Remove _has_gym checks

Add code coverage, more end-to-end tests

.gitattributes

@@ -1 +1,2 @@
 *.memmap filter=lfs diff=lfs merge=lfs -text
+*.stl filter=lfs diff=lfs merge=lfs -text

.github/poetry/cpu/pyproject.toml

@@ -1,19 +1,24 @@
 [tool.poetry]
 name = "lerobot"
 version = "0.1.0"
-description = "Le robot is learning"
+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 = "MIT"
+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 :: MIT License",
+    "License :: OSI Approved :: Apache Software License",
     "Programming Language :: Python :: 3.10",
 ]
 packages = [{include = "lerobot"}]
@@ -21,10 +26,8 @@ packages = [{include = "lerobot"}]
 
 [tool.poetry.dependencies]
 python = "^3.10"
-cython = "^3.0.8"
 termcolor = "^2.4.0"
 omegaconf = "^2.3.0"
-dm-env = "^1.6"
 pandas = "^2.2.1"
 wandb = "^0.16.3"
 moviepy = "^1.0.3"
@@ -35,29 +38,37 @@ einops = "^0.7.0"
 pygame = "^2.5.2"
 pymunk = "^6.6.0"
 zarr = "^2.17.0"
-shapely = "^2.0.3"
-scikit-image = "^0.22.0"
 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"}
-mujoco = "^3.1.2"
-mujoco-py = "^2.1.2.14"
-gym = "^0.26.2"
 opencv-python = "^4.9.0.80"
 diffusers = "^0.26.3"
 torchvision = {version = "^0.17.1", source = "torch-cpu"}
 h5py = "^3.10.0"
-dm = "^1.3"
-dm-control = "^1.0.16"
+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]]

.github/workflows/test.yml

@@ -1,4 +1,4 @@
-name: Test
+name: Tests
 
 on:
   pull_request:
@@ -10,20 +10,15 @@ on:
       - main
 
 jobs:
-  test:
+  tests:
     if: |
       ${{ github.event_name == 'pull_request' && contains(github.event.pull_request.labels.*.name, 'CI') }} ||
       ${{ github.event_name == 'push' }}
     runs-on: ubuntu-latest
     env:
-      POETRY_VERSION: 1.8.1
+      POETRY_VERSION: 1.8.2
       DATA_DIR: tests/data
-      TMPDIR: ~/tmp
-      TEMP: ~/tmp
-      TMP: ~/tmp
-      PYOPENGL_PLATFORM: egl
       MUJOCO_GL: egl
-      LEROBOT_TESTS_DEVICE: cpu
     steps:
       #----------------------------------------------
       #       check-out repo and set-up python
@@ -86,9 +81,13 @@ jobs:
 
       - name: Install dependencies
         if: steps.restore-dependencies-cache.outputs.cache-hit != 'true'
+        env:
+          TMPDIR: ~/tmp
+          TEMP: ~/tmp
+          TMP: ~/tmp
         run: |
           mkdir ~/tmp
-          poetry install --no-interaction --no-root
+          poetry install --no-interaction --no-root --without dev --all-extras
 
       - name: Save cached venv
         if: |
@@ -107,38 +106,129 @@ jobs:
       #             install project
       #----------------------------------------------
       - name: Install project
-        run: poetry install --no-interaction
+        run: poetry install --no-interaction --without dev --all-extras
 
       #----------------------------------------------
-      #               run tests
+      #            run tests & coverage
       #----------------------------------------------
       - name: Run tests
+        env:
+          LEROBOT_TESTS_DEVICE: cpu
         run: |
           source .venv/bin/activate
-          pytest tests
+          pytest --cov=./lerobot --cov-report=xml tests
 
-      - name: Test train pusht end-to-end
+      #   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 \
-            hydra.job.name=pusht \
+            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=1 \
-            hydra.run.dir=tests/outputs/
+            save_freq=2 \
+            hydra.run.dir=tests/outputs/diffusion/
 
-      - name: Test eval pusht end-to-end
+      - name: Test eval Diffusion on PushT end-to-end
         run: |
           source .venv/bin/activate
           python lerobot/scripts/eval.py \
-            hydra.job.name=pusht \
-            env=pusht \
-            wandb.enable=False \
+            --config tests/outputs/diffusion/.hydra/config.yaml \
             eval_episodes=1 \
             env.episode_length=8 \
             device=cpu \
-            policy.pretrained_model_path=tests/outputs/models/1.pt
+            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

.pre-commit-config.yaml

@@ -14,11 +14,11 @@ repos:
       - id: end-of-file-fixer
       - id: trailing-whitespace
   - repo: https://github.com/asottile/pyupgrade
-    rev: v3.15.1
+    rev: v3.15.2
     hooks:
     -   id: pyupgrade
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.2.2
+    rev: v0.3.4
     hooks:
       - id: ruff
         args: [--fix]

LICENSE

@@ -253,6 +253,31 @@ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 SOFTWARE.
 
 
+## Some of lerobot's code is derived from simxarm, which is subject to the following copyright notice:
+
+MIT License
+
+Copyright (c) 2023 Nicklas Hansen & Yanjie Ze
+
+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.
+
+
 ## Some of lerobot's code is derived from ALOHA, which is subject to the following copyright notice:
 
