Add mobile Aloha and visu with rerun.io

Co-authored-by: Remi <re.cadene@gmail.com>

.gitattributes

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

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -0,0 +1,54 @@
+name: "\U0001F41B Bug Report"
+description: Submit a bug report to help us improve LeRobot
+body:
+  - type: markdown
+    attributes:
+      value: |
+        Thanks for taking the time to submit a bug report! 🐛
+        If this is not a bug related to the LeRobot library directly, but instead a general question about your code or the library specifically please use our [discord](https://discord.gg/s3KuuzsPFb).
+
+  - type: textarea
+    id: system-info
+    attributes:
+      label: System Info
+      description: If needed, you can share your lerobot configuration with us by running `python -m lerobot.scripts.display_sys_info` and copy-pasting its outputs below
+      render: Shell
+      placeholder: lerobot version, OS, python version, numpy version, torch version, and lerobot's configuration
+    validations:
+      required: true
+
+  - type: checkboxes
+    id: information-scripts-examples
+    attributes:
+      label: Information
+      description: 'The problem arises when using:'
+      options:
+        - label: "One of the scripts in the examples/ folder of LeRobot"
+        - label: "My own task or dataset (give details below)"
+
+  - type: textarea
+    id: reproduction
+    validations:
+      required: true
+    attributes:
+      label: Reproduction
+      description: |
+        If needed, provide a simple code sample that reproduces the problem you ran into. It can be a Colab link or just a code snippet.
+        Sharing error messages or stack traces could be useful as well!
+        Important! Use code tags to correctly format your code. See https://help.github.com/en/github/writing-on-github/creating-and-highlighting-code-blocks#syntax-highlighting
+        Try to avoid screenshots, as they are hard to read and don't allow copy-and-pasting.
+
+      placeholder: |
+        Steps to reproduce the behavior:
+
+          1.
+          2.
+          3.
+
+  - type: textarea
+    id: expected-behavior
+    validations:
+      required: true
+    attributes:
+      label: Expected behavior
+      description: "A clear and concise description of what you would expect to happen."

.github/PULL_REQUEST_TEMPLATE.md

@@ -0,0 +1,15 @@
+# What does this PR do?
+
+Example: Fixes # (issue)
+
+
+## Before submitting
+- Read the [contributor guideline](https://github.com/huggingface/lerobot/blob/main/CONTRIBUTING.md#submitting-a-pull-request-pr).
+- Provide a minimal code example for the reviewer to checkout & try.
+- Explain how you tested your changes.
+
+
+## Who can review?
+
+Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
+members/contributors who may be interested in your PR. Try to avoid tagging more than 3 people.

.github/poetry/cpu/pyproject.toml

@@ -1,19 +1,25 @@
 [tool.poetry]
 name = "lerobot"
 version = "0.1.0"
-description = "Le robot is learning"
+description = "🤗 LeRobot: State-of-the-art Machine Learning for Real-World Robotics in Pytorch"
 authors = [
     "Rémi Cadène <re.cadene@gmail.com>",
+    "Alexander Soare <alexander.soare159@gmail.com>",
+    "Quentin Gallouédec <quentin.gallouedec@ec-lyon.fr>",
     "Simon Alibert <alibert.sim@gmail.com>",
+    "Thomas Wolf <thomaswolfcontact@gmail.com>",
 ]
-repository = "https://github.com/Cadene/lerobot"
+repository = "https://github.com/huggingface/lerobot"
 readme = "README.md"
-license = "MIT"
+license = "Apache-2.0"
 classifiers=[
     "Development Status :: 3 - Alpha",
     "Intended Audience :: Developers",
+    "Intended Audience :: Education",
+    "Intended Audience :: Science/Research",
     "Topic :: Software Development :: Build Tools",
-    "License :: OSI Approved :: MIT License",
+    "Topic :: Scientific/Engineering :: Artificial Intelligence",
+    "License :: OSI Approved :: Apache Software License",
     "Programming Language :: Python :: 3.10",
 ]
 packages = [{include = "lerobot"}]
@@ -21,43 +27,41 @@ 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"
-imageio = {extras = ["pyav"], version = "^2.34.0"}
+imageio = {extras = ["ffmpeg"], version = "^2.34.0"}
 gdown = "^5.1.0"
 hydra-core = "^1.3.2"
 einops = "^0.7.0"
-pygame = "^2.5.2"
 pymunk = "^6.6.0"
 zarr = "^2.17.0"
-shapely = "^2.0.3"
-scikit-image = "^0.22.0"
 numba = "^0.59.0"
-mpmath = "^1.3.0"
 torch = {version = "^2.2.1", source = "torch-cpu"}
-tensordict = {git = "https://github.com/pytorch/tensordict"}
-torchrl = {git = "https://github.com/pytorch/rl", rev = "13bef426dcfa5887c6e5034a6e9697993fa92c37"}
-mujoco = "^3.1.2"
-mujoco-py = "^2.1.2.14"
-gym = "^0.26.2"
 opencv-python = "^4.9.0.80"
 diffusers = "^0.26.3"
 torchvision = {version = "^0.17.1", source = "torch-cpu"}
 h5py = "^3.10.0"
-dm = "^1.3"
-dm-control = "^1.0.16"
 huggingface-hub = "^0.21.4"
+robomimic = "0.2.0"
+gymnasium = "^0.29.1"
+cmake = "^3.29.0.1"
+gym-pusht = { git = "git@github.com:huggingface/gym-pusht.git", optional = true}
+gym-xarm = { git = "git@github.com:huggingface/gym-xarm.git", optional = true}
+gym-aloha = { git = "git@github.com:huggingface/gym-aloha.git", optional = true}
+pre-commit = {version = "^3.7.0", optional = true}
+debugpy = {version = "^1.8.1", optional = true}
+pytest = {version = "^8.1.0", optional = true}
+pytest-cov = {version = "^5.0.0", optional = true}
+datasets = "^2.18.0"
 
 
-[tool.poetry.group.dev.dependencies]
-pre-commit = "^3.6.2"
-debugpy = "^1.8.1"
-pytest = "^8.1.0"
+[tool.poetry.extras]
+pusht = ["gym-pusht"]
+xarm = ["gym-xarm"]
+aloha = ["gym-aloha"]
+dev = ["pre-commit", "debugpy"]
+test = ["pytest", "pytest-cov"]
 
 
 [[tool.poetry.source]]
@@ -98,10 +102,6 @@ exclude = [
 select = ["E4", "E7", "E9", "F", "I", "N", "B", "C4", "SIM"]
 
 
-[tool.poetry-dynamic-versioning]
-enable = true
-
-
 [build-system]
-requires = ["poetry-core>=1.0.0", "poetry-dynamic-versioning>=1.0.0,<2.0.0"]
-build-backend = "poetry_dynamic_versioning.backend"
+requires = ["poetry-core>=1.5.0"]
+build-backend = "poetry.core.masonry.api"

.github/workflows/test.yml

@@ -1,4 +1,4 @@
-name: Test
+name: Tests
 
 on:
   pull_request:
@@ -10,20 +10,15 @@ on:
       - main
 
 jobs:
-  test:
+  tests:
     if: |
       ${{ github.event_name == 'pull_request' && contains(github.event.pull_request.labels.*.name, 'CI') }} ||
       ${{ github.event_name == 'push' }}
     runs-on: ubuntu-latest
     env:
-      POETRY_VERSION: 1.8.1
+      POETRY_VERSION: 1.8.2
       DATA_DIR: tests/data
-      TMPDIR: ~/tmp
-      TEMP: ~/tmp
-      TMP: ~/tmp
-      PYOPENGL_PLATFORM: egl
       MUJOCO_GL: egl
-      LEROBOT_TESTS_DEVICE: cpu
     steps:
       #----------------------------------------------
       #       check-out repo and set-up python
@@ -39,6 +34,11 @@ jobs:
         with:
           python-version: '3.10'
 
+      - name: Add SSH key for installing envs
+        uses: webfactory/ssh-agent@v0.9.0
+        with:
+          ssh-private-key: ${{ secrets.SSH_PRIVATE_KEY }}
+
       #----------------------------------------------
       #         install & configure poetry
       #----------------------------------------------
@@ -86,9 +86,13 @@ jobs:
 
       - name: Install dependencies
         if: steps.restore-dependencies-cache.outputs.cache-hit != 'true'
+        env:
+          TMPDIR: ~/tmp
+          TEMP: ~/tmp
+          TMP: ~/tmp
         run: |
           mkdir ~/tmp
-          poetry install --no-interaction --no-root
+          poetry install --no-interaction --no-root --all-extras
 
       - name: Save cached venv
         if: |
@@ -107,38 +111,102 @@ jobs:
       #             install project
       #----------------------------------------------
       - name: Install project
-        run: poetry install --no-interaction
+        run: poetry install --no-interaction --all-extras
 
       #----------------------------------------------
-      #               run tests
+      #            run tests & coverage
       #----------------------------------------------
       - name: Run tests
         run: |
           source .venv/bin/activate
-          pytest tests
+          pytest -v --cov=./lerobot --cov-report=xml tests
 
-      - name: Test train pusht end-to-end
+      #   TODO(aliberts): Link with HF Codecov account
+      # - name: Upload coverage reports to Codecov with GitHub Action
+      #   uses: codecov/codecov-action@v4
+      #   with:
+      #     files: ./coverage.xml
+      #     verbose: true
+
+      #----------------------------------------------
+      #            run end-to-end tests
+      #----------------------------------------------
+      - name: Test train ACT on ALOHA end-to-end
         run: |
           source .venv/bin/activate
           python lerobot/scripts/train.py \
-            hydra.job.name=pusht \
+            policy=act \
+            env=aloha \
+            wandb.enable=False \
+            offline_steps=2 \
+            online_steps=0 \
+            eval_episodes=1 \
+            device=cpu \
+            save_model=true \
+            save_freq=2 \
+            policy.n_action_steps=20 \
+            policy.chunk_size=20 \
+            policy.batch_size=2 \
+            hydra.run.dir=tests/outputs/act/
+
+      - 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
+
+      - name: Test train Diffusion on PushT end-to-end
+        run: |
+          source .venv/bin/activate
+          python lerobot/scripts/train.py \
+            policy=diffusion \
             env=pusht \
             wandb.enable=False \
             offline_steps=2 \
             online_steps=0 \
+            eval_episodes=1 \
             device=cpu \
             save_model=true \
-            save_freq=1 \
-            hydra.run.dir=tests/outputs/
+            save_freq=2 \
+            policy.batch_size=2 \
+            hydra.run.dir=tests/outputs/diffusion/
 
-      - name: Test eval pusht end-to-end
+      - name: Test eval Diffusion on PushT end-to-end
         run: |
           source .venv/bin/activate
           python lerobot/scripts/eval.py \
-            hydra.job.name=pusht \
-            env=pusht \
-            wandb.enable=False \
+            --config tests/outputs/diffusion/.hydra/config.yaml \
             eval_episodes=1 \
             env.episode_length=8 \
             device=cpu \
-            policy.pretrained_model_path=tests/outputs/models/1.pt
+            policy.pretrained_model_path=tests/outputs/diffusion/models/2.pt
+
+      - name: Test train TDMPC on Simxarm end-to-end
+        run: |
+          source .venv/bin/activate
+          python lerobot/scripts/train.py \
+            policy=tdmpc \
+            env=xarm \
+            wandb.enable=False \
+            offline_steps=1 \
+            online_steps=1 \
+            eval_episodes=1 \
+            device=cpu \
+            save_model=true \
+            save_freq=2 \
+            policy.batch_size=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

