use Path type instead of str

2024-03-15 13:15:34 +00:00
231 changed files with 1283 additions and 6315 deletions
--- a/.gitattributes
+++ b/.gitattributes
@@ -1,2 +1 @@
 *.memmap filter=lfs diff=lfs merge=lfs -text
-*.stl filter=lfs diff=lfs merge=lfs -text
--- a/.github/poetry/cpu/poetry.lock
+++ b/.github/poetry/cpu/poetry.lock
--- a/.github/poetry/cpu/pyproject.toml
+++ b/.github/poetry/cpu/pyproject.toml
@@ -1,24 +1,19 @@
 [tool.poetry]
 name = "lerobot"
 version = "0.1.0"
-description = "🤗 LeRobot: State-of-the-art Machine Learning for Real-World Robotics in Pytorch"
+description = "Le robot is learning"
 authors = [
    "Rémi Cadène <re.cadene@gmail.com>",
-]
-maintainers = [
-    "Alexander Soare <alexander.soare159@gmail.com>",
-    "Quentin Gallouédec <quentin.gallouedec@ec-lyon.fr>",
    "Simon Alibert <alibert.sim@gmail.com>",
 ]
 repository = "https://github.com/Cadene/lerobot"
 readme = "README.md"
-license = "Apache-2.0"
-keywords = ["robotics, deep, reinforcement, learning, pytorch"]
+license = "MIT"
 classifiers=[
    "Development Status :: 3 - Alpha",
    "Intended Audience :: Developers",
    "Topic :: Software Development :: Build Tools",
-    "License :: OSI Approved :: Apache Software License",
+    "License :: OSI Approved :: MIT License",
    "Programming Language :: Python :: 3.10",
 ]
 packages = [{include = "lerobot"}]
@@ -26,8 +21,10 @@ 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"
@@ -38,37 +35,29 @@ 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"
-robomimic = "0.2.0"
+dm = "^1.3"
+dm-control = "^1.0.16"
 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]]
--- a/.github/workflows/test.yml
+++ b/.github/workflows/test.yml
@@ -1,4 +1,4 @@
-name: Tests
+name: Test

 on:
  pull_request:
@@ -10,15 +10,20 @@ on:
      - main

 jobs:
-  tests:
+  test:
    if: |
      ${{ github.event_name == 'pull_request' && contains(github.event.pull_request.labels.*.name, 'CI') }} ||
      ${{ github.event_name == 'push' }}
    runs-on: ubuntu-latest
    env:
-      POETRY_VERSION: 1.8.2
+      POETRY_VERSION: 1.8.1
      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
@@ -81,13 +86,9 @@ 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 --without dev --all-extras
+          poetry install --no-interaction --no-root

      - name: Save cached venv
        if: |
@@ -106,129 +107,38 @@ jobs:
      #             install project
      #----------------------------------------------
      - name: Install project
-        run: poetry install --no-interaction --without dev --all-extras
+        run: poetry install --no-interaction

      #----------------------------------------------
-      #            run tests & coverage
+      #               run tests
      #----------------------------------------------
      - name: Run tests
-        env:
-          LEROBOT_TESTS_DEVICE: cpu
        run: |
          source .venv/bin/activate
-          pytest --cov=./lerobot --cov-report=xml tests
+          pytest tests

-      #   TODO(aliberts): Link with HF Codecov account
-      # - name: Upload coverage reports to Codecov with GitHub Action
-      #   uses: codecov/codecov-action@v4
-      #   with:
-      #     files: ./coverage.xml
-      #     verbose: true
-
-      #----------------------------------------------
-      #            run end-to-end tests
-      #----------------------------------------------
-      - name: Test train ACT on ALOHA end-to-end
+      - name: Test train pusht end-to-end
        run: |
          source .venv/bin/activate
          python lerobot/scripts/train.py \
-            policy=act \
-            env=aloha \
-            wandb.enable=False \
-            offline_steps=2 \
-            online_steps=0 \
-            device=cpu \
-            save_model=true \
-            save_freq=2 \
-            horizon=20 \
-            policy.batch_size=2 \
-            hydra.run.dir=tests/outputs/act/
-
-      - name: Test eval ACT on ALOHA end-to-end
-        run: |
-          source .venv/bin/activate
-          python lerobot/scripts/eval.py \
-            --config tests/outputs/act/.hydra/config.yaml \
-            eval_episodes=1 \
-            env.episode_length=8 \
-            device=cpu \
-            policy.pretrained_model_path=tests/outputs/act/models/2.pt
-
-      # TODO(aliberts): This takes ~2mn to run, needs to be improved
-      # - name: Test eval ACT on ALOHA end-to-end (policy is None)
-      #   run: |
-      #     source .venv/bin/activate
-      #     python lerobot/scripts/eval.py \
-      #       --config lerobot/configs/default.yaml \
-      #       policy=act \
-      #       env=aloha \
-      #       eval_episodes=1 \
-      #       device=cpu
-
-      - name: Test train Diffusion on PushT end-to-end
-        run: |
-          source .venv/bin/activate
-          python lerobot/scripts/train.py \
-            policy=diffusion \
+            hydra.job.name=pusht \
            env=pusht \
            wandb.enable=False \
            offline_steps=2 \
            online_steps=0 \
            device=cpu \
            save_model=true \
-            save_freq=2 \
-            hydra.run.dir=tests/outputs/diffusion/
+            save_freq=1 \
+            hydra.run.dir=tests/outputs/