 MIT License
@@ -276,3 +301,207 @@ 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.
+
+## Some of lerobot's code is derived from DETR, which is subject to the following copyright notice:
+
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
+      control with that entity. For the purposes of this definition,
+      "control" means (i) the power, direct or indirect, to cause the
+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
+
+      "Source" form shall mean the preferred form for making modifications,
+      including but not limited to software source code, documentation
+      source, and configuration files.
+
+      "Object" form shall mean any form resulting from mechanical
+      transformation or translation of a Source form, including but
+      not limited to compiled object code, generated documentation,
+      and conversions to other media types.
+
+      "Work" shall mean the work of authorship, whether in Source or
+      Object form, made available under the License, as indicated by a
+      copyright notice that is included in or attached to the work
+      (an example is provided in the Appendix below).
+
+      "Derivative Works" shall mean any work, whether in Source or Object
+      form, that is based on (or derived from) the Work and for which the
+      editorial revisions, annotations, elaborations, or other modifications
+      represent, as a whole, an original work of authorship. For the purposes
+      of this License, Derivative Works shall not include works that remain
+      separable from, or merely link (or bind by name) to the interfaces of,
+      the Work and Derivative Works thereof.
+
+      "Contribution" shall mean any work of authorship, including
+      the original version of the Work and any modifications or additions
+      to that Work or Derivative Works thereof, that is intentionally
+      submitted to Licensor for inclusion in the Work by the copyright owner
+      or by an individual or Legal Entity authorized to submit on behalf of
+      the copyright owner. For the purposes of this definition, "submitted"
+      means any form of electronic, verbal, or written communication sent
+      to the Licensor or its representatives, including but not limited to
+      communication on electronic mailing lists, source code control systems,
+      and issue tracking systems that are managed by, or on behalf of, the
+      Licensor for the purpose of discussing and improving the Work, but
+      excluding communication that is conspicuously marked or otherwise
+      designated in writing by the copyright owner as "Not a Contribution."
+
+      "Contributor" shall mean Licensor and any individual or Legal Entity
+      on behalf of whom a Contribution has been received by Licensor and
+      subsequently incorporated within the Work.
+
+   2. Grant of Copyright License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      copyright license to reproduce, prepare Derivative Works of,
+      publicly display, publicly perform, sublicense, and distribute the
+      Work and such Derivative Works in Source or Object form.
+
+   3. Grant of Patent License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      (except as stated in this section) patent license to make, have made,
+      use, offer to sell, sell, import, and otherwise transfer the Work,
+      where such license applies only to those patent claims licensable
+      by such Contributor that are necessarily infringed by their
+      Contribution(s) alone or by combination of their Contribution(s)
+      with the Work to which such Contribution(s) was submitted. If You
+      institute patent litigation against any entity (including a
+      cross-claim or counterclaim in a lawsuit) alleging that the Work
+      or a Contribution incorporated within the Work constitutes direct
+      or contributory patent infringement, then any patent licenses
+      granted to You under this License for that Work shall terminate
+      as of the date such litigation is filed.
+
+   4. Redistribution. You may reproduce and distribute copies of the
+      Work or Derivative Works thereof in any medium, with or without
+      modifications, and in Source or Object form, provided that You
+      meet the following conditions:
+
+      (a) You must give any other recipients of the Work or
+          Derivative Works a copy of this License; and
+
+      (b) You must cause any modified files to carry prominent notices
+          stating that You changed the files; and
+
+      (c) You must retain, in the Source form of any Derivative Works
+          that You distribute, all copyright, patent, trademark, and
+          attribution notices from the Source form of the Work,
+          excluding those notices that do not pertain to any part of
+          the Derivative Works; and
+
+      (d) If the Work includes a "NOTICE" text file as part of its
+          distribution, then any Derivative Works that You distribute must
+          include a readable copy of the attribution notices contained
+          within such NOTICE file, excluding those notices that do not
+          pertain to any part of the Derivative Works, in at least one
+          of the following places: within a NOTICE text file distributed
+          as part of the Derivative Works; within the Source form or
+          documentation, if provided along with the Derivative Works; or,
+          within a display generated by the Derivative Works, if and
+          wherever such third-party notices normally appear. The contents
+          of the NOTICE file are for informational purposes only and
+          do not modify the License. You may add Your own attribution
+          notices within Derivative Works that You distribute, alongside
+          or as an addendum to the NOTICE text from the Work, provided
+          that such additional attribution notices cannot be construed
+          as modifying the License.
+
+      You may add Your own copyright statement to Your modifications and
+      may provide additional or different license terms and conditions
+      for use, reproduction, or distribution of Your modifications, or
+      for any such Derivative Works as a whole, provided Your use,
+      reproduction, and distribution of the Work otherwise complies with
+      the conditions stated in this License.
+
+   5. Submission of Contributions. Unless You explicitly state otherwise,
+      any Contribution intentionally submitted for inclusion in the Work
+      by You to the Licensor shall be under the terms and conditions of
+      this License, without any additional terms or conditions.
+      Notwithstanding the above, nothing herein shall supersede or modify
+      the terms of any separate license agreement you may have executed
+      with Licensor regarding such Contributions.
+
+   6. Trademarks. This License does not grant permission to use the trade
+      names, trademarks, service marks, or product names of the Licensor,
+      except as required for reasonable and customary use in describing the
+      origin of the Work and reproducing the content of the NOTICE file.
+
+   7. Disclaimer of Warranty. Unless required by applicable law or
+      agreed to in writing, Licensor provides the Work (and each
+      Contributor provides its Contributions) on an "AS IS" BASIS,
+      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+      implied, including, without limitation, any warranties or conditions
+      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
+      PARTICULAR PURPOSE. You are solely responsible for determining the
+      appropriateness of using or redistributing the Work and assume any
+      risks associated with Your exercise of permissions under this License.
+
+   8. Limitation of Liability. In no event and under no legal theory,
+      whether in tort (including negligence), contract, or otherwise,
+      unless required by applicable law (such as deliberate and grossly
+      negligent acts) or agreed to in writing, shall any Contributor be
+      liable to You for damages, including any direct, indirect, special,
+      incidental, or consequential damages of any character arising as a
+      result of this License or out of the use or inability to use the
+      Work (including but not limited to damages for loss of goodwill,
+      work stoppage, computer failure or malfunction, or any and all
+      other commercial damages or losses), even if such Contributor
+      has been advised of the possibility of such damages.
+
+   9. Accepting Warranty or Additional Liability. While redistributing
+      the Work or Derivative Works thereof, You may choose to offer,
+      and charge a fee for, acceptance of support, warranty, indemnity,
+      or other liability obligations and/or rights consistent with this
+      License. However, in accepting such obligations, You may act only
+      on Your own behalf and on Your sole responsibility, not on behalf
+      of any other Contributor, and only if You agree to indemnify,
+      defend, and hold each Contributor harmless for any liability
+      incurred by, or claims asserted against, such Contributor by reason
+      of your accepting any such warranty or additional liability.
+
+   END OF TERMS AND CONDITIONS
+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
+      boilerplate notice, with the fields enclosed by brackets "[]"
+      replaced with your own identifying information. (Don't include
+      the brackets!)  The text should be enclosed in the appropriate
+      comment syntax for the file format. We also recommend that a
+      file or class name and description of purpose be included on the
+      same "printed page" as the copyright notice for easier
+      identification within third-party archives.
+
+   Copyright 2020 - present, Facebook, Inc
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.