.gitignore

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

.pre-commit-config.yaml

@@ -1,9 +1,9 @@
-exclude: ^(data/|tests/)
+exclude: ^(data/|tests/data)
 default_language_version:
     python: python3.10
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.5.0
+    rev: v4.6.0
     hooks:
       - id: check-added-large-files
       - id: debug-statements
@@ -14,11 +14,11 @@ repos:
       - id: end-of-file-fixer
       - id: trailing-whitespace
   - repo: https://github.com/asottile/pyupgrade
-    rev: v3.15.1
+    rev: v3.15.2
     hooks:
     -   id: pyupgrade
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.2.2
+    rev: v0.3.7
     hooks:
       - id: ruff
         args: [--fix]

CODE_OF_CONDUCT.md

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

CONTRIBUTING.md

@@ -0,0 +1,274 @@
+# How to contribute to 🤗 LeRobot?
+
+Everyone is welcome to contribute, and we value everybody's contribution. Code
+is thus not the only way to help the community. Answering questions, helping
+others, reaching out and improving the documentations are immensely valuable to
+the community.
+
+It also helps us if you spread the word: reference the library from blog posts
+on the awesome projects it made possible, shout out on Twitter when it has
+helped you, or simply ⭐️ the repo to say "thank you".
+
+Whichever way you choose to contribute, please be mindful to respect our
+[code of conduct](https://github.com/huggingface/lerobot/blob/main/CODE_OF_CONDUCT.md).
+
+## You can contribute in so many ways!
+
+Some of the ways you can contribute to 🤗 LeRobot:
+* Fixing outstanding issues with the existing code.
+* Implementing new models, datasets or simulation environments.
+* Contributing to the examples or to the documentation.
+* Submitting issues related to bugs or desired new features.
+
+Following the guides below, feel free to open issues and PRs and to coordinate your efforts with the community on our [Discord Channel](https://discord.gg/VjFz58wn3R). For specific inquiries, reach out to [Remi Cadene](remi.cadene@huggingface.co).
+
+If you are not sure how to contribute or want to know the next features we working on, look on this project page: [LeRobot TODO](https://github.com/orgs/huggingface/projects/46)
+
+## Submitting a new issue or feature request
+
+Do your best to follow these guidelines when submitting an issue or a feature
+request. It will make it easier for us to come back to you quickly and with good
+feedback.
+
+### Did you find a bug?
+
+The 🤗 LeRobot library is robust and reliable thanks to the users who notify us of
+the problems they encounter. So thank you for reporting an issue.
+
+First, we would really appreciate it if you could **make sure the bug was not
+already reported** (use the search bar on Github under Issues).
+
+Did not find it? :( So we can act quickly on it, please follow these steps:
+
+* Include your **OS type and version**, the versions of **Python** and **PyTorch**.
+* A short, self-contained, code snippet that allows us to reproduce the bug in
+  less than 30s.
+* The full traceback if an exception is raised.
+* Attach any other additional information, like screenshots, you think may help.
+
+### Do you want a new feature?
+
+A good feature request addresses the following points:
+
+1. Motivation first:
+* Is it related to a problem/frustration with the library? If so, please explain
+  why. Providing a code snippet that demonstrates the problem is best.
+* Is it related to something you would need for a project? We'd love to hear
+  about it!
+* Is it something you worked on and think could benefit the community?
+  Awesome! Tell us what problem it solved for you.
+2. Write a *paragraph* describing the feature.
+3. Provide a **code snippet** that demonstrates its future use.
+4. In case this is related to a paper, please attach a link.
+5. Attach any additional information (drawings, screenshots, etc.) you think may help.
+
+If your issue is well written we're already 80% of the way there by the time you
+post it.
+
+## Adding new policies, datasets or environments
+
+Look at our implementations for [datasets](./lerobot/common/datasets/), [policies](./lerobot/common/policies/),
+environments ([aloha](https://github.com/huggingface/gym-aloha),
+[xarm](https://github.com/huggingface/gym-xarm),
+[pusht](https://github.com/huggingface/gym-pusht))
+and follow the same api design.
+
+When implementing a new dataset class (e.g. `AlohaDataset`) follow these steps:
+- Update `available_datasets` in `lerobot/__init__.py`
+- Copy it in the required `available_datasets` class attribute
+
+When implementing a new environment (e.g. `gym_aloha`), follow these steps:
+- Update `available_envs`, `available_tasks_per_env` and `available_datasets` in `lerobot/__init__.py`
+
+When implementing a new policy class (e.g. `DiffusionPolicy`) follow these steps:
+- Update `available_policies` in `lerobot/__init__.py`
+- Set the required `name` class attribute.
+- Update variables in `tests/test_available.py` by importing your new Policy class
+
+## Submitting a pull request (PR)
+
+Before writing code, we strongly advise you to search through the existing PRs or
+issues to make sure that nobody is already working on the same thing. If you are
+unsure, it is always a good idea to open an issue to get some feedback.
+
+You will need basic `git` proficiency to be able to contribute to
+🤗 LeRobot. `git` is not the easiest tool to use but it has the greatest
+manual. Type `git --help` in a shell and enjoy. If you prefer books, [Pro
+Git](https://git-scm.com/book/en/v2) is a very good reference.
+
+Follow these steps to start contributing:
+
+1. Fork the [repository](https://github.com/huggingface/lerobot) by
+   clicking on the 'Fork' button on the repository's page. This creates a copy of the code
+   under your GitHub user account.
+
+2. Clone your fork to your local disk, and add the base repository as a remote. The following command
+   assumes you have your public SSH key uploaded to GitHub. See the following guide for more
+   [information](https://docs.github.com/en/repositories/creating-and-managing-repositories/cloning-a-repository).
+
+   ```bash
+   git clone git@github.com:<your Github handle>/lerobot.git
+   cd lerobot
+   git remote add upstream https://github.com/huggingface/lerobot.git
+   ```
+
+3. Create a new branch to hold your development changes, and do this for every new PR you work on.
+
+   Start by synchronizing your `main` branch with the `upstream/main` branch (more details in the [GitHub Docs](https://docs.github.com/en/github/collaborating-with-issues-and-pull-requests/syncing-a-fork)):
+
+   ```bash
+   git checkout main
+   git fetch upstream
+   git rebase upstream/main
+   ```
+
+   Once your `main` branch is synchronized, create a new branch from it:
+
+   ```bash
+   git checkout -b a-descriptive-name-for-my-changes
+   ```
+
+   🚨 **Do not** work on the `main` branch.
+
+4. 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.
+   Set up a development environment by running the following command in a conda or a virtual environment you've created for working on this library:
+   Install the project with dev dependencies and all environments:
+   ```bash
+   poetry install --sync --with dev --all-extras
+   ```
+   This command should be run when pulling code with and updated version of `pyproject.toml` and `poetry.lock` in order to synchronize your virtual environment with the dependencies.
+
+   To selectively install environments (for example aloha and pusht) use:
+   ```bash
+   poetry install --sync --with dev --extras "aloha pusht"
+   ```
+
+   The equivalent of `pip install some-package`, would just be:
+   ```bash
+   poetry add some-package
+   ```
+
+   When changes are made to the poetry sections of the `pyproject.toml`, you should run the following command to lock dependencies.
+   ```bash
+   poetry lock --no-update
+   ```
+
+   **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
+   ```
+
+5. Develop the features on your branch.
+
+   As you work on the features, you should make sure that the test suite
+   passes. You should run the tests impacted by your changes like this (see
+   below an explanation regarding the environment variable):
+
+   ```bash
+   pytest tests/<TEST_TO_RUN>.py
+   ```
+
+6. Follow our style.
+
+   `lerobot` relies on `ruff` to format its source code
+   consistently. Set up [`pre-commit`](https://pre-commit.com/) to run these checks
+   automatically as Git commit hooks.
+
+   Install `pre-commit` hooks:
+   ```bash
+   pre-commit install
+   ```
+
+   You can run these hooks whenever you need on staged files with:
+   ```bash
+   pre-commit
+   ```
+
+   Once you're happy with your changes, add changed files using `git add` and
+   make a commit with `git commit` to record your changes locally:
+
+   ```bash
+   git add modified_file.py
+   git commit
+   ```
+
+   Please write [good commit messages](https://chris.beams.io/posts/git-commit/).
+
+   It is a good idea to sync your copy of the code with the original
+   repository regularly. This way you can quickly account for changes:
+
+   ```bash
+   git fetch upstream
+   git rebase upstream/main
+   ```
+
+   Push the changes to your account using:
+
+   ```bash
+   git push -u origin a-descriptive-name-for-my-changes
+   ```
+
+6. Once you are satisfied (**and the checklist below is happy too**), go to the
+   webpage of your fork on GitHub. Click on 'Pull request' to send your changes
+   to the project maintainers for review.
+
+7. It's ok if maintainers ask you for changes. It happens to core contributors
+   too! So everyone can see the changes in the Pull request, work in your local
+   branch and push the changes to your fork. They will automatically appear in
+   the pull request.
+
+
+### Checklist
+
+1. The title of your pull request should be a summary of its contribution;
+2. If your pull request addresses an issue, please mention the issue number in
+   the pull request description to make sure they are linked (and people
+   consulting the issue know you are working on it);
+3. To indicate a work in progress please prefix the title with `[WIP]`, or preferably mark
+   the PR as a draft PR. These are useful to avoid duplicated work, and to differentiate
+   it from PRs ready to be merged;
+4. Make sure existing tests pass;
+<!-- 5. Add high-coverage tests. No quality testing = no merge.
+
+See an example of a good PR here: https://github.com/huggingface/lerobot/pull/ -->
+
+### Tests
+
+An extensive test suite is included to test the library behavior and several examples. Library tests can be found in the [tests folder](https://github.com/huggingface/lerobot/tree/main/tests).
+
+Install [git lfs](https://git-lfs.com/) to retrieve test artifacts (if you don't have it already).
+
+On Mac:
+```bash
+brew install git-lfs
+git lfs install
+```
+
+On Ubuntu:
+```bash
+sudo apt-get install git-lfs
+git lfs install
+```
+
+Pull artifacts if they're not in [tests/data](tests/data)
+```bash
+git lfs pull
+```
+
+We use `pytest` in order to run the tests. From the root of the
+repository, here's how to run tests with `pytest` for the library:
+
+```bash
+DATA_DIR="tests/data" python -m pytest -sv ./tests
+```
+
+
+You can specify a smaller set of tests in order to test only the feature
+you're working on.

LICENSE

@@ -253,6 +253,31 @@ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 SOFTWARE.
 
 
+## Some of lerobot's code is derived from simxarm, which is subject to the following copyright notice:
+
+MIT License
+
+Copyright (c) 2023 Nicklas Hansen & Yanjie Ze
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+
+
 ## Some of lerobot's code is derived from ALOHA, which is subject to the following copyright notice:
 
 MIT License
@@ -276,3 +301,207 @@ AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 SOFTWARE.
+
+## Some of lerobot's code is derived from DETR, which is subject to the following copyright notice:
+
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+
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README.md

@@ -1,72 +1,297 @@
-# LeRobot
+<p align="center">
+  <picture>
+    <source media="(prefers-color-scheme: dark)" srcset="media/lerobot-logo-thumbnail.png">
+    <source media="(prefers-color-scheme: light)" srcset="media/lerobot-logo-thumbnail.png">
+    <img alt="LeRobot, Hugging Face Robotics Library" src="media/lerobot-logo-thumbnail.png" style="max-width: 100%;">
+  </picture>
+  <br/>
+  <br/>
+</p>
+
+<div align="center">
+
+[![Tests](https://github.com/huggingface/lerobot/actions/workflows/test.yml/badge.svg?branch=main)](https://github.com/huggingface/lerobot/actions/workflows/test.yml?query=branch%3Amain)
+[![Coverage](https://codecov.io/gh/huggingface/lerobot/branch/main/graph/badge.svg?token=TODO)](https://codecov.io/gh/huggingface/lerobot)
+[![Python versions](https://img.shields.io/pypi/pyversions/lerobot)](https://www.python.org/downloads/)
+[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://github.com/huggingface/lerobot/blob/main/LICENSE)
+[![Status](https://img.shields.io/pypi/status/lerobot)](https://pypi.org/project/lerobot/)
+[![Version](https://img.shields.io/pypi/v/lerobot)](https://pypi.org/project/lerobot/)
+[![Examples](https://img.shields.io/badge/Examples-green.svg)](https://github.com/huggingface/lerobot/tree/main/examples)
+[![Contributor Covenant](https://img.shields.io/badge/Contributor%20Covenant-v2.1%20adopted-ff69b4.svg)](https://github.com/huggingface/lerobot/blob/main/CODE_OF_CONDUCT.md)
+[![Discord](https://dcbadge.vercel.app/api/server/C5P34WJ68S?style=flat)](https://discord.gg/s3KuuzsPFb)
+
+</div>
+
+<h3 align="center">
+    <p>State-of-the-art Machine Learning for real-world robotics</p>
+</h3>
+
+---
+
+
+🤗 LeRobot aims to provide models, datasets, and tools for real-world robotics in PyTorch. The goal is to lower the barrier for entry to robotics so that everyone can contribute and benefit from sharing datasets and pretrained models.
+
+🤗 LeRobot contains state-of-the-art approaches that have been shown to transfer to the real-world with a focus on imitation learning and reinforcement learning.
+
+🤗 LeRobot already provides a set of pretrained models, datasets with human collected demonstrations, and simulated environments so that everyone can get started. In the coming weeks, the plan is to add more and more support for real-world robotics on the most affordable and capable robots out there.
+
+🤗 LeRobot hosts pretrained models and datasets on this HuggingFace community page: [huggingface.co/lerobot](https://huggingface.co/lerobot)
+
+#### Examples of pretrained models and environments
+
+<table>
+  <tr>
+    <td><img src="http://remicadene.com/assets/gif/aloha_act.gif" width="100%" alt="ACT policy on ALOHA env"/></td>
+    <td><img src="http://remicadene.com/assets/gif/simxarm_tdmpc.gif" width="100%" alt="TDMPC policy on SimXArm env"/></td>
+    <td><img src="http://remicadene.com/assets/gif/pusht_diffusion.gif" width="100%" alt="Diffusion policy on PushT env"/></td>
+  </tr>
+  <tr>
+    <td align="center">ACT policy on ALOHA env</td>
+    <td align="center">TDMPC policy on SimXArm env</td>
+    <td align="center">Diffusion policy on PushT env</td>
+  </tr>
+</table>
+
+### Acknowledgment
+
+- ACT policy and ALOHA environment are adapted from [ALOHA](https://tonyzhaozh.github.io/aloha/)
+- Diffusion policy and Pusht environment are adapted from [Diffusion Policy](https://diffusion-policy.cs.columbia.edu/)
+- TDMPC policy and Simxarm environment are adapted from [FOWM](https://www.yunhaifeng.com/FOWM/)
+- Abstractions and utilities for Reinforcement Learning come from [TorchRL](https://github.com/pytorch/rl)
 
 ## Installation
 
-Create a virtual environment with Python 3.10, e.g. using `conda`:
-```
-conda create -y -n lerobot python=3.10
-conda activate lerobot
+Download our source code:
+```bash
+git clone https://github.com/huggingface/lerobot.git && cd lerobot
 ```
 
