Remove _has_gym checks

Remove envs dependencies
Update CI
2024-03-29 18:37:37 +01:00 · 2024-03-29 17:27:42 +01:00 · 2024-03-29 16:53:25 +01:00 · 2024-03-29 16:53:00 +01:00 · 2024-03-29 16:47:18 +01:00 · 2024-03-29 15:22:32 +01:00
101 changed files with 2391 additions and 456 deletions
--- a/.github/poetry/cpu/poetry.lock
+++ b/.github/poetry/cpu/poetry.lock
@@ -517,21 +517,11 @@ files = [
    {file = "distlib-0.3.8.tar.gz", hash = "sha256:1530ea13e350031b6312d8580ddb6b27a104275a31106523b8f123787f494f64"},
 ]

-[[package]]
-name = "dm"
-version = "1.3"
-description = "Dict to Data mapper"
-optional = false
-python-versions = "*"
-files = [
-    {file = "dm-1.3.tar.gz", hash = "sha256:ce77537bf346b5d8c0dc0b5d679cfc4a946faadcd5315e6c80ef6f3af824130d"},
-]
-
 [[package]]
 name = "dm-control"
 version = "1.0.14"
 description = "Continuous control environments and MuJoCo Python bindings."
-optional = false
+optional = true
 python-versions = ">=3.8"
 files = [
    {file = "dm_control-1.0.14-py3-none-any.whl", hash = "sha256:883c63244a7ebf598700a97564ed19fffd3479ca79efd090aed881609cdb9fc6"},
@@ -562,7 +552,7 @@ hdf5 = ["h5py"]
 name = "dm-env"
 version = "1.6"
 description = "A Python interface for Reinforcement Learning environments."
-optional = false
+optional = true
 python-versions = ">=3.7"
 files = [
    {file = "dm-env-1.6.tar.gz", hash = "sha256:a436eb1c654c39e0c986a516cee218bea7140b510fceff63f97eb4fcff3d93de"},
@@ -578,7 +568,7 @@ numpy = "*"
 name = "dm-tree"
 version = "0.1.8"
 description = "Tree is a library for working with nested data structures."
-optional = false
+optional = true
 python-versions = "*"
 files = [
    {file = "dm-tree-0.1.8.tar.gz", hash = "sha256:0fcaabbb14e7980377439e7140bd05552739ca5e515ecb3119f234acee4b9430"},
@@ -682,7 +672,7 @@ test = ["pytest (>=6)"]
 name = "farama-notifications"
 version = "0.0.4"
 description = "Notifications for all Farama Foundation maintained libraries."
-optional = false
+optional = true
 python-versions = "*"
 files = [
    {file = "Farama-Notifications-0.0.4.tar.gz", hash = "sha256:13fceff2d14314cf80703c8266462ebf3733c7d165336eee998fc58e545efd18"},
@@ -806,7 +796,7 @@ test = ["black", "coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mock", "mypy", "pre
 name = "glfw"
 version = "2.7.0"
 description = "A ctypes-based wrapper for GLFW3."
-optional = false
+optional = true
 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"},
@@ -893,7 +883,7 @@ protobuf = ["grpcio-tools (>=1.62.1)"]
 name = "gymnasium"
 version = "0.29.1"
 description = "A standard API for reinforcement learning and a diverse set of reference environments (formerly Gym)."
-optional = false
+optional = true
 python-versions = ">=3.8"
 files = [
    {file = "gymnasium-0.29.1-py3-none-any.whl", hash = "sha256:61c3384b5575985bb7f85e43213bcb40f36fcdff388cae6bc229304c71f2843e"},
@@ -923,7 +913,7 @@ toy-text = ["pygame (>=2.1.3)", "pygame (>=2.1.3)"]
 name = "gymnasium-robotics"
 version = "1.2.4"
 description = "Robotics environments for the Gymnasium repo."
-optional = false
+optional = true
 python-versions = ">=3.8"
 files = [
    {file = "gymnasium-robotics-1.2.4.tar.gz", hash = "sha256:d304192b066f8b800599dfbe3d9d90bba9b761ee884472bdc4d05968a8bc61cb"},
@@ -1155,7 +1145,7 @@ i18n = ["Babel (>=2.7)"]
 name = "labmaze"
 version = "1.0.6"
 description = "LabMaze: DeepMind Lab's text maze generator."
-optional = false
+optional = true
 python-versions = "*"
 files = [
    {file = "labmaze-1.0.6-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:b2ddef976dfd8d992b19cfa6c633f2eba7576d759c2082da534e3f727479a84a"},
@@ -1199,7 +1189,7 @@ setuptools = "!=50.0.0"
 name = "lazy-loader"
 version = "0.3"
 description = "lazy_loader"
-optional = false
+optional = true
 python-versions = ">=3.7"
 files = [
    {file = "lazy_loader-0.3-py3-none-any.whl", hash = "sha256:1e9e76ee8631e264c62ce10006718e80b2cfc74340d17d1031e0f84af7478554"},
@@ -1244,7 +1234,7 @@ files = [
 name = "lxml"
 version = "5.1.0"
 description = "Powerful and Pythonic XML processing library combining libxml2/libxslt with the ElementTree API."
-optional = false
+optional = true
 python-versions = ">=3.6"
 files = [
    {file = "lxml-5.1.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:704f5572ff473a5f897745abebc6df40f22d4133c1e0a1f124e4f2bd3330ff7e"},
@@ -1462,7 +1452,7 @@ tests = ["pytest (>=4.6)"]
 name = "mujoco"
 version = "2.3.7"
 description = "MuJoCo Physics Simulator"
-optional = false
+optional = true
 python-versions = ">=3.8"
 files = [
    {file = "mujoco-2.3.7-cp310-cp310-macosx_10_16_x86_64.whl", hash = "sha256:e8714a5ff6a1561b364b7b4648d4c0c8d13e751874cf7401c309b9d23fa9598b"},
@@ -1776,7 +1766,7 @@ xml = ["lxml (>=4.9.2)"]
 name = "pettingzoo"
 version = "1.24.3"
 description = "Gymnasium for multi-agent reinforcement learning."
-optional = false
+optional = true
 python-versions = ">=3.8"
 files = [
    {file = "pettingzoo-1.24.3-py3-none-any.whl", hash = "sha256:23ed90517d2e8a7098bdaf5e31234b3a7f7b73ca578d70d1ca7b9d0cb0e37982"},
@@ -2144,7 +2134,7 @@ dev = ["aafigure", "matplotlib", "pygame", "pyglet (<2.0.0)", "sphinx", "wheel"]
 name = "pyopengl"
 version = "3.1.7"
 description = "Standard OpenGL bindings for Python"
-optional = false
+optional = true
 python-versions = "*"
 files = [
    {file = "PyOpenGL-3.1.7-py3-none-any.whl", hash = "sha256:a6ab19cf290df6101aaf7470843a9c46207789855746399d0af92521a0a92b7a"},
@@ -2155,7 +2145,7 @@ files = [
 name = "pyparsing"
 version = "3.1.2"
 description = "pyparsing module - Classes and methods to define and execute parsing grammars"
-optional = false
+optional = true
 python-versions = ">=3.6.8"
 files = [
    {file = "pyparsing-3.1.2-py3-none-any.whl", hash = "sha256:f9db75911801ed778fe61bb643079ff86601aca99fcae6345aa67292038fb742"},
@@ -2586,7 +2576,7 @@ torch = ["safetensors[numpy]", "torch (>=1.10)"]
 name = "scikit-image"
 version = "0.22.0"
 description = "Image processing in Python"
-optional = false
+optional = true
 python-versions = ">=3.9"
 files = [
    {file = "scikit_image-0.22.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:74ec5c1d4693506842cc7c9487c89d8fc32aed064e9363def7af08b8f8cbb31d"},
@@ -2634,7 +2624,7 @@ test = ["asv", "matplotlib (>=3.5)", "numpydoc (>=1.5)", "pooch (>=1.6.0)", "pyt
 name = "scipy"
 version = "1.12.0"
 description = "Fundamental algorithms for scientific computing in Python"
-optional = false
+optional = true
 python-versions = ">=3.9"
 files = [
    {file = "scipy-1.12.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:78e4402e140879387187f7f25d91cc592b3501a2e51dfb320f48dfb73565f10b"},
@@ -2839,7 +2829,7 @@ testing-integration = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "jar
 name = "shapely"
 version = "2.0.3"
 description = "Manipulation and analysis of geometric objects"
-optional = false
+optional = true
 python-versions = ">=3.7"
 files = [
    {file = "shapely-2.0.3-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:af7e9abe180b189431b0f490638281b43b84a33a960620e6b2e8d3e3458b61a1"},
@@ -2892,6 +2882,55 @@ numpy = ">=1.14,<2"
 docs = ["matplotlib", "numpydoc (==1.1.*)", "sphinx", "sphinx-book-theme", "sphinx-remove-toctrees"]
 test = ["pytest", "pytest-cov"]