-      - name: Test eval Diffusion on PushT end-to-end
+      - name: Test eval pusht end-to-end
        run: |
          source .venv/bin/activate
          python lerobot/scripts/eval.py \
-            --config tests/outputs/diffusion/.hydra/config.yaml \
-            eval_episodes=1 \
-            env.episode_length=8 \
-            device=cpu \
-            policy.pretrained_model_path=tests/outputs/diffusion/models/2.pt
-
-      - name: Test eval Diffusion on PushT end-to-end (policy is None)
-        run: |
-          source .venv/bin/activate
-          python lerobot/scripts/eval.py \
-            --config lerobot/configs/default.yaml \
-            policy=diffusion  \
+            hydra.job.name=pusht \
            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
+            policy.pretrained_model_path=tests/outputs/models/1.pt
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -14,11 +14,11 @@ repos:
      - id: end-of-file-fixer
      - id: trailing-whitespace
  - repo: https://github.com/asottile/pyupgrade
-    rev: v3.15.2
+    rev: v3.15.1
    hooks:
    -   id: pyupgrade
  - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.3.4
+    rev: v0.2.2
    hooks:
      - id: ruff
        args: [--fix]
--- a/229
+++ b/229
@@ -253,31 +253,6 @@ 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
@@ -301,207 +276,3 @@ 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/
-
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--- a/README.md
+++ b/README.md
@@ -1,374 +1,72 @@
-<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)
+# LeRobot

 ## Installation

-Download our source code:
-```bash
-git clone https://github.com/huggingface/lerobot.git
-cd lerobot
+Create a virtual environment with Python 3.10, e.g. using `conda`:
 ```
-
-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
 ```

-Then, install 🤗 LeRobot:
-```bash
-python -m pip install .
+[Install `poetry`](https://python-poetry.org/docs/#installation) (if you don't have it already)
+```
+curl -sSL https://install.python-poetry.org | python -
 ```

-To use [Weights and Biases](https://docs.wandb.ai/quickstart) for experiments tracking, log in with
-```bash
-wandb login
+Install dependencies
 ```
-
-## Walkthrough
-
-```
-.
-├── 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 \
-env=aloha \
-task=sim_sim_transfer_cube_human \
-hydra.run.dir=outputs/visualize_dataset/example
-# >>> ['outputs/visualize_dataset/example/episode_0.mp4']
-```
-
-### Evaluate a pretrained policy
-
-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 \
--hub-id lerobot/diffusion_policy_pusht_image \
-eval_episodes=10 \
-hydra.run.dir=outputs/eval/example_hub
-```
-
-After launching training of your own policy, you can also re-evaluate the checkpoints with:
-```bash
-python lerobot/scripts/eval.py \
--config PATH/TO/FOLDER/config.yaml \
-policy.pretrained_model_path=PATH/TO/FOLDER/weights.pth \
-eval_episodes=10 \
-hydra.run.dir=outputs/eval/example_dir
-```
-
-See `python lerobot/scripts/eval.py --help` for more instructions.
-
-### Train your own policy
-
-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
+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'
 ```

-**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
+To use [Weights and Biases](https://docs.wandb.ai/quickstart) for experiments tracking, log in with
+```
+wandb login
 ```

-### 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)
+## Usage


-Finally, you might want to mock the dataset if you need to update the unit tests as well:
-```bash
-python tests/scripts/mock_dataset.py --in-data-dir data/$DATASET --out-data-dir tests/data/$DATASET
-```
-
-### Add a pretrained policy
-
-Once you have trained a policy you may upload it to the 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):
+### Train

 ```
-to_upload
-    ├── config.yaml
-    ├── model.pt
-    └── stats.pth
+python lerobot/scripts/train.py \
+hydra.job.name=pusht \
+env=pusht
 ```

-With the folder prepared, run the following with a desired revision ID.
+### Visualize offline buffer

-```bash
-huggingface-cli upload $HUB_ID to_upload --revision $REVISION_ID
+```
+python lerobot/scripts/visualize_dataset.py \
+hydra.run.dir=tmp/$(date +"%Y_%m_%d") \
+env=pusht
 ```

-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):
+### Visualize online buffer / Eval

-```bash
-huggingface-cli upload $HUB_ID to_upload
+```
+python lerobot/scripts/eval.py \
+hydra.run.dir=tmp/$(date +"%Y_%m_%d") \
+env=pusht
 ```

-See `eval.py` for an example of how a user may use your policy.
+
+## 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/users/Cadene/projects/1)
+
+Ask [Remi Cadene](re.cadene@gmail.com) for access if needed.