-[Install `poetry`](https://python-poetry.org/docs/#installation) (if you don't have it already)
-```
-curl -sSL https://install.python-poetry.org | python -
+Create a virtual environment with Python 3.10 and activate it, e.g. with [`miniconda`](https://docs.anaconda.com/free/miniconda/index.html):
+```bash
+conda create -y -n lerobot python=3.10 && conda activate lerobot
 ```
 
-Install dependencies
-```
-poetry install
+Install 🤗 LeRobot:
+```bash
+python -m pip install .
 ```
 
-If you encounter a disk space error, try to change your tmp dir to a location where you have enough disk space, e.g.
-```
-mkdir ~/tmp
-export TMPDIR='~/tmp'
+For simulations, 🤗 LeRobot comes with gymnasium environments that can be installed as extras:
+- [aloha](https://github.com/huggingface/gym-aloha)
+- [xarm](https://github.com/huggingface/gym-xarm)
+- [pusht](https://github.com/huggingface/gym-pusht)
+
+For instance, to install 🤗 LeRobot with aloha and pusht, use:
+```bash
+python -m pip install ".[aloha, pusht]"
 ```
 
 To use [Weights and Biases](https://docs.wandb.ai/quickstart) for experiments tracking, log in with
-```
+```bash
 wandb login
 ```
 
-## Usage
-
-
-### Train
+## Walkthrough
 
 ```
-python lerobot/scripts/train.py \
-hydra.job.name=pusht \
-env=pusht
-```
-
-### Visualize offline buffer
+.
+├── lerobot
+|   ├── configs          # contains hydra yaml files with all options that you can override in the command line
+|   |   ├── default.yaml   # selected by default, it loads pusht environment and diffusion policy
+|   |   ├── env            # various sim environments and their datasets: aloha.yaml, pusht.yaml, xarm.yaml
+|   |   └── policy         # various policies: act.yaml, diffusion.yaml, tdmpc.yaml
+|   ├── common           # contains classes and utilities
+|   |   ├── datasets       # various datasets of human demonstrations: aloha, pusht, xarm
+|   |   ├── envs           # various sim environments: aloha, pusht, xarm
+|   |   └── policies       # various policies: act, diffusion, tdmpc
+|   └── 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 os
+from pathlib import Path
+
+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']
+
+# TODO(rcadene): remove DATA_DIR
+dataset = AlohaDataset("pusht", root=Path(os.environ.get("DATA_DIR")))
+
+video_paths = render_dataset(
+    dataset,
+    out_dir="outputs/visualize_dataset/example",
+    max_num_episodes=1,
+)
+print(video_paths)
+# ['outputs/visualize_dataset/example/episode_0.mp4']
+```
+
+Or you can achieve the same result by executing our script from the command line:
+```bash
 python lerobot/scripts/visualize_dataset.py \
-hydra.run.dir=tmp/$(date +"%Y_%m_%d") \
-env=pusht
+env=pusht \
+hydra.run.dir=outputs/visualize_dataset/example
+# >>> ['outputs/visualize_dataset/example/episode_0.mp4']
 ```
 
-### Visualize online buffer / Eval
+### Evaluate a pretrained policy
 
-```
+Check out [example 2](./examples/2_evaluate_pretrained_policy.py) to see how you can load a pretrained policy from HuggingFace hub, load up the corresponding environment and model, and run an evaluation.
+
+Or you can achieve the same result by executing our script from the command line:
+```bash
 python lerobot/scripts/eval.py \
-hydra.run.dir=tmp/$(date +"%Y_%m_%d") \
-env=pusht
+--hub-id lerobot/diffusion_policy_pusht_image \
+eval_episodes=10 \
+hydra.run.dir=outputs/eval/example_hub
 ```
 
+After training your own policy, you can also re-evaluate the checkpoints with:
+```bash
+python lerobot/scripts/eval.py \
+--config PATH/TO/FOLDER/config.yaml \
+policy.pretrained_model_path=PATH/TO/FOLDER/weights.pth \
+eval_episodes=10 \
+hydra.run.dir=outputs/eval/example_dir
+```
 
-## TODO
+See `python lerobot/scripts/eval.py --help` for more instructions.
 
-If you are not sure how to contribute or want to know the next features we working on, look on this project page: [LeRobot TODO](https://github.com/users/Cadene/projects/1)
+### Train your own policy
 
-Ask [Remi Cadene](re.cadene@gmail.com) for access if needed.
+You can import our dataset, environment, policy classes, and use our training utilities (if some data is missing, it will be automatically downloaded from HuggingFace hub): check out [example 3](./examples/3_train_policy.py). After you run this, you may want to revisit [example 2](./examples/2_evaluate_pretrained_policy.py) to evaluate your training output!
+
+In general, you can use our training script to 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
+
+If you would like to contribute to 🤗 LeRobot, please check out our [contribution guide](https://github.com/huggingface/lerobot/blob/main/CONTRIBUTING.md).
+
+### 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/$DATASET \
+--repo-type dataset  \
+--revision v1.0
+```
+
+You will need to set the corresponding version as a default argument in your dataset class:
+```python
+  version: str | None = "v1.1",
+```
+See: [`lerobot/common/datasets/pusht.py`](https://github.com/Cadene/lerobot/blob/main/lerobot/common/datasets/pusht.py)
+
+For instance, for [lerobot/pusht](https://huggingface.co/datasets/lerobot/pusht), we used:
+```bash
+HF_USER=lerobot
+DATASET=pusht
+```
+
+If you want to improve an existing dataset, you can download it locally with:
+```bash
+mkdir -p data/$DATASET
+HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download ${HF_USER}/$DATASET \
+--repo-type dataset \
+--local-dir data/$DATASET \
+--local-dir-use-symlinks=False \
+--revision v1.0
+```
+
+Iterate on your code and dataset with:
+```bash
+DATA_DIR=data python train.py
+```
+
+Upload a new version (v2.0 or v1.1 if the changes are respectively more or less significant):
+```bash
+HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli upload $HF_USER/$DATASET data/$DATASET \
+--repo-type dataset \
+--revision v1.1 \
+--delete "*"
+```
+
+Then you will need to set the corresponding version as a default argument in your dataset class:
+```python
+  version: str | None = "v1.1",
+```
+See: [`lerobot/common/datasets/pusht.py`](https://github.com/Cadene/lerobot/blob/main/lerobot/common/datasets/pusht.py)
 
 
-## Profile
+Finally, you might want to mock the dataset if you need to update the unit tests as well:
+```bash
+python tests/scripts/mock_dataset.py --in-data-dir data/$DATASET --out-data-dir tests/data/$DATASET
+```
 
-**Example**
+### Add a pretrained policy
+
+Once you have trained a policy you may upload it to the HuggingFace hub.
+
+Firstly, make sure you have a model repository set up on the hub. The hub ID looks like HF_USER/REPO_NAME.
+
+Secondly, assuming you have trained a policy, you need:
+
+- `config.yaml` which you can get from the `.hydra` directory of your training output folder.
+- `model.pt` which should be one of the saved models in the `models` directory of your training output folder (they won't be named `model.pt` but you will need to choose one).
+- `stats.pth` which should point to the same file in the dataset directory (found in `data/{dataset_name}`).
+
+To upload these to the hub, prepare a folder with the following structure (you can use symlinks rather than copying):
+
+```
+to_upload
+    ├── config.yaml
+    ├── model.pt
+    └── stats.pth
+```
+
+With the folder prepared, run the following with a desired revision ID.
+
+```bash
+huggingface-cli upload $HUB_ID to_upload --revision $REVISION_ID
+```
+
+If you want this to be the default revision also run the following (don't worry, it won't upload the files again; it will just adjust the file pointers):
+
+```bash
+huggingface-cli upload $HUB_ID to_upload
+```
+
+See `eval.py` for an example of how a user may use your policy.
+
+
+### Improve your code with profiling
+
+An example of a code snippet to profile the evaluation of a policy:
 ```python
 from torch.profiler import profile, record_function, ProfilerActivity
 