+[[package]]
+name = "sim-aloha"
+version = "0.1.2"
+description = "ALOHA environment for LeRobot"
+optional = true
+python-versions = "<4.0,>=3.10"
+files = [
+    {file = "sim_aloha-0.1.2-py3-none-any.whl", hash = "sha256:3b13c1ee474481f5d686b57bf1a9ed350e01ca4da7d65f7501446eb74b02653a"},
+    {file = "sim_aloha-0.1.2.tar.gz", hash = "sha256:44d36bbdb13e98e0c74f4d2a682f38683f4f63951618da28175f89cbb1c6f324"},
+]
+
+[package.dependencies]
+dm-control = "1.0.14"
+
+[[package]]
+name = "sim-pusht"
+version = "0.1.0"
+description = "PushT environment for LeRobot"
+optional = true
+python-versions = "<4.0,>=3.10"
+files = [
+    {file = "sim_pusht-0.1.0-py3-none-any.whl", hash = "sha256:1348dcab5ea8460eff2dc97b7d62dd40f2a382df92bfdc69ff5c0224900690b0"},
+    {file = "sim_pusht-0.1.0.tar.gz", hash = "sha256:d8f6a2207fd781348156206728329aa6338e9785cfc07679c5c48889b34d9b14"},
+]
+
+[package.dependencies]
+gymnasium = ">=0.29.1,<0.30.0"
+opencv-python = ">=4.9.0.80,<5.0.0.0"
+pygame = ">=2.5.2,<3.0.0"
+pymunk = ">=6.6.0,<7.0.0"
+scikit-image = ">=0.22.0,<0.23.0"
+shapely = ">=2.0.3,<3.0.0"
+
+[[package]]
+name = "sim-xarm"
+version = "0.1.0"
+description = "xArm environment for LeRobot"
+optional = true
+python-versions = "<4.0,>=3.10"
+files = [
+    {file = "sim_xarm-0.1.0-py3-none-any.whl", hash = "sha256:2771ca0e8d775dc7d9ccb3360e7fcf42507c5d4791525692e409f53ff5c83eaa"},
+    {file = "sim_xarm-0.1.0.tar.gz", hash = "sha256:90342394369ab37636a8c41a995ba20f1dac79c50563b1b1e4b38eeffbc5588d"},
+]
+
+[package.dependencies]
+gymnasium = ">=0.29.1,<0.30.0"
+gymnasium-robotics = ">=1.2.4,<2.0.0"
+mujoco = ">=2.3.7,<3.0.0"
+
 [[package]]
 name = "six"
 version = "1.16.0"
@@ -3031,7 +3070,7 @@ tests = ["pytest", "pytest-cov"]
 name = "tifffile"
 version = "2024.2.12"
 description = "Read and write TIFF files"
-optional = false
+optional = true
 python-versions = ">=3.9"
 files = [
    {file = "tifffile-2024.2.12-py3-none-any.whl", hash = "sha256:870998f82fbc94ff7c3528884c1b0ae54863504ff51dbebea431ac3fa8fb7c21"},
@@ -3327,7 +3366,12 @@ files = [
 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)"]

+[extras]
+aloha = ["sim-aloha"]
+pusht = ["sim-pusht"]
+xarm = ["sim-xarm"]
+
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.10"
-content-hash = "8800bb8b24312d17b765cd2ce2799f49436171dd5fbf1bec3b07f853cfa9befd"
+content-hash = "1203f5ffb62ef7146ce79be8353d37f0262ae5e6e63934b76084f50d569b10b9"
--- a/.github/poetry/cpu/pyproject.toml
+++ b/.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"}]
@@ -23,7 +28,6 @@ packages = [{include = "lerobot"}]
 python = "^3.10"
 termcolor = "^2.4.0"
 omegaconf = "^2.3.0"
-dm-env = "^1.6"
 pandas = "^2.2.1"
 wandb = "^0.16.3"
 moviepy = "^1.0.3"
@@ -34,30 +38,35 @@ 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 = "^2.3.7"
 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 = "1.0.14"
 robomimic = "0.2.0"
 huggingface-hub = "^0.21.4"
-gymnasium-robotics = "^1.2.4"
-gymnasium = "^0.29.1"
 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"

--- a/.github/workflows/test.yml
+++ b/.github/workflows/test.yml
@@ -87,7 +87,7 @@ jobs:
          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: |
@@ -106,7 +106,7 @@ jobs:
      #             install project
      #----------------------------------------------
      - name: Install project
-        run: poetry install --no-interaction
+        run: poetry install --no-interaction --without dev --all-extras

      #----------------------------------------------
      #            run tests & coverage
--- a/README.md
+++ b/README.md
@@ -146,7 +146,11 @@ hydra.run.dir=outputs/visualize_dataset/example

 ### Evaluate a pretrained policy

-Check out [example 2](./examples/2_evaluate_pretrained_policy.py) to see how you can load a pretrained policy from HuggingFace hub, load up the corresponding environment and model, and run an evaluation.
+You can import our environment class, download pretrained policies from the HuggingFace hub, and use our rollout utilities with rendering:
+```python
+""" Copy pasted from `examples/2_evaluate_pretrained_policy.py`
+# TODO
+```

 Or you can achieve the same result by executing our script from the command line:
 ```bash
@@ -156,7 +160,7 @@ eval_episodes=10 \
 hydra.run.dir=outputs/eval/example_hub
 ```

-After training your own policy, you can also re-evaluate the checkpoints with:
+After launching training of your own policy, you can also re-evaluate the checkpoints with:
 ```bash
 python lerobot/scripts/eval.py \
 --config PATH/TO/FOLDER/config.yaml \
@@ -169,9 +173,19 @@ See `python lerobot/scripts/eval.py --help` for more instructions.

 ### Train your own policy

-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): check out [example 3](./examples/3_train_policy.py). After you run this, you may want to revisit [example 2](./examples/2_evaluate_pretrained_policy.py) to evaluate your training output!
+You can import our dataset, environment, policy classes, and use our training utilities (if some data is missing, it will be automatically downloaded from HuggingFace hub):
+```python
+""" Copy pasted from `examples/3_train_policy.py`
+# TODO
+```

-In general, you can use our training script to easily train any policy on any environment:
+Or you can achieve the same result by executing our script from the command line:
+```bash
+python lerobot/scripts/train.py \
+hydra.run.dir=outputs/train/example
+```
+
+You can easily train any policy on any environment:
 ```bash
 python lerobot/scripts/train.py \
 env=aloha \
--- a/envs/sim_aloha/README.md
+++ b/envs/sim_aloha/README.md
@@ -0,0 +1 @@
+# ALOHA environment for LeRobot
--- a/lerobot/common/envs/simxarm/simxarm/tasks/init.py
+++ b/lerobot/common/envs/simxarm/simxarm/tasks/init.py
--- a/lerobot/common/envs/aloha/assets/bimanual_viperx_end_effector_insertion.xml
+++ b/lerobot/common/envs/aloha/assets/bimanual_viperx_end_effector_insertion.xml
--- a/lerobot/common/envs/aloha/assets/bimanual_viperx_end_effector_transfer_cube.xml
+++ b/lerobot/common/envs/aloha/assets/bimanual_viperx_end_effector_transfer_cube.xml
--- a/lerobot/common/envs/aloha/assets/bimanual_viperx_insertion.xml
+++ b/lerobot/common/envs/aloha/assets/bimanual_viperx_insertion.xml
--- a/lerobot/common/envs/aloha/assets/bimanual_viperx_transfer_cube.xml
+++ b/lerobot/common/envs/aloha/assets/bimanual_viperx_transfer_cube.xml
--- a/lerobot/common/envs/aloha/assets/scene.xml
+++ b/lerobot/common/envs/aloha/assets/scene.xml
--- a/lerobot/common/envs/aloha/assets/tabletop.stl
+++ b/lerobot/common/envs/aloha/assets/tabletop.stl
--- a/lerobot/common/envs/aloha/assets/vx300s_10_custom_finger_left.stl
+++ b/lerobot/common/envs/aloha/assets/vx300s_10_custom_finger_left.stl
--- a/lerobot/common/envs/aloha/assets/vx300s_10_custom_finger_right.stl
+++ b/lerobot/common/envs/aloha/assets/vx300s_10_custom_finger_right.stl
--- a/lerobot/common/envs/aloha/assets/vx300s_10_gripper_finger.stl
+++ b/lerobot/common/envs/aloha/assets/vx300s_10_gripper_finger.stl
--- a/lerobot/common/envs/aloha/assets/vx300s_11_ar_tag.stl
+++ b/lerobot/common/envs/aloha/assets/vx300s_11_ar_tag.stl
--- a/lerobot/common/envs/aloha/assets/vx300s_1_base.stl
+++ b/lerobot/common/envs/aloha/assets/vx300s_1_base.stl
--- a/lerobot/common/envs/aloha/assets/vx300s_2_shoulder.stl
+++ b/lerobot/common/envs/aloha/assets/vx300s_2_shoulder.stl
--- a/lerobot/common/envs/aloha/assets/vx300s_3_upper_arm.stl
+++ b/lerobot/common/envs/aloha/assets/vx300s_3_upper_arm.stl
--- a/lerobot/common/envs/aloha/assets/vx300s_4_upper_forearm.stl
+++ b/lerobot/common/envs/aloha/assets/vx300s_4_upper_forearm.stl
--- a/lerobot/common/envs/aloha/assets/vx300s_5_lower_forearm.stl
+++ b/lerobot/common/envs/aloha/assets/vx300s_5_lower_forearm.stl
--- a/lerobot/common/envs/aloha/assets/vx300s_6_wrist.stl
+++ b/lerobot/common/envs/aloha/assets/vx300s_6_wrist.stl
--- a/lerobot/common/envs/aloha/assets/vx300s_7_gripper.stl
+++ b/lerobot/common/envs/aloha/assets/vx300s_7_gripper.stl
--- a/lerobot/common/envs/aloha/assets/vx300s_8_gripper_prop.stl
+++ b/lerobot/common/envs/aloha/assets/vx300s_8_gripper_prop.stl
--- a/lerobot/common/envs/aloha/assets/vx300s_9_gripper_bar.stl
+++ b/lerobot/common/envs/aloha/assets/vx300s_9_gripper_bar.stl
--- a/lerobot/common/envs/aloha/assets/vx300s_dependencies.xml
+++ b/lerobot/common/envs/aloha/assets/vx300s_dependencies.xml
--- a/lerobot/common/envs/aloha/assets/vx300s_left.xml
+++ b/lerobot/common/envs/aloha/assets/vx300s_left.xml
--- a/lerobot/common/envs/aloha/assets/vx300s_right.xml
+++ b/lerobot/common/envs/aloha/assets/vx300s_right.xml
--- a/lerobot/common/envs/aloha/constants.py
+++ b/lerobot/common/envs/aloha/constants.py
--- a/envs/sim_aloha/aloha/env.py
+++ b/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
--- a/lerobot/common/envs/aloha/tasks/sim.py
+++ b/lerobot/common/envs/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

