0 1" limited="true" range="-2.059 2.0944" damping="10" frictionloss="1" />
+                <geom name="j2"  type="mesh" mesh="link2" material="robot0:head_mat" rgba="1 1 1 1"/>
+                <body name="link3" pos="0 -0.293 0" quat="0.707105 0.707108 0 0">
+                    <inertial pos="0.04223 -0.023258 -0.0096674" quat="0.883205 0.339803 0.323238 0.000542237" mass="0.44463" diaginertia="0.00133227 0.00119126 0.000780475" />
+                    <joint name="joint3" pos="0 0 0" axis="0 0 1" limited="true" range="-6.28319 6.28319" damping="5" frictionloss="1" />
+                    <geom name="j3" type="mesh" mesh="link3" material="robot0:gripper_mat" rgba="1 1 1 1"/>
+                    <body name="link4" pos="0.0525 0 0" quat="0.707105 0.707108 0 0">
+                        <inertial pos="0.067148 -0.10732 0.024479" quat="0.0654142 0.483317 -0.738663 0.465298" mass="0.52387" diaginertia="0.00288984 0.00282705 0.000894409" />
+                        <joint name="joint4" pos="0 0 0" axis="0 0 1" limited="true" range="-0.19198 3.927" damping="5" frictionloss="1" />
+                        <geom name="j4" type="mesh" mesh="link4" material="robot0:arm_mat" rgba="1 1 1 1"/>
+                        <body name="link5" pos="0.0775 -0.3425 0" quat="0.707105 0.707108 0 0">
+                            <inertial pos="-0.00023397 0.036705 -0.080064" quat="0.981064 -0.19003 0.00637998 0.0369004" mass="0.18554" diaginertia="0.00099553 0.000988613 0.000247126" />
+                            <joint name="joint5" pos="0 0 0" axis="0 0 1" limited="true" range="-6.28319 6.28319" damping="5" frictionloss="1" />
+                            <geom name="j5" type="mesh"  material="robot0:gripper_mat" rgba="1 1 1 1" mesh="link5" />
+                            <body name="link6" pos="0 0 0" quat="0.707105 0.707108 0 0">
+                                <inertial pos="0.058911 0.028469 0.0068428" quat="-0.188705 0.793535 0.166088 0.554173" mass="0.31344" diaginertia="0.000827892 0.000768871 0.000386708" />
+                                <joint name="joint6" pos="0 0 0" axis="0 0 1" limited="true" range="-1.69297 3.14159" damping="2" frictionloss="1" />
+                                <geom name="j6" type="mesh" material="robot0:gripper_mat" rgba="1 1 1 1" mesh="link6" />
+                                <body name="link7" pos="0.076 0.097 0" quat="0.707105 -0.707108 0 0">
+                                    <inertial pos="-0.000420033 -0.00287433 0.0257078" quat="0.999372 -0.0349129 -0.00605634 0.000551744" mass="0.85624" diaginertia="0.00137671 0.00118744 0.000514968" />
+                                    <joint name="joint7" pos="0 0 0" axis="0 0 1" limited="true" range="-6.28319 6.28319" damping="2" frictionloss="1" />
+                                    <geom name="j8" material="robot0:gripper_mat" type="mesh" rgba="0.753 0.753 0.753 1" mesh="link7" />
+                                    <geom name="j9" material="robot0:gripper_mat" type="mesh" rgba="1 1 1 1" mesh="base_link" />
+                                    <site name="grasp" pos="0 0 0.16" rgba="1 0 0 0" type="sphere" size="0.01" group="1"/>
+                                    <body name="left_outer_knuckle" pos="0 0.035 0.059098">
+                                        <inertial pos="0 0.021559 0.015181" quat="0.47789 0.87842 0 0" mass="0.033618" diaginertia="1.9111e-05 1.79089e-05 1.90167e-06" />
+                                        <joint name="drive_joint" pos="0 0 0" axis="1 0 0" limited="true" range="0 0.85" />
+                                        <geom type="mesh" rgba="0 0 0 1" conaffinity="1" contype="0"  mesh="left_outer_knuckle" />
+                                        <body name="left_finger" pos="0 0.035465 0.042039">
+                                            <inertial pos="0 -0.016413 0.029258" quat="0.697634 0.115353 -0.115353 0.697634" mass="0.048304" diaginertia="1.88037e-05 1.7493e-05 3.56792e-06" />
+                                            <joint name="left_finger_joint" pos="0 0 0" axis="-1 0 0" limited="true" range="0 0.85" />
+                                            <geom name="j10" material="robot0:gripper_finger_mat" type="mesh" rgba="0 0 0 1" conaffinity="3" contype="2"  mesh="left_finger" friction='1.5 1.5 1.5' solref='0.01 1' solimp='0.99 0.99 0.01'/>
+                                            <body name="right_hand" pos="0 -0.03 0.05" quat="-0.7071 0 0 0.7071">
+                                                <site name="ee" pos="0 0 0" rgba="0 0 1 0" type="sphere" group="1"/>
+                                                <site name="ee_x" pos="0 0 0" size="0.005 .1"  quat="0.707105 0.707108 0 0 " rgba="1 0 0 0" type="cylinder" group="1"/>
+                                                <site name="ee_z" pos="0 0 0" size="0.005 .1" quat="0.707105 0 0 0.707108" rgba="0 0 1 0" type="cylinder" group="1"/>
+                                                <site name="ee_y" pos="0 0 0" size="0.005 .1" quat="0.707105 0 0.707108 0 " rgba="0 1 0 0" type="cylinder" group="1"/>
+                                            </body>
+                                        </body>
+                                    </body>
+                                    <body name="left_inner_knuckle" pos="0 0.02 0.074098">
+                                        <inertial pos="1.86601e-06 0.0220468 0.0261335" quat="0.664139 -0.242732 0.242713 0.664146" mass="0.0230126" diaginertia="8.34216e-06 6.0949e-06 2.75601e-06" />
+                                        <joint name="left_inner_knuckle_joint" pos="0 0 0" axis="1 0 0" limited="true" range="0 0.85" />
+                                        <geom type="mesh" rgba="0 0 0 1" conaffinity="1" contype="0"  mesh="left_inner_knuckle" friction='1.5 1.5 1.5' solref='0.01 1' solimp='0.99 0.99 0.01'/>
+                                    </body>
+                                    <body name="right_outer_knuckle" pos="0 -0.035 0.059098">
+                                        <inertial pos="0 -0.021559 0.015181" quat="0.87842 0.47789 0 0" mass="0.033618" diaginertia="1.9111e-05 1.79089e-05 1.90167e-06" />
+                                        <joint name="right_outer_knuckle_joint" pos="0 0 0" axis="-1 0 0" limited="true" range="0 0.85" />
+                                        <geom type="mesh" rgba="0 0 0 1" conaffinity="1" contype="0"  mesh="right_outer_knuckle" />
+                                        <body name="right_finger" pos="0 -0.035465 0.042039">
+                                            <inertial pos="0 0.016413 0.029258" quat="0.697634 -0.115356 0.115356 0.697634" mass="0.048304" diaginertia="1.88038e-05 1.7493e-05 3.56779e-06" />
+                                            <joint name="right_finger_joint" pos="0 0 0" axis="1 0 0" limited="true" range="0 0.85" />
+                                            <geom name="j11" material="robot0:gripper_finger_mat" type="mesh" rgba="0 0 0 1" conaffinity="3" contype="2" mesh="right_finger" friction='1.5 1.5 1.5' solref='0.01 1' solimp='0.99 0.99 0.01'/>
+                                            <body name="left_hand" pos="0 0.03 0.05" quat="-0.7071 0 0 0.7071">
+                                                <site name="ee_2" pos="0 0 0" rgba="1 0 0 0" type="sphere" size="0.01" group="1"/>
+                                            </body>
+                                        </body>
+                                    </body>
+                                    <body name="right_inner_knuckle" pos="0 -0.02 0.074098">
+                                        <inertial pos="1.866e-06 -0.022047 0.026133" quat="0.66415 0.242702 -0.242721 0.664144" mass="0.023013" diaginertia="8.34209e-06 6.0949e-06 2.75601e-06" />
+                                        <joint name="right_inner_knuckle_joint" pos="0 0 0" axis="-1 0 0" limited="true" range="0 0.85" />
+                                        <geom type="mesh" rgba="0 0 0 1" conaffinity="1" contype="0" mesh="right_inner_knuckle" friction='1.5 1.5 1.5' solref='0.01 1' solimp='0.99 0.99 0.01'/>
+                                    </body>
+                                </body>
+                            </body>
+                        </body>
+                    </body>
+                </body>
+            </body>
+        </body>
+    </body>
+</mujoco>