@@ -85,87 +310,12 @@ with profile(
     with record_function("eval_policy"):
         for i in range(num_episodes):
             prof.step()
+            # insert code to profile, potentially whole body of eval_policy function
 ```
 
 ```bash
 python lerobot/scripts/eval.py \
-pretrained_model_path=/home/rcadene/code/fowm/logs/xarm_lift/all/default/2/models/final.pt \
+--config outputs/pusht/.hydra/config.yaml \
+pretrained_model_path=outputs/pusht/model.pt \
 eval_episodes=7
 ```
-
-## Contribute
-
-**Style**
-```
-# install if needed
-pre-commit install
-# apply style and linter checks before git commit
-pre-commit run -a
-```
-
-**Adding dependencies (temporary)**
-
-Right now, for the CI to work, whenever a new dependency is added it needs to be also added to the cpu env, eg:
-
-```
-# Run in this directory, adds the package to the main env with cuda
-poetry add some-package
-
-# Adds the same package to the cpu env
-cd .github/poetry/cpu && poetry add some-package
-```
-
-**Tests**
-
-Install [git lfs](https://git-lfs.com/) to retrieve test artifacts (if you don't have it already).
-
-On Mac:
-```
-brew install git-lfs
-git lfs install
-```
-
-On Ubuntu:
-```
-sudo apt-get install git-lfs
-git lfs install
-```
-
-Pull artifacts if they're not in [tests/data](tests/data)
-```
-git lfs pull
-```
-
-When adding a new dataset, mock it with
-```
-python tests/scripts/mock_dataset.py --in-data-dir data/<dataset_id> --out-data-dir tests/data/<dataset_id>
-```
-
-Run tests
-```
-DATA_DIR="tests/data" pytest -sx tests
-```
-
-**Datasets**
-
-To add a pytorch rl dataset to the hub, first login and use a token generated from [huggingface settings](https://huggingface.co/settings/tokens) with write access:
-```
-huggingface-cli login --token $HUGGINGFACE_TOKEN --add-to-git-credential
-```
-
-Then you can upload it to the hub with:
-```
-HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli upload --repo-type dataset $HF_USER/$DATASET data/$DATASET
-```
-
-For instance, for [cadene/pusht](https://huggingface.co/datasets/cadene/pusht), we used:
-```
-HF_USER=cadene
-DATASET=pusht
-```
-
-
-## Acknowledgment
-- Our Diffusion policy and Pusht environment are adapted from [Diffusion Policy](https://diffusion-policy.cs.columbia.edu/)
-- Our TDMPC policy and Simxarm environment are adapted from [FOWM](https://www.yunhaifeng.com/FOWM/)
-- Our ACT policy and ALOHA environment are adapted from [ALOHA](https://tonyzhaozh.github.io/aloha/)

download_and_upload_dataset.py

@@ -0,0 +1,656 @@
+"""
+This file contains all obsolete download scripts. They are centralized here to not have to load
+useless dependencies when using datasets.
+"""
+
+import io
+import json
+import pickle
+import shutil
+from pathlib import Path
+
+import einops
+import h5py
+import numpy as np
+import torch
+import tqdm
+from datasets import Dataset, Features, Image, Sequence, Value
+from huggingface_hub import HfApi
+from PIL import Image as PILImage
+from safetensors.torch import save_file
+
+from lerobot.common.datasets.utils import compute_stats, flatten_dict, hf_transform_to_torch
+
+
+def download_and_upload(root, revision, dataset_id):
+    if "pusht" in dataset_id:
+        download_and_upload_pusht(root, revision, dataset_id)
+    elif "xarm" in dataset_id:
+        download_and_upload_xarm(root, revision, dataset_id)
+    elif "aloha_sim" in dataset_id:
+        download_and_upload_aloha(root, revision, dataset_id)
+    elif "aloha_mobile" in dataset_id:
+        download_and_upload_aloha_mobile(root, revision, dataset_id)
+    else:
+        raise ValueError(dataset_id)
+
+
+def download_and_extract_zip(url: str, destination_folder: Path) -> bool:
+    import zipfile
+
+    import requests
+
+    print(f"downloading from {url}")
+    response = requests.get(url, stream=True)
+    if response.status_code == 200:
+        total_size = int(response.headers.get("content-length", 0))
+        progress_bar = tqdm.tqdm(total=total_size, unit="B", unit_scale=True)
+
+        zip_file = io.BytesIO()
+        for chunk in response.iter_content(chunk_size=1024):
+            if chunk:
+                zip_file.write(chunk)
+                progress_bar.update(len(chunk))
+
+        progress_bar.close()
+
+        zip_file.seek(0)
+
+        with zipfile.ZipFile(zip_file, "r") as zip_ref:
+            zip_ref.extractall(destination_folder)
+        return True
+    else:
+        return False
+
+
+def concatenate_episodes(ep_dicts):
+    data_dict = {}
+
+    keys = ep_dicts[0].keys()
+    for key in keys:
+        if torch.is_tensor(ep_dicts[0][key][0]):
+            data_dict[key] = torch.cat([ep_dict[key] for ep_dict in ep_dicts])
+        else:
+            if key not in data_dict:
+                data_dict[key] = []
+            for ep_dict in ep_dicts:
+                for x in ep_dict[key]:
+                    data_dict[key].append(x)
+
+    total_frames = data_dict["frame_index"].shape[0]
+    data_dict["index"] = torch.arange(0, total_frames, 1)
+    return data_dict
+
+
+def push_to_hub(hf_dataset, episode_data_index, info, stats, root, revision, dataset_id):
+    # push to main to indicate latest version
+    hf_dataset.push_to_hub(f"lerobot/{dataset_id}", token=True)
+
+    # push to version branch
+    hf_dataset.push_to_hub(f"lerobot/{dataset_id}", token=True, revision=revision)
+
+    # create and store meta_data
+    meta_data_dir = root / dataset_id / "meta_data"
+    meta_data_dir.mkdir(parents=True, exist_ok=True)
+
+    api = HfApi()
+
+    # info
+    info_path = meta_data_dir / "info.json"
+    with open(str(info_path), "w") as f:
+        json.dump(info, f, indent=4)
+    api.upload_file(
+        path_or_fileobj=info_path,
+        path_in_repo=str(info_path).replace(f"{root}/{dataset_id}", ""),
+        repo_id=f"lerobot/{dataset_id}",
+        repo_type="dataset",
+    )
+    api.upload_file(
+        path_or_fileobj=info_path,
+        path_in_repo=str(info_path).replace(f"{root}/{dataset_id}", ""),
+        repo_id=f"lerobot/{dataset_id}",
+        repo_type="dataset",
+        revision=revision,
+    )
+
+    # stats
+    stats_path = meta_data_dir / "stats.safetensors"
+    save_file(flatten_dict(stats), stats_path)
+    api.upload_file(
+        path_or_fileobj=stats_path,
+        path_in_repo=str(stats_path).replace(f"{root}/{dataset_id}", ""),
+        repo_id=f"lerobot/{dataset_id}",
+        repo_type="dataset",
+    )
+    api.upload_file(
+        path_or_fileobj=stats_path,
+        path_in_repo=str(stats_path).replace(f"{root}/{dataset_id}", ""),
+        repo_id=f"lerobot/{dataset_id}",
+        repo_type="dataset",
+        revision=revision,
+    )
+
+    # episode_data_index
+    episode_data_index = {key: torch.tensor(episode_data_index[key]) for key in episode_data_index}
+    ep_data_idx_path = meta_data_dir / "episode_data_index.safetensors"
+    save_file(episode_data_index, ep_data_idx_path)
+    api.upload_file(
+        path_or_fileobj=ep_data_idx_path,
+        path_in_repo=str(ep_data_idx_path).replace(f"{root}/{dataset_id}", ""),
+        repo_id=f"lerobot/{dataset_id}",
+        repo_type="dataset",
+    )
+    api.upload_file(
+        path_or_fileobj=ep_data_idx_path,
+        path_in_repo=str(ep_data_idx_path).replace(f"{root}/{dataset_id}", ""),
+        repo_id=f"lerobot/{dataset_id}",
+        repo_type="dataset",
+        revision=revision,
+    )
+
+    # copy in tests folder, the first episode and the meta_data directory
+    num_items_first_ep = episode_data_index["to"][0] - episode_data_index["from"][0]
+    hf_dataset.select(range(num_items_first_ep)).with_format("torch").save_to_disk(
+        f"tests/data/{dataset_id}/train"
+    )
+    if Path(f"tests/data/{dataset_id}/meta_data").exists():
+        shutil.rmtree(f"tests/data/{dataset_id}/meta_data")
+    shutil.copytree(meta_data_dir, f"tests/data/{dataset_id}/meta_data")
+
+
+def download_and_upload_pusht(root, revision, dataset_id="pusht", fps=10):
+    try:
+        import pymunk
+        from gym_pusht.envs.pusht import PushTEnv, pymunk_to_shapely
+
+        from lerobot.common.datasets._diffusion_policy_replay_buffer import (
+            ReplayBuffer as DiffusionPolicyReplayBuffer,
+        )
+    except ModuleNotFoundError as e:
+        print("`gym_pusht` is not installed. Please install it with `pip install 'lerobot[gym_pusht]'`")
+        raise e
+
+    # as define in env
+    success_threshold = 0.95  # 95% coverage,
+
+    pusht_url = "https://diffusion-policy.cs.columbia.edu/data/training/pusht.zip"
+    pusht_zarr = Path("pusht/pusht_cchi_v7_replay.zarr")
+
+    root = Path(root)
+    raw_dir = root / f"{dataset_id}_raw"
+    zarr_path = (raw_dir / pusht_zarr).resolve()
+    if not zarr_path.is_dir():
+        raw_dir.mkdir(parents=True, exist_ok=True)
+        download_and_extract_zip(pusht_url, raw_dir)
+
+    # load
+    dataset_dict = DiffusionPolicyReplayBuffer.copy_from_path(zarr_path)  # , keys=['img', 'state', 'action'])
+
+    episode_ids = torch.from_numpy(dataset_dict.get_episode_idxs())
+    num_episodes = dataset_dict.meta["episode_ends"].shape[0]
+    assert len(
+        {dataset_dict[key].shape[0] for key in dataset_dict.keys()}  # noqa: SIM118
+    ), "Some data type dont have the same number of total frames."
+
+    # TODO: verify that goal pose is expected to be fixed
+    goal_pos_angle = np.array([256, 256, np.pi / 4])  # x, y, theta (in radians)
+    goal_body = PushTEnv.get_goal_pose_body(goal_pos_angle)
+
+    imgs = torch.from_numpy(dataset_dict["img"])  # b h w c
+    states = torch.from_numpy(dataset_dict["state"])
+    actions = torch.from_numpy(dataset_dict["action"])
+
+    ep_dicts = []
+    episode_data_index = {"from": [], "to": []}
+
+    id_from = 0
+    for episode_id in tqdm.tqdm(range(num_episodes)):
+        id_to = dataset_dict.meta["episode_ends"][episode_id]
+
+        num_frames = id_to - id_from
+
+        assert (episode_ids[id_from:id_to] == episode_id).all()
+
+        image = imgs[id_from:id_to]
+        assert image.min() >= 0.0
+        assert image.max() <= 255.0
+        image = image.type(torch.uint8)
+
+        state = states[id_from:id_to]
+        agent_pos = state[:, :2]
+        block_pos = state[:, 2:4]
+        block_angle = state[:, 4]
+
+        reward = torch.zeros(num_frames)
+        success = torch.zeros(num_frames, dtype=torch.bool)
+        done = torch.zeros(num_frames, dtype=torch.bool)
+        for i in range(num_frames):
+            space = pymunk.Space()
+            space.gravity = 0, 0
+            space.damping = 0
+
+            # Add walls.
+            walls = [
+                PushTEnv.add_segment(space, (5, 506), (5, 5), 2),
+                PushTEnv.add_segment(space, (5, 5), (506, 5), 2),