README.md

@@ -1,72 +1,374 @@
-# LeRobot
+<p align="center">
+  <picture>
+    <source media="(prefers-color-scheme: dark)" srcset="media/lerobot-logo-thumbnail.png">
+    <source media="(prefers-color-scheme: light)" srcset="media/lerobot-logo-thumbnail.png">
+    <img alt="LeRobot, Hugging Face Robotics Library" src="media/lerobot-logo-thumbnail.png" style="max-width: 100%;">
+  </picture>
+  <br/>
+  <br/>
+</p>
+
+<div align="center">
+
+[![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)
+[![Discord](https://dcbadge.vercel.app/api/server/C5P34WJ68S?style=flat)](https://discord.gg/s3KuuzsPFb)
+
+</div>
+
+<h3 align="center">
+    <p>State-of-the-art Machine Learning for real-world robotics</p>
+</h3>
+
+---
+
+
+🤗 LeRobot aims to provide models, datasets, and tools for real-world robotics in PyTorch. The goal is to lower the barrier for entry to robotics so that everyone can contribute and benefit from sharing datasets and pretrained models.
+
+🤗 LeRobot contains state-of-the-art approaches that have been shown to transfer to the real-world with a focus on imitation learning and reinforcement learning.
+
+🤗 LeRobot already provides a set of pretrained models, datasets with human collected demonstrations, and simulated environments so that everyone can get started. In the coming weeks, the plan is to add more and more support for real-world robotics on the most affordable and capable robots out there.
+
+🤗 LeRobot hosts pretrained models and datasets on this HuggingFace community page: [huggingface.co/lerobot](https://huggingface.co/lerobot)
+
+#### Examples of pretrained models and environments
+
+<table>
+  <tr>
+    <td><img src="http://remicadene.com/assets/gif/aloha_act.gif" width="100%" alt="ACT policy on ALOHA env"/></td>
+    <td><img src="http://remicadene.com/assets/gif/simxarm_tdmpc.gif" width="100%" alt="TDMPC policy on SimXArm env"/></td>
+    <td><img src="http://remicadene.com/assets/gif/pusht_diffusion.gif" width="100%" alt="Diffusion policy on PushT env"/></td>
+  </tr>
+  <tr>
+    <td align="center">ACT policy on ALOHA env</td>
+    <td align="center">TDMPC policy on SimXArm env</td>
+    <td align="center">Diffusion policy on PushT env</td>
+  </tr>
+</table>
+
+### Acknowledgment
+
+- ACT policy and ALOHA environment are adapted from [ALOHA](https://tonyzhaozh.github.io/aloha/)
+- Diffusion policy and Pusht environment are adapted from [Diffusion Policy](https://diffusion-policy.cs.columbia.edu/)
+- TDMPC policy and Simxarm environment are adapted from [FOWM](https://www.yunhaifeng.com/FOWM/)
+- Abstractions and utilities for Reinforcement Learning come from [TorchRL](https://github.com/pytorch/rl)
 
 ## Installation
 
-Create a virtual environment with Python 3.10, e.g. using `conda`:
+Download our source code:
+```bash
+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
 ```
 
-[Install `poetry`](https://python-poetry.org/docs/#installation) (if you don't have it already)
-```
-curl -sSL https://install.python-poetry.org | python -
-```
-
-Install dependencies
-```
-poetry install
-```
-
-If you encounter a disk space error, try to change your tmp dir to a location where you have enough disk space, e.g.
-```
-mkdir ~/tmp
-export TMPDIR='~/tmp'
+Then, install 🤗 LeRobot:
+```bash
+python -m pip install .
 ```
 
 To use [Weights and Biases](https://docs.wandb.ai/quickstart) for experiments tracking, log in with
-```
+```bash
 wandb login
 ```
 
-## Usage
-
-
-### Train
+## Walkthrough
 
 ```
-python lerobot/scripts/train.py \
-hydra.job.name=pusht \
-env=pusht
-```
-
-### Visualize offline buffer
+.
+├── 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, simxarm.yaml
+|   |   └── policy         # various policies: act.yaml, diffusion.yaml, tdmpc.yaml
+|   ├── common           # contains classes and utilities
+|   |   ├── datasets       # various datasets of human demonstrations: aloha, pusht, simxarm
+|   |   ├── envs           # various sim environments: aloha, pusht, simxarm
+|   |   └── policies       # various policies: act, diffusion, tdmpc
+|   └── scripts                  # contains functions to execute via command line
+|       ├── visualize_dataset.py  # load a dataset and render its demonstrations
+|       ├── eval.py               # load policy and evaluate it on an environment
+|       └── train.py              # train a policy via imitation learning and/or reinforcement learning
+├── outputs               # contains results of scripts execution: logs, videos, model checkpoints
+├── .github
+|   └── workflows
+|       └── test.yml      # defines install settings for continuous integration and specifies end-to-end tests
+└── tests                 # contains pytest utilities for continuous integration
 
 ```
+
+### Visualize datasets
+
+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 \
-hydra.run.dir=tmp/$(date +"%Y_%m_%d") \
-env=pusht
+env=aloha \
+task=sim_sim_transfer_cube_human \
+hydra.run.dir=outputs/visualize_dataset/example
+# >>> ['outputs/visualize_dataset/example/episode_0.mp4']
 ```
 