--- a/lerobot/common/envs/aloha/tasks/sim_end_effector.py
+++ b/lerobot/common/envs/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.
--- a/lerobot/common/envs/aloha/utils.py
+++ b/lerobot/common/envs/aloha/utils.py
--- a/envs/sim_aloha/poetry.lock
+++ b/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.
+
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+]
+
+[[package]]
+name = "certifi"
+version = "2024.2.2"
+description = "Python package for providing Mozilla's CA Bundle."
+optional = false
+python-versions = ">=3.6"
+files = [
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+]
+
+[[package]]
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--- a/envs/sim_aloha/pyproject.toml
+++ b/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"
--- a/envs/sim_pusht/README.md
+++ b/envs/sim_pusht/README.md
@@ -0,0 +1 @@
+# PushT environment for LeRobot
--- a/envs/sim_pusht/poetry.lock
+++ b/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.
+
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+[package.extras]
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+test = ["pytest", "pytest-cov"]
+
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+optional = false
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+
+[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 = [
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+lock-version = "2.0"
+python-versions = "^3.10"
+content-hash = "bedbec07c63d805de4503e1158d9f656e675831e9dd69a1e79f993dcf6da0295"
--- a/envs/sim_pusht/pusht/init.py
+++ b/envs/sim_pusht/pusht/init.py
--- a/lerobot/common/envs/pusht/pusht_env.py
+++ b/lerobot/common/envs/pusht/pusht_env.py
@@ -11,7 +11,7 @@ import skimage.transform as st
 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):
--- a/lerobot/common/envs/pusht/pusht_image_env.py
+++ b/lerobot/common/envs/pusht/pusht_image_env.py
@@ -1,7 +1,7 @@
 import numpy as np
 from gymnasium import spaces

-from lerobot.common.envs.pusht.pusht_env import PushTEnv
+from pusht.pusht_env import PushTEnv


 class PushTImageEnv(PushTEnv):
--- a/lerobot/common/envs/pusht/pymunk_override.py
+++ b/lerobot/common/envs/pusht/pymunk_override.py
--- a/envs/sim_pusht/pyproject.toml
+++ b/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"
--- a/envs/sim_xarm/README.md
+++ b/envs/sim_xarm/README.md
@@ -0,0 +1 @@
+# xArm environment for LeRobot
--- a/envs/sim_xarm/poetry.lock
+++ b/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]]
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+[[package]]
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+version = "3.0.0"
+description = "Pickler class to extend the standard pickle.Pickler functionality"
+optional = false
+python-versions = ">=3.8"
+files = [
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+version = "0.0.4"
+description = "Notifications for all Farama Foundation maintained libraries."
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+files = [
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+optional = false
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+
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+]
+
+[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"
--- a/envs/sim_xarm/pyproject.toml
+++ b/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"
--- a/lerobot/common/envs/simxarm/simxarm/init.py
+++ b/lerobot/common/envs/simxarm/simxarm/init.py
@@ -4,11 +4,11 @@ import gymnasium as gym
 import numpy as np
 from gymnasium.wrappers import TimeLimit

-from lerobot.common.envs.simxarm.simxarm.tasks.base import Base as Base
-from lerobot.common.envs.simxarm.simxarm.tasks.lift import Lift
-from lerobot.common.envs.simxarm.simxarm.tasks.peg_in_box import PegInBox
-from lerobot.common.envs.simxarm.simxarm.tasks.push import Push
-from lerobot.common.envs.simxarm.simxarm.tasks.reach import Reach
+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(
    (
--- a/envs/sim_xarm/xarm/tasks/init.py
+++ b/envs/sim_xarm/xarm/tasks/init.py
--- a/lerobot/common/envs/simxarm/simxarm/tasks/assets/lift.xml
+++ b/lerobot/common/envs/simxarm/simxarm/tasks/assets/lift.xml
--- a/lerobot/common/envs/simxarm/simxarm/tasks/assets/mesh/base_link.stl
+++ b/lerobot/common/envs/simxarm/simxarm/tasks/assets/mesh/base_link.stl
--- a/lerobot/common/envs/simxarm/simxarm/tasks/assets/mesh/block_inner.stl
+++ b/lerobot/common/envs/simxarm/simxarm/tasks/assets/mesh/block_inner.stl
--- a/lerobot/common/envs/simxarm/simxarm/tasks/assets/mesh/block_inner2.stl
+++ b/lerobot/common/envs/simxarm/simxarm/tasks/assets/mesh/block_inner2.stl
--- a/lerobot/common/envs/simxarm/simxarm/tasks/assets/mesh/block_outer.stl
+++ b/lerobot/common/envs/simxarm/simxarm/tasks/assets/mesh/block_outer.stl
--- a/lerobot/common/envs/simxarm/simxarm/tasks/assets/mesh/left_finger.stl
+++ b/lerobot/common/envs/simxarm/simxarm/tasks/assets/mesh/left_finger.stl
--- a/lerobot/common/envs/simxarm/simxarm/tasks/assets/mesh/left_inner_knuckle.stl
+++ b/lerobot/common/envs/simxarm/simxarm/tasks/assets/mesh/left_inner_knuckle.stl
--- a/lerobot/common/envs/simxarm/simxarm/tasks/assets/mesh/left_outer_knuckle.stl
+++ b/lerobot/common/envs/simxarm/simxarm/tasks/assets/mesh/left_outer_knuckle.stl
--- a/lerobot/common/envs/simxarm/simxarm/tasks/assets/mesh/link1.stl
+++ b/lerobot/common/envs/simxarm/simxarm/tasks/assets/mesh/link1.stl
--- a/lerobot/common/envs/simxarm/simxarm/tasks/assets/mesh/link2.stl
+++ b/lerobot/common/envs/simxarm/simxarm/tasks/assets/mesh/link2.stl
--- a/lerobot/common/envs/simxarm/simxarm/tasks/assets/mesh/link3.stl
+++ b/lerobot/common/envs/simxarm/simxarm/tasks/assets/mesh/link3.stl
--- a/lerobot/common/envs/simxarm/simxarm/tasks/assets/mesh/link4.stl
+++ b/lerobot/common/envs/simxarm/simxarm/tasks/assets/mesh/link4.stl
--- a/lerobot/common/envs/simxarm/simxarm/tasks/assets/mesh/link5.stl
+++ b/lerobot/common/envs/simxarm/simxarm/tasks/assets/mesh/link5.stl
--- a/lerobot/common/envs/simxarm/simxarm/tasks/assets/mesh/link6.stl
+++ b/lerobot/common/envs/simxarm/simxarm/tasks/assets/mesh/link6.stl
--- a/lerobot/common/envs/simxarm/simxarm/tasks/assets/mesh/link7.stl
+++ b/lerobot/common/envs/simxarm/simxarm/tasks/assets/mesh/link7.stl
--- a/lerobot/common/envs/simxarm/simxarm/tasks/assets/mesh/link_base.stl
+++ b/lerobot/common/envs/simxarm/simxarm/tasks/assets/mesh/link_base.stl
--- a/lerobot/common/envs/simxarm/simxarm/tasks/assets/mesh/right_finger.stl
+++ b/lerobot/common/envs/simxarm/simxarm/tasks/assets/mesh/right_finger.stl
--- a/lerobot/common/envs/simxarm/simxarm/tasks/assets/mesh/right_inner_knuckle.stl
+++ b/lerobot/common/envs/simxarm/simxarm/tasks/assets/mesh/right_inner_knuckle.stl
--- a/lerobot/common/envs/simxarm/simxarm/tasks/assets/mesh/right_outer_knuckle.stl
+++ b/lerobot/common/envs/simxarm/simxarm/tasks/assets/mesh/right_outer_knuckle.stl
--- a/lerobot/common/envs/simxarm/simxarm/tasks/assets/peg_in_box.xml
+++ b/lerobot/common/envs/simxarm/simxarm/tasks/assets/peg_in_box.xml
--- a/lerobot/common/envs/simxarm/simxarm/tasks/assets/push.xml
+++ b/lerobot/common/envs/simxarm/simxarm/tasks/assets/push.xml
--- a/lerobot/common/envs/simxarm/simxarm/tasks/assets/reach.xml
+++ b/lerobot/common/envs/simxarm/simxarm/tasks/assets/reach.xml
--- a/lerobot/common/envs/simxarm/simxarm/tasks/assets/shared.xml
+++ b/lerobot/common/envs/simxarm/simxarm/tasks/assets/shared.xml
--- a/lerobot/common/envs/simxarm/simxarm/tasks/assets/xarm.xml
+++ b/lerobot/common/envs/simxarm/simxarm/tasks/assets/xarm.xml
--- a/lerobot/common/envs/simxarm/simxarm/tasks/base.py
+++ b/lerobot/common/envs/simxarm/simxarm/tasks/base.py
@@ -4,7 +4,7 @@ import mujoco
 import numpy as np
 from gymnasium_robotics.envs import robot_env