+                PushTEnv.add_segment(space, (506, 5), (506, 506), 2),
+                PushTEnv.add_segment(space, (5, 506), (506, 506), 2),
+            ]
+            space.add(*walls)
+
+            block_body = PushTEnv.add_tee(space, block_pos[i].tolist(), block_angle[i].item())
+            goal_geom = pymunk_to_shapely(goal_body, block_body.shapes)
+            block_geom = pymunk_to_shapely(block_body, block_body.shapes)
+            intersection_area = goal_geom.intersection(block_geom).area
+            goal_area = goal_geom.area
+            coverage = intersection_area / goal_area
+            reward[i] = np.clip(coverage / success_threshold, 0, 1)
+            success[i] = coverage > success_threshold
+
+        # last step of demonstration is considered done
+        done[-1] = True
+
+        ep_dict = {
+            "observation.image": [PILImage.fromarray(x.numpy()) for x in image],
+            "observation.state": agent_pos,
+            "action": actions[id_from:id_to],
+            "episode_index": torch.tensor([episode_id] * num_frames, dtype=torch.int),
+            "frame_index": torch.arange(0, num_frames, 1),
+            "timestamp": torch.arange(0, num_frames, 1) / fps,
+            # "next.observation.image": image[1:],
+            # "next.observation.state": agent_pos[1:],
+            # TODO(rcadene): verify that reward and done are aligned with image and agent_pos
+            "next.reward": torch.cat([reward[1:], reward[[-1]]]),
+            "next.done": torch.cat([done[1:], done[[-1]]]),
+            "next.success": torch.cat([success[1:], success[[-1]]]),
+        }
+        ep_dicts.append(ep_dict)
+
+        episode_data_index["from"].append(id_from)
+        episode_data_index["to"].append(id_from + num_frames)
+
+        id_from += num_frames
+
+    data_dict = concatenate_episodes(ep_dicts)
+
+    features = {
+        "observation.image": Image(),
+        "observation.state": Sequence(
+            length=data_dict["observation.state"].shape[1], feature=Value(dtype="float32", id=None)
+        ),
+        "action": Sequence(length=data_dict["action"].shape[1], feature=Value(dtype="float32", id=None)),
+        "episode_index": Value(dtype="int64", id=None),
+        "frame_index": Value(dtype="int64", id=None),
+        "timestamp": Value(dtype="float32", id=None),
+        "next.reward": Value(dtype="float32", id=None),
+        "next.done": Value(dtype="bool", id=None),
+        "next.success": Value(dtype="bool", id=None),
+        "index": Value(dtype="int64", id=None),
+    }
+    features = Features(features)
+    hf_dataset = Dataset.from_dict(data_dict, features=features)
+    hf_dataset.set_transform(hf_transform_to_torch)
+
+    info = {
+        "fps": fps,
+    }
+    stats = compute_stats(hf_dataset)
+    push_to_hub(hf_dataset, episode_data_index, info, stats, root, revision, dataset_id)
+
+
+def download_and_upload_xarm(root, revision, dataset_id, fps=15):
+    root = Path(root)
+    raw_dir = root / "xarm_datasets_raw"
+    if not raw_dir.exists():
+        import zipfile
+
+        import gdown
+
+        raw_dir.mkdir(parents=True, exist_ok=True)
+        # from https://github.com/fyhMer/fowm/blob/main/scripts/download_datasets.py
+        url = "https://drive.google.com/uc?id=1nhxpykGtPDhmQKm-_B8zBSywVRdgeVya"
+        zip_path = raw_dir / "data.zip"
+        gdown.download(url, str(zip_path), quiet=False)
+        print("Extracting...")
+        with zipfile.ZipFile(str(zip_path), "r") as zip_f:
+            for member in zip_f.namelist():
+                if member.startswith("data/xarm") and member.endswith(".pkl"):
+                    print(member)
+                    zip_f.extract(member=member)
+        zip_path.unlink()
+
+    dataset_path = root / f"{dataset_id}" / "buffer.pkl"
+    print(f"Using offline dataset '{dataset_path}'")
+    with open(dataset_path, "rb") as f:
+        dataset_dict = pickle.load(f)
+
+    ep_dicts = []
+    episode_data_index = {"from": [], "to": []}
+
+    id_from = 0
+    id_to = 0
+    episode_id = 0
+    total_frames = dataset_dict["actions"].shape[0]
+    for i in tqdm.tqdm(range(total_frames)):
+        id_to += 1
+
+        if not dataset_dict["dones"][i]:
+            continue
+
+        num_frames = id_to - id_from
+
+        image = torch.tensor(dataset_dict["observations"]["rgb"][id_from:id_to])
+        image = einops.rearrange(image, "b c h w -> b h w c")
+        state = torch.tensor(dataset_dict["observations"]["state"][id_from:id_to])
+        action = torch.tensor(dataset_dict["actions"][id_from:id_to])
+        # TODO(rcadene): we have a missing last frame which is the observation when the env is done
+        # it is critical to have this frame for tdmpc to predict a "done observation/state"
+        # next_image = torch.tensor(dataset_dict["next_observations"]["rgb"][id_from:id_to])
+        # next_state = torch.tensor(dataset_dict["next_observations"]["state"][id_from:id_to])
+        next_reward = torch.tensor(dataset_dict["rewards"][id_from:id_to])
+        next_done = torch.tensor(dataset_dict["dones"][id_from:id_to])
+
+        ep_dict = {
+            "observation.image": [PILImage.fromarray(x.numpy()) for x in image],
+            "observation.state": state,
+            "action": action,
+            "episode_index": torch.tensor([episode_id] * num_frames, dtype=torch.int),
+            "frame_index": torch.arange(0, num_frames, 1),
+            "timestamp": torch.arange(0, num_frames, 1) / fps,
+            # "next.observation.image": next_image,
+            # "next.observation.state": next_state,
+            "next.reward": next_reward,
+            "next.done": next_done,
+        }
+        ep_dicts.append(ep_dict)
+
+        episode_data_index["from"].append(id_from)
+        episode_data_index["to"].append(id_from + num_frames)
+
+        id_from = id_to
+        episode_id += 1
+
+    data_dict = concatenate_episodes(ep_dicts)
+
+    features = {
+        "observation.image": Image(),
+        "observation.state": Sequence(
+            length=data_dict["observation.state"].shape[1], feature=Value(dtype="float32", id=None)
+        ),
+        "action": Sequence(length=data_dict["action"].shape[1], feature=Value(dtype="float32", id=None)),
+        "episode_index": Value(dtype="int64", id=None),
+        "frame_index": Value(dtype="int64", id=None),
+        "timestamp": Value(dtype="float32", id=None),
+        "next.reward": Value(dtype="float32", id=None),
+        "next.done": Value(dtype="bool", id=None),
+        #'next.success': Value(dtype='bool', id=None),
+        "index": Value(dtype="int64", id=None),
+    }
+    features = Features(features)
+    hf_dataset = Dataset.from_dict(data_dict, features=features)
+    hf_dataset.set_transform(hf_transform_to_torch)
+
+    info = {
+        "fps": fps,
+    }
+    stats = compute_stats(hf_dataset)
+    push_to_hub(hf_dataset, episode_data_index, info, stats, root, revision, dataset_id)
+
+
+def download_and_upload_aloha(root, revision, dataset_id, fps=50):
+    folder_urls = {
+        "aloha_sim_insertion_human": "https://drive.google.com/drive/folders/1RgyD0JgTX30H4IM5XZn8I3zSV_mr8pyF",
+        "aloha_sim_insertion_scripted": "https://drive.google.com/drive/folders/1TsojQQSXtHEoGnqgJ3gmpPQR2DPLtS2N",
+        "aloha_sim_transfer_cube_human": "https://drive.google.com/drive/folders/1sc-E4QYW7A0o23m1u2VWNGVq5smAsfCo",
+        "aloha_sim_transfer_cube_scripted": "https://drive.google.com/drive/folders/1aRyoOhQwxhyt1J8XgEig4s6kzaw__LXj",
+    }
+
+    ep48_urls = {
+        "aloha_sim_insertion_human": "https://drive.google.com/file/d/18Cudl6nikDtgRolea7je8iF_gGKzynOP/view?usp=drive_link",
+        "aloha_sim_insertion_scripted": "https://drive.google.com/file/d/1wfMSZ24oOh5KR_0aaP3Cnu_c4ZCveduB/view?usp=drive_link",
+        "aloha_sim_transfer_cube_human": "https://drive.google.com/file/d/18smMymtr8tIxaNUQ61gW6dG50pt3MvGq/view?usp=drive_link",
+        "aloha_sim_transfer_cube_scripted": "https://drive.google.com/file/d/1pnGIOd-E4-rhz2P3VxpknMKRZCoKt6eI/view?usp=drive_link",
+    }
+
+    ep49_urls = {
+        "aloha_sim_insertion_human": "https://drive.google.com/file/d/1C1kZYyROzs-PrLc0SkDgUgMi4-L3lauE/view?usp=drive_link",
+        "aloha_sim_insertion_scripted": "https://drive.google.com/file/d/17EuCUWS6uCCr6yyNzpXdcdE-_TTNCKtf/view?usp=drive_link",
+        "aloha_sim_transfer_cube_human": "https://drive.google.com/file/d/1Nk7l53d9sJoGDBKAOnNrExX5nLacATc6/view?usp=drive_link",
+        "aloha_sim_transfer_cube_scripted": "https://drive.google.com/file/d/1GKReZHrXU73NMiC5zKCq_UtqPVtYq8eo/view?usp=drive_link",
+    }
+
+    num_episodes = {
+        "aloha_sim_insertion_human": 50,
+        "aloha_sim_insertion_scripted": 50,
+        "aloha_sim_transfer_cube_human": 50,
+        "aloha_sim_transfer_cube_scripted": 50,
+    }
+
+    episode_len = {
+        "aloha_sim_insertion_human": 500,
+        "aloha_sim_insertion_scripted": 400,
+        "aloha_sim_transfer_cube_human": 400,
+        "aloha_sim_transfer_cube_scripted": 400,
+    }
+
+    cameras = {
+        "aloha_sim_insertion_human": ["top"],
+        "aloha_sim_insertion_scripted": ["top"],
+        "aloha_sim_transfer_cube_human": ["top"],
+        "aloha_sim_transfer_cube_scripted": ["top"],
+    }
+
+    root = Path(root)
+    raw_dir = root / f"{dataset_id}_raw"
+    if not raw_dir.is_dir():
+        import gdown
+
+        assert dataset_id in folder_urls
+        assert dataset_id in ep48_urls
+        assert dataset_id in ep49_urls
+
+        raw_dir.mkdir(parents=True, exist_ok=True)
+
+        gdown.download_folder(folder_urls[dataset_id], output=str(raw_dir))
+
+        # because of the 50 files limit per directory, two files episode 48 and 49 were missing
+        gdown.download(ep48_urls[dataset_id], output=str(raw_dir / "episode_48.hdf5"), fuzzy=True)
+        gdown.download(ep49_urls[dataset_id], output=str(raw_dir / "episode_49.hdf5"), fuzzy=True)
+
+    ep_dicts = []
+    episode_data_index = {"from": [], "to": []}
+
+    id_from = 0
+    for ep_id in tqdm.tqdm(range(num_episodes[dataset_id])):
+        ep_path = raw_dir / f"episode_{ep_id}.hdf5"
+        with h5py.File(ep_path, "r") as ep:
+            num_frames = ep["/action"].shape[0]
+            assert episode_len[dataset_id] == num_frames
+
+            # last step of demonstration is considered done
+            done = torch.zeros(num_frames, dtype=torch.bool)
+            done[-1] = True
+
+            state = torch.from_numpy(ep["/observations/qpos"][:])
+            action = torch.from_numpy(ep["/action"][:])
+
+            ep_dict = {}
+
+            for cam in cameras[dataset_id]:
+                image = torch.from_numpy(ep[f"/observations/images/{cam}"][:])  # b h w c