-### Improve your code with profiling
+## Profile

-An example of a code snippet to profile the evaluation of a policy:
+**Example**
 ```python
 from torch.profiler import profile, record_function, ProfilerActivity

@@ -387,12 +85,87 @@ 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 \
--config outputs/pusht/.hydra/config.yaml \
-pretrained_model_path=outputs/pusht/model.pt \
+pretrained_model_path=/home/rcadene/code/fowm/logs/xarm_lift/all/default/2/models/final.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/)
--- a/envs/sim_aloha/README.md
+++ b/envs/sim_aloha/README.md
@@ -1 +0,0 @@
-# ALOHA environment for LeRobot
--- a/envs/sim_aloha/aloha/init.py
+++ b/envs/sim_aloha/aloha/init.py
--- a/envs/sim_aloha/aloha/assets/tabletop.stl
+++ b/envs/sim_aloha/aloha/assets/tabletop.stl
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:76a1571d1aa36520f2bd81c268991b99816c2a7819464d718e0fd9976fe30dce
-size 684
--- a/envs/sim_aloha/aloha/assets/vx300s_10_custom_finger_left.stl
+++ b/envs/sim_aloha/aloha/assets/vx300s_10_custom_finger_left.stl
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:df73ae5b9058e5d50a6409ac2ab687dade75053a86591bb5e23ab051dbf2d659
-size 83384
--- a/envs/sim_aloha/aloha/assets/vx300s_10_custom_finger_right.stl
+++ b/envs/sim_aloha/aloha/assets/vx300s_10_custom_finger_right.stl
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:56fb3cc1236d4193106038adf8e457c7252ae9e86c7cee6dabf0578c53666358
-size 83384
--- a/envs/sim_aloha/aloha/assets/vx300s_10_gripper_finger.stl
+++ b/envs/sim_aloha/aloha/assets/vx300s_10_gripper_finger.stl
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:a4baacd9a64df1be60ea5e98f50f3c660e1b7a1fe9684aace6004c5058c09483
-size 42884
--- a/envs/sim_aloha/aloha/assets/vx300s_11_ar_tag.stl
+++ b/envs/sim_aloha/aloha/assets/vx300s_11_ar_tag.stl
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:a18a1601074d29ed1d546ead70cd18fbb063f1db7b5b96b9f0365be714f3136a
-size 3884
--- a/envs/sim_aloha/aloha/assets/vx300s_1_base.stl
+++ b/envs/sim_aloha/aloha/assets/vx300s_1_base.stl
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:d100cafe656671ca8fde98fb6a4cf2d1b746995c51c61c25ad9ea2715635d146
-size 99984
--- a/envs/sim_aloha/aloha/assets/vx300s_2_shoulder.stl
+++ b/envs/sim_aloha/aloha/assets/vx300s_2_shoulder.stl
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:139745a74055cb0b23430bb5bc032bf68cf7bea5e4975c8f4c04107ae005f7f0
-size 63884
--- a/envs/sim_aloha/aloha/assets/vx300s_3_upper_arm.stl
+++ b/envs/sim_aloha/aloha/assets/vx300s_3_upper_arm.stl
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:900f236320dd3d500870c5fde763b2d47502d51e043a5c377875e70237108729
-size 102984
--- a/envs/sim_aloha/aloha/assets/vx300s_4_upper_forearm.stl
+++ b/envs/sim_aloha/aloha/assets/vx300s_4_upper_forearm.stl
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:4104fc54bbfb8a9b533029f1e7e3ade3d54d638372b3195daa0c98f57e0295b5
-size 49584
--- a/envs/sim_aloha/aloha/assets/vx300s_5_lower_forearm.stl
+++ b/envs/sim_aloha/aloha/assets/vx300s_5_lower_forearm.stl
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:66814e27fa728056416e25e02e89eb7d34c51d51c51e7c3df873829037ddc6b8
-size 99884
--- a/envs/sim_aloha/aloha/assets/vx300s_6_wrist.stl
+++ b/envs/sim_aloha/aloha/assets/vx300s_6_wrist.stl
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:90eb145c85627968c3776ae6de23ccff7e112c9dd713c46bc9acdfdaa859a048
-size 70784
--- a/envs/sim_aloha/aloha/assets/vx300s_7_gripper.stl
+++ b/envs/sim_aloha/aloha/assets/vx300s_7_gripper.stl
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:786c1077bfd226f14219581b11d5f19464ca95b17132e0bb7532503568f5af90
-size 450084
--- a/envs/sim_aloha/aloha/assets/vx300s_8_gripper_prop.stl
+++ b/envs/sim_aloha/aloha/assets/vx300s_8_gripper_prop.stl
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:d1275a93fe2157c83dbc095617fb7e672888bdd48ec070a35ef4ab9ebd9755b0
-size 31684
--- a/envs/sim_aloha/aloha/assets/vx300s_9_gripper_bar.stl
+++ b/envs/sim_aloha/aloha/assets/vx300s_9_gripper_bar.stl
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:a4de62c9a2ed2c78433010e4c05530a1254b1774a7651967f406120c9bf8973e
-size 379484
--- a/envs/sim_aloha/aloha/env.py
+++ b/envs/sim_aloha/aloha/env.py
@@ -1,40 +0,0 @@
-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/envs/sim_aloha/poetry.lock
+++ b/envs/sim_aloha/poetry.lock
@@ -1,766 +0,0 @@
-# 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
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-    {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"
--- a/envs/sim_xarm/pyproject.toml
+++ b/envs/sim_xarm/pyproject.toml
@@ -1,34 +0,0 @@
-[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/envs/sim_xarm/xarm/init.py