-from lerobot.common.envs.simxarm.simxarm.tasks import mocap
+from xarm.tasks import mocap


 class Base(robot_env.MujocoRobotEnv):
--- a/lerobot/common/envs/simxarm/simxarm/tasks/lift.py
+++ b/lerobot/common/envs/simxarm/simxarm/tasks/lift.py
@@ -1,6 +1,6 @@
 import numpy as np

-from lerobot.common.envs.simxarm.simxarm import Base
+from xarm import Base


 class Lift(Base):
--- a/lerobot/common/envs/simxarm/simxarm/tasks/mocap.py
+++ b/lerobot/common/envs/simxarm/simxarm/tasks/mocap.py
--- a/lerobot/common/envs/simxarm/simxarm/tasks/peg_in_box.py
+++ b/lerobot/common/envs/simxarm/simxarm/tasks/peg_in_box.py
@@ -1,6 +1,6 @@
 import numpy as np

-from lerobot.common.envs.simxarm.simxarm import Base
+from xarm import Base


 class PegInBox(Base):
--- a/lerobot/common/envs/simxarm/simxarm/tasks/push.py
+++ b/lerobot/common/envs/simxarm/simxarm/tasks/push.py
@@ -1,6 +1,6 @@
 import numpy as np

-from lerobot.common.envs.simxarm.simxarm import Base
+from xarm import Base


 class Push(Base):
--- a/lerobot/common/envs/simxarm/simxarm/tasks/reach.py
+++ b/lerobot/common/envs/simxarm/simxarm/tasks/reach.py
@@ -1,6 +1,6 @@
 import numpy as np

-from lerobot.common.envs.simxarm.simxarm import Base
+from xarm import Base


 class Reach(Base):
--- a/examples/2_evaluate_pretrained_policy.py
+++ b/examples/2_evaluate_pretrained_policy.py
@@ -1,39 +1 @@
-"""
-This scripts demonstrates how to evaluate a pretrained policy from the HuggingFace Hub or from your local
-training outputs directory. In the latter case, you might want to run examples/3_train_policy.py first.
-"""
-
-from pathlib import Path
-
-from huggingface_hub import snapshot_download
-
-from lerobot.common.utils import init_hydra_config
-from lerobot.scripts.eval import eval
-
-# Get a pretrained policy from the hub.
-hub_id = "lerobot/diffusion_policy_pusht_image"
-folder = Path(snapshot_download(hub_id))
-# OR uncomment the following to evaluate a policy from the local outputs/train folder.
-# folder = Path("outputs/train/example_pusht_diffusion")
-
-config_path = folder / "config.yaml"
-weights_path = folder / "model.pt"
-stats_path = folder / "stats.pth"  # normalization stats
-
-# Override some config parameters to do with evaluation.
-overrides = [
-    f"policy.pretrained_model_path={weights_path}",
-    "eval_episodes=10",
-    "rollout_batch_size=10",
-    "device=cuda",
-]
-
-# Create a Hydra config.
-cfg = init_hydra_config(config_path, overrides)
-
-# Evaluate the policy and save the outputs including metrics and videos.
-eval(
-    cfg,
-    out_dir=f"outputs/eval/example_{cfg.env.name}_{cfg.policy.name}",
-    stats_path=stats_path,
-)
+# TODO
--- a/examples/3_train_policy.py
+++ b/examples/3_train_policy.py
@@ -1,55 +1 @@
-"""This scripts demonstrates how to train Diffusion Policy on the PushT environment.
-
-Once you have trained a model with this script, you can try to evaluate it on
-examples/2_evaluate_pretrained_policy.py
-"""
-
-import os
-from pathlib import Path
-
-import torch
-from omegaconf import OmegaConf
-from tqdm import trange
-
-from lerobot.common.datasets.factory import make_offline_buffer
-from lerobot.common.policies.diffusion.policy import DiffusionPolicy
-from lerobot.common.utils import init_hydra_config
-
-output_directory = Path("outputs/train/example_pusht_diffusion")
-os.makedirs(output_directory, exist_ok=True)
-
-overrides = [
-    "env=pusht",
-    "policy=diffusion",
-    # Adjust as you prefer. 5000 steps are needed to get something worth evaluating.
-    "offline_steps=5000",
-    "log_freq=250",
-    "device=cuda",
-]
-
-cfg = init_hydra_config("lerobot/configs/default.yaml", overrides)
-
-policy = DiffusionPolicy(
-    cfg=cfg.policy,
-    cfg_device=cfg.device,
-    cfg_noise_scheduler=cfg.noise_scheduler,
-    cfg_rgb_model=cfg.rgb_model,
-    cfg_obs_encoder=cfg.obs_encoder,
-    cfg_optimizer=cfg.optimizer,
-    cfg_ema=cfg.ema,
-    n_action_steps=cfg.n_action_steps + cfg.n_latency_steps,
-    **cfg.policy,
-)
-policy.train()
-
-offline_buffer = make_offline_buffer(cfg)
-
-for offline_step in trange(cfg.offline_steps):
-    train_info = policy.update(offline_buffer, offline_step)
-    if offline_step % cfg.log_freq == 0:
-        print(train_info)
-
-# Save the policy, configuration, and normalization stats for later use.
-policy.save_pretrained(output_directory / "model.pt")
-OmegaConf.save(cfg, output_directory / "config.yaml")
-torch.save(offline_buffer.transform[-1].stats, output_directory / "stats.pth")
+# TODO
--- a/lerobot/common/datasets/pusht.py
+++ b/lerobot/common/datasets/pusht.py
@@ -15,8 +15,8 @@ from torchrl.data.replay_buffers.writers import Writer

 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,
--- a/lerobot/common/envs/aloha/env.py
+++ b/lerobot/common/envs/aloha/env.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,
@@ -17,21 +15,9 @@ 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_global_seed

-_has_gym = importlib.util.find_spec("gymnasium") is not None
+_has_aloha = importlib.util.find_spec("aloha") is not None


 class AlohaEnv(AbstractEnv):
@@ -64,49 +50,23 @@ class AlohaEnv(AbstractEnv):
        )

    def _make_env(self):
-        if not _has_gym:
-            raise ImportError("Cannot import gymnasium.")
-
        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())
@@ -124,6 +84,9 @@ class AlohaEnv(AbstractEnv):
        return obs

    def _reset(self, tensordict: Optional[TensorDict] = None):
+        from aloha.tasks.sim import BOX_POSE
+        from aloha.utils import sample_box_pose, sample_insertion_pose
+
        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
@@ -200,9 +163,14 @@ 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)
--- a/lerobot/common/envs/factory.py
+++ b/lerobot/common/envs/factory.py
@@ -17,18 +17,18 @@ def make_env(cfg, transform=None):
    }

    if cfg.env.name == "simxarm":
-        from lerobot.common.envs.simxarm.env import SimxarmEnv
+        from lerobot.common.envs.xarm import SimxarmEnv

        kwargs["task"] = cfg.env.task
        clsfunc = SimxarmEnv
    elif cfg.env.name == "pusht":
-        from lerobot.common.envs.pusht.env import PushtEnv
+        from lerobot.common.envs.pusht import PushtEnv

        # assert kwargs["seed"] > 200, "Seed 0-200 are used for the demonstration dataset, so we don't want to seed the eval env with this range."

        clsfunc = PushtEnv
    elif cfg.env.name == "aloha":
-        from lerobot.common.envs.aloha.env import AlohaEnv
+        from lerobot.common.envs.aloha import AlohaEnv

        kwargs["task"] = cfg.env.task
        clsfunc = AlohaEnv
--- a/lerobot/common/envs/pusht/env.py
+++ b/lerobot/common/envs/pusht/env.py
@@ -18,7 +18,7 @@ from torchrl.envs.libs.gym import _gym_to_torchrl_spec_transform
 from lerobot.common.envs.abstract import AbstractEnv
 from lerobot.common.utils import set_global_seed

-_has_gym = importlib.util.find_spec("gymnasium") is not None
+_has_pusht = importlib.util.find_spec("pusht") is not None


 class PushtEnv(AbstractEnv):
@@ -51,15 +51,17 @@ class PushtEnv(AbstractEnv):
        )

    def _make_env(self):
-        if not _has_gym:
-            raise ImportError("Cannot import gymnasium.")
-
        # TODO(rcadene) (PushTEnv is similar to PushTImageEnv, but without the image rendering, it's faster to iterate on)
        # from lerobot.common.envs.pusht.pusht_env import PushTEnv
-
        if not self.from_pixels:
            raise NotImplementedError("Use PushTEnv, instead of PushTImageEnv")
-        from lerobot.common.envs.pusht.pusht_image_env import PushTImageEnv
+
+        if not _has_pusht:
+            raise ImportError(
+                "Cannot import pusht env. Please install it with `python -m pip install 'lerobot[pusht]'`"
+            )
+
+        from pusht.pusht_image_env import PushTImageEnv

        self._env = PushTImageEnv(render_size=self.image_size)