envs/sim_xarm/xarm/tasks/base.py

@@ -0,0 +1,145 @@
+import os
+
+import mujoco
+import numpy as np
+from gymnasium_robotics.envs import robot_env
+
+from xarm.tasks import mocap
+
+
+class Base(robot_env.MujocoRobotEnv):
+    """
+    Superclass for all simxarm environments.
+    Args:
+            xml_name (str): name of the xml environment file
+            gripper_rotation (list): initial rotation of the gripper (given as a quaternion)
+    """
+
+    def __init__(self, xml_name, gripper_rotation=None):
+        if gripper_rotation is None:
+            gripper_rotation = [0, 1, 0, 0]
+        self.gripper_rotation = np.array(gripper_rotation, dtype=np.float32)
+        self.center_of_table = np.array([1.655, 0.3, 0.63625])
+        self.max_z = 1.2
+        self.min_z = 0.2
+        super().__init__(
+            model_path=os.path.join(os.path.dirname(__file__), "assets", xml_name + ".xml"),
+            n_substeps=20,
+            n_actions=4,
+            initial_qpos={},
+        )
+
+    @property
+    def dt(self):
+        return self.n_substeps * self.model.opt.timestep
+
+    @property
+    def eef(self):
+        return self._utils.get_site_xpos(self.model, self.data, "grasp")
+
+    @property
+    def obj(self):
+        return self._utils.get_site_xpos(self.model, self.data, "object_site")
+
+    @property
+    def robot_state(self):
+        gripper_angle = self._utils.get_joint_qpos(self.model, self.data, "right_outer_knuckle_joint")
+        return np.concatenate([self.eef, gripper_angle])
+
+    def is_success(self):
+        return NotImplementedError()
+
+    def get_reward(self):
+        raise NotImplementedError()
+
+    def _sample_goal(self):
+        raise NotImplementedError()
+
+    def get_obs(self):
+        return self._get_obs()
+
+    def _step_callback(self):
+        self._mujoco.mj_forward(self.model, self.data)
+
+    def _limit_gripper(self, gripper_pos, pos_ctrl):
+        if gripper_pos[0] > self.center_of_table[0] - 0.105 + 0.15:
+            pos_ctrl[0] = min(pos_ctrl[0], 0)
+        if gripper_pos[0] < self.center_of_table[0] - 0.105 - 0.3:
+            pos_ctrl[0] = max(pos_ctrl[0], 0)
+        if gripper_pos[1] > self.center_of_table[1] + 0.3:
+            pos_ctrl[1] = min(pos_ctrl[1], 0)
+        if gripper_pos[1] < self.center_of_table[1] - 0.3:
+            pos_ctrl[1] = max(pos_ctrl[1], 0)
+        if gripper_pos[2] > self.max_z:
+            pos_ctrl[2] = min(pos_ctrl[2], 0)
+        if gripper_pos[2] < self.min_z:
+            pos_ctrl[2] = max(pos_ctrl[2], 0)
+        return pos_ctrl
+
+    def _apply_action(self, action):
+        assert action.shape == (4,)
+        action = action.copy()
+        pos_ctrl, gripper_ctrl = action[:3], action[3]
+        pos_ctrl = self._limit_gripper(
+            self._utils.get_site_xpos(self.model, self.data, "grasp"), pos_ctrl
+        ) * (1 / self.n_substeps)
+        gripper_ctrl = np.array([gripper_ctrl, gripper_ctrl])
+        mocap.apply_action(
+            self.model,
+            self._model_names,
+            self.data,
+            np.concatenate([pos_ctrl, self.gripper_rotation, gripper_ctrl]),
+        )
+
+    def _render_callback(self):
+        self._mujoco.mj_forward(self.model, self.data)
+
+    def _reset_sim(self):
+        self.data.time = self.initial_time
+        self.data.qpos[:] = np.copy(self.initial_qpos)
+        self.data.qvel[:] = np.copy(self.initial_qvel)
+        self._sample_goal()
+        self._mujoco.mj_step(self.model, self.data, nstep=10)
+        return True
+
+    def _set_gripper(self, gripper_pos, gripper_rotation):
+        self._utils.set_mocap_pos(self.model, self.data, "robot0:mocap", gripper_pos)
+        self._utils.set_mocap_quat(self.model, self.data, "robot0:mocap", gripper_rotation)
+        self._utils.set_joint_qpos(self.model, self.data, "right_outer_knuckle_joint", 0)
+        self.data.qpos[10] = 0.0
+        self.data.qpos[12] = 0.0
+
+    def _env_setup(self, initial_qpos):
+        for name, value in initial_qpos.items():
+            self.data.set_joint_qpos(name, value)
+        mocap.reset(self.model, self.data)
+        mujoco.mj_forward(self.model, self.data)
+        self._sample_goal()
+        mujoco.mj_forward(self.model, self.data)
+
+    def reset(self):
+        self._reset_sim()
+        return self._get_obs()
+
+    def step(self, action):
+        assert action.shape == (4,)
+        assert self.action_space.contains(action), "{!r} ({}) invalid".format(action, type(action))
+        self._apply_action(action)
+        self._mujoco.mj_step(self.model, self.data, nstep=2)
+        self._step_callback()
+        obs = self._get_obs()
+        reward = self.get_reward()
+        done = False
+        info = {"is_success": self.is_success(), "success": self.is_success()}
+        return obs, reward, done, info
+
+    def render(self, mode="rgb_array", width=384, height=384):
+        self._render_callback()
+        # HACK
+        self.model.vis.global_.offwidth = width
+        self.model.vis.global_.offheight = height
+        return self.mujoco_renderer.render(mode)
+
+    def close(self):
+        if self.mujoco_renderer is not None:
+            self.mujoco_renderer.close()

envs/sim_xarm/xarm/tasks/lift.py

@@ -0,0 +1,100 @@
+import numpy as np
+
+from xarm import Base
+
+
+class Lift(Base):
+    def __init__(self):
+        self._z_threshold = 0.15
+        super().__init__("lift")
+
+    @property
+    def z_target(self):
+        return self._init_z + self._z_threshold
+
+    def is_success(self):
+        return self.obj[2] >= self.z_target
+
+    def get_reward(self):
+        reach_dist = np.linalg.norm(self.obj - self.eef)
+        reach_dist_xy = np.linalg.norm(self.obj[:-1] - self.eef[:-1])
+        pick_completed = self.obj[2] >= (self.z_target - 0.01)
+        obj_dropped = (self.obj[2] < (self._init_z + 0.005)) and (reach_dist > 0.02)
+
+        # Reach
+        if reach_dist < 0.05:
+            reach_reward = -reach_dist + max(self._action[-1], 0) / 50
+        elif reach_dist_xy < 0.05:
+            reach_reward = -reach_dist
+        else:
+            z_bonus = np.linalg.norm(np.linalg.norm(self.obj[-1] - self.eef[-1]))
+            reach_reward = -reach_dist - 2 * z_bonus
+
+        # Pick
+        if pick_completed and not obj_dropped:
+            pick_reward = self.z_target
+        elif (reach_dist < 0.1) and (self.obj[2] > (self._init_z + 0.005)):
+            pick_reward = min(self.z_target, self.obj[2])
+        else:
+            pick_reward = 0
+
+        return reach_reward / 100 + pick_reward
+
+    def _get_obs(self):
+        eef_velp = self._utils.get_site_xvelp(self.model, self.data, "grasp") * self.dt
+        gripper_angle = self._utils.get_joint_qpos(self.model, self.data, "right_outer_knuckle_joint")
+        eef = self.eef - self.center_of_table
+
+        obj = self.obj - self.center_of_table
+        obj_rot = self._utils.get_joint_qpos(self.model, self.data, "object_joint0")[-4:]
+        obj_velp = self._utils.get_site_xvelp(self.model, self.data, "object_site") * self.dt
+        obj_velr = self._utils.get_site_xvelr(self.model, self.data, "object_site") * self.dt
+
+        obs = np.concatenate(
+            [
+                eef,
+                eef_velp,
+                obj,
+                obj_rot,
+                obj_velp,
+                obj_velr,
+                eef - obj,
+                np.array(
+                    [
+                        np.linalg.norm(eef - obj),
+                        np.linalg.norm(eef[:-1] - obj[:-1]),
+                        self.z_target,
+                        self.z_target - obj[-1],
+                        self.z_target - eef[-1],
+                    ]
+                ),
+                gripper_angle,
+            ],
+            axis=0,
+        )
+        return {"observation": obs, "state": eef, "achieved_goal": eef, "desired_goal": eef}
+
+    def _sample_goal(self):
+        # Gripper
+        gripper_pos = np.array([1.280, 0.295, 0.735]) + self.np_random.uniform(-0.05, 0.05, size=3)
+        super()._set_gripper(gripper_pos, self.gripper_rotation)
+
+        # Object
+        object_pos = self.center_of_table - np.array([0.15, 0.10, 0.07])
+        object_pos[0] += self.np_random.uniform(-0.05, 0.05, size=1)
+        object_pos[1] += self.np_random.uniform(-0.05, 0.05, size=1)
+        object_qpos = self._utils.get_joint_qpos(self.model, self.data, "object_joint0")
+        object_qpos[:3] = object_pos
+        self._utils.set_joint_qpos(self.model, self.data, "object_joint0", object_qpos)
+        self._init_z = object_pos[2]
+
+        # Goal
+        return object_pos + np.array([0, 0, self._z_threshold])
+
+    def reset(self):
+        self._action = np.zeros(4)
+        return super().reset()
+
+    def step(self, action):
+        self._action = action.copy()
+        return super().step(action)