+                # image = einops.rearrange(image, "b h w c -> b c h w").contiguous()
+                ep_dict[f"observation.images.{cam}"] = [PILImage.fromarray(x.numpy()) for x in image]
+                # ep_dict[f"next.observation.images.{cam}"] = image
+
+            ep_dict.update(
+                {
+                    "observation.state": state,
+                    "action": action,
+                    "episode_index": torch.tensor([ep_id] * num_frames),
+                    "frame_index": torch.arange(0, num_frames, 1),
+                    "timestamp": torch.arange(0, num_frames, 1) / fps,
+                    # "next.observation.state": state,
+                    # TODO(rcadene): compute reward and success
+                    # "next.reward": reward,
+                    "next.done": done,
+                    # "next.success": success,
+                }
+            )
+
+            assert isinstance(ep_id, int)
+            ep_dicts.append(ep_dict)
+
+            episode_data_index["from"].append(id_from)
+            episode_data_index["to"].append(id_from + num_frames)
+
+        id_from += num_frames
+
+    data_dict = concatenate_episodes(ep_dicts)
+
+    features = {
+        "observation.images.top": Image(),
+        "observation.state": Sequence(
+            length=data_dict["observation.state"].shape[1], feature=Value(dtype="float32", id=None)
+        ),
+        "action": Sequence(length=data_dict["action"].shape[1], feature=Value(dtype="float32", id=None)),
+        "episode_index": Value(dtype="int64", id=None),
+        "frame_index": Value(dtype="int64", id=None),
+        "timestamp": Value(dtype="float32", id=None),
+        #'next.reward': Value(dtype='float32', id=None),
+        "next.done": Value(dtype="bool", id=None),
+        #'next.success': Value(dtype='bool', id=None),
+        "index": Value(dtype="int64", id=None),
+    }
+    features = Features(features)
+    hf_dataset = Dataset.from_dict(data_dict, features=features)
+    hf_dataset.set_transform(hf_transform_to_torch)
+
+    info = {
+        "fps": fps,
+    }
+    stats = compute_stats(hf_dataset)
+    push_to_hub(hf_dataset, episode_data_index, info, stats, root, revision, dataset_id)
+
+
+def download_and_upload_aloha_mobile(root, revision, dataset_id, fps=50):
+    num_episodes = {
+        "aloha_mobile_trossen_block_handoff": 5,
+    }
+
+    # episode_len = {
+    #     "aloha_sim_insertion_human": 500,
+    #     "aloha_sim_insertion_scripted": 400,
+    #     "aloha_sim_transfer_cube_human": 400,
+    #     "aloha_sim_transfer_cube_scripted": 400,
+    # }
+
+    cameras = {
+        "aloha_mobile_trossen_block_handoff": ['cam_high', 'cam_left_wrist', 'cam_right_wrist'],
+    }
+
+    root = Path(root)
+    raw_dir = root / f"{dataset_id}_raw"
+
+    ep_dicts = []
+    episode_data_index = {"from": [], "to": []}
+
+    id_from = 0
+    for ep_id in tqdm.tqdm(range(num_episodes[dataset_id])):
+        ep_path = raw_dir / f"episode_{ep_id}.hdf5"
+        with h5py.File(ep_path, "r") as ep:
+            num_frames = ep["/action"].shape[0]
+
+            #assert episode_len[dataset_id] == num_frames
+
+            # last step of demonstration is considered done
+            done = torch.zeros(num_frames, dtype=torch.bool)
+            done[-1] = True
+
+            state = torch.from_numpy(ep["/observations/qpos"][:num_frames])
+            action = torch.from_numpy(ep["/action"][:num_frames])
+
+            ep_dict = {}
+
+            for cam in cameras[dataset_id]:
+                image = ep[f"/observations/images/{cam}"][:num_frames]  # b h w c
+
+                import cv2
+                # un-pad and uncompress from: https://github.com/MarkFzp/act-plus-plus/blob/26bab0789d05b7496bacef04f5c6b2541a4403b5/postprocess_episodes.py#L50
+                image = np.array([cv2.imdecode(x, 1) for x in image])
+                image = [PILImage.fromarray(x) for x in image]
+                ep_dict[f"observation.images.{cam}"] = image
+
+            ep_dict.update(
+                {
+                    "observation.state": state,
+                    "action": action,
+                    "episode_index": torch.tensor([ep_id] * num_frames),
+                    "frame_index": torch.arange(0, num_frames, 1),
+                    "timestamp": torch.arange(0, num_frames, 1) / fps,
+                    # "next.observation.state": state,
+                    # TODO(rcadene): compute reward and success
+                    # "next.reward": reward,
+                    "next.done": done,
+                    # "next.success": success,
+                }
+            )
+
+            assert isinstance(ep_id, int)
+            ep_dicts.append(ep_dict)
+
+            episode_data_index["from"].append(id_from)
+            episode_data_index["to"].append(id_from + num_frames)
+
+        id_from += num_frames
+
+        break
+
+    data_dict = concatenate_episodes(ep_dicts)
+
+    features = {}
+    for cam in cameras[dataset_id]:
+        features[f"observation.images.{cam}"] = Image()
+    features.update({
+        "observation.state": Sequence(
+            length=data_dict["observation.state"].shape[1], feature=Value(dtype="float32", id=None)
+        ),
+        "action": Sequence(length=data_dict["action"].shape[1], feature=Value(dtype="float32", id=None)),
+        "episode_index": Value(dtype="int64", id=None),
+        "frame_index": Value(dtype="int64", id=None),
+        "timestamp": Value(dtype="float32", id=None),
+        #'next.reward': Value(dtype='float32', id=None),
+        "next.done": Value(dtype="bool", id=None),
+        #'next.success': Value(dtype='bool', id=None),
+        "index": Value(dtype="int64", id=None),
+    })
+    
+    features = Features(features)
+    hf_dataset = Dataset.from_dict(data_dict, features=features)
+    hf_dataset.set_transform(hf_transform_to_torch)
+
+    info = {
+        "fps": fps,
+    }
+    stats = compute_stats(hf_dataset)
+    push_to_hub(hf_dataset, episode_data_index, info, stats, root, revision, dataset_id)
+
+
+
+if __name__ == "__main__":
+    root = "data"
+    revision = "v1.1"
+
+    dataset_ids = [
+        # "pusht",
+        # "xarm_lift_medium",
+        # "xarm_lift_medium_replay",
+        # "xarm_push_medium",
+        # "xarm_push_medium_replay",
+        # "aloha_sim_insertion_human",
+        # "aloha_sim_insertion_scripted",
+        # "aloha_sim_transfer_cube_human",
+        # "aloha_sim_transfer_cube_scripted",
+        "aloha_mobile_trossen_block_handoff",
+    ]
+    for dataset_id in dataset_ids:
+        download_and_upload(root, revision, dataset_id)

examples/1_load_hugging_face_dataset.py

@@ -0,0 +1,67 @@
+"""
+This script demonstrates the visualization of various robotic datasets from Hugging Face hub.
+It covers the steps from loading the datasets, filtering specific episodes, and converting the frame data to MP4 videos.
+Importantly, the dataset format is agnostic to any deep learning library and doesn't require using `lerobot` functions.
+It is compatible with pytorch, jax, numpy, etc.
+
+As an example, this script saves frames of episode number 5 of the PushT dataset to a mp4 video and saves the result here:
+`outputs/examples/1_visualize_hugging_face_datasets/episode_5.mp4`
+
+This script supports several Hugging Face datasets, among which:
+1. [Pusht](https://huggingface.co/datasets/lerobot/pusht)
+2. [Xarm Lift Medium](https://huggingface.co/datasets/lerobot/xarm_lift_medium)
+3. [Xarm Lift Medium](https://huggingface.co/datasets/lerobot/xarm_lift_medium_replay)
+4. [Xarm Lift Medium](https://huggingface.co/datasets/lerobot/xarm_push_medium)
+5. [Xarm Lift Medium](https://huggingface.co/datasets/lerobot/xarm_push_medium_replay)
+6. [Aloha Sim Insertion Human](https://huggingface.co/datasets/lerobot/aloha_sim_insertion_human)
+7. [Aloha Sim Insertion Scripted](https://huggingface.co/datasets/lerobot/aloha_sim_insertion_scripted)
+8. [Aloha Sim Transfer Cube Human](https://huggingface.co/datasets/lerobot/aloha_sim_transfer_cube_human)
+9. [Aloha Sim Transfer Cube Scripted](https://huggingface.co/datasets/lerobot/aloha_sim_transfer_cube_scripted)
+
+To try a different Hugging Face dataset, you can replace this line:
+```python
+hf_dataset, fps = load_dataset("lerobot/pusht", split="train"), 10
+```
+by one of these:
+```python
+hf_dataset, fps = load_dataset("lerobot/xarm_lift_medium", split="train"), 15
+hf_dataset, fps = load_dataset("lerobot/xarm_lift_medium_replay", split="train"), 15
+hf_dataset, fps = load_dataset("lerobot/xarm_push_medium", split="train"), 15
+hf_dataset, fps = load_dataset("lerobot/xarm_push_medium_replay", split="train"), 15
+hf_dataset, fps = load_dataset("lerobot/aloha_sim_insertion_human", split="train"), 50
+hf_dataset, fps = load_dataset("lerobot/aloha_sim_insertion_scripted", split="train"), 50
+hf_dataset, fps = load_dataset("lerobot/aloha_sim_transfer_cube_human", split="train"), 50
+hf_dataset, fps = load_dataset("lerobot/aloha_sim_transfer_cube_scripted", split="train"), 50
+```
+"""
+
+from pathlib import Path
+
+import imageio
+from datasets import load_dataset
+
+# TODO(rcadene): list available datasets on lerobot page using `datasets`
+
+# download/load hugging face dataset in pyarrow format
+hf_dataset, fps = load_dataset("lerobot/pusht", split="train"), 10
+
+# display name of dataset and its features
+print(f"{hf_dataset=}")
+print(f"{hf_dataset.features=}")
+
+# display useful statistics about frames and episodes, which are sequences of frames from the same video
+print(f"number of frames: {len(hf_dataset)=}")
+print(f"number of episodes: {len(hf_dataset.unique('episode_index'))=}")
+print(
+    f"average number of frames per episode: {len(hf_dataset) / len(hf_dataset.unique('episode_index')):.3f}"
+)
+
+# select the frames belonging to episode number 5
+hf_dataset = hf_dataset.filter(lambda frame: frame["episode_index"] == 5)
+
+# load all frames of episode 5 in RAM in PIL format
+frames = hf_dataset["observation.image"]
+
+# save episode frames to a mp4 video
+Path("outputs/examples/1_load_hugging_face_dataset").mkdir(parents=True, exist_ok=True)
+imageio.mimsave("outputs/examples/1_load_hugging_face_dataset/episode_5.mp4", frames, fps=fps)

examples/2_load_lerobot_dataset.py

@@ -0,0 +1,102 @@
+"""
+This script demonstrates the use of the PushtDataset class for handling and processing robotic datasets from Hugging Face.
+It illustrates how to load datasets, manipulate them, and apply transformations suitable for machine learning tasks in PyTorch.
+
+Features included in this script:
+- Loading a dataset and accessing its properties.
+- Filtering data by episode number.
+- Converting tensor data for visualization.