--- a/lerobot/common/envs/simxarm/env.py
+++ b/lerobot/common/envs/simxarm/env.py
@@ -21,6 +21,7 @@ from lerobot.common.utils import set_global_seed
 MAX_NUM_ACTIONS = 4

 _has_gym = importlib.util.find_spec("gymnasium") is not None
+_has_xarm = importlib.util.find_spec("xarm") is not None


 class SimxarmEnv(AbstractEnv):
@@ -52,12 +53,16 @@ class SimxarmEnv(AbstractEnv):
        )

    def _make_env(self):
+        if not _has_xarm:
+            raise ImportError(
+                "Cannot import xarm env. Please install it with `python -m pip install 'lerobot[xarm]'`"
+            )
        if not _has_gym:
            raise ImportError("Cannot import gymnasium.")

        import gymnasium

-        from lerobot.common.envs.simxarm.simxarm import TASKS
+        from xarm import TASKS

        if self.task not in TASKS:
            raise ValueError(f"Unknown task {self.task}. Must be one of {list(TASKS.keys())}")
--- a/lerobot/common/logger.py
+++ b/lerobot/common/logger.py
@@ -5,7 +5,6 @@ from pathlib import Path
 from omegaconf import OmegaConf
 from termcolor import colored

-from lerobot.common.policies.abstract import AbstractPolicy

 def log_output_dir(out_dir):
    logging.info(colored("Output dir:", "yellow", attrs=["bold"]) + f" {out_dir}")
@@ -68,11 +67,11 @@ class Logger:
            logging.info(f"Track this run --> {colored(wandb.run.get_url(), 'yellow', attrs=['bold'])}")
            self._wandb = wandb

-    def save_model(self, policy: AbstractPolicy, identifier):
+    def save_model(self, policy, identifier):
        if self._save_model:
            self._model_dir.mkdir(parents=True, exist_ok=True)
            fp = self._model_dir / f"{str(identifier)}.pt"
-            policy.save_pretrained(fp)
+            policy.save(fp)
            if self._wandb and not self._disable_wandb_artifact:
                # note wandb artifact does not accept ":" in its name
                artifact = self._wandb.Artifact(
--- a/lerobot/common/policies/abstract.py
+++ b/lerobot/common/policies/abstract.py
@@ -2,25 +2,14 @@ from collections import deque

 import torch
 from torch import Tensor, nn
-from huggingface_hub import PyTorchModelHubMixin


-class AbstractPolicy(nn.Module, PyTorchModelHubMixin):
+class AbstractPolicy(nn.Module):
    """Base policy which all policies should be derived from.

    The forward method should generally not be overriden as it plays the role of handling multi-step policies. See its
    documentation for more information.

-    The policy is a PyTorchModelHubMixin, which means that it can be saved and loaded from the Hugging Face Hub and/or to a local directory.
-    # Save policy weights to local directory
-    >>> policy.save_pretrained("my-awesome-policy")
-
-    # Push policy weights to the Hub
-    >>> policy.push_to_hub("my-awesome-policy")
-
-    # Download and initialize policy from the Hub
-    >>> policy = MyPolicy.from_pretrained("username/my-awesome-policy")
-
    Note:
        When implementing a concrete class (e.g. `AlohaDataset`, `PushtEnv`, `DiffusionPolicy`), you need to:
            1. set the required class attributes:
@@ -33,7 +22,7 @@ class AbstractPolicy(nn.Module, PyTorchModelHubMixin):

    name: str | None = None  # same name should be used to instantiate the policy in factory.py

-    def __init__(self, n_action_steps: int | None = None):
+    def __init__(self, n_action_steps: int | None):
        """
        n_action_steps: Sets the cache size for storing action trajectories. If None, it is assumed that a single
            action is returned by `select_actions` and that doesn't have a horizon dimension. The `forward` method then
@@ -48,10 +37,10 @@ class AbstractPolicy(nn.Module, PyTorchModelHubMixin):
        """One step of the policy's learning algorithm."""
        raise NotImplementedError("Abstract method")

-    def save(self, fp):  # TODO: remove this method since we are using PyTorchModelHubMixin
+    def save(self, fp):
        torch.save(self.state_dict(), fp)

-    def load(self, fp):  # TODO: remove this method since we are using PyTorchModelHubMixin
+    def load(self, fp):
        d = torch.load(fp)
        self.load_state_dict(d)

--- a/lerobot/common/policies/act/policy.py
+++ b/lerobot/common/policies/act/policy.py
@@ -136,8 +136,8 @@ class ActionChunkingTransformerPolicy(AbstractPolicy):
    def save(self, fp):
        torch.save(self.state_dict(), fp)

-    def load(self, fp, device=None):
-        d = torch.load(fp, map_location=device)
+    def load(self, fp):
+        d = torch.load(fp)
        self.load_state_dict(d)

    def compute_loss(self, batch):
--- a/lerobot/common/policies/diffusion/diffusion_unet_image_policy.py
+++ b/lerobot/common/policies/diffusion/diffusion_unet_image_policy.py
@@ -32,7 +32,7 @@ assert len(unexpected_keys) == 0
 Then in that same runtime you can also save the weights with the new aligned state_dict:

 ```
-policy.save_pretrained("my-policy")
+policy.save("weights.pt")
 ```

 Now you can remove the breakpoint and extra code and load in the weights just like with any other lerobot checkpoint.
--- a/lerobot/common/policies/diffusion/policy.py
+++ b/lerobot/common/policies/diffusion/policy.py
@@ -203,8 +203,8 @@ class DiffusionPolicy(AbstractPolicy):
    def save(self, fp):
        torch.save(self.state_dict(), fp)

-    def load(self, fp, device=None):
-        d = torch.load(fp, map_location=device)
+    def load(self, fp):
+        d = torch.load(fp)
        missing_keys, unexpected_keys = self.load_state_dict(d, strict=False)
        if len(missing_keys) > 0:
            assert all(k.startswith("ema_diffusion.") for k in missing_keys)
--- a/lerobot/common/policies/factory.py
+++ b/lerobot/common/policies/factory.py
@@ -1,53 +1,35 @@
-""" Factory for policies
-"""
-
-from lerobot.common.policies.abstract import AbstractPolicy
-
-
-def make_policy(cfg: dict) -> AbstractPolicy:
-    """ Instantiate a policy from the configuration.
-        Currently supports TD-MPC, Diffusion, and ACT: select the policy with cfg.policy.name: tdmpc, diffusion, act.
-    
-    Args:
-        cfg: The configuration (DictConfig)
-
-    """
-    policy_kwargs = {}
+def make_policy(cfg):
    if cfg.policy.name != "diffusion" and cfg.rollout_batch_size > 1:
        raise NotImplementedError("Only diffusion policy supports rollout_batch_size > 1 for the time being.")

    if cfg.policy.name == "tdmpc":
        from lerobot.common.policies.tdmpc.policy import TDMPCPolicy

-        policy_cls = TDMPCPolicy
-        policy_kwargs = {"cfg": cfg.policy, "device": cfg.device}
+        policy = TDMPCPolicy(cfg.policy, cfg.device)
    elif cfg.policy.name == "diffusion":
        from lerobot.common.policies.diffusion.policy import DiffusionPolicy

-        policy_cls = DiffusionPolicy
-        policy_kwargs = {
-            "cfg": cfg.policy,
-            "cfg_device": cfg.device,
-            "cfg_noise_scheduler": cfg.noise_scheduler,
-            "cfg_rgb_model": cfg.rgb_model,
-            "cfg_obs_encoder": cfg.obs_encoder,
-            "cfg_optimizer": cfg.optimizer,
-            "cfg_ema": cfg.ema,
-            "n_action_steps": cfg.n_action_steps + cfg.n_latency_steps,
+        policy = DiffusionPolicy(
+            cfg=cfg.policy,
+            cfg_device=cfg.device,
+            cfg_noise_scheduler=cfg.noise_scheduler,
+            cfg_rgb_model=cfg.rgb_model,
+            cfg_obs_encoder=cfg.obs_encoder,
+            cfg_optimizer=cfg.optimizer,
+            cfg_ema=cfg.ema,
+            n_action_steps=cfg.n_action_steps + cfg.n_latency_steps,
            **cfg.policy,
-        }
+        )
    elif cfg.policy.name == "act":
        from lerobot.common.policies.act.policy import ActionChunkingTransformerPolicy

-        policy_cls = ActionChunkingTransformerPolicy
-        policy_kwargs = {"cfg": cfg.policy, "device": cfg.device, "n_action_steps": cfg.n_action_steps + cfg.n_latency_steps}
+        policy = ActionChunkingTransformerPolicy(
+            cfg.policy, cfg.device, n_action_steps=cfg.n_action_steps + cfg.n_latency_steps
+        )
    else:
        raise ValueError(cfg.policy.name)

    if cfg.policy.pretrained_model_path:
-        # policy.load(cfg.policy.pretrained_model_path, device=cfg.device)
-        policy = policy_cls.from_pretrained(cfg.policy.pretrained_model_path, map_location=cfg.device, **policy_kwargs)
-    
        # TODO(rcadene): hack for old pretrained models from fowm
        if cfg.policy.name == "tdmpc" and "fowm" in cfg.policy.pretrained_model_path:
            if "offline" in cfg.pretrained_model_path:
@@ -56,5 +38,6 @@ def make_policy(cfg: dict) -> AbstractPolicy:
                policy.step[0] = 100000
            else:
                raise NotImplementedError()
+        policy.load(cfg.policy.pretrained_model_path)

    return policy
--- a/lerobot/common/policies/tdmpc/policy.py
+++ b/lerobot/common/policies/tdmpc/policy.py
@@ -122,9 +122,9 @@ class TDMPCPolicy(AbstractPolicy):
        """Save state dict of TOLD model to filepath."""
        torch.save(self.state_dict(), fp)