envs/sim_xarm/xarm/tasks/mocap.py

@@ -0,0 +1,67 @@
+# import mujoco_py
+import mujoco
+import numpy as np
+
+
+def apply_action(model, model_names, data, action):
+    if model.nmocap > 0:
+        pos_action, gripper_action = np.split(action, (model.nmocap * 7,))
+        if data.ctrl is not None:
+            for i in range(gripper_action.shape[0]):
+                data.ctrl[i] = gripper_action[i]
+        pos_action = pos_action.reshape(model.nmocap, 7)
+        pos_delta, quat_delta = pos_action[:, :3], pos_action[:, 3:]
+        reset_mocap2body_xpos(model, model_names, data)
+        data.mocap_pos[:] = data.mocap_pos + pos_delta
+        data.mocap_quat[:] = data.mocap_quat + quat_delta
+
+
+def reset(model, data):
+    if model.nmocap > 0 and model.eq_data is not None:
+        for i in range(model.eq_data.shape[0]):
+            # if sim.model.eq_type[i] == mujoco_py.const.EQ_WELD:
+            if model.eq_type[i] == mujoco.mjtEq.mjEQ_WELD:
+                # model.eq_data[i, :] = np.array([0., 0., 0., 1., 0., 0., 0.])
+                model.eq_data[i, :] = np.array(
+                    [
+                        0.0,
+                        0.0,
+                        0.0,
+                        1.0,
+                        0.0,
+                        0.0,
+                        0.0,
+                        0.0,
+                        0.0,
+                        0.0,
+                        0.0,
+                    ]
+                )
+    # sim.forward()
+    mujoco.mj_forward(model, data)
+
+
+def reset_mocap2body_xpos(model, model_names, data):
+    if model.eq_type is None or model.eq_obj1id is None or model.eq_obj2id is None:
+        return
+
+    # For all weld constraints
+    for eq_type, obj1_id, obj2_id in zip(model.eq_type, model.eq_obj1id, model.eq_obj2id, strict=False):
+        # if eq_type != mujoco_py.const.EQ_WELD:
+        if eq_type != mujoco.mjtEq.mjEQ_WELD:
+            continue
+        # body2 = model.body_id2name(obj2_id)
+        body2 = model_names.body_id2name[obj2_id]
+        if body2 == "B0" or body2 == "B9" or body2 == "B1":
+            continue
+        mocap_id = model.body_mocapid[obj1_id]
+        if mocap_id != -1:
+            # obj1 is the mocap, obj2 is the welded body
+            body_idx = obj2_id
+        else:
+            # obj2 is the mocap, obj1 is the welded body
+            mocap_id = model.body_mocapid[obj2_id]
+            body_idx = obj1_id
+        assert mocap_id != -1
+        data.mocap_pos[mocap_id][:] = data.xpos[body_idx]
+        data.mocap_quat[mocap_id][:] = data.xquat[body_idx]

envs/sim_xarm/xarm/tasks/peg_in_box.py

@@ -0,0 +1,86 @@
+import numpy as np
+
+from xarm import Base
+
+
+class PegInBox(Base):
+    def __init__(self):
+        super().__init__("peg_in_box")
+
+    def _reset_sim(self):
+        self._act_magnitude = 0
+        super()._reset_sim()
+        for _ in range(10):
+            self._apply_action(np.array([0, 0, 0, 1], dtype=np.float32))
+            self.sim.step()
+
+    @property
+    def box(self):
+        return self.sim.data.get_site_xpos("box_site")
+
+    def is_success(self):
+        return np.linalg.norm(self.obj - self.box) <= 0.05
+
+    def get_reward(self):
+        dist_xy = np.linalg.norm(self.obj[:2] - self.box[:2])
+        dist_xyz = np.linalg.norm(self.obj - self.box)
+        return float(dist_xy <= 0.045) * (2 - 6 * dist_xyz) - 0.2 * np.square(self._act_magnitude) - dist_xy
+
+    def _get_obs(self):
+        eef_velp = self.sim.data.get_site_xvelp("grasp") * self.dt
+        gripper_angle = self.sim.data.get_joint_qpos("right_outer_knuckle_joint")
+        eef, box = self.eef - self.center_of_table, self.box - self.center_of_table
+
+        obj = self.obj - self.center_of_table
+        obj_rot = self.sim.data.get_joint_qpos("object_joint0")[-4:]
+        obj_velp = self.sim.data.get_site_xvelp("object_site") * self.dt
+        obj_velr = self.sim.data.get_site_xvelr("object_site") * self.dt
+
+        obs = np.concatenate(
+            [
+                eef,
+                eef_velp,
+                box,
+                obj,
+                obj_rot,
+                obj_velp,
+                obj_velr,
+                eef - box,
+                eef - obj,
+                obj - box,
+                np.array(
+                    [
+                        np.linalg.norm(eef - box),
+                        np.linalg.norm(eef - obj),
+                        np.linalg.norm(obj - box),
+                        gripper_angle,
+                    ]
+                ),
+            ],
+            axis=0,
+        )
+        return {"observation": obs, "state": eef, "achieved_goal": eef, "desired_goal": box}
+
+    def _sample_goal(self):
+        # Gripper
+        gripper_pos = np.array([1.280, 0.295, 0.9]) + self.np_random.uniform(-0.05, 0.05, size=3)
+        super()._set_gripper(gripper_pos, self.gripper_rotation)
+
+        # Object
+        object_pos = gripper_pos - np.array([0, 0, 0.06]) + self.np_random.uniform(-0.005, 0.005, size=3)
+        object_qpos = self.sim.data.get_joint_qpos("object_joint0")
+        object_qpos[:3] = object_pos
+        self.sim.data.set_joint_qpos("object_joint0", object_qpos)
+
+        # Box
+        box_pos = np.array([1.61, 0.18, 0.58])
+        box_pos[:2] += self.np_random.uniform(-0.11, 0.11, size=2)
+        box_qpos = self.sim.data.get_joint_qpos("box_joint0")
+        box_qpos[:3] = box_pos
+        self.sim.data.set_joint_qpos("box_joint0", box_qpos)
+
+        return self.box
+
+    def step(self, action):
+        self._act_magnitude = np.linalg.norm(action[:3])
+        return super().step(action)