-### Visualize online buffer / Eval
+### Evaluate a pretrained policy
 
+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
 python lerobot/scripts/eval.py \
-hydra.run.dir=tmp/$(date +"%Y_%m_%d") \
-env=pusht
+--hub-id lerobot/diffusion_policy_pusht_image \
+eval_episodes=10 \
+hydra.run.dir=outputs/eval/example_hub
 ```
 
+After launching training of your own policy, you can also re-evaluate the checkpoints with:
+```bash
+python lerobot/scripts/eval.py \
+--config PATH/TO/FOLDER/config.yaml \
+policy.pretrained_model_path=PATH/TO/FOLDER/weights.pth \
+eval_episodes=10 \
+hydra.run.dir=outputs/eval/example_dir
+```
 
-## TODO
+See `python lerobot/scripts/eval.py --help` for more instructions.
 
-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/users/Cadene/projects/1)
+### Train your own policy
 
-Ask [Remi Cadene](re.cadene@gmail.com) for access 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
+```
+
+Or you can achieve the same result by executing our script from the command line:
+```bash
+python lerobot/scripts/train.py \
+hydra.run.dir=outputs/train/example
+```
+
+You can easily train any policy on any environment:
+```bash
+python lerobot/scripts/train.py \
+env=aloha \
+task=sim_insertion \
+dataset_id=aloha_sim_insertion_scripted \
+policy=act \
+hydra.run.dir=outputs/train/aloha_act
+```
+
+## Contribute
+
+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
+
+To add a dataset to the hub, first login and use a token generated from [huggingface settings](https://huggingface.co/settings/tokens) with write access:
+```bash
+huggingface-cli login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential
+```
+
+Then you can upload it to the hub with:
+```bash
+HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli upload $HF_USER/$DATASET data/$DATASET \
+--repo-type dataset  \
+--revision v1.0
+```
+
+You will need to set the corresponding version as a default argument in your dataset class:
+```python
+  version: str | None = "v1.0",
+```
+See: [`lerobot/common/datasets/pusht.py`](https://github.com/Cadene/lerobot/blob/main/lerobot/common/datasets/pusht.py)
+
+For instance, for [lerobot/pusht](https://huggingface.co/datasets/lerobot/pusht), we used:
+```bash
+HF_USER=lerobot
+DATASET=pusht
+```
+
+If you want to improve an existing dataset, you can download it locally with:
+```bash
+mkdir -p data/$DATASET
+HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download ${HF_USER}/$DATASET \
+--repo-type dataset \
+--local-dir data/$DATASET \
+--local-dir-use-symlinks=False \
+--revision v1.0
+```
+
+Iterate on your code and dataset with:
+```bash
+DATA_DIR=data python train.py
+```
+
+Upload a new version (v2.0 or v1.1 if the changes are respectively more or less significant):
+```bash
+HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli upload $HF_USER/$DATASET data/$DATASET \
+--repo-type dataset \
+--revision v1.1 \
+--delete "*"
+```
+
+Then you will need to set the corresponding version as a default argument in your dataset class:
+```python
+  version: str | None = "v1.1",
+```
+See: [`lerobot/common/datasets/pusht.py`](https://github.com/Cadene/lerobot/blob/main/lerobot/common/datasets/pusht.py)
 
 
-## Profile
+Finally, you might want to mock the dataset if you need to update the unit tests as well:
+```bash
+python tests/scripts/mock_dataset.py --in-data-dir data/$DATASET --out-data-dir tests/data/$DATASET
+```
 
-**Example**
+### Add a pretrained policy
+
+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.
+
+Secondly, assuming you have trained a policy, you need:
+
+- `config.yaml` which you can get from the `.hydra` directory of your training output folder.
+- `model.pt` which should be one of the saved models in the `models` directory of your training output folder (they won't be named `model.pt` but you will need to choose one).
+- `stats.pth` which should point to the same file in the dataset directory (found in `data/{dataset_name}`).
+
+To upload these to the hub, prepare a folder with the following structure (you can use symlinks rather than copying):
+
+```
+to_upload
+    ├── config.yaml
+    ├── model.pt
+    └── stats.pth
+```
+
+With the folder prepared, run the following with a desired revision ID.
+
+```bash
+huggingface-cli upload $HUB_ID to_upload --revision $REVISION_ID
+```
+
+If you want this to be the default revision also run the following (don't worry, it won't upload the files again; it will just adjust the file pointers):
+
+```bash
+huggingface-cli upload $HUB_ID to_upload
+```
+
+See `eval.py` for an example of how a user may use your policy.
+
+
+### Improve your code with profiling
+
+An example of a code snippet to profile the evaluation of a policy:
 ```python
 from torch.profiler import profile, record_function, ProfilerActivity
 
@@ -85,87 +387,12 @@ with profile(
     with record_function("eval_policy"):
         for i in range(num_episodes):
             prof.step()
+            # insert code to profile, potentially whole body of eval_policy function
 ```
 