+++ b/envs/sim_xarm/xarm/init.py
@@ -1,166 +0,0 @@
-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
--- a/envs/sim_xarm/xarm/tasks/init.py
+++ b/envs/sim_xarm/xarm/tasks/init.py
--- a/envs/sim_xarm/xarm/tasks/assets/lift.xml
+++ b/envs/sim_xarm/xarm/tasks/assets/lift.xml
@@ -1,53 +0,0 @@
-<?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>
--- a/envs/sim_xarm/xarm/tasks/assets/mesh/base_link.stl
+++ b/envs/sim_xarm/xarm/tasks/assets/mesh/base_link.stl
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:21fb81ae7fba19e3c6b2d2ca60c8051712ba273357287eb5a397d92d61c7a736
-size 1211434
--- a/envs/sim_xarm/xarm/tasks/assets/mesh/block_inner.stl
+++ b/envs/sim_xarm/xarm/tasks/assets/mesh/block_inner.stl
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:be68ce180d11630a667a5f37f4dffcc3feebe4217d4bb3912c813b6d9ca3ec66
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--- a/envs/sim_xarm/xarm/tasks/assets/mesh/block_inner2.stl
+++ b/envs/sim_xarm/xarm/tasks/assets/mesh/block_inner2.stl
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:2c6448552bf6b1c4f17334d686a5320ce051bcdfe31431edf69303d8a570d1de
-size 3284
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+++ b/envs/sim_xarm/xarm/tasks/assets/mesh/block_outer.stl
@@ -1,3 +0,0 @@
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+++ b/envs/sim_xarm/xarm/tasks/assets/mesh/left_finger.stl
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+++ b/envs/sim_xarm/xarm/tasks/assets/mesh/left_inner_knuckle.stl
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--- a/envs/sim_xarm/xarm/tasks/assets/mesh/right_inner_knuckle.stl
+++ b/envs/sim_xarm/xarm/tasks/assets/mesh/right_inner_knuckle.stl
@@ -1,3 +0,0 @@
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-size 366284
--- a/envs/sim_xarm/xarm/tasks/assets/mesh/right_outer_knuckle.stl
+++ b/envs/sim_xarm/xarm/tasks/assets/mesh/right_outer_knuckle.stl
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:75ca1107d0a42a0f03802a9a49cab48419b31851ee8935f8f1ca06be1c1c91e8
-size 22284
--- a/envs/sim_xarm/xarm/tasks/assets/peg_in_box.xml
+++ b/envs/sim_xarm/xarm/tasks/assets/peg_in_box.xml
@@ -1,74 +0,0 @@
-<?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>
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-	<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>
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-		<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>
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-		<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>
--- a/envs/sim_xarm/xarm/tasks/assets/push.xml
+++ b/envs/sim_xarm/xarm/tasks/assets/push.xml
@@ -1,54 +0,0 @@
-<?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>
--- a/envs/sim_xarm/xarm/tasks/assets/reach.xml
+++ b/envs/sim_xarm/xarm/tasks/assets/reach.xml
@@ -1,48 +0,0 @@
-<?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>
--- a/envs/sim_xarm/xarm/tasks/assets/shared.xml
+++ b/envs/sim_xarm/xarm/tasks/assets/shared.xml
@@ -1,51 +0,0 @@
-<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>
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-    <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>
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-        <default class="robot0:grey">
-            <geom rgba="0.356 0.361 0.376 1.0"></geom>
-        </default>
-    </default>
-
-</mujoco>
--- a/envs/sim_xarm/xarm/tasks/assets/xarm.xml
+++ b/envs/sim_xarm/xarm/tasks/assets/xarm.xml
@@ -1,88 +0,0 @@
-<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>
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-    <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"/>
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-            <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">
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-                    <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">
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-                        <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>
--- a/envs/sim_xarm/xarm/tasks/base.py
+++ b/envs/sim_xarm/xarm/tasks/base.py
@@ -1,145 +0,0 @@
-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()
--- a/envs/sim_xarm/xarm/tasks/lift.py
+++ b/envs/sim_xarm/xarm/tasks/lift.py
@@ -1,100 +0,0 @@
-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)
--- a/envs/sim_xarm/xarm/tasks/mocap.py
+++ b/envs/sim_xarm/xarm/tasks/mocap.py
@@ -1,67 +0,0 @@
-# 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]
--- a/envs/sim_xarm/xarm/tasks/peg_in_box.py
+++ b/envs/sim_xarm/xarm/tasks/peg_in_box.py
@@ -1,86 +0,0 @@
-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)