+- Saving video files from dataset frames.
+- Using advanced dataset features like timestamp-based frame selection.
+- Demonstrating compatibility with PyTorch DataLoader for batch processing.
+
+The script ends with examples of how to batch process data using PyTorch's DataLoader.
+
+To try a different Hugging Face dataset, you can replace:
+```python
+dataset = PushtDataset()
+```
+by one of these:
+```python
+dataset = XarmDataset("xarm_lift_medium")
+dataset = XarmDataset("xarm_lift_medium_replay")
+dataset = XarmDataset("xarm_push_medium")
+dataset = XarmDataset("xarm_push_medium_replay")
+dataset = AlohaDataset("aloha_sim_insertion_human")
+dataset = AlohaDataset("aloha_sim_insertion_scripted")
+dataset = AlohaDataset("aloha_sim_transfer_cube_human")
+dataset = AlohaDataset("aloha_sim_transfer_cube_scripted")
+```
+"""
+
+from pathlib import Path
+
+import imageio
+import torch
+
+from lerobot.common.datasets.pusht import PushtDataset
+
+# TODO(rcadene): List available datasets and their dataset ids (e.g. PushtDataset, AlohaDataset(dataset_id="aloha_sim_insertion_human"))
+# print("List of available datasets", 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']
+
+
+# You can easily load datasets from LeRobot
+dataset = PushtDataset()
+
+# All LeRobot datasets are actually a thin wrapper around an underlying Hugging Face dataset  (see https://huggingface.co/docs/datasets/index for more information).
+print(f"{dataset=}")
+print(f"{dataset.hf_dataset=}")
+
+# and provide additional utilities for robotics and compatibility with pytorch
+print(f"number of samples/frames: {dataset.num_samples=}")
+print(f"number of episodes: {dataset.num_episodes=}")
+print(f"average number of frames per episode: {dataset.num_samples / dataset.num_episodes:.3f}")
+print(f"frames per second used during data collection: {dataset.fps=}")
+print(f"keys to access images from cameras: {dataset.image_keys=}")
+
+# While the LeRobot dataset adds helpers for working within our library, we still expose the underling Hugging Face dataset. It may be freely replaced or modified in place. Here we use the filtering to keep only frames from episode 5.
+dataset.hf_dataset = dataset.hf_dataset.filter(lambda frame: frame["episode_index"] == 5)
+
+# LeRobot datsets actually subclass PyTorch datasets. So you can do everything you know and love from working with the latter, for example: iterating through the dataset. Here we grap all the image frames.
+frames = [sample["observation.image"] for sample in dataset]
+
+# but frames are now float32 range [0,1] channel first to follow pytorch convention,
+# to view them, we convert to uint8 range [0,255] channel last
+frames = [(frame * 255).type(torch.uint8) for frame in frames]
+frames = [frame.permute((1, 2, 0)).numpy() for frame in frames]
+
+# and finally save them to a mp4 video
+Path("outputs/examples/2_load_lerobot_dataset").mkdir(parents=True, exist_ok=True)
+imageio.mimsave("outputs/examples/2_load_lerobot_dataset/episode_5.mp4", frames, fps=dataset.fps)
+
+# For many machine learning applications we need to load histories of past observations, or trajectorys of future actions. Our datasets can load previous and future frames for each key/modality,
+# using timestamps differences with the current loaded frame. For instance:
+delta_timestamps = {
+    # loads 4 images: 1 second before current frame, 500 ms before, 200 ms before, and current frame
+    "observation.image": [-1, -0.5, -0.20, 0],
+    # loads 8 state vectors: 1.5 seconds before, 1 second before, ... 20 ms, 10 ms, and current frame
+    "observation.state": [-1.5, -1, -0.5, -0.20, -0.10, -0.02, -0.01, 0],
+    # loads 64 action vectors: current frame, 1 frame in the future, 2 frames, ... 63 frames in the future
+    "action": [t / dataset.fps for t in range(64)],
+}
+dataset = PushtDataset(delta_timestamps=delta_timestamps)
+print(f"{dataset[0]['observation.image'].shape=}")  # (4,c,h,w)
+print(f"{dataset[0]['observation.state'].shape=}")  # (8,c)
+print(f"{dataset[0]['action'].shape=}")  # (64,c)
+
+# Finally, our datasets are fully compatible with PyTorch dataloaders and samplers
+# because they are just PyTorch datasets.
+dataloader = torch.utils.data.DataLoader(
+    dataset,
+    num_workers=0,
+    batch_size=32,
+    shuffle=True,
+)
+for batch in dataloader:
+    print(f"{batch['observation.image'].shape=}")  # (32,4,c,h,w)
+    print(f"{batch['observation.state'].shape=}")  # (32,8,c)
+    print(f"{batch['action'].shape=}")  # (32,64,c)
+    break

examples/3_evaluate_pretrained_policy.py

@@ -0,0 +1,40 @@
+"""
+This scripts demonstrates how to evaluate a pretrained policy from the HuggingFace Hub or from your local
+training outputs directory. In the latter case, you might want to run examples/3_train_policy.py first.
+"""
+
+from pathlib import Path
+
+from huggingface_hub import snapshot_download
+
+from lerobot.common.utils.utils import init_hydra_config
+from lerobot.scripts.eval import eval
+
+# Get a pretrained policy from the hub.
+# TODO(alexander-soare): This no longer works until we upload a new model that uses the current configs.
+hub_id = "lerobot/diffusion_policy_pusht_image"
+folder = Path(snapshot_download(hub_id))
+# OR uncomment the following to evaluate a policy from the local outputs/train folder.
+# folder = Path("outputs/train/example_pusht_diffusion")
+
+config_path = folder / "config.yaml"
+weights_path = folder / "model.pt"
+stats_path = folder / "stats.pth"  # normalization stats
+
+# Override some config parameters to do with evaluation.
+overrides = [
+    f"policy.pretrained_model_path={weights_path}",
+    "eval_episodes=10",
+    "rollout_batch_size=10",
+    "device=cuda",
+]
+
+# Create a Hydra config.
+cfg = init_hydra_config(config_path, overrides)
+
+# Evaluate the policy and save the outputs including metrics and videos.
+eval(
+    cfg,
+    out_dir=f"outputs/eval/example_{cfg.env.name}_{cfg.policy.name}",
+    stats_path=stats_path,
+)

examples/4_train_policy.py

@@ -0,0 +1,68 @@
+"""This scripts demonstrates how to train Diffusion Policy on the PushT environment.
+
+Once you have trained a model with this script, you can try to evaluate it on
+examples/2_evaluate_pretrained_policy.py
+"""
+
+import os
+from pathlib import Path
+
+import torch
+from omegaconf import OmegaConf
+
+from lerobot.common.datasets.factory import make_dataset
+from lerobot.common.policies.diffusion.configuration_diffusion import DiffusionConfig
+from lerobot.common.policies.diffusion.modeling_diffusion import DiffusionPolicy
+from lerobot.common.utils.utils import init_hydra_config
+
+output_directory = Path("outputs/train/example_pusht_diffusion")
+os.makedirs(output_directory, exist_ok=True)
+
+# Number of offline training steps (we'll only do offline training for this example.
+# Adjust as you prefer. 5000 steps are needed to get something worth evaluating.
+training_steps = 5000
+device = torch.device("cuda")
+log_freq = 250
+
+# Set up the dataset.
+hydra_cfg = init_hydra_config("lerobot/configs/default.yaml", overrides=["env=pusht"])
+dataset = make_dataset(hydra_cfg)
+
+# Set up the the policy.
+# Policies are initialized with a configuration class, in this case `DiffusionConfig`.
+# For this example, no arguments need to be passed because the defaults are set up for PushT.
+# If you're doing something different, you will likely need to change at least some of the defaults.
+cfg = DiffusionConfig()
+# TODO(alexander-soare): Remove LR scheduler from the policy.
+policy = DiffusionPolicy(cfg, lr_scheduler_num_training_steps=training_steps)
+policy.train()
+policy.to(device)
+
+# Create dataloader for offline training.
+dataloader = torch.utils.data.DataLoader(
+    dataset,
+    num_workers=4,
+    batch_size=cfg.batch_size,
+    shuffle=True,
+    pin_memory=device != torch.device("cpu"),
+    drop_last=True,
+)
+
+# Run training loop.
+step = 0
+done = False
+while not done:
+    for batch in dataloader:
+        batch = {k: v.to(device, non_blocking=True) for k, v in batch.items()}
+        info = policy.update(batch)
+        if step % log_freq == 0:
+            print(f"step: {step} loss: {info['loss']:.3f} update_time: {info['update_s']:.3f} (seconds)")
+        step += 1
+        if step >= training_steps:
+            done = True
+            break
+
+# Save the policy, configuration, and normalization stats for later use.
+policy.save(output_directory / "model.pt")
+OmegaConf.save(hydra_cfg, output_directory / "config.yaml")
+torch.save(dataset.transform.transforms[-1].stats, output_directory / "stats.pth")

lerobot/__init__.py

@@ -1 +1,80 @@
+"""
+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)
+        print(lerobot.available_policies)
+        print(lerobot.available_policies_per_env)
+    ```
+
+When implementing a new dataset class (e.g. `AlohaDataset`) follow these steps:
+- Update `available_datasets` in `lerobot/__init__.py`
+- Set the required `available_datasets` class attribute using the previously updated `lerobot.available_datasets`
+
+When implementing a new environment (e.g. `gym_aloha`), follow these steps:
+- Update `available_envs`, `available_tasks_per_env` and `available_datasets` in `lerobot/__init__.py`
+
+When implementing a new policy class (e.g. `DiffusionPolicy`) follow these steps:
+- Update `available_policies` in `lerobot/__init__.py`
+- Set the required `name` class attribute.
+- Update variables in `tests/test_available.py` by importing your new Policy class
+"""
+
 from lerobot.__version__ import __version__  # noqa: F401
+
+available_envs = [
+    "aloha",
+    "pusht",
+    "xarm",
+]
+
+available_tasks_per_env = {
+    "aloha": [
+        "AlohaInsertion-v0",
+        "AlohaTransferCube-v0",
+    ],
+    "pusht": ["PushT-v0"],
+    "xarm": ["XarmLift-v0"],
+}
+
+available_datasets = {
+    "aloha": [
+        "aloha_sim_insertion_human",
+        "aloha_sim_insertion_scripted",
+        "aloha_sim_transfer_cube_human",
+        "aloha_sim_transfer_cube_scripted",
+    ],
+    "pusht": ["pusht"],
+    "xarm": [
+        "xarm_lift_medium",
+        "xarm_lift_medium_replay",
+        "xarm_push_medium",
+        "xarm_push_medium_replay",
+    ],
+}
+
+available_policies = [
+    "act",
+    "diffusion",
+    "tdmpc",
+]
+
+available_policies_per_env = {
+    "aloha": ["act"],
+    "pusht": ["diffusion"],
+    "xarm": ["tdmpc"],
+}
+
+env_task_pairs = [(env, task) for env, tasks in available_tasks_per_env.items() for task in tasks]
+env_dataset_pairs = [(env, dataset) for env, datasets in available_datasets.items() for dataset in datasets]
+env_dataset_policy_triplets = [
+    (env, dataset, policy)
+    for env, datasets in available_datasets.items()
+    for dataset in datasets
+    for policy in available_policies_per_env[env]
+]