envs/sim_xarm/xarm/tasks/push.py

@@ -0,0 +1,78 @@
+import numpy as np
+
+from xarm import Base
+
+
+class Push(Base):
+    def __init__(self):
+        super().__init__("push")
+
+    def _reset_sim(self):
+        self._act_magnitude = 0
+        super()._reset_sim()
+
+    def is_success(self):
+        return np.linalg.norm(self.obj - self.goal) <= 0.05
+
+    def get_reward(self):
+        dist = np.linalg.norm(self.obj - self.goal)
+        penalty = self._act_magnitude**2
+        return -(dist + 0.15 * penalty)
+
+    def _get_obs(self):
+        eef_velp = self.sim.data.get_site_xvelp("grasp") * self.dt
+        gripper_angle = self.sim.data.get_joint_qpos("right_outer_knuckle_joint")
+        eef, goal = self.eef - self.center_of_table, self.goal - self.center_of_table
+
+        obj = self.obj - self.center_of_table
+        obj_rot = self.sim.data.get_joint_qpos("object_joint0")[-4:]
+        obj_velp = self.sim.data.get_site_xvelp("object_site") * self.dt
+        obj_velr = self.sim.data.get_site_xvelr("object_site") * self.dt
+
+        obs = np.concatenate(
+            [
+                eef,
+                eef_velp,
+                goal,
+                obj,
+                obj_rot,
+                obj_velp,
+                obj_velr,
+                eef - goal,
+                eef - obj,
+                obj - goal,
+                np.array(
+                    [
+                        np.linalg.norm(eef - goal),
+                        np.linalg.norm(eef - obj),
+                        np.linalg.norm(obj - goal),
+                        gripper_angle,
+                    ]
+                ),
+            ],
+            axis=0,
+        )
+        return {"observation": obs, "state": eef, "achieved_goal": eef, "desired_goal": goal}
+
+    def _sample_goal(self):
+        # Gripper
+        gripper_pos = np.array([1.280, 0.295, 0.735]) + self.np_random.uniform(-0.05, 0.05, size=3)
+        super()._set_gripper(gripper_pos, self.gripper_rotation)
+
+        # Object
+        object_pos = self.center_of_table - np.array([0.25, 0, 0.07])
+        object_pos[0] += self.np_random.uniform(-0.08, 0.08, size=1)
+        object_pos[1] += self.np_random.uniform(-0.08, 0.08, size=1)
+        object_qpos = self.sim.data.get_joint_qpos("object_joint0")
+        object_qpos[:3] = object_pos
+        self.sim.data.set_joint_qpos("object_joint0", object_qpos)
+
+        # Goal
+        self.goal = np.array([1.600, 0.200, 0.545])
+        self.goal[:2] += self.np_random.uniform(-0.1, 0.1, size=2)
+        self.sim.model.site_pos[self.sim.model.site_name2id("target0")] = self.goal
+        return self.goal
+
+    def step(self, action):
+        self._act_magnitude = np.linalg.norm(action[:3])
+        return super().step(action)

envs/sim_xarm/xarm/tasks/reach.py

@@ -0,0 +1,44 @@
+import numpy as np
+
+from xarm import Base
+
+
+class Reach(Base):
+    def __init__(self):
+        super().__init__("reach")
+
+    def _reset_sim(self):
+        self._act_magnitude = 0
+        super()._reset_sim()
+
+    def is_success(self):
+        return np.linalg.norm(self.eef - self.goal) <= 0.05
+
+    def get_reward(self):
+        dist = np.linalg.norm(self.eef - self.goal)
+        penalty = self._act_magnitude**2
+        return -(dist + 0.15 * penalty)
+
+    def _get_obs(self):
+        eef_velp = self.sim.data.get_site_xvelp("grasp") * self.dt
+        gripper_angle = self.sim.data.get_joint_qpos("right_outer_knuckle_joint")
+        eef, goal = self.eef - self.center_of_table, self.goal - self.center_of_table
+        obs = np.concatenate(
+            [eef, eef_velp, goal, eef - goal, np.array([np.linalg.norm(eef - goal), gripper_angle])], axis=0
+        )
+        return {"observation": obs, "state": eef, "achieved_goal": eef, "desired_goal": goal}
+
+    def _sample_goal(self):
+        # Gripper
+        gripper_pos = np.array([1.280, 0.295, 0.735]) + self.np_random.uniform(-0.05, 0.05, size=3)
+        super()._set_gripper(gripper_pos, self.gripper_rotation)
+
+        # Goal
+        self.goal = np.array([1.550, 0.287, 0.580])
+        self.goal[:2] += self.np_random.uniform(-0.125, 0.125, size=2)
+        self.sim.model.site_pos[self.sim.model.site_name2id("target0")] = self.goal
+        return self.goal
+
+    def step(self, action):
+        self._act_magnitude = np.linalg.norm(action[:3])
+        return super().step(action)

examples/1_load_lerobot_dataset.py

@@ -1,91 +0,0 @@
-"""
-This script demonstrates the use of `LeRobotDataset` class for handling and processing robotic datasets from Hugging Face.
-It illustrates how to load datasets, manipulate them, and apply transformations suitable for machine learning tasks in PyTorch.
-
-Features included in this script:
-- Loading a dataset and accessing its properties.
-- Filtering data by episode number.
-- Converting tensor data for visualization.
-- Saving video files from dataset frames.
-- Using advanced dataset features like timestamp-based frame selection.
-- Demonstrating compatibility with PyTorch DataLoader for batch processing.
-
-The script ends with examples of how to batch process data using PyTorch's DataLoader.
-"""
-
-from pathlib import Path
-
-import imageio
-import torch
-
-import lerobot
-from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
-
-print("List of available datasets", lerobot.available_datasets)
-# # >>> ['lerobot/aloha_sim_insertion_human', 'lerobot/aloha_sim_insertion_scripted',
-# #     'lerobot/aloha_sim_transfer_cube_human', 'lerobot/aloha_sim_transfer_cube_scripted',
-# #     'lerobot/pusht', 'lerobot/xarm_lift_medium']
-
-repo_id = "lerobot/pusht"
-
-# You can easily load a dataset from a Hugging Face repositery
-dataset = LeRobotDataset(repo_id)
-
-# LeRobotDataset is actually a thin wrapper around an underlying Hugging Face dataset  (see https://huggingface.co/docs/datasets/index for more information).
-# TODO(rcadene): update to make the print pretty
-print(f"{dataset=}")
-print(f"{dataset.hf_dataset=}")
-
-# and provides additional utilities for robotics and compatibility with pytorch
-print(f"number of samples/frames: {dataset.num_samples=}")
-print(f"number of episodes: {dataset.num_episodes=}")
-print(f"average number of frames per episode: {dataset.num_samples / dataset.num_episodes:.3f}")
-print(f"frames per second used during data collection: {dataset.fps=}")
-print(f"keys to access images from cameras: {dataset.image_keys=}")
-
-# While the LeRobotDataset adds helpers for working within our library, we still expose the underling Hugging Face dataset.
-# It may be freely replaced or modified in place. Here we use the filtering to keep only frames from episode 5.
-# TODO(rcadene): remove this example of accessing hf_dataset
-dataset.hf_dataset = dataset.hf_dataset.filter(lambda frame: frame["episode_index"] == 5)
-
-# LeRobot datsets actually subclass PyTorch datasets. So you can do everything you know and love from working with the latter, for example: iterating through the dataset. Here we grab all the image frames.
-frames = [sample["observation.image"] for sample in dataset]
-
-# but frames are now float32 range [0,1] channel first (c,h,w) to follow pytorch convention,
-# to view them, we convert to uint8 range [0,255]
-frames = [(frame * 255).type(torch.uint8) for frame in frames]
-# and to channel last (h,w,c)
-frames = [frame.permute((1, 2, 0)).numpy() for frame in frames]
-
-# and finally save them to a mp4 video
-Path("outputs/examples/1_load_lerobot_dataset").mkdir(parents=True, exist_ok=True)
-imageio.mimsave("outputs/examples/1_load_lerobot_dataset/episode_5.mp4", frames, fps=dataset.fps)
-
-# For many machine learning applications we need to load histories of past observations, or trajectorys of future actions. Our datasets can load previous and future frames for each key/modality,
-# using timestamps differences with the current loaded frame. For instance:
-delta_timestamps = {
-    # loads 4 images: 1 second before current frame, 500 ms before, 200 ms before, and current frame
-    "observation.image": [-1, -0.5, -0.20, 0],
-    # loads 8 state vectors: 1.5 seconds before, 1 second before, ... 20 ms, 10 ms, and current frame
-    "observation.state": [-1.5, -1, -0.5, -0.20, -0.10, -0.02, -0.01, 0],
-    # loads 64 action vectors: current frame, 1 frame in the future, 2 frames, ... 63 frames in the future
-    "action": [t / dataset.fps for t in range(64)],
-}
-dataset = LeRobotDataset(repo_id, delta_timestamps=delta_timestamps)
-print(f"{dataset[0]['observation.image'].shape=}")  # (4,c,h,w)
-print(f"{dataset[0]['observation.state'].shape=}")  # (8,c)
-print(f"{dataset[0]['action'].shape=}")  # (64,c)
-
-# Finally, our datasets are fully compatible with PyTorch dataloaders and samplers
-# because they are just PyTorch datasets.
-dataloader = torch.utils.data.DataLoader(
-    dataset,
-    num_workers=0,
-    batch_size=32,
-    shuffle=True,
-)
-for batch in dataloader:
-    print(f"{batch['observation.image'].shape=}")  # (32,4,c,h,w)
-    print(f"{batch['observation.state'].shape=}")  # (32,8,c)
-    print(f"{batch['action'].shape=}")  # (32,64,c)
-    break