--- a/envs/sim_xarm/xarm/tasks/push.py
+++ b/envs/sim_xarm/xarm/tasks/push.py
@@ -1,78 +0,0 @@
-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)
--- a/envs/sim_xarm/xarm/tasks/reach.py
+++ b/envs/sim_xarm/xarm/tasks/reach.py
@@ -1,44 +0,0 @@
-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)
--- a/examples/1_visualize_dataset.py
+++ b/examples/1_visualize_dataset.py
@@ -1,24 +0,0 @@
-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']
--- a/examples/2_evaluate_pretrained_policy.py
+++ b/examples/2_evaluate_pretrained_policy.py
@@ -1 +0,0 @@
-# TODO
--- a/examples/3_train_policy.py
+++ b/examples/3_train_policy.py
@@ -1 +0,0 @@
-# TODO
--- a/lerobot/init.py
+++ b/lerobot/init.py
@@ -1,59 +1 @@
-"""
-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",
-]
--- a/lerobot/version.py
+++ b/lerobot/version.py
@@ -1,4 +1,4 @@
-"""To enable `lerobot.__version__`"""
+""" To enable `lerobot.__version__` """

 from importlib.metadata import PackageNotFoundError, version

--- a/lerobot/common/datasets/abstract.py
+++ b/lerobot/common/datasets/abstract.py
@@ -9,74 +9,30 @@ import tqdm
 from huggingface_hub import snapshot_download
 from tensordict import TensorDict
 from torchrl.data.replay_buffers.replay_buffers import TensorDictReplayBuffer
-from torchrl.data.replay_buffers.samplers import Sampler
+from torchrl.data.replay_buffers.samplers import SliceSampler
 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,
-        version: str | None = None,
-        batch_size: int | None = None,
+        batch_size: int = None,
        *,
        shuffle: bool = True,
        root: Path | None = None,
        pin_memory: bool = False,
        prefetch: int = None,
-        sampler: Sampler | None = None,
-        collate_fn: Callable | None = None,
-        writer: Writer | None = None,
+        sampler: SliceSampler = None,
+        collate_fn: Callable = None,
+        writer: Writer = None,
        transform: "torchrl.envs.Transform" = None,
    ):
-        assert (
-            self.available_datasets is not None
-        ), "Subclasses of `AbstractDataset` should set the `available_datasets` class attribute."
-        assert (
-            dataset_id in self.available_datasets
-        ), f"The provided dataset ({dataset_id}) is not on the list of available datasets {self.available_datasets}."
-
        self.dataset_id = dataset_id
-        self.version = version
        self.shuffle = shuffle
-        self.root = root if root is None else Path(root)
-
-        if self.root is not None and self.version is not None:
-            logging.warning(
-                f"The version of the dataset ({self.version}) is not enforced when root is provided ({self.root})."
-            )
-
+        self.root = root
        storage = self._download_or_load_dataset()

        super().__init__(
@@ -93,9 +49,9 @@ class AbstractDataset(TensorDictReplayBuffer):
    @property
    def stats_patterns(self) -> dict:
        return {
-            ("observation", "state"): "b c -> c",
-            ("observation", "image"): "b c h w -> c 1 1",
-            ("action",): "b c -> c",
+            ("observation", "state"): "b c -> 1 c",
+            ("observation", "image"): "b c h w -> 1 c 1 1",
+            ("action",): "b c -> 1 c",
        }

    @property
@@ -129,7 +85,7 @@ class AbstractDataset(TensorDictReplayBuffer):
            self._transform = transform

    def compute_or_load_stats(self, num_batch=100, batch_size=32) -> TensorDict:
-        stats_path = self.data_dir / "stats.pth"
+        stats_path = Path(self.data_dir) / "stats.pth"
        if stats_path.exists():
            stats = torch.load(stats_path)
        else:
@@ -140,14 +96,10 @@ class AbstractDataset(TensorDictReplayBuffer):

    def _download_or_load_dataset(self) -> torch.StorageBase:
        if self.root is None:
-            self.data_dir = Path(
-                snapshot_download(
-                    repo_id=f"{HF_USER}/{self.dataset_id}", repo_type="dataset", revision=self.version
-                )
-            )
+            self.data_dir = snapshot_download(repo_id=f"cadene/{self.dataset_id}", repo_type="dataset")
        else:
            self.data_dir = self.root / self.dataset_id
-        return TensorStorage(TensorDict.load_memmap(self.data_dir / "replay_buffer"))
+        return TensorStorage(TensorDict.load_memmap(self.data_dir))

    def _compute_stats(self, num_batch=100, batch_size=32):
        rb = TensorDictReplayBuffer(
--- a/lerobot/common/datasets/aloha.py
+++ b/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 Sampler
+from torchrl.data.replay_buffers.samplers import SliceSampler
 from torchrl.data.replay_buffers.storages import TensorStorage