-    def load(self, fp, device=None):
+    def load(self, fp):
        """Load a saved state dict from filepath into current agent."""
-        d = torch.load(fp, map_location=device)
+        d = torch.load(fp)
        self.model.load_state_dict(d["model"])
        self.model_target.load_state_dict(d["model_target"])

--- a/lerobot/common/utils.py
+++ b/lerobot/common/utils.py
@@ -1,13 +1,9 @@
 import logging
-import os.path as osp
 import random
 from datetime import datetime
-from pathlib import Path

-import hydra
 import numpy as np
 import torch
-from omegaconf import DictConfig


 def get_safe_torch_device(cfg_device: str, log: bool = False) -> torch.device:
@@ -67,31 +63,3 @@ def format_big_number(num):
        num /= divisor

    return num
-
-
-def _relative_path_between(path1: Path, path2: Path) -> Path:
-    """Returns path1 relative to path2."""
-    path1 = path1.absolute()
-    path2 = path2.absolute()
-    try:
-        return path1.relative_to(path2)
-    except ValueError:  # most likely because path1 is not a subpath of path2
-        common_parts = Path(osp.commonpath([path1, path2])).parts
-        return Path(
-            "/".join([".."] * (len(path2.parts) - len(common_parts)) + list(path1.parts[len(common_parts) :]))
-        )
-
-
-def init_hydra_config(config_path: str, overrides: list[str] | None = None) -> DictConfig:
-    """Initialize a Hydra config given only the path to the relevant config file.
-
-    For config resolution, it is assumed that the config file's parent is the Hydra config dir.
-    """
-    # TODO(alexander-soare): Resolve configs without Hydra initialization.
-    hydra.core.global_hydra.GlobalHydra.instance().clear()
-    # Hydra needs a path relative to this file.
-    hydra.initialize(
-        str(_relative_path_between(Path(config_path).absolute().parent, Path(__file__).absolute().parent))
-    )
-    cfg = hydra.compose(Path(config_path).stem, overrides)
-    return cfg
--- a/lerobot/scripts/eval.py
+++ b/lerobot/scripts/eval.py
@@ -30,13 +30,14 @@ python lerobot/scripts/eval.py --hub-id HUB/ID --revision v1.0 eval_episodes=10
 import argparse
 import json
 import logging
+import os.path as osp
 import threading
 import time
-from typing import Tuple, Union
 from datetime import datetime as dt
 from pathlib import Path

 import einops
+import hydra
 import imageio
 import numpy as np
 import torch
@@ -51,7 +52,7 @@ from lerobot.common.envs.factory import make_env
 from lerobot.common.logger import log_output_dir
 from lerobot.common.policies.abstract import AbstractPolicy
 from lerobot.common.policies.factory import make_policy
-from lerobot.common.utils import get_safe_torch_device, init_hydra_config, init_logging, set_global_seed
+from lerobot.common.utils import get_safe_torch_device, init_logging, set_global_seed


 def write_video(video_path, stacked_frames, fps):
@@ -67,19 +68,7 @@ def eval_policy(
    video_dir: Path = None,
    fps: int = 15,
    return_first_video: bool = False,
-) -> Union[dict, Tuple[dict, torch.Tensor]]:
-    """ Evaluate a policy on an environment by running rollouts and computing metrics.
-
-    Args:
-        env: The environment to evaluate.
-        policy: The policy to evaluate.
-        num_episodes: The number of episodes to evaluate.
-        max_steps: The maximum number of steps per episode.
-        save_video: Whether to save videos of the evaluation episodes.
-        video_dir: The directory to save the videos.
-        fps: The frames per second for the videos.
-        return_first_video: Whether to return the first video as a tensor.
-    """
+):
    if policy is not None:
        policy.eval()
    start = time.time()
@@ -158,7 +147,7 @@ def eval_policy(
    for thread in threads:
        thread.join()

-    info = {  # TODO: change to dataclass
+    info = {
        "per_episode": [
            {
                "episode_ix": i,
@@ -191,13 +180,6 @@ def eval_policy(


 def eval(cfg: dict, out_dir=None, stats_path=None):
-    """ Evaluate a policy.
-
-    Args:
-        cfg: The configuration (DictConfig).
-        out_dir: The directory to save the evaluation results (JSON file and videos)
-        stats_path: The path to the stats file.
-    """
    if out_dir is None:
        raise NotImplementedError()

@@ -213,7 +195,6 @@ def eval(cfg: dict, out_dir=None, stats_path=None):
    log_output_dir(out_dir)

    logging.info("Making transforms.")
-    # TODO(alexander-soare): Completely decouple datasets from evaluation.
    offline_buffer = make_offline_buffer(cfg, stats_path=stats_path)

    logging.info("Making environment.")
@@ -248,6 +229,19 @@ def eval(cfg: dict, out_dir=None, stats_path=None):
    logging.info("End of eval")


+def _relative_path_between(path1: Path, path2: Path) -> Path:
+    """Returns path1 relative to path2."""
+    path1 = path1.absolute()
+    path2 = path2.absolute()
+    try:
+        return path1.relative_to(path2)
+    except ValueError:  # most likely because path1 is not a subpath of path2
+        common_parts = Path(osp.commonpath([path1, path2])).parts
+        return Path(
+            "/".join([".."] * (len(path2.parts) - len(common_parts)) + list(path1.parts[len(common_parts) :]))
+        )
+
+
 if __name__ == "__main__":
    parser = argparse.ArgumentParser(
        description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter
@@ -265,15 +259,19 @@ if __name__ == "__main__":

    if args.config is not None:
        # Note: For the config_path, Hydra wants a path relative to this script file.
-        cfg = init_hydra_config(args.config, args.overrides)
+        hydra.initialize(
+            config_path=str(
+                _relative_path_between(Path(args.config).absolute().parent, Path(__file__).parent)
+            )
+        )
+        cfg = hydra.compose(Path(args.config).stem, args.overrides)
        # TODO(alexander-soare): Save and load stats in trained model directory.
        stats_path = None
    elif args.hub_id is not None:
        folder = Path(snapshot_download(args.hub_id, revision=args.revision))
-        cfg = init_hydra_config(
-            folder / "config.yaml", [*args.overrides]
-            # folder / "config.yaml"  # , [f"policy.pretrained_model_path={folder / 'model.pt'}", *args.overrides]
-        )
+        cfg = hydra.initialize(config_path=str(_relative_path_between(folder, Path(__file__).parent)))
+        cfg = hydra.compose("config", args.overrides)
+        cfg.policy.pretrained_model_path = folder / "model.pt"
        stats_path = folder / "stats.pth"