examples/1_visualize_dataset.py

@@ -0,0 +1,24 @@
+import os
+
+from torchrl.data.replay_buffers import SamplerWithoutReplacement
+
+import lerobot
+from lerobot.common.datasets.aloha import AlohaDataset
+from lerobot.scripts.visualize_dataset import render_dataset
+
+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, root=os.environ.get("DATA_DIR"))
+
+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']

examples/2_evaluate_pretrained_policy.py

@@ -1,38 +1 @@
-"""
-This scripts demonstrates how to evaluate a pretrained policy from the HuggingFace Hub or from your local
-training outputs directory. In the latter case, you might want to run examples/3_train_policy.py first.
-"""
-
-from pathlib import Path
-
-from huggingface_hub import snapshot_download
-
-from lerobot.common.utils.utils import init_hydra_config
-from lerobot.scripts.eval import eval
-
-# Get a pretrained policy from the hub.
-# TODO(alexander-soare): This no longer works until we upload a new model that uses the current configs.
-hub_id = "lerobot/diffusion_policy_pusht_image"
-folder = Path(snapshot_download(hub_id))
-# OR uncomment the following to evaluate a policy from the local outputs/train folder.
-# folder = Path("outputs/train/example_pusht_diffusion")
-
-config_path = folder / "config.yaml"
-weights_path = folder / "model.pt"
-
-# Override some config parameters to do with evaluation.
-overrides = [
-    f"policy.pretrained_model_path={weights_path}",
-    "eval_episodes=10",
-    "rollout_batch_size=10",
-    "device=cuda",
-]
-
-# Create a Hydra config.
-cfg = init_hydra_config(config_path, overrides)
-
-# Evaluate the policy and save the outputs including metrics and videos.
-eval(
-    cfg,
-    out_dir=f"outputs/eval/example_{cfg.env.name}_{cfg.policy.name}",
-)
+# TODO

examples/3_train_policy.py

@@ -1,67 +1 @@
-"""This scripts demonstrates how to train Diffusion Policy on the PushT environment.
-
-Once you have trained a model with this script, you can try to evaluate it on
-examples/2_evaluate_pretrained_policy.py
-"""
-
-import os
-from pathlib import Path
-
-import torch
-from omegaconf import OmegaConf
-
-from lerobot.common.datasets.factory import make_dataset
-from lerobot.common.policies.diffusion.configuration_diffusion import DiffusionConfig
-from lerobot.common.policies.diffusion.modeling_diffusion import DiffusionPolicy
-from lerobot.common.utils.utils import init_hydra_config
-
-output_directory = Path("outputs/train/example_pusht_diffusion")
-os.makedirs(output_directory, exist_ok=True)
-
-# Number of offline training steps (we'll only do offline training for this example.
-# Adjust as you prefer. 5000 steps are needed to get something worth evaluating.
-training_steps = 5000
-device = torch.device("cuda")
-log_freq = 250
-
-# Set up the dataset.
-hydra_cfg = init_hydra_config("lerobot/configs/default.yaml", overrides=["env=pusht"])
-dataset = make_dataset(hydra_cfg)
-
-# Set up the the policy.
-# Policies are initialized with a configuration class, in this case `DiffusionConfig`.
-# For this example, no arguments need to be passed because the defaults are set up for PushT.
-# If you're doing something different, you will likely need to change at least some of the defaults.
-cfg = DiffusionConfig()
-# TODO(alexander-soare): Remove LR scheduler from the policy.
-policy = DiffusionPolicy(cfg, lr_scheduler_num_training_steps=training_steps, dataset_stats=dataset.stats)
-policy.train()
-policy.to(device)
-
-# Create dataloader for offline training.
-dataloader = torch.utils.data.DataLoader(
-    dataset,
-    num_workers=4,
-    batch_size=cfg.batch_size,
-    shuffle=True,
-    pin_memory=device != torch.device("cpu"),
-    drop_last=True,
-)
-
-# Run training loop.
-step = 0
-done = False
-while not done:
-    for batch in dataloader:
-        batch = {k: v.to(device, non_blocking=True) for k, v in batch.items()}
-        info = policy.update(batch)
-        if step % log_freq == 0:
-            print(f"step: {step} loss: {info['loss']:.3f} update_time: {info['update_s']:.3f} (seconds)")
-        step += 1
-        if step >= training_steps:
-            done = True
-            break
-
-# Save the policy and configuration for later use.
-policy.save(output_directory / "model.pt")
-OmegaConf.save(hydra_cfg, output_directory / "config.yaml")
+# TODO

lerobot/__init__.py

@@ -7,86 +7,53 @@ Example:
         import lerobot
         print(lerobot.available_envs)
         print(lerobot.available_tasks_per_env)
-        print(lerobot.available_datasets)
         print(lerobot.available_datasets_per_env)
+        print(lerobot.available_datasets)
         print(lerobot.available_policies)
-        print(lerobot.available_policies_per_env)
     ```
 
-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
+Note:
+    When implementing a concrete class (e.g. `AlohaDataset`, `PushtEnv`, `DiffusionPolicy`), you need to:
+        1. set the required class attributes:
+            - for classes inheriting from `AbstractDataset`: `available_datasets`
+            - for classes inheriting from `AbstractEnv`: `name`, `available_tasks`
+            - for classes inheriting from `AbstractPolicy`: `name`
+        2. update variables in `lerobot/__init__.py` (e.g. `available_envs`, `available_datasets_per_envs`, `available_policies`)
+        3. update variables in `tests/test_available.py` by importing your new class
 """
 
-import itertools
-
 from lerobot.__version__ import __version__  # noqa: F401
 
+available_envs = [
+    "aloha",
+    "pusht",
+    "simxarm",
+]
+
 available_tasks_per_env = {
     "aloha": [
-        "AlohaInsertion-v0",
-        "AlohaTransferCube-v0",
+        "sim_insertion",
+        "sim_transfer_cube",
     ],
-    "pusht": ["PushT-v0"],
-    "xarm": ["XarmLift-v0"],
+    "pusht": ["pusht"],
+    "simxarm": ["lift"],
 }
-available_envs = list(available_tasks_per_env.keys())
 
 available_datasets_per_env = {
     "aloha": [
-        "lerobot/aloha_sim_insertion_human",
-        "lerobot/aloha_sim_insertion_scripted",
-        "lerobot/aloha_sim_transfer_cube_human",
-        "lerobot/aloha_sim_transfer_cube_scripted",
-    ],
-    "pusht": ["lerobot/pusht"],
-    "xarm": [
-        "lerobot/xarm_lift_medium",
-        "lerobot/xarm_lift_medium_replay",
-        "lerobot/xarm_push_medium",
-        "lerobot/xarm_push_medium_replay",
+        "aloha_sim_insertion_human",
+        "aloha_sim_insertion_scripted",
+        "aloha_sim_transfer_cube_human",
+        "aloha_sim_transfer_cube_scripted",
     ],
+    "pusht": ["pusht"],
+    "simxarm": ["xarm_lift_medium"],
 }
 