 ```bash
 python lerobot/scripts/eval.py \
-pretrained_model_path=/home/rcadene/code/fowm/logs/xarm_lift/all/default/2/models/final.pt \
+--config outputs/pusht/.hydra/config.yaml \
+pretrained_model_path=outputs/pusht/model.pt \
 eval_episodes=7
 ```
-
-## Contribute
-
-**Style**
-```
-# install if needed
-pre-commit install
-# apply style and linter checks before git commit
-pre-commit run -a
-```
-
-**Adding dependencies (temporary)**
-
-Right now, for the CI to work, whenever a new dependency is added it needs to be also added to the cpu env, eg:
-
-```
-# Run in this directory, adds the package to the main env with cuda
-poetry add some-package
-
-# Adds the same package to the cpu env
-cd .github/poetry/cpu && poetry add some-package
-```
-
-**Tests**
-
-Install [git lfs](https://git-lfs.com/) to retrieve test artifacts (if you don't have it already).
-
-On Mac:
-```
-brew install git-lfs
-git lfs install
-```
-
-On Ubuntu:
-```
-sudo apt-get install git-lfs
-git lfs install
-```
-
-Pull artifacts if they're not in [tests/data](tests/data)
-```
-git lfs pull
-```
-
-When adding a new dataset, mock it with
-```
-python tests/scripts/mock_dataset.py --in-data-dir data/<dataset_id> --out-data-dir tests/data/<dataset_id>
-```
-
-Run tests
-```
-DATA_DIR="tests/data" pytest -sx tests
-```
-
-**Datasets**
-
-To add a pytorch rl dataset to the hub, first login and use a token generated from [huggingface settings](https://huggingface.co/settings/tokens) with write access:
-```
-huggingface-cli login --token $HUGGINGFACE_TOKEN --add-to-git-credential
-```
-
-Then you can upload it to the hub with:
-```
-HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli upload --repo-type dataset $HF_USER/$DATASET data/$DATASET
-```
-
-For instance, for [cadene/pusht](https://huggingface.co/datasets/cadene/pusht), we used:
-```
-HF_USER=cadene
-DATASET=pusht
-```
-
-
-## Acknowledgment
-- Our Diffusion policy and Pusht environment are adapted from [Diffusion Policy](https://diffusion-policy.cs.columbia.edu/)
-- Our TDMPC policy and Simxarm environment are adapted from [FOWM](https://www.yunhaifeng.com/FOWM/)
-- Our ACT policy and ALOHA environment are adapted from [ALOHA](https://tonyzhaozh.github.io/aloha/)

envs/sim_aloha/README.md

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

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/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

@@ -1,14 +1,13 @@
 import collections
 
 import numpy as np
-from dm_control.suite import base
-
-from lerobot.common.envs.aloha.constants import (
+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
 

envs/sim_aloha/aloha/tasks/sim_end_effector.py

@@ -1,16 +1,15 @@
 import collections
 
 import numpy as np
-from dm_control.suite import base
-
-from lerobot.common.envs.aloha.constants import (
+from aloha.constants import (
     PUPPET_GRIPPER_POSITION_CLOSE,
     START_ARM_POSE,
     normalize_puppet_gripper_position,
     normalize_puppet_gripper_velocity,
     unnormalize_puppet_gripper_position,
 )
-from lerobot.common.envs.aloha.utils import sample_box_pose, sample_insertion_pose
+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.

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

@@ -1,17 +1,17 @@
 import collections
 
 import cv2
-import gym
+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 gym import spaces
+from gymnasium import spaces
 from pymunk.vec2d import Vec2d
 
-from lerobot.common.envs.pusht.pymunk_override import DrawOptions
+from pusht.pymunk_override import DrawOptions
 
 
 def pymunk_to_shapely(body, shapes):
@@ -33,7 +33,7 @@ class PushTEnv(gym.Env):
 
     def __init__(
         self,
-        legacy=False,
+        legacy=True,  # compatibility with original
         block_cog=None,
         damping=None,
         render_action=True,

envs/sim_pusht/pusht/pusht_image_env.py

@@ -1,14 +1,14 @@
-import cv2
 import numpy as np
-from gym import spaces
+from gymnasium import spaces
 
-from lerobot.common.envs.pusht.pusht_env import PushTEnv
+from pusht.pusht_env import PushTEnv
 
 
 class PushTImageEnv(PushTEnv):
     metadata = {"render.modes": ["rgb_array"], "video.frames_per_second": 10}
 
-    def __init__(self, legacy=False, block_cog=None, damping=None, render_size=96):
+    # 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
         )
@@ -28,20 +28,6 @@ class PushTImageEnv(PushTEnv):
         img_obs = np.moveaxis(img, -1, 0)
         obs = {"image": img_obs, "agent_pos": agent_pos}
 
-        # draw action
-        if 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,
-            )
         self.render_cache = img
 
         return obs

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_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

@@ -0,0 +1 @@
+# TODO

examples/3_train_policy.py

@@ -0,0 +1 @@
+# TODO

lerobot/__init__.py

@@ -1 +1,59 @@
+"""
+This file contains lists of available environments, dataset and policies to reflect the current state of LeRobot library.
+We do not want to import all the dependencies, but instead we keep it lightweight to ensure fast access to these variables.
+
+Example:
+    ```python
+        import lerobot
+        print(lerobot.available_envs)
+        print(lerobot.available_tasks_per_env)
+        print(lerobot.available_datasets_per_env)
+        print(lerobot.available_datasets)
+        print(lerobot.available_policies)
+    ```
+
+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
+"""
+
 from lerobot.__version__ import __version__  # noqa: F401
+
+available_envs = [
+    "aloha",
+    "pusht",
+    "simxarm",
+]
+
+available_tasks_per_env = {
+    "aloha": [
+        "sim_insertion",
+        "sim_transfer_cube",
+    ],
+    "pusht": ["pusht"],
+    "simxarm": ["lift"],
+}
+
+available_datasets_per_env = {
+    "aloha": [
+        "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 = [dataset for env in available_envs for dataset in available_datasets_per_env[env]]
+
+available_policies = [
+    "act",
+    "diffusion",
+    "tdmpc",
+]

lerobot/__version__.py

@@ -1,4 +1,4 @@
-""" To enable `lerobot.__version__` """
+"""To enable `lerobot.__version__`"""
 
 from importlib.metadata import PackageNotFoundError, version
 

lerobot/common/datasets/abstract.py

@@ -9,30 +9,74 @@ 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 SliceSampler
+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
 
-class AbstractExperienceReplay(TensorDictReplayBuffer):
     def __init__(
         self,
         dataset_id: str,
-        batch_size: int = None,
+        version: str | None = None,
+        batch_size: int | None = None,
         *,
         shuffle: bool = True,
         root: Path | None = None,
         pin_memory: bool = False,
         prefetch: int = None,
-        sampler: SliceSampler = None,
-        collate_fn: Callable = None,
-        writer: Writer = 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
+        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__(
@@ -49,9 +93,9 @@ class AbstractExperienceReplay(TensorDictReplayBuffer):
     @property
     def stats_patterns(self) -> dict:
         return {
-            ("observation", "state"): "b c -> 1 c",
-            ("observation", "image"): "b c h w -> 1 c 1 1",
-            ("action",): "b c -> 1 c",
+            ("observation", "state"): "b c -> c",
+            ("observation", "image"): "b c h w -> c 1 1",
+            ("action",): "b c -> c",
         }
 
     @property
@@ -85,7 +129,7 @@ class AbstractExperienceReplay(TensorDictReplayBuffer):
             self._transform = transform
 