    eval(
--- a/poetry.lock
+++ b/poetry.lock
@@ -521,7 +521,7 @@ files = [
 name = "dm-control"
 version = "1.0.14"
 description = "Continuous control environments and MuJoCo Python bindings."
-optional = false
+optional = true
 python-versions = ">=3.8"
 files = [
    {file = "dm_control-1.0.14-py3-none-any.whl", hash = "sha256:883c63244a7ebf598700a97564ed19fffd3479ca79efd090aed881609cdb9fc6"},
@@ -552,7 +552,7 @@ hdf5 = ["h5py"]
 name = "dm-env"
 version = "1.6"
 description = "A Python interface for Reinforcement Learning environments."
-optional = false
+optional = true
 python-versions = ">=3.7"
 files = [
    {file = "dm-env-1.6.tar.gz", hash = "sha256:a436eb1c654c39e0c986a516cee218bea7140b510fceff63f97eb4fcff3d93de"},
@@ -568,7 +568,7 @@ numpy = "*"
 name = "dm-tree"
 version = "0.1.8"
 description = "Tree is a library for working with nested data structures."
-optional = false
+optional = true
 python-versions = "*"
 files = [
    {file = "dm-tree-0.1.8.tar.gz", hash = "sha256:0fcaabbb14e7980377439e7140bd05552739ca5e515ecb3119f234acee4b9430"},
@@ -672,7 +672,7 @@ test = ["pytest (>=6)"]
 name = "farama-notifications"
 version = "0.0.4"
 description = "Notifications for all Farama Foundation maintained libraries."
-optional = false
+optional = true
 python-versions = "*"
 files = [
    {file = "Farama-Notifications-0.0.4.tar.gz", hash = "sha256:13fceff2d14314cf80703c8266462ebf3733c7d165336eee998fc58e545efd18"},
@@ -796,7 +796,7 @@ test = ["black", "coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mock", "mypy", "pre
 name = "glfw"
 version = "2.7.0"
 description = "A ctypes-based wrapper for GLFW3."
-optional = false
+optional = true
 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"},
@@ -883,7 +883,7 @@ protobuf = ["grpcio-tools (>=1.62.1)"]
 name = "gymnasium"
 version = "0.29.1"
 description = "A standard API for reinforcement learning and a diverse set of reference environments (formerly Gym)."
-optional = false
+optional = true
 python-versions = ">=3.8"
 files = [
    {file = "gymnasium-0.29.1-py3-none-any.whl", hash = "sha256:61c3384b5575985bb7f85e43213bcb40f36fcdff388cae6bc229304c71f2843e"},
@@ -913,7 +913,7 @@ toy-text = ["pygame (>=2.1.3)", "pygame (>=2.1.3)"]
 name = "gymnasium-robotics"
 version = "1.2.4"
 description = "Robotics environments for the Gymnasium repo."
-optional = false
+optional = true
 python-versions = ">=3.8"
 files = [
    {file = "gymnasium-robotics-1.2.4.tar.gz", hash = "sha256:d304192b066f8b800599dfbe3d9d90bba9b761ee884472bdc4d05968a8bc61cb"},
@@ -1218,7 +1218,7 @@ i18n = ["Babel (>=2.7)"]
 name = "labmaze"
 version = "1.0.6"
 description = "LabMaze: DeepMind Lab's text maze generator."
-optional = false
+optional = true
 python-versions = "*"
 files = [
    {file = "labmaze-1.0.6-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:b2ddef976dfd8d992b19cfa6c633f2eba7576d759c2082da534e3f727479a84a"},
@@ -1262,7 +1262,7 @@ setuptools = "!=50.0.0"
 name = "lazy-loader"
 version = "0.3"
 description = "lazy_loader"
-optional = false
+optional = true
 python-versions = ">=3.7"
 files = [
    {file = "lazy_loader-0.3-py3-none-any.whl", hash = "sha256:1e9e76ee8631e264c62ce10006718e80b2cfc74340d17d1031e0f84af7478554"},
@@ -1307,7 +1307,7 @@ files = [
 name = "lxml"
 version = "5.1.0"
 description = "Powerful and Pythonic XML processing library combining libxml2/libxslt with the ElementTree API."
-optional = false
+optional = true
 python-versions = ">=3.6"
 files = [
    {file = "lxml-5.1.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:704f5572ff473a5f897745abebc6df40f22d4133c1e0a1f124e4f2bd3330ff7e"},
@@ -1525,7 +1525,7 @@ tests = ["pytest (>=4.6)"]
 name = "mujoco"
 version = "2.3.7"
 description = "MuJoCo Physics Simulator"
-optional = false
+optional = true
 python-versions = ">=3.8"
 files = [
    {file = "mujoco-2.3.7-cp310-cp310-macosx_10_16_x86_64.whl", hash = "sha256:e8714a5ff6a1561b364b7b4648d4c0c8d13e751874cf7401c309b9d23fa9598b"},
@@ -1980,7 +1980,7 @@ xml = ["lxml (>=4.9.2)"]
 name = "pettingzoo"
 version = "1.24.3"
 description = "Gymnasium for multi-agent reinforcement learning."
-optional = false
+optional = true
 python-versions = ">=3.8"
 files = [
    {file = "pettingzoo-1.24.3-py3-none-any.whl", hash = "sha256:23ed90517d2e8a7098bdaf5e31234b3a7f7b73ca578d70d1ca7b9d0cb0e37982"},
@@ -2348,7 +2348,7 @@ dev = ["aafigure", "matplotlib", "pygame", "pyglet (<2.0.0)", "sphinx", "wheel"]
 name = "pyopengl"
 version = "3.1.7"
 description = "Standard OpenGL bindings for Python"
-optional = false
+optional = true
 python-versions = "*"
 files = [
    {file = "PyOpenGL-3.1.7-py3-none-any.whl", hash = "sha256:a6ab19cf290df6101aaf7470843a9c46207789855746399d0af92521a0a92b7a"},
@@ -2359,7 +2359,7 @@ files = [
 name = "pyparsing"
 version = "3.1.2"
 description = "pyparsing module - Classes and methods to define and execute parsing grammars"
-optional = false
+optional = true
 python-versions = ">=3.6.8"
 files = [
    {file = "pyparsing-3.1.2-py3-none-any.whl", hash = "sha256:f9db75911801ed778fe61bb643079ff86601aca99fcae6345aa67292038fb742"},
@@ -2790,7 +2790,7 @@ torch = ["safetensors[numpy]", "torch (>=1.10)"]
 name = "scikit-image"
 version = "0.22.0"
 description = "Image processing in Python"
-optional = false
+optional = true
 python-versions = ">=3.9"
 files = [
    {file = "scikit_image-0.22.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:74ec5c1d4693506842cc7c9487c89d8fc32aed064e9363def7af08b8f8cbb31d"},
@@ -2838,7 +2838,7 @@ test = ["asv", "matplotlib (>=3.5)", "numpydoc (>=1.5)", "pooch (>=1.6.0)", "pyt
 name = "scipy"
 version = "1.12.0"
 description = "Fundamental algorithms for scientific computing in Python"
-optional = false
+optional = true
 python-versions = ">=3.9"
 files = [
    {file = "scipy-1.12.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:78e4402e140879387187f7f25d91cc592b3501a2e51dfb320f48dfb73565f10b"},
@@ -3043,7 +3043,7 @@ testing-integration = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "jar
 name = "shapely"
 version = "2.0.3"
 description = "Manipulation and analysis of geometric objects"
-optional = false
+optional = true
 python-versions = ">=3.7"
 files = [
    {file = "shapely-2.0.3-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:af7e9abe180b189431b0f490638281b43b84a33a960620e6b2e8d3e3458b61a1"},
@@ -3096,6 +3096,55 @@ numpy = ">=1.14,<2"
 docs = ["matplotlib", "numpydoc (==1.1.*)", "sphinx", "sphinx-book-theme", "sphinx-remove-toctrees"]
 test = ["pytest", "pytest-cov"]

+[[package]]
+name = "sim-aloha"
+version = "0.1.2"
+description = "ALOHA environment for LeRobot"
+optional = true
+python-versions = "<4.0,>=3.10"
+files = [
+    {file = "sim_aloha-0.1.2-py3-none-any.whl", hash = "sha256:3b13c1ee474481f5d686b57bf1a9ed350e01ca4da7d65f7501446eb74b02653a"},
+    {file = "sim_aloha-0.1.2.tar.gz", hash = "sha256:44d36bbdb13e98e0c74f4d2a682f38683f4f63951618da28175f89cbb1c6f324"},
+]
+
+[package.dependencies]
+dm-control = "1.0.14"
+
+[[package]]
+name = "sim-pusht"
+version = "0.1.0"
+description = "PushT environment for LeRobot"
+optional = true
+python-versions = "<4.0,>=3.10"
+files = [
+    {file = "sim_pusht-0.1.0-py3-none-any.whl", hash = "sha256:1348dcab5ea8460eff2dc97b7d62dd40f2a382df92bfdc69ff5c0224900690b0"},
+    {file = "sim_pusht-0.1.0.tar.gz", hash = "sha256:d8f6a2207fd781348156206728329aa6338e9785cfc07679c5c48889b34d9b14"},
+]
+
+[package.dependencies]
+gymnasium = ">=0.29.1,<0.30.0"
+opencv-python = ">=4.9.0.80,<5.0.0.0"
+pygame = ">=2.5.2,<3.0.0"
+pymunk = ">=6.6.0,<7.0.0"
+scikit-image = ">=0.22.0,<0.23.0"
+shapely = ">=2.0.3,<3.0.0"
+
+[[package]]
+name = "sim-xarm"
+version = "0.1.0"
+description = "xArm environment for LeRobot"
+optional = true
+python-versions = "<4.0,>=3.10"
+files = [
+    {file = "sim_xarm-0.1.0-py3-none-any.whl", hash = "sha256:2771ca0e8d775dc7d9ccb3360e7fcf42507c5d4791525692e409f53ff5c83eaa"},
+    {file = "sim_xarm-0.1.0.tar.gz", hash = "sha256:90342394369ab37636a8c41a995ba20f1dac79c50563b1b1e4b38eeffbc5588d"},
+]
+
+[package.dependencies]
+gymnasium = ">=0.29.1,<0.30.0"
+gymnasium-robotics = ">=1.2.4,<2.0.0"
+mujoco = ">=2.3.7,<3.0.0"
+
 [[package]]
 name = "six"
 version = "1.16.0"
@@ -3235,7 +3284,7 @@ tests = ["pytest", "pytest-cov"]
 name = "tifffile"
 version = "2024.2.12"
 description = "Read and write TIFF files"
-optional = false
+optional = true
 python-versions = ">=3.9"
 files = [
    {file = "tifffile-2024.2.12-py3-none-any.whl", hash = "sha256:870998f82fbc94ff7c3528884c1b0ae54863504ff51dbebea431ac3fa8fb7c21"},
@@ -3586,7 +3635,12 @@ files = [
 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)"]