-available_datasets_without_env = ["lerobot/umi_cup_in_the_wild"]
-
-available_datasets = list(
-    itertools.chain(*available_datasets_per_env.values(), available_datasets_without_env)
-)
-
-# TODO(rcadene, aliberts, alexander-soare): Add real-world env with a gym API
-available_datasets_without_env = ["lerobot/umi_cup_in_the_wild"]
-
-available_datasets = list(
-    itertools.chain(*available_datasets_per_env.values(), available_datasets_without_env)
-)
+available_datasets = [dataset for env in available_envs for dataset in available_datasets_per_env[env]]
 
 available_policies = [
     "act",
     "diffusion",
     "tdmpc",
 ]
-
-available_policies_per_env = {
-    "aloha": ["act"],
-    "pusht": ["diffusion"],
-    "xarm": ["tdmpc"],
-}
-
-env_task_pairs = [(env, task) for env, tasks in available_tasks_per_env.items() for task in tasks]
-env_dataset_pairs = [
-    (env, dataset) for env, datasets in available_datasets_per_env.items() for dataset in datasets
-]
-env_dataset_policy_triplets = [
-    (env, dataset, policy)
-    for env, datasets in available_datasets_per_env.items()
-    for dataset in datasets
-    for policy in available_policies_per_env[env]
-]

lerobot/common/datasets/abstract.py

@@ -0,0 +1,207 @@
+import logging
+from pathlib import Path
+from typing import Callable
+
+import einops
+import torch
+import torchrl
+import tqdm
+from huggingface_hub import snapshot_download
+from tensordict import TensorDict
+from torchrl.data.replay_buffers.replay_buffers import TensorDictReplayBuffer
+from torchrl.data.replay_buffers.samplers import Sampler
+from torchrl.data.replay_buffers.storages import TensorStorage, _collate_id
+from torchrl.data.replay_buffers.writers import ImmutableDatasetWriter, Writer
+from torchrl.envs.transforms.transforms import Compose
+
+HF_USER = "lerobot"
+
+
+class AbstractDataset(TensorDictReplayBuffer):
+    """
+    AbstractDataset represents a dataset in the context of imitation learning or reinforcement learning.
+    This class is designed to be subclassed by concrete implementations that specify particular types of datasets.
+    These implementations can vary based on the source of the data, the environment the data pertains to,
+    or the specific kind of data manipulation applied.
+
+    Note:
+        - `TensorDictReplayBuffer` is the base class from which `AbstractDataset` inherits. It provides the foundational
+           functionality for storing and retrieving `TensorDict`-like data.
+        - `available_datasets` should be overridden by concrete subclasses to list the specific dataset variants supported.
+           It is expected that these variants correspond to a HuggingFace dataset on the hub.
+           For instance, the `AlohaDataset` which inherites from `AbstractDataset` has 4 available dataset variants:
+            - [aloha_sim_transfer_cube_scripted](https://huggingface.co/datasets/lerobot/aloha_sim_transfer_cube_scripted)
+            - [aloha_sim_insertion_scripted](https://huggingface.co/datasets/lerobot/aloha_sim_insertion_scripted)
+            - [aloha_sim_transfer_cube_human](https://huggingface.co/datasets/lerobot/aloha_sim_transfer_cube_human)
+            - [aloha_sim_insertion_human](https://huggingface.co/datasets/lerobot/aloha_sim_insertion_human)
+        - When implementing a concrete class (e.g. `AlohaDataset`, `PushtEnv`, `DiffusionPolicy`), you need to:
+            1. set the required class attributes:
+                - for classes inheriting from `AbstractDataset`: `available_datasets`
+                - for classes inheriting from `AbstractEnv`: `name`, `available_tasks`
+                - for classes inheriting from `AbstractPolicy`: `name`
+            2. update variables in `lerobot/__init__.py` (e.g. `available_envs`, `available_datasets_per_envs`, `available_policies`)
+            3. update variables in `tests/test_available.py` by importing your new class
+    """
+
+    available_datasets: list[str] | None = None
+
+    def __init__(
+        self,
+        dataset_id: str,
+        version: str | None = None,
+        batch_size: int | None = None,
+        *,
+        shuffle: bool = True,
+        root: Path | None = None,
+        pin_memory: bool = False,
+        prefetch: int = None,
+        sampler: Sampler | None = None,
+        collate_fn: Callable | None = None,
+        writer: Writer | None = None,
+        transform: "torchrl.envs.Transform" = None,
+    ):
+        assert (
+            self.available_datasets is not None
+        ), "Subclasses of `AbstractDataset` should set the `available_datasets` class attribute."
+        assert (
+            dataset_id in self.available_datasets
+        ), f"The provided dataset ({dataset_id}) is not on the list of available datasets {self.available_datasets}."
+
+        self.dataset_id = dataset_id
+        self.version = version
+        self.shuffle = shuffle
+        self.root = root if root is None else Path(root)
+
+        if self.root is not None and self.version is not None:
+            logging.warning(
+                f"The version of the dataset ({self.version}) is not enforced when root is provided ({self.root})."
+            )
+
+        storage = self._download_or_load_dataset()
+
+        super().__init__(
+            storage=storage,
+            sampler=sampler,
+            writer=ImmutableDatasetWriter() if writer is None else writer,
+            collate_fn=_collate_id if collate_fn is None else collate_fn,
+            pin_memory=pin_memory,
+            prefetch=prefetch,
+            batch_size=batch_size,
+            transform=transform,
+        )
+
+    @property
+    def stats_patterns(self) -> dict:
+        return {
+            ("observation", "state"): "b c -> c",
+            ("observation", "image"): "b c h w -> c 1 1",
+            ("action",): "b c -> c",
+        }
+
+    @property
+    def image_keys(self) -> list:
+        return [("observation", "image")]
+
+    @property
+    def num_cameras(self) -> int:
+        return len(self.image_keys)
+
+    @property
+    def num_samples(self) -> int:
+        return len(self)
+
+    @property
+    def num_episodes(self) -> int:
+        return len(self._storage._storage["episode"].unique())
+
+    @property
+    def transform(self):
+        return self._transform
+
+    def set_transform(self, transform):
+        if not isinstance(transform, Compose):
+            # required since torchrl calls `len(self._transform)` downstream
+            if isinstance(transform, list):
+                self._transform = Compose(*transform)
+            else:
+                self._transform = Compose(transform)
+        else:
+            self._transform = transform
+
+    def compute_or_load_stats(self, num_batch=100, batch_size=32) -> TensorDict:
+        stats_path = self.data_dir / "stats.pth"
+        if stats_path.exists():
+            stats = torch.load(stats_path)
+        else:
+            logging.info(f"compute_stats and save to {stats_path}")
+            stats = self._compute_stats(num_batch, batch_size)
+            torch.save(stats, stats_path)
+        return stats
+
+    def _download_or_load_dataset(self) -> torch.StorageBase:
+        if self.root is None:
+            self.data_dir = Path(
+                snapshot_download(
+                    repo_id=f"{HF_USER}/{self.dataset_id}", repo_type="dataset", revision=self.version
+                )
+            )
+        else:
+            self.data_dir = self.root / self.dataset_id
+        return TensorStorage(TensorDict.load_memmap(self.data_dir / "replay_buffer"))
+
+    def _compute_stats(self, num_batch=100, batch_size=32):
+        rb = TensorDictReplayBuffer(
+            storage=self._storage,
+            batch_size=batch_size,
+            prefetch=True,
+        )
+
+        mean, std, max, min = {}, {}, {}, {}
+
+        # compute mean, min, max
+        for _ in tqdm.tqdm(range(num_batch)):
+            batch = rb.sample()
+            for key, pattern in self.stats_patterns.items():
+                batch[key] = batch[key].float()
+                if key not in mean:
+                    # first batch initialize mean, min, max
+                    mean[key] = einops.reduce(batch[key], pattern, "mean")
+                    max[key] = einops.reduce(batch[key], pattern, "max")
+                    min[key] = einops.reduce(batch[key], pattern, "min")
+                else:
+                    mean[key] += einops.reduce(batch[key], pattern, "mean")
+                    max[key] = torch.maximum(max[key], einops.reduce(batch[key], pattern, "max"))
+                    min[key] = torch.minimum(min[key], einops.reduce(batch[key], pattern, "min"))
+                batch = rb.sample()
+
+        for key in self.stats_patterns:
+            mean[key] /= num_batch
+
+        # compute std, min, max
+        for _ in tqdm.tqdm(range(num_batch)):
+            batch = rb.sample()
+            for key, pattern in self.stats_patterns.items():
+                batch[key] = batch[key].float()
+                batch_mean = einops.reduce(batch[key], pattern, "mean")
+                if key not in std:
+                    # first batch initialize std
+                    std[key] = (batch_mean - mean[key]) ** 2
+                else:
+                    std[key] += (batch_mean - mean[key]) ** 2
+                max[key] = torch.maximum(max[key], einops.reduce(batch[key], pattern, "max"))
+                min[key] = torch.minimum(min[key], einops.reduce(batch[key], pattern, "min"))
+
+        for key in self.stats_patterns:
+            std[key] = torch.sqrt(std[key] / num_batch)
+
+        stats = TensorDict({}, batch_size=[])
+        for key in self.stats_patterns:
+            stats[(*key, "mean")] = mean[key]
+            stats[(*key, "std")] = std[key]
+            stats[(*key, "max")] = max[key]
+            stats[(*key, "min")] = min[key]
+
+            if key[0] == "observation":
+                # use same stats for the next observations
+                stats[("next", *key)] = stats[key]
+        return stats