 from torchrl.data.replay_buffers.writers import Writer

-from lerobot.common.datasets.abstract import AbstractDataset
+from lerobot.common.datasets.abstract import AbstractExperienceReplay

 DATASET_IDS = [
    "aloha_sim_insertion_human",
@@ -80,27 +80,25 @@ def download(data_dir, dataset_id):
    gdown.download(EP49_URLS[dataset_id], output=str(data_dir / "episode_49.hdf5"), fuzzy=True)


-class AlohaDataset(AbstractDataset):
-    available_datasets = DATASET_IDS
-
+class AlohaExperienceReplay(AbstractExperienceReplay):
    def __init__(
        self,
        dataset_id: str,
-        version: str | None = "v1.2",
-        batch_size: int | None = None,
+        batch_size: int = None,
        *,
        shuffle: bool = True,
        root: Path | None = None,
        pin_memory: bool = False,
        prefetch: int = None,
-        sampler: Sampler | None = None,
-        collate_fn: Callable | None = None,
-        writer: Writer | None = None,
+        sampler: SliceSampler = None,
+        collate_fn: Callable = None,
+        writer: Writer = None,
        transform: "torchrl.envs.Transform" = None,
    ):
+        assert dataset_id in DATASET_IDS
+
        super().__init__(
            dataset_id,
-            version,
            batch_size,
            shuffle=shuffle,
            root=root,
@@ -115,11 +113,11 @@ class AlohaDataset(AbstractDataset):
    @property
    def stats_patterns(self) -> dict:
        d = {
-            ("observation", "state"): "b c -> c",
-            ("action",): "b c -> c",
+            ("observation", "state"): "b c -> 1 c",
+            ("action",): "b c -> 1 c",
        }
        for cam in CAMERAS[self.dataset_id]:
-            d[("observation", "image", cam)] = "b c h w -> c 1 1"
+            d[("observation", "image", cam)] = "b c h w -> 1 c 1 1"
        return d

    @property
--- a/lerobot/common/datasets/factory.py
+++ b/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.transforms import NormalizeTransform, Prod
+from lerobot.common.envs.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`
@@ -14,13 +14,7 @@ DATA_DIR = Path(os.environ["DATA_DIR"]) if "DATA_DIR" in os.environ else None


 def make_offline_buffer(
-    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,
+    cfg, overwrite_sampler=None, normalize=True, overwrite_batch_size=None, overwrite_prefetch=None
 ):
    if cfg.policy.balanced_sampling:
        assert cfg.online_steps > 0
@@ -65,24 +59,27 @@ def make_offline_buffer(
        sampler = overwrite_sampler

    if cfg.env.name == "simxarm":
-        from lerobot.common.datasets.simxarm import SimxarmDataset
+        from lerobot.common.datasets.simxarm import SimxarmExperienceReplay

-        clsfunc = SimxarmDataset
+        clsfunc = SimxarmExperienceReplay
+        dataset_id = f"xarm_{cfg.env.task}_medium"

    elif cfg.env.name == "pusht":
-        from lerobot.common.datasets.pusht import PushtDataset
+        from lerobot.common.datasets.pusht import PushtExperienceReplay

-        clsfunc = PushtDataset
+        clsfunc = PushtExperienceReplay
+        dataset_id = "pusht"

    elif cfg.env.name == "aloha":
-        from lerobot.common.datasets.aloha import AlohaDataset
+        from lerobot.common.datasets.aloha import AlohaExperienceReplay

-        clsfunc = AlohaDataset
+        clsfunc = AlohaExperienceReplay
+        dataset_id = f"aloha_{cfg.env.task}"
    else:
        raise ValueError(cfg.env.name)

    offline_buffer = clsfunc(
-        dataset_id=cfg.dataset_id,
+        dataset_id=dataset_id,
        sampler=sampler,
        batch_size=batch_size,
        root=DATA_DIR,
@@ -98,15 +95,13 @@ def make_offline_buffer(
    else:
        img_keys = offline_buffer.image_keys

+    transforms = [Prod(in_keys=img_keys, prod=1 / 255)]
+
    if normalize:
-        transforms = [Prod(in_keys=img_keys, prod=1 / 255)]
+        # TODO(rcadene): make normalization strategy configurable between mean_std, min_max, manual_min_max, min_max_from_spec
+        stats = offline_buffer.compute_or_load_stats()

-        # 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)
+        # 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":
@@ -127,7 +122,7 @@ 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)
--- a/lerobot/common/datasets/pusht.py
+++ b/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 Sampler
+from torchrl.data.replay_buffers.samplers import SliceSampler
 from torchrl.data.replay_buffers.storages import TensorStorage
 from torchrl.data.replay_buffers.writers import Writer

-from lerobot.common.datasets.abstract import AbstractDataset
+from lerobot.common.datasets.abstract import AbstractExperienceReplay