+[extras]
+aloha = ["sim-aloha"]
+pusht = ["sim-pusht"]
+xarm = ["sim-xarm"]
+
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.10"
-content-hash = "174c7d42f8039eedd2c447a4e6cae5169782cbd94346b5606572a0010194ca05"
+content-hash = "1a27121cca3d38e1bafba74157e3bb68cc57a420bb7a6d6e8edbec0fb90368d6"
--- a/pyproject.toml
+++ b/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"}]
@@ -23,7 +28,6 @@ packages = [{include = "lerobot"}]
 python = "^3.10"
 termcolor = "^2.4.0"
 omegaconf = "^2.3.0"
-dm-env = "^1.6"
 pandas = "^2.2.1"
 wandb = "^0.16.3"
 moviepy = "^1.0.3"
@@ -34,29 +38,35 @@ 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 = "^2.2.1"
 tensordict = {git = "https://github.com/pytorch/tensordict"}
 torchrl = {git = "https://github.com/pytorch/rl", rev = "13bef426dcfa5887c6e5034a6e9697993fa92c37"}
-mujoco = "^2.3.7"
 opencv-python = "^4.9.0.80"
 diffusers = "^0.26.3"
 torchvision = "^0.17.1"
 h5py = "^3.10.0"
-dm-control = "1.0.14"
 huggingface-hub = {extras = ["hf-transfer"], version = "^0.21.4"}
 robomimic = "0.2.0"
-gymnasium-robotics = "^1.2.4"
-gymnasium = "^0.29.1"
 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"

@@ -93,6 +103,9 @@ exclude = [
 select = ["E4", "E7", "E9", "F", "I", "N", "B", "C4", "SIM"]
 ignore-init-module-imports = true

+[tool.ruff.lint.isort]
+known-first-party = ["pusht", "xarm", "aloha"]
+
 [tool.poetry-dynamic-versioning]
 enable = true

--- a/tests/test_available.py
+++ b/tests/test_available.py
@@ -15,9 +15,9 @@ Note:
 import pytest
 import lerobot

-from lerobot.common.envs.aloha.env import AlohaEnv
-from lerobot.common.envs.pusht.env import PushtEnv
-from lerobot.common.envs.simxarm.env import SimxarmEnv
+from lerobot.common.envs.aloha import AlohaEnv
+from lerobot.common.envs.pusht import PushtEnv
+from lerobot.common.envs.xarm import SimxarmEnv

 from lerobot.common.datasets.simxarm import SimxarmDataset
 from lerobot.common.datasets.aloha import AlohaDataset
--- a/tests/test_datasets.py
+++ b/tests/test_datasets.py
@@ -2,9 +2,8 @@ import pytest
 import torch

 from lerobot.common.datasets.factory import make_offline_buffer
-from lerobot.common.utils import init_hydra_config

-from .utils import DEVICE, DEFAULT_CONFIG_PATH
+from .utils import DEVICE, init_config


@pytest.mark.parametrize(
@@ -19,10 +18,7 @@ from .utils import DEVICE, DEFAULT_CONFIG_PATH
    ],
 )
 def test_factory(env_name, dataset_id):
-    cfg = init_hydra_config(
-        DEFAULT_CONFIG_PATH,
-        overrides=[f"env={env_name}", f"env.task={dataset_id}", f"device={DEVICE}"]
-    )
+    cfg = init_config(overrides=[f"env={env_name}", f"env.task={dataset_id}", f"device={DEVICE}"])
    offline_buffer = make_offline_buffer(cfg)
    for key in offline_buffer.image_keys:
        img = offline_buffer[0].get(key)
--- a/tests/test_envs.py
+++ b/tests/test_envs.py
@@ -4,13 +4,12 @@ import torch
 from torchrl.envs.utils import check_env_specs, step_mdp
 from lerobot.common.datasets.factory import make_offline_buffer

-from lerobot.common.envs.aloha.env import AlohaEnv
 from lerobot.common.envs.factory import make_env
-from lerobot.common.envs.pusht.env import PushtEnv
-from lerobot.common.envs.simxarm.env import SimxarmEnv
-from lerobot.common.utils import init_hydra_config
+from lerobot.common.envs.pusht import PushtEnv
+from lerobot.common.envs.xarm import SimxarmEnv
+from lerobot.common.envs.aloha import AlohaEnv

-from .utils import DEVICE, DEFAULT_CONFIG_PATH
+from .utils import DEVICE, init_config


 def print_spec_rollout(env):
@@ -111,10 +110,7 @@ def test_pusht(from_pixels, pixels_only):
    ],
 )
 def test_factory(env_name):
-    cfg = init_hydra_config(
-        DEFAULT_CONFIG_PATH,
-        overrides=[f"env={env_name}", f"device={DEVICE}"],
-    )
+    cfg = init_config(overrides=[f"env={env_name}", f"device={DEVICE}"])

    offline_buffer = make_offline_buffer(cfg)

--- a/tests/test_examples.py
+++ b/tests/test_examples.py
@@ -1,70 +1,19 @@
+import pytest
 from pathlib import Path

+@pytest.mark.parametrize(
+    "path",
+    [
+        "examples/1_visualize_dataset.py",
+        "examples/2_evaluate_pretrained_policy.py",
+        "examples/3_train_policy.py",
+    ],
+)
+def test_example(path):

-def _find_and_replace(text: str, finds: list[str], replaces: list[str]) -> str:
-    for f, r in zip(finds, replaces):
-        assert f in text
-        text = text.replace(f, r)
-    return text
-
-
-def test_example_1():
-    path = "examples/1_visualize_dataset.py"
-
-    with open(path, "r") as file:
+    with open(path, 'r') as file:
        file_contents = file.read()
    exec(file_contents)

-    assert Path("outputs/visualize_dataset/example/episode_0.mp4").exists()
-
-
-def test_examples_3_and_2():
-    """
-    Train a model with example 3, check the outputs.
-    Evaluate the trained model with example 2, check the outputs.
-    """
-
-    path = "examples/3_train_policy.py"
-
-    with open(path, "r") as file:
-        file_contents = file.read()
-
-    # Do less steps and use CPU.
-    file_contents = _find_and_replace(
-        file_contents,
-        ['"offline_steps=5000"', '"device=cuda"'],
-        ['"offline_steps=1"', '"device=cpu"'],
-    )
-
-    exec(file_contents)
-
-    for file_name in ["model.pt", "stats.pth", "config.yaml"]:
-        assert Path(f"outputs/train/example_pusht_diffusion/{file_name}").exists()
-
-    path = "examples/2_evaluate_pretrained_policy.py"
-
-    with open(path, "r") as file:
-        file_contents = file.read()
-
-    # Do less evals, use CPU, and use the local model.
-    file_contents = _find_and_replace(
-        file_contents,
-        [
-            '"eval_episodes=10"',
-            '"rollout_batch_size=10"',
-            '"device=cuda"',
-            '# folder = Path("outputs/train/example_pusht_diffusion")',
-            'hub_id = "lerobot/diffusion_policy_pusht_image"',
-            "folder = Path(snapshot_download(hub_id)",
-        ],
-        [
-            '"eval_episodes=1"',
-            '"rollout_batch_size=1"',
-            '"device=cpu"',
-            'folder = Path("outputs/train/example_pusht_diffusion")',
-            "",
-            "",
-        ],
-    )
-
-    assert Path(f"outputs/train/example_pusht_diffusion").exists()
+    if path == "examples/1_visualize_dataset.py":
+        assert Path("outputs/visualize_dataset/example/episode_0.mp4").exists()
--- a/tests/test_policies.py
+++ b/tests/test_policies.py
@@ -1,3 +1,4 @@
+from omegaconf import open_dict
 import pytest
 from tensordict import TensorDict
 from tensordict.nn import TensorDictModule
@@ -9,8 +10,8 @@ from lerobot.common.policies.factory import make_policy
 from lerobot.common.envs.factory import make_env
 from lerobot.common.datasets.factory import make_offline_buffer
 from lerobot.common.policies.abstract import AbstractPolicy
-from lerobot.common.utils import init_hydra_config
-from .utils import DEVICE, DEFAULT_CONFIG_PATH
+
+from .utils import DEVICE, init_config

@pytest.mark.parametrize(
    "env_name,policy_name,extra_overrides",
@@ -33,8 +34,7 @@ def test_concrete_policy(env_name, policy_name, extra_overrides):
        - Updating the policy.
        - Using the policy to select actions at inference time.
    """
-    cfg = init_hydra_config(
-        DEFAULT_CONFIG_PATH,
+    cfg = init_config(
        overrides=[
            f"env={env_name}",
            f"policy={policy_name}",
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Simon Alibert	624efd50c6	Remove _has_gym checks	2024-03-29 18:37:37 +01:00
Simon Alibert	7df64ba9b1	Remove envs dependencies	2024-03-29 17:27:42 +01:00
Simon Alibert	8505952e5a	Update CI	2024-03-29 16:53:25 +01:00
Simon Alibert	f17b600198	Add aloha as package	2024-03-29 16:53:00 +01:00
Simon Alibert	8fc1008809	Fix imports	2024-03-29 16:47:18 +01:00
Simon Alibert	3d53e0fe0f	Move make_env_task logic to aloha	2024-03-29 15:22:32 +01:00
Simon Alibert	b7b6c9bbf1	Package aloha	2024-03-29 14:45:21 +01:00
Simon Alibert	c41abc9a72	Update CI	2024-03-29 14:05:44 +01:00
Simon Alibert	8260a7010a	Add xarm as package	2024-03-29 14:04:44 +01:00
Simon Alibert	3713a3a87b	Package xarm	2024-03-29 13:59:09 +01:00
Simon Alibert	dfa6cc777f	CI fix	2024-03-29 12:30:07 +01:00
Simon Alibert	c6002bb88f	Add pusht as package	2024-03-29 12:03:49 +01:00
Simon Alibert	1d7ff65dc5	Package pusht	2024-03-29 11:29:05 +01:00