lerobot/common/datasets/aloha.py

@@ -0,0 +1,185 @@
+import logging
+from pathlib import Path
+from typing import Callable
+
+import einops
+import gdown
+import h5py
+import torch
+import torchrl
+import tqdm
+from tensordict import TensorDict
+from torchrl.data.replay_buffers.samplers import Sampler
+from torchrl.data.replay_buffers.storages import TensorStorage
+from torchrl.data.replay_buffers.writers import Writer
+
+from lerobot.common.datasets.abstract import AbstractDataset
+
+DATASET_IDS = [
+    "aloha_sim_insertion_human",
+    "aloha_sim_insertion_scripted",
+    "aloha_sim_transfer_cube_human",
+    "aloha_sim_transfer_cube_scripted",
+]
+
+FOLDER_URLS = {
+    "aloha_sim_insertion_human": "https://drive.google.com/drive/folders/1RgyD0JgTX30H4IM5XZn8I3zSV_mr8pyF",
+    "aloha_sim_insertion_scripted": "https://drive.google.com/drive/folders/1TsojQQSXtHEoGnqgJ3gmpPQR2DPLtS2N",
+    "aloha_sim_transfer_cube_human": "https://drive.google.com/drive/folders/1sc-E4QYW7A0o23m1u2VWNGVq5smAsfCo",
+    "aloha_sim_transfer_cube_scripted": "https://drive.google.com/drive/folders/1aRyoOhQwxhyt1J8XgEig4s6kzaw__LXj",
+}
+
+EP48_URLS = {
+    "aloha_sim_insertion_human": "https://drive.google.com/file/d/18Cudl6nikDtgRolea7je8iF_gGKzynOP/view?usp=drive_link",
+    "aloha_sim_insertion_scripted": "https://drive.google.com/file/d/1wfMSZ24oOh5KR_0aaP3Cnu_c4ZCveduB/view?usp=drive_link",
+    "aloha_sim_transfer_cube_human": "https://drive.google.com/file/d/18smMymtr8tIxaNUQ61gW6dG50pt3MvGq/view?usp=drive_link",
+    "aloha_sim_transfer_cube_scripted": "https://drive.google.com/file/d/1pnGIOd-E4-rhz2P3VxpknMKRZCoKt6eI/view?usp=drive_link",
+}
+
+EP49_URLS = {
+    "aloha_sim_insertion_human": "https://drive.google.com/file/d/1C1kZYyROzs-PrLc0SkDgUgMi4-L3lauE/view?usp=drive_link",
+    "aloha_sim_insertion_scripted": "https://drive.google.com/file/d/17EuCUWS6uCCr6yyNzpXdcdE-_TTNCKtf/view?usp=drive_link",
+    "aloha_sim_transfer_cube_human": "https://drive.google.com/file/d/1Nk7l53d9sJoGDBKAOnNrExX5nLacATc6/view?usp=drive_link",
+    "aloha_sim_transfer_cube_scripted": "https://drive.google.com/file/d/1GKReZHrXU73NMiC5zKCq_UtqPVtYq8eo/view?usp=drive_link",
+}
+
+NUM_EPISODES = {
+    "aloha_sim_insertion_human": 50,
+    "aloha_sim_insertion_scripted": 50,
+    "aloha_sim_transfer_cube_human": 50,
+    "aloha_sim_transfer_cube_scripted": 50,
+}
+
+EPISODE_LEN = {
+    "aloha_sim_insertion_human": 500,
+    "aloha_sim_insertion_scripted": 400,
+    "aloha_sim_transfer_cube_human": 400,
+    "aloha_sim_transfer_cube_scripted": 400,
+}
+
+CAMERAS = {
+    "aloha_sim_insertion_human": ["top"],
+    "aloha_sim_insertion_scripted": ["top"],
+    "aloha_sim_transfer_cube_human": ["top"],
+    "aloha_sim_transfer_cube_scripted": ["top"],
+}
+
+
+def download(data_dir, dataset_id):
+    assert dataset_id in DATASET_IDS
+    assert dataset_id in FOLDER_URLS
+    assert dataset_id in EP48_URLS
+    assert dataset_id in EP49_URLS
+
+    data_dir.mkdir(parents=True, exist_ok=True)
+
+    gdown.download_folder(FOLDER_URLS[dataset_id], output=str(data_dir))
+
+    # because of the 50 files limit per directory, two files episode 48 and 49 were missing
+    gdown.download(EP48_URLS[dataset_id], output=str(data_dir / "episode_48.hdf5"), fuzzy=True)
+    gdown.download(EP49_URLS[dataset_id], output=str(data_dir / "episode_49.hdf5"), fuzzy=True)
+
+
+class AlohaDataset(AbstractDataset):
+    available_datasets = DATASET_IDS
+
+    def __init__(
+        self,
+        dataset_id: str,
+        version: str | None = "v1.2",
+        batch_size: int | None = None,
+        *,
+        shuffle: bool = True,
+        root: Path | None = None,
+        pin_memory: bool = False,
+        prefetch: int = None,
+        sampler: Sampler | None = None,
+        collate_fn: Callable | None = None,
+        writer: Writer | None = None,
+        transform: "torchrl.envs.Transform" = None,
+    ):
+        super().__init__(
+            dataset_id,
+            version,
+            batch_size,
+            shuffle=shuffle,
+            root=root,
+            pin_memory=pin_memory,
+            prefetch=prefetch,
+            sampler=sampler,
+            collate_fn=collate_fn,
+            writer=writer,
+            transform=transform,
+        )
+
+    @property
+    def stats_patterns(self) -> dict:
+        d = {
+            ("observation", "state"): "b c -> c",
+            ("action",): "b c -> c",
+        }
+        for cam in CAMERAS[self.dataset_id]:
+            d[("observation", "image", cam)] = "b c h w -> c 1 1"
+        return d
+
+    @property
+    def image_keys(self) -> list:
+        return [("observation", "image", cam) for cam in CAMERAS[self.dataset_id]]
+
+    def _download_and_preproc_obsolete(self):
+        assert self.root is not None
+        raw_dir = self.root / f"{self.dataset_id}_raw"
+        if not raw_dir.is_dir():
+            download(raw_dir, self.dataset_id)
+
+        total_num_frames = 0
+        logging.info("Compute total number of frames to initialize offline buffer")
+        for ep_id in range(NUM_EPISODES[self.dataset_id]):
+            ep_path = raw_dir / f"episode_{ep_id}.hdf5"
+            with h5py.File(ep_path, "r") as ep:
+                total_num_frames += ep["/action"].shape[0] - 1
+        logging.info(f"{total_num_frames=}")
+
+        logging.info("Initialize and feed offline buffer")
+        idxtd = 0
+        for ep_id in tqdm.tqdm(range(NUM_EPISODES[self.dataset_id])):
+            ep_path = raw_dir / f"episode_{ep_id}.hdf5"
+            with h5py.File(ep_path, "r") as ep:
+                ep_num_frames = ep["/action"].shape[0]
+
+                # last step of demonstration is considered done
+                done = torch.zeros(ep_num_frames, 1, dtype=torch.bool)
+                done[-1] = True
+
+                state = torch.from_numpy(ep["/observations/qpos"][:])
+                action = torch.from_numpy(ep["/action"][:])
+
+                ep_td = TensorDict(
+                    {
+                        ("observation", "state"): state[:-1],
+                        "action": action[:-1],
+                        "episode": torch.tensor([ep_id] * (ep_num_frames - 1)),
+                        "frame_id": torch.arange(0, ep_num_frames - 1, 1),
+                        ("next", "observation", "state"): state[1:],
+                        # TODO: compute reward and success
+                        # ("next", "reward"): reward[1:],
+                        ("next", "done"): done[1:],
+                        # ("next", "success"): success[1:],
+                    },
+                    batch_size=ep_num_frames - 1,
+                )
+
+                for cam in CAMERAS[self.dataset_id]:
+                    image = torch.from_numpy(ep[f"/observations/images/{cam}"][:])
+                    image = einops.rearrange(image, "b h w c -> b c h w").contiguous()
+                    ep_td["observation", "image", cam] = image[:-1]
+                    ep_td["next", "observation", "image", cam] = image[1:]
+
+                if ep_id == 0:
+                    # hack to initialize tensordict data structure to store episodes
+                    td_data = ep_td[0].expand(total_num_frames).memmap_like(self.root / f"{self.dataset_id}")
+
+                td_data[idxtd : idxtd + len(ep_td)] = ep_td
+                idxtd = idxtd + len(ep_td)
+
+        return TensorStorage(td_data.lock_())