 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,
@@ -83,27 +83,23 @@ def add_tee(
    return body


-class PushtDataset(AbstractDataset):
-    available_datasets = ["pusht"]
-
+class PushtExperienceReplay(AbstractExperienceReplay):
    def __init__(
        self,
        dataset_id: str,
-        version: str | None = "v1.2",
-        batch_size: int | None = None,
+        batch_size: int = None,
        *,
        shuffle: bool = True,
        root: Path | None = None,
        pin_memory: bool = False,
        prefetch: int = None,
-        sampler: Sampler | None = None,
-        collate_fn: Callable | None = None,
-        writer: Writer | None = None,
+        sampler: SliceSampler = None,
+        collate_fn: Callable = None,
+        writer: Writer = None,
        transform: "torchrl.envs.Transform" = None,
    ):
        super().__init__(
            dataset_id,
-            version,
            batch_size,
            shuffle=shuffle,
            root=root,
--- a/lerobot/common/datasets/simxarm.py
+++ b/lerobot/common/datasets/simxarm.py
@@ -8,12 +8,12 @@ import torchrl
 import tqdm
 from tensordict import TensorDict
 from torchrl.data.replay_buffers.samplers import (
-    Sampler,
+    SliceSampler,
 )
 from torchrl.data.replay_buffers.storages import TensorStorage
 from torchrl.data.replay_buffers.writers import Writer

-from lerobot.common.datasets.abstract import AbstractDataset
+from lerobot.common.datasets.abstract import AbstractExperienceReplay


 def download():
@@ -32,7 +32,7 @@ def download():
    Path(download_path).unlink()


-class SimxarmDataset(AbstractDataset):
+class SimxarmExperienceReplay(AbstractExperienceReplay):
    available_datasets = [
        "xarm_lift_medium",
    ]
@@ -40,21 +40,19 @@ class SimxarmDataset(AbstractDataset):
    def __init__(
        self,
        dataset_id: str,
-        version: str | None = "v1.1",
-        batch_size: int | None = None,
+        batch_size: int = None,
        *,
        shuffle: bool = True,
        root: Path | None = None,
        pin_memory: bool = False,
        prefetch: int = None,
-        sampler: Sampler | None = None,
-        collate_fn: Callable | None = None,
-        writer: Writer | None = None,
+        sampler: SliceSampler = None,
+        collate_fn: Callable = None,
+        writer: Writer = None,
        transform: "torchrl.envs.Transform" = None,
    ):
        super().__init__(
            dataset_id,
-            version,
            batch_size,
            shuffle=shuffle,
            root=root,
@@ -67,11 +65,11 @@ class SimxarmDataset(AbstractDataset):
        )

    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}" / "buffer.pkl"
+        dataset_path = self.root / f"{self.dataset_id}_raw" / "buffer.pkl"
        print(f"Using offline dataset '{dataset_path}'")
        with open(dataset_path, "rb") as f:
            dataset_dict = pickle.load(f)
@@ -105,19 +103,15 @@ class SimxarmDataset(AbstractDataset):
                    "frame_id": torch.arange(0, num_frames, 1),
                    ("next", "observation", "image"): next_image,
                    ("next", "observation", "state"): next_state,
-                    ("next", "reward"): next_reward,
-                    ("next", "done"): next_done,
+                    ("next", "observation", "reward"): next_reward,
+                    ("next", "observation", "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}" / "replay_buffer")
-                )
+                td_data = episode[0].expand(total_frames).memmap_like(self.root / f"{self.dataset_id}")

            td_data[idx0:idx1] = episode

--- a/lerobot/common/envs/abstract.py
+++ b/lerobot/common/envs/abstract.py
@@ -1,27 +1,12 @@
+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,
@@ -35,14 +20,6 @@ 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
@@ -50,6 +27,7 @@ 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
@@ -58,13 +36,7 @@ class AbstractEnv(EnvBase):

        self._make_env()
        self._make_spec()
-
-        # 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)
+        self._current_seed = self.set_seed(seed)

        if self.num_prev_obs > 0:
            self._prev_obs_image_queue = deque(maxlen=self.num_prev_obs)
@@ -73,20 +45,36 @@ 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("Abstract method")
+        raise NotImplementedError()

+    @abc.abstractmethod
    def _reset(self, tensordict: Optional[TensorDict] = None):
-        raise NotImplementedError("Abstract method")
+        raise NotImplementedError()

+    @abc.abstractmethod
    def _step(self, tensordict: TensorDict):
-        raise NotImplementedError("Abstract method")