     def compute_or_load_stats(self, num_batch=100, batch_size=32) -> TensorDict:
-        stats_path = Path(self.data_dir) / "stats.pth"
+        stats_path = self.data_dir / "stats.pth"
         if stats_path.exists():
             stats = torch.load(stats_path)
         else:
@@ -96,10 +140,14 @@ class AbstractExperienceReplay(TensorDictReplayBuffer):
 
     def _download_or_load_dataset(self) -> torch.StorageBase:
         if self.root is None:
-            self.data_dir = snapshot_download(repo_id=f"cadene/{self.dataset_id}", repo_type="dataset")
+            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))
+        return TensorStorage(TensorDict.load_memmap(self.data_dir / "replay_buffer"))
 
     def _compute_stats(self, num_batch=100, batch_size=32):
         rb = TensorDictReplayBuffer(

lerobot/common/datasets/aloha.py

@@ -9,11 +9,11 @@ import torch
 import torchrl
 import tqdm
 from tensordict import TensorDict
-from torchrl.data.replay_buffers.samplers import SliceSampler
+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 AbstractExperienceReplay
+from lerobot.common.datasets.abstract import AbstractDataset
 
 DATASET_IDS = [
     "aloha_sim_insertion_human",
@@ -80,25 +80,27 @@ def download(data_dir, dataset_id):
     gdown.download(EP49_URLS[dataset_id], output=str(data_dir / "episode_49.hdf5"), fuzzy=True)
 
 
-class AlohaExperienceReplay(AbstractExperienceReplay):
+class AlohaDataset(AbstractDataset):
+    available_datasets = DATASET_IDS
+
     def __init__(
         self,
         dataset_id: str,
-        batch_size: int = None,
+        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: SliceSampler = None,
-        collate_fn: Callable = None,
-        writer: Writer = None,
+        sampler: Sampler | None = None,
+        collate_fn: Callable | None = None,
+        writer: Writer | None = None,
         transform: "torchrl.envs.Transform" = None,
     ):
-        assert dataset_id in DATASET_IDS
-
         super().__init__(
             dataset_id,
+            version,
             batch_size,
             shuffle=shuffle,
             root=root,
@@ -113,11 +115,11 @@ class AlohaExperienceReplay(AbstractExperienceReplay):
     @property
     def stats_patterns(self) -> dict:
         d = {
-            ("observation", "state"): "b c -> 1 c",
-            ("action",): "b c -> 1 c",
+            ("observation", "state"): "b c -> c",
+            ("action",): "b c -> c",
         }
         for cam in CAMERAS[self.dataset_id]:
-            d[("observation", "image", cam)] = "b c h w -> 1 c 1 1"
+            d[("observation", "image", cam)] = "b c h w -> c 1 1"
         return d
 
     @property

lerobot/common/datasets/factory.py

@@ -5,7 +5,7 @@ from pathlib import Path
 import torch
 from torchrl.data.replay_buffers import PrioritizedSliceSampler, SliceSampler
 
-from lerobot.common.envs.transforms import NormalizeTransform, Prod
+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`
@@ -14,7 +14,13 @@ DATA_DIR = Path(os.environ["DATA_DIR"]) if "DATA_DIR" in os.environ else None
 
 
 def make_offline_buffer(
-    cfg, overwrite_sampler=None, normalize=True, overwrite_batch_size=None, overwrite_prefetch=None
+    cfg,
+    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.policy.balanced_sampling:
         assert cfg.online_steps > 0
@@ -59,27 +65,24 @@ def make_offline_buffer(
         sampler = overwrite_sampler
 
     if cfg.env.name == "simxarm":
-        from lerobot.common.datasets.simxarm import SimxarmExperienceReplay
+        from lerobot.common.datasets.simxarm import SimxarmDataset
 
-        clsfunc = SimxarmExperienceReplay
-        dataset_id = f"xarm_{cfg.env.task}_medium"
+        clsfunc = SimxarmDataset
 
     elif cfg.env.name == "pusht":
-        from lerobot.common.datasets.pusht import PushtExperienceReplay
+        from lerobot.common.datasets.pusht import PushtDataset
 
-        clsfunc = PushtExperienceReplay
-        dataset_id = "pusht"
+        clsfunc = PushtDataset
 
     elif cfg.env.name == "aloha":
-        from lerobot.common.datasets.aloha import AlohaExperienceReplay
+        from lerobot.common.datasets.aloha import AlohaDataset
 
-        clsfunc = AlohaExperienceReplay
-        dataset_id = f"aloha_{cfg.env.task}"
+        clsfunc = AlohaDataset
     else:
         raise ValueError(cfg.env.name)
 
     offline_buffer = clsfunc(
-        dataset_id=dataset_id,
+        dataset_id=cfg.dataset_id,
         sampler=sampler,
         batch_size=batch_size,
         root=DATA_DIR,
@@ -95,13 +98,15 @@ def make_offline_buffer(
     else:
         img_keys = offline_buffer.image_keys
 
-    transforms = [Prod(in_keys=img_keys, prod=1 / 255)]
-
     if normalize:
-        # 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()
+        transforms = [Prod(in_keys=img_keys, prod=1 / 255)]
 
-        # we only normalize the state and action, since the images are usually normalized inside the model for now (except for tdmpc: see the following)
+        # 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":
@@ -122,7 +127,7 @@ def make_offline_buffer(
         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)
+        offline_buffer.set_transform(transforms)
 
     if not overwrite_sampler:
         index = torch.arange(0, offline_buffer.num_samples, 1)

lerobot/common/datasets/pusht.py

@@ -9,14 +9,14 @@ import torch
 import torchrl
 import tqdm
 from tensordict import TensorDict
-from torchrl.data.replay_buffers.samplers import SliceSampler
+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 AbstractExperienceReplay
+from lerobot.common.datasets.abstract import AbstractDataset
 from lerobot.common.datasets.utils import download_and_extract_zip
-from lerobot.common.envs.pusht.pusht_env import pymunk_to_shapely
 from lerobot.common.policies.diffusion.replay_buffer import ReplayBuffer as DiffusionPolicyReplayBuffer
+from pusht.pusht_env import pymunk_to_shapely
 
 # as define in env
 SUCCESS_THRESHOLD = 0.95  # 95% coverage,
@@ -83,23 +83,27 @@ def add_tee(
     return body
 
 
-class PushtExperienceReplay(AbstractExperienceReplay):
+class PushtDataset(AbstractDataset):
+    available_datasets = ["pusht"]
+
     def __init__(
         self,
         dataset_id: str,
-        batch_size: int = None,
+        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: SliceSampler = None,
-        collate_fn: Callable = None,
-        writer: Writer = 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,

lerobot/common/datasets/simxarm.py

@@ -8,12 +8,12 @@ import torchrl
 import tqdm
 from tensordict import TensorDict
 from torchrl.data.replay_buffers.samplers import (
-    SliceSampler,
+    Sampler,
 )
 from torchrl.data.replay_buffers.storages import TensorStorage
 from torchrl.data.replay_buffers.writers import Writer
 
-from lerobot.common.datasets.abstract import AbstractExperienceReplay
+from lerobot.common.datasets.abstract import AbstractDataset
 
 
 def download():
@@ -32,7 +32,7 @@ def download():
     Path(download_path).unlink()
 
 
-class SimxarmExperienceReplay(AbstractExperienceReplay):
+class SimxarmDataset(AbstractDataset):
     available_datasets = [
         "xarm_lift_medium",
     ]
@@ -40,19 +40,21 @@ class SimxarmExperienceReplay(AbstractExperienceReplay):
     def __init__(
         self,
         dataset_id: str,
-        batch_size: int = None,
+        version: str | None = "v1.1",
+        batch_size: int | None = None,
         *,
         shuffle: bool = True,
         root: Path | None = None,
         pin_memory: bool = False,
         prefetch: int = None,
-        sampler: SliceSampler = None,