lerobot/common/datasets/factory.py

@@ -3,42 +3,134 @@ import os
 from pathlib import Path
 
 import torch
-from omegaconf import OmegaConf
+from torchrl.data.replay_buffers import PrioritizedSliceSampler, SliceSampler
 
-from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.common.transforms import NormalizeTransform, Prod
 
+# DATA_DIR specifies to location where datasets are loaded. By default, DATA_DIR is None and
+# we load from `$HOME/.cache/huggingface/hub/datasets`. For our unit tests, we set `DATA_DIR=tests/data`
+# to load a subset of our datasets for faster continuous integration.
 DATA_DIR = Path(os.environ["DATA_DIR"]) if "DATA_DIR" in os.environ else None
 
 
-def make_dataset(
+def make_offline_buffer(
     cfg,
-    split="train",
+    overwrite_sampler=None,
+    # set normalize=False to remove all transformations and keep images unnormalized in [0,255]
+    normalize=True,
+    overwrite_batch_size=None,
+    overwrite_prefetch=None,
+    stats_path=None,
 ):
-    if cfg.env.name not in cfg.dataset.repo_id:
-        logging.warning(
-            f"There might be a mismatch between your training dataset ({cfg.dataset.repo_id=}) and your environment ({cfg.env.name=})."
-        )
+    if cfg.policy.balanced_sampling:
+        assert cfg.online_steps > 0
+        batch_size = None
+        pin_memory = False
+        prefetch = None
+    else:
+        assert cfg.online_steps == 0
+        num_slices = cfg.policy.batch_size
+        batch_size = cfg.policy.horizon * num_slices
+        pin_memory = cfg.device == "cuda"
+        prefetch = cfg.prefetch
 
-    delta_timestamps = cfg.policy.get("delta_timestamps")
-    if delta_timestamps is not None:
-        for key in delta_timestamps:
-            if isinstance(delta_timestamps[key], str):
-                delta_timestamps[key] = eval(delta_timestamps[key])
+    if overwrite_batch_size is not None:
+        batch_size = overwrite_batch_size
 
-    # TODO(rcadene): add data augmentations
+    if overwrite_prefetch is not None:
+        prefetch = overwrite_prefetch
 
-    dataset = LeRobotDataset(
-        cfg.dataset.repo_id,
-        split=split,
+    if overwrite_sampler is None:
+        # TODO(rcadene): move batch_size outside
+        num_traj_per_batch = cfg.policy.batch_size  # // cfg.horizon
+        # TODO(rcadene): Sampler outputs a batch_size <= cfg.batch_size.
+        # We would need to add a transform to pad the tensordict to ensure batch_size == cfg.batch_size.
+
+        if cfg.offline_prioritized_sampler:
+            logging.info("use prioritized sampler for offline dataset")
+            sampler = PrioritizedSliceSampler(
+                max_capacity=100_000,
+                alpha=cfg.policy.per_alpha,
+                beta=cfg.policy.per_beta,
+                num_slices=num_traj_per_batch,
+                strict_length=False,
+            )
+        else:
+            logging.info("use simple sampler for offline dataset")
+            sampler = SliceSampler(
+                num_slices=num_traj_per_batch,
+                strict_length=False,
+            )
+    else:
+        sampler = overwrite_sampler
+
+    if cfg.env.name == "simxarm":
+        from lerobot.common.datasets.simxarm import SimxarmDataset
+
+        clsfunc = SimxarmDataset
+
+    elif cfg.env.name == "pusht":
+        from lerobot.common.datasets.pusht import PushtDataset
+
+        clsfunc = PushtDataset
+
+    elif cfg.env.name == "aloha":
+        from lerobot.common.datasets.aloha import AlohaDataset
+
+        clsfunc = AlohaDataset
+    else:
+        raise ValueError(cfg.env.name)
+
+    offline_buffer = clsfunc(
+        dataset_id=cfg.dataset_id,
+        sampler=sampler,
+        batch_size=batch_size,
         root=DATA_DIR,
-        delta_timestamps=delta_timestamps,
+        pin_memory=pin_memory,
+        prefetch=prefetch if isinstance(prefetch, int) else None,
     )
 
-    if cfg.get("override_dataset_stats"):
-        for key, stats_dict in cfg.override_dataset_stats.items():
-            for stats_type, listconfig in stats_dict.items():
-                # example of stats_type: min, max, mean, std
-                stats = OmegaConf.to_container(listconfig, resolve=True)
-                dataset.stats[key][stats_type] = torch.tensor(stats, dtype=torch.float32)
+    if cfg.policy.name == "tdmpc":
+        img_keys = []
+        for key in offline_buffer.image_keys:
+            img_keys.append(("next", *key))
+        img_keys += offline_buffer.image_keys
+    else:
+        img_keys = offline_buffer.image_keys
 
-    return dataset
+    if normalize:
+        transforms = [Prod(in_keys=img_keys, prod=1 / 255)]
+
+        # TODO(rcadene): make normalization strategy configurable between mean_std, min_max, manual_min_max,
+        # min_max_from_spec
+        stats = offline_buffer.compute_or_load_stats() if stats_path is None else torch.load(stats_path)
+
+        # we only normalize the state and action, since the images are usually normalized inside the model for
+        # now (except for tdmpc: see the following)
+        in_keys = [("observation", "state"), ("action")]
+
+        if cfg.policy.name == "tdmpc":
+            # TODO(rcadene): we add img_keys to the keys to normalize for tdmpc only, since diffusion and act policies normalize the image inside the model for now
+            in_keys += img_keys
+            # TODO(racdene): since we use next observations in tdmpc, we also add them to the normalization. We are wasting a bit of compute on this for now.
+            in_keys += [("next", *key) for key in img_keys]
+            in_keys.append(("next", "observation", "state"))
+
+        if cfg.policy.name == "diffusion" and cfg.env.name == "pusht":
+            # TODO(rcadene): we overwrite stats to have the same as pretrained model, but we should remove this
+            stats["observation", "state", "min"] = torch.tensor([13.456424, 32.938293], dtype=torch.float32)
+            stats["observation", "state", "max"] = torch.tensor([496.14618, 510.9579], dtype=torch.float32)
+            stats["action", "min"] = torch.tensor([12.0, 25.0], dtype=torch.float32)
+            stats["action", "max"] = torch.tensor([511.0, 511.0], dtype=torch.float32)
+
+        # TODO(rcadene): remove this and put it in config. Ideally we want to reproduce SOTA results just with mean_std
+        normalization_mode = "mean_std" if cfg.env.name == "aloha" else "min_max"
+        transforms.append(NormalizeTransform(stats, in_keys, mode=normalization_mode))
+
+        offline_buffer.set_transform(transforms)
+
+    if not overwrite_sampler:
+        index = torch.arange(0, offline_buffer.num_samples, 1)
+        sampler.extend(index)
+
+    return offline_buffer