+        raise NotImplementedError()

+    @abc.abstractmethod
    def _make_env(self):
-        raise NotImplementedError("Abstract method")
+        raise NotImplementedError()

+    @abc.abstractmethod
    def _make_spec(self):
-        raise NotImplementedError("Abstract method")
+        raise NotImplementedError()

+    @abc.abstractmethod
    def _set_seed(self, seed: Optional[int]):
-        set_global_seed(seed)
+        raise NotImplementedError()
--- 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/lerobot/common/envs/aloha/env.py
+++ b/lerobot/common/envs/aloha/env.py
@@ -6,6 +6,8 @@ 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,
@@ -15,16 +17,24 @@ from torchrl.data.tensor_specs import (
 )

 from lerobot.common.envs.abstract import AbstractEnv
-from lerobot.common.utils import set_global_seed
+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

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


 class AlohaEnv(AbstractEnv):
-    name = "aloha"
-    available_tasks = ["sim_insertion", "sim_transfer_cube"]
-    _reset_warning_issued = False
-
    def __init__(
        self,
        task,
@@ -50,23 +60,49 @@ class AlohaEnv(AbstractEnv):
        )

    def _make_env(self):
+        if not _has_gym:
+            raise ImportError("Cannot import gym.")
+
        if not self.from_pixels:
            raise NotImplementedError()

-        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)
+        self._env = self._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())
@@ -84,77 +120,91 @@ 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
+        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)

-        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
+            # 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

-        # Seed the environment and update the seed to be used for the next reset.
-        self._next_seed = self.set_seed(self._next_seed)
+            raw_obs = self._env.reset()
+            # TODO(rcadene): add assert
+            # assert self._current_seed == self._env._seed

-        # 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
+            obs = self._format_raw_obs(raw_obs.observation)

-        raw_obs = self._env.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

-        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=[],
-        )
+            td = TensorDict(
+                {
+                    "observation": TensorDict(obs, batch_size=[]),
+                    "done": torch.tensor([False], dtype=torch.bool),
+                },
+                batch_size=[],
+            )
+        else:
+            raise NotImplementedError()

+        self.call_rendering_hooks()
        return td

    def _step(self, tensordict: TensorDict):
        td = tensordict
        action = td["action"].numpy()
-        assert action.ndim == 1
+        # step expects shape=(4,) so we pad if necessary
        # TODO(rcadene): add info["is_success"] and info["success"] ?
+        sum_reward = 0

-        _, reward, _, raw_obs = self._env.step(action)
+        if action.ndim == 1:
+            action = einops.repeat(action, "c -> t c", t=self.frame_skip)
+        else:
+            if self.frame_skip > 1:
+                raise NotImplementedError()

-        # TODO(rcadene): add an enum
-        success = done = reward == 4
-        obs = self._format_raw_obs(raw_obs)
+        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

-        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
+            # 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()

        td = TensorDict(
            {
                "observation": TensorDict(obs, batch_size=[]),
-                "reward": torch.tensor([reward], dtype=torch.float32),
-                # success and done are true when coverage > self.success_threshold in env
+                "reward": torch.tensor([sum_reward], dtype=torch.float32),
+                # succes 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),
            },
@@ -163,18 +213,13 @@ 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) or isinstance(self.image_size, list):
+            elif OmegaConf.is_list(self.image_size):
                assert len(self.image_size) == 3  # c h w
                assert self.image_size[0] == 3  # c is RGB
                image_shape = tuple(self.image_size)
@@ -260,7 +305,7 @@ class AlohaEnv(AbstractEnv):
        )

    def _set_seed(self, seed: Optional[int]):
-        set_global_seed(seed)
+        set_seed(seed)
        # TODO(rcadene): seed the env
        # self._env.seed(seed)
        logging.warning("Aloha env is not seeded")
--- a/Show More
+++ b/Show More