-        collate_fn: Callable = None,
-        writer: Writer = 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,
@@ -65,11 +67,11 @@ class SimxarmExperienceReplay(AbstractExperienceReplay):
         )
 
     def _download_and_preproc_obsolete(self):
-        assert self.root is not None
+        # assert self.root is not None
         # TODO(rcadene): finish download
-        download()
+        # download()
 
-        dataset_path = self.root / f"{self.dataset_id}_raw" / "buffer.pkl"
+        dataset_path = self.root / f"{self.dataset_id}" / "buffer.pkl"
         print(f"Using offline dataset '{dataset_path}'")
         with open(dataset_path, "rb") as f:
             dataset_dict = pickle.load(f)
@@ -103,15 +105,19 @@ class SimxarmExperienceReplay(AbstractExperienceReplay):
                     "frame_id": torch.arange(0, num_frames, 1),
                     ("next", "observation", "image"): next_image,
                     ("next", "observation", "state"): next_state,
-                    ("next", "observation", "reward"): next_reward,
-                    ("next", "observation", "done"): next_done,
+                    ("next", "reward"): next_reward,
+                    ("next", "done"): next_done,
                 },
                 batch_size=num_frames,
             )
 
             if episode_id == 0:
                 # hack to initialize tensordict data structure to store episodes
-                td_data = episode[0].expand(total_frames).memmap_like(self.root / f"{self.dataset_id}")
+                td_data = (
+                    episode[0]
+                    .expand(total_frames)
+                    .memmap_like(self.root / f"{self.dataset_id}" / "replay_buffer")
+                )
 
             td_data[idx0:idx1] = episode
 

lerobot/common/envs/abstract.py

@@ -1,12 +1,27 @@
-import abc
 from collections import deque
 from typing import Optional
 
 from tensordict import TensorDict
 from torchrl.envs import EnvBase
 
+from lerobot.common.utils import set_global_seed
+
 
 class AbstractEnv(EnvBase):
+    """
+    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
+    """
+
+    name: str | None = None  # same name should be used to instantiate the environment in factory.py
+    available_tasks: list[str] | None = None  # for instance: sim_insertion, sim_transfer_cube, pusht, lift
+
     def __init__(
         self,
         task,
@@ -20,6 +35,14 @@ class AbstractEnv(EnvBase):
         num_prev_action=0,
     ):
         super().__init__(device=device, batch_size=[])
+        assert self.name is not None, "Subclasses of `AbstractEnv` should set the `name` class attribute."
+        assert (
+            self.available_tasks is not None
+        ), "Subclasses of `AbstractEnv` should set the `available_tasks` class attribute."
+        assert (
+            task in self.available_tasks
+        ), f"The provided task ({task}) is not on the list of available tasks {self.available_tasks}."
+
         self.task = task
         self.frame_skip = frame_skip
         self.from_pixels = from_pixels
@@ -27,7 +50,6 @@ class AbstractEnv(EnvBase):
         self.image_size = image_size
         self.num_prev_obs = num_prev_obs
         self.num_prev_action = num_prev_action
-        self._rendering_hooks = []
 
         if pixels_only:
             assert from_pixels
@@ -36,7 +58,13 @@ class AbstractEnv(EnvBase):
 
         self._make_env()
         self._make_spec()
-        self._current_seed = self.set_seed(seed)
+
+        # self._next_seed will be used for the next reset. It is recommended that when self.set_seed is called
+        # you store the return value in self._next_seed (it will be a new randomly generated seed).
+        self._next_seed = seed
+        # Don't store the result of this in self._next_seed, as we want to make sure that the first time
+        # self._reset is called, we use seed.
+        self.set_seed(seed)
 
         if self.num_prev_obs > 0:
             self._prev_obs_image_queue = deque(maxlen=self.num_prev_obs)
@@ -45,36 +73,20 @@ class AbstractEnv(EnvBase):
             raise NotImplementedError()
             # self._prev_action_queue = deque(maxlen=self.num_prev_action)
 
-    def register_rendering_hook(self, func):
-        self._rendering_hooks.append(func)
-
-    def call_rendering_hooks(self):
-        for func in self._rendering_hooks:
-            func(self)
-
-    def reset_rendering_hooks(self):
-        self._rendering_hooks = []
-
-    @abc.abstractmethod
     def render(self, mode="rgb_array", width=640, height=480):
-        raise NotImplementedError()
+        raise NotImplementedError("Abstract method")
 
-    @abc.abstractmethod
     def _reset(self, tensordict: Optional[TensorDict] = None):
-        raise NotImplementedError()
+        raise NotImplementedError("Abstract method")
 
-    @abc.abstractmethod
     def _step(self, tensordict: TensorDict):
-        raise NotImplementedError()
+        raise NotImplementedError("Abstract method")
 
-    @abc.abstractmethod
     def _make_env(self):
-        raise NotImplementedError()
+        raise NotImplementedError("Abstract method")
 
-    @abc.abstractmethod
     def _make_spec(self):
-        raise NotImplementedError()
+        raise NotImplementedError("Abstract method")
 
-    @abc.abstractmethod
     def _set_seed(self, seed: Optional[int]):
-        raise NotImplementedError()
+        set_global_seed(seed)

lerobot/common/envs/aloha.py

@@ -6,8 +6,6 @@ from typing import Optional
 import einops
 import numpy as np
 import torch
-from dm_control import mujoco
-from dm_control.rl import control
 from tensordict import TensorDict
 from torchrl.data.tensor_specs import (
     BoundedTensorSpec,
@@ -17,24 +15,16 @@ from torchrl.data.tensor_specs import (
 )
 
 from lerobot.common.envs.abstract import AbstractEnv
-from lerobot.common.envs.aloha.constants import (
-    ACTIONS,
-    ASSETS_DIR,
-    DT,
-    JOINTS,
-)
-from lerobot.common.envs.aloha.tasks.sim import BOX_POSE, InsertionTask, TransferCubeTask
-from lerobot.common.envs.aloha.tasks.sim_end_effector import (
-    InsertionEndEffectorTask,
-    TransferCubeEndEffectorTask,
-)
-from lerobot.common.envs.aloha.utils import sample_box_pose, sample_insertion_pose
-from lerobot.common.utils import set_seed
+from lerobot.common.utils import set_global_seed
 
-_has_gym = importlib.util.find_spec("gym") is not None
+_has_aloha = importlib.util.find_spec("aloha") is not None
 
 
 class AlohaEnv(AbstractEnv):
+    name = "aloha"
+    available_tasks = ["sim_insertion", "sim_transfer_cube"]
+    _reset_warning_issued = False
+
     def __init__(
         self,
         task,
@@ -60,49 +50,23 @@ class AlohaEnv(AbstractEnv):
         )
 
     def _make_env(self):
-        if not _has_gym:
-            raise ImportError("Cannot import gym.")
-
         if not self.from_pixels:
             raise NotImplementedError()
 
-        self._env = self._make_env_task(self.task)
+        if not _has_aloha:
+            raise ImportError(
+                "Cannot import aloha env. Please install it with `python -m pip install 'lerobot[aloha]'`"
+            )
+
+        from aloha.env import make_env_task
+
+        self._env = make_env_task(self.task)
 
     def render(self, mode="rgb_array", width=640, height=480):
         # TODO(rcadene): render and visualizer several cameras (e.g. angle, front_close)
         image = self._env.physics.render(height=height, width=width, camera_id="top")
         return image
 
-    def _make_env_task(self, 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
-
     def _format_raw_obs(self, raw_obs):
         if self.from_pixels:
             image = torch.from_numpy(raw_obs["images"]["top"].copy())
@@ -120,91 +84,77 @@ class AlohaEnv(AbstractEnv):
         return obs
 
     def _reset(self, tensordict: Optional[TensorDict] = None):
-        td = tensordict
-        if td is None or td.is_empty():
-            # we need to handle seed iteration, since self._env.reset() rely an internal _seed.
-            self._current_seed += 1
-            self.set_seed(self._current_seed)
+        from aloha.tasks.sim import BOX_POSE
+        from aloha.utils import sample_box_pose, sample_insertion_pose
 
-            # TODO(rcadene): do not use global variable for this
-            if "sim_transfer_cube" in self.task:
-                BOX_POSE[0] = sample_box_pose()  # used in sim reset
-            elif "sim_insertion" in self.task:
-                BOX_POSE[0] = np.concatenate(sample_insertion_pose())  # used in sim reset
+        if tensordict is not None and not AlohaEnv._reset_warning_issued:
+            logging.warning(f"{self.__class__.__name__}._reset ignores the provided tensordict.")
+            AlohaEnv._reset_warning_issued = True
 
-            raw_obs = self._env.reset()
-            # TODO(rcadene): add assert
-            # assert self._current_seed == self._env._seed
+        # Seed the environment and update the seed to be used for the next reset.
+        self._next_seed = self.set_seed(self._next_seed)
 
-            obs = self._format_raw_obs(raw_obs.observation)
+        # TODO(rcadene): do not use global variable for this
+        if "sim_transfer_cube" in self.task:
+            BOX_POSE[0] = sample_box_pose()  # used in sim reset
+        elif "sim_insertion" in self.task:
+            BOX_POSE[0] = np.concatenate(sample_insertion_pose())  # used in sim reset
 
-            if self.num_prev_obs > 0:
-                stacked_obs = {}
-                if "image" in obs:
-                    self._prev_obs_image_queue = deque(
-                        [obs["image"]["top"]] * (self.num_prev_obs + 1), maxlen=(self.num_prev_obs + 1)
-                    )
-                    stacked_obs["image"] = {"top": torch.stack(list(self._prev_obs_image_queue))}
-                if "state" in obs:
-                    self._prev_obs_state_queue = deque(
-                        [obs["state"]] * (self.num_prev_obs + 1), maxlen=(self.num_prev_obs + 1)
-                    )
-                    stacked_obs["state"] = torch.stack(list(self._prev_obs_state_queue))
-                obs = stacked_obs
+        raw_obs = self._env.reset()
 
-            td = TensorDict(
-                {
-                    "observation": TensorDict(obs, batch_size=[]),
-                    "done": torch.tensor([False], dtype=torch.bool),
-                },
-                batch_size=[],
-            )
-        else:
-            raise NotImplementedError()
+        obs = self._format_raw_obs(raw_obs.observation)
+
+        if self.num_prev_obs > 0:
+            stacked_obs = {}
+            if "image" in obs:
+                self._prev_obs_image_queue = deque(
+                    [obs["image"]["top"]] * (self.num_prev_obs + 1), maxlen=(self.num_prev_obs + 1)
+                )
+                stacked_obs["image"] = {"top": torch.stack(list(self._prev_obs_image_queue))}
+            if "state" in obs:
+                self._prev_obs_state_queue = deque(
+                    [obs["state"]] * (self.num_prev_obs + 1), maxlen=(self.num_prev_obs + 1)
+                )
+                stacked_obs["state"] = torch.stack(list(self._prev_obs_state_queue))
+            obs = stacked_obs
+
+        td = TensorDict(
+            {
+                "observation": TensorDict(obs, batch_size=[]),
+                "done": torch.tensor([False], dtype=torch.bool),
+            },
+            batch_size=[],
+        )
 
-        self.call_rendering_hooks()
         return td
 
     def _step(self, tensordict: TensorDict):
         td = tensordict
         action = td["action"].numpy()
-        # step expects shape=(4,) so we pad if necessary
+        assert action.ndim == 1
         # TODO(rcadene): add info["is_success"] and info["success"] ?
-        sum_reward = 0
 
-        if action.ndim == 1:
-            action = einops.repeat(action, "c -> t c", t=self.frame_skip)
-        else:
-            if self.frame_skip > 1:
-                raise NotImplementedError()
+        _, reward, _, raw_obs = self._env.step(action)
 
-        num_action_steps = action.shape[0]
-        for i in range(num_action_steps):
-            _, reward, discount, raw_obs = self._env.step(action[i])
-            del discount  # not used
+        # TODO(rcadene): add an enum
+        success = done = reward == 4
+        obs = self._format_raw_obs(raw_obs)
 
-            # TOOD(rcadene): add an enum
-            success = done = reward == 4
-            sum_reward += reward
-            obs = self._format_raw_obs(raw_obs)
-
-            if self.num_prev_obs > 0:
-                stacked_obs = {}
-                if "image" in obs:
-                    self._prev_obs_image_queue.append(obs["image"]["top"])
-                    stacked_obs["image"] = {"top": torch.stack(list(self._prev_obs_image_queue))}
-                if "state" in obs:
-                    self._prev_obs_state_queue.append(obs["state"])
-                    stacked_obs["state"] = torch.stack(list(self._prev_obs_state_queue))
-                obs = stacked_obs
-
-            self.call_rendering_hooks()
+        if self.num_prev_obs > 0:
+            stacked_obs = {}
+            if "image" in obs:
+                self._prev_obs_image_queue.append(obs["image"]["top"])
+                stacked_obs["image"] = {"top": torch.stack(list(self._prev_obs_image_queue))}
+            if "state" in obs:
+                self._prev_obs_state_queue.append(obs["state"])
+                stacked_obs["state"] = torch.stack(list(self._prev_obs_state_queue))
+            obs = stacked_obs
 
         td = TensorDict(
             {
                 "observation": TensorDict(obs, batch_size=[]),
-                "reward": torch.tensor([sum_reward], dtype=torch.float32),
-                # succes and done are true when coverage > self.success_threshold in env
+                "reward": torch.tensor([reward], dtype=torch.float32),
+                # success and done are true when coverage > self.success_threshold in env
                 "done": torch.tensor([done], dtype=torch.bool),
                 "success": torch.tensor([success], dtype=torch.bool),
             },
@@ -213,13 +163,18 @@ class AlohaEnv(AbstractEnv):
         return td
 
     def _make_spec(self):
-        obs = {}
         from omegaconf import OmegaConf
 
+        from aloha.constants import (
+            ACTIONS,
+            JOINTS,
+        )
+
+        obs = {}
         if self.from_pixels:
             if isinstance(self.image_size, int):
                 image_shape = (3, self.image_size, self.image_size)
-            elif OmegaConf.is_list(self.image_size):
+            elif OmegaConf.is_list(self.image_size) or isinstance(self.image_size, list):
                 assert len(self.image_size) == 3  # c h w
                 assert self.image_size[0] == 3  # c is RGB
                 image_shape = tuple(self.image_size)
@@ -305,7 +260,7 @@ class AlohaEnv(AbstractEnv):
         )
 
     def _set_seed(self, seed: Optional[int]):
-        set_seed(seed)
+        set_global_seed(seed)
         # TODO(rcadene): seed the env
         # self._env.seed(seed)
         logging.warning("Aloha env is not seeded")