lerobot/__version__.py

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

lerobot/common/datasets/_diffusion_policy_replay_buffer.py

@@ -1,3 +1,8 @@
+"""Helper code for loading PushT dataset from Diffusion Policy (https://diffusion-policy.cs.columbia.edu/)
+
+Copied from the original Diffusion Policy repository and used in our `download_and_upload_dataset.py` script.
+"""
+
 from __future__ import annotations
 
 import math

lerobot/common/datasets/abstract.py

@@ -1,159 +0,0 @@
-import logging
-from pathlib import Path
-from typing import Callable
-
-import einops
-import torch
-import torchrl
-import tqdm
-from huggingface_hub import snapshot_download
-from tensordict import TensorDict
-from torchrl.data.replay_buffers.replay_buffers import TensorDictReplayBuffer
-from torchrl.data.replay_buffers.samplers import 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
-
-
-class AbstractExperienceReplay(TensorDictReplayBuffer):
-    def __init__(
-        self,
-        dataset_id: str,
-        batch_size: int = None,
-        *,
-        shuffle: bool = True,
-        root: Path | None = None,
-        pin_memory: bool = False,
-        prefetch: int = None,
-        sampler: SliceSampler = None,
-        collate_fn: Callable = None,
-        writer: Writer = None,
-        transform: "torchrl.envs.Transform" = None,
-    ):
-        self.dataset_id = dataset_id
-        self.shuffle = shuffle
-        self.root = root
-        storage = self._download_or_load_dataset()
-
-        super().__init__(
-            storage=storage,
-            sampler=sampler,
-            writer=ImmutableDatasetWriter() if writer is None else writer,
-            collate_fn=_collate_id if collate_fn is None else collate_fn,
-            pin_memory=pin_memory,
-            prefetch=prefetch,
-            batch_size=batch_size,
-            transform=transform,
-        )
-
-    @property
-    def stats_patterns(self) -> dict:
-        return {
-            ("observation", "state"): "b c -> 1 c",
-            ("observation", "image"): "b c h w -> 1 c 1 1",
-            ("action",): "b c -> 1 c",
-        }
-
-    @property
-    def image_keys(self) -> list:
-        return [("observation", "image")]
-
-    @property
-    def num_cameras(self) -> int:
-        return len(self.image_keys)
-
-    @property
-    def num_samples(self) -> int:
-        return len(self)
-
-    @property
-    def num_episodes(self) -> int:
-        return len(self._storage._storage["episode"].unique())
-
-    @property
-    def transform(self):
-        return self._transform
-
-    def set_transform(self, transform):
-        if not isinstance(transform, Compose):
-            # required since torchrl calls `len(self._transform)` downstream
-            if isinstance(transform, list):
-                self._transform = Compose(*transform)
-            else:
-                self._transform = Compose(transform)
-        else:
-            self._transform = transform
-
-    def compute_or_load_stats(self, num_batch=100, batch_size=32) -> TensorDict:
-        stats_path = self.data_dir / "stats.pth"
-        if stats_path.exists():
-            stats = torch.load(stats_path)
-        else:
-            logging.info(f"compute_stats and save to {stats_path}")
-            stats = self._compute_stats(num_batch, batch_size)
-            torch.save(stats, stats_path)
-        return stats
-
-    def _download_or_load_dataset(self) -> torch.StorageBase:
-        if self.root is None:
-            self.data_dir = 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))
-
-    def _compute_stats(self, num_batch=100, batch_size=32):
-        rb = TensorDictReplayBuffer(
-            storage=self._storage,
-            batch_size=batch_size,
-            prefetch=True,
-        )
-
-        mean, std, max, min = {}, {}, {}, {}
-
-        # compute mean, min, max
-        for _ in tqdm.tqdm(range(num_batch)):
-            batch = rb.sample()
-            for key, pattern in self.stats_patterns.items():
-                batch[key] = batch[key].float()
-                if key not in mean:
-                    # first batch initialize mean, min, max
-                    mean[key] = einops.reduce(batch[key], pattern, "mean")
-                    max[key] = einops.reduce(batch[key], pattern, "max")
-                    min[key] = einops.reduce(batch[key], pattern, "min")
-                else:
-                    mean[key] += einops.reduce(batch[key], pattern, "mean")
-                    max[key] = torch.maximum(max[key], einops.reduce(batch[key], pattern, "max"))
-                    min[key] = torch.minimum(min[key], einops.reduce(batch[key], pattern, "min"))
-                batch = rb.sample()
-
-        for key in self.stats_patterns:
-            mean[key] /= num_batch
-
-        # compute std, min, max
-        for _ in tqdm.tqdm(range(num_batch)):
-            batch = rb.sample()
-            for key, pattern in self.stats_patterns.items():
-                batch[key] = batch[key].float()
-                batch_mean = einops.reduce(batch[key], pattern, "mean")
-                if key not in std:
-                    # first batch initialize std
-                    std[key] = (batch_mean - mean[key]) ** 2
-                else:
-                    std[key] += (batch_mean - mean[key]) ** 2
-                max[key] = torch.maximum(max[key], einops.reduce(batch[key], pattern, "max"))
-                min[key] = torch.minimum(min[key], einops.reduce(batch[key], pattern, "min"))
-
-        for key in self.stats_patterns:
-            std[key] = torch.sqrt(std[key] / num_batch)
-
-        stats = TensorDict({}, batch_size=[])
-        for key in self.stats_patterns:
-            stats[(*key, "mean")] = mean[key]
-            stats[(*key, "std")] = std[key]
-            stats[(*key, "max")] = max[key]
-            stats[(*key, "min")] = min[key]
-
-            if key[0] == "observation":
-                # use same stats for the next observations
-                stats[("next", *key)] = stats[key]
-        return stats

lerobot/common/datasets/aloha.py

@@ -1,183 +1,78 @@
-import logging
 from pathlib import Path
-from typing import Callable
 
-import einops
-import gdown
-import h5py
 import torch
-import torchrl
-import tqdm
-from tensordict import TensorDict
-from torchrl.data.replay_buffers.samplers import SliceSampler
-from torchrl.data.replay_buffers.storages import TensorStorage
-from torchrl.data.replay_buffers.writers import Writer
 
-from lerobot.common.datasets.abstract import AbstractExperienceReplay
-
-DATASET_IDS = [
-    "aloha_sim_insertion_human",
-    "aloha_sim_insertion_scripted",
-    "aloha_sim_transfer_cube_human",
-    "aloha_sim_transfer_cube_scripted",
-]
-
-FOLDER_URLS = {
-    "aloha_sim_insertion_human": "https://drive.google.com/drive/folders/1RgyD0JgTX30H4IM5XZn8I3zSV_mr8pyF",
-    "aloha_sim_insertion_scripted": "https://drive.google.com/drive/folders/1TsojQQSXtHEoGnqgJ3gmpPQR2DPLtS2N",
-    "aloha_sim_transfer_cube_human": "https://drive.google.com/drive/folders/1sc-E4QYW7A0o23m1u2VWNGVq5smAsfCo",
-    "aloha_sim_transfer_cube_scripted": "https://drive.google.com/drive/folders/1aRyoOhQwxhyt1J8XgEig4s6kzaw__LXj",
-}
-
-EP48_URLS = {
-    "aloha_sim_insertion_human": "https://drive.google.com/file/d/18Cudl6nikDtgRolea7je8iF_gGKzynOP/view?usp=drive_link",
-    "aloha_sim_insertion_scripted": "https://drive.google.com/file/d/1wfMSZ24oOh5KR_0aaP3Cnu_c4ZCveduB/view?usp=drive_link",
-    "aloha_sim_transfer_cube_human": "https://drive.google.com/file/d/18smMymtr8tIxaNUQ61gW6dG50pt3MvGq/view?usp=drive_link",
-    "aloha_sim_transfer_cube_scripted": "https://drive.google.com/file/d/1pnGIOd-E4-rhz2P3VxpknMKRZCoKt6eI/view?usp=drive_link",
-}
-
-EP49_URLS = {
-    "aloha_sim_insertion_human": "https://drive.google.com/file/d/1C1kZYyROzs-PrLc0SkDgUgMi4-L3lauE/view?usp=drive_link",
-    "aloha_sim_insertion_scripted": "https://drive.google.com/file/d/17EuCUWS6uCCr6yyNzpXdcdE-_TTNCKtf/view?usp=drive_link",
-    "aloha_sim_transfer_cube_human": "https://drive.google.com/file/d/1Nk7l53d9sJoGDBKAOnNrExX5nLacATc6/view?usp=drive_link",
-    "aloha_sim_transfer_cube_scripted": "https://drive.google.com/file/d/1GKReZHrXU73NMiC5zKCq_UtqPVtYq8eo/view?usp=drive_link",
-}
-
-NUM_EPISODES = {
-    "aloha_sim_insertion_human": 50,
-    "aloha_sim_insertion_scripted": 50,
-    "aloha_sim_transfer_cube_human": 50,
-    "aloha_sim_transfer_cube_scripted": 50,
-}
-
-EPISODE_LEN = {
-    "aloha_sim_insertion_human": 500,
-    "aloha_sim_insertion_scripted": 400,
-    "aloha_sim_transfer_cube_human": 400,
-    "aloha_sim_transfer_cube_scripted": 400,
-}
-
-CAMERAS = {
-    "aloha_sim_insertion_human": ["top"],
-    "aloha_sim_insertion_scripted": ["top"],
-    "aloha_sim_transfer_cube_human": ["top"],
-    "aloha_sim_transfer_cube_scripted": ["top"],
-}
+from lerobot.common.datasets.utils import (
+    load_episode_data_index,
+    load_hf_dataset,
+    load_previous_and_future_frames,
+    load_stats,
+)
 
 
-def download(data_dir, dataset_id):
-    assert dataset_id in DATASET_IDS
-    assert dataset_id in FOLDER_URLS
-    assert dataset_id in EP48_URLS
-    assert dataset_id in EP49_URLS
+class AlohaDataset(torch.utils.data.Dataset):
+    """
+    https://huggingface.co/datasets/lerobot/aloha_sim_insertion_human
+    https://huggingface.co/datasets/lerobot/aloha_sim_insertion_scripted
+    https://huggingface.co/datasets/lerobot/aloha_sim_transfer_cube_human
+    https://huggingface.co/datasets/lerobot/aloha_sim_transfer_cube_scripted
+    """
 
-    data_dir.mkdir(parents=True, exist_ok=True)
+    # Copied from lerobot/__init__.py
+    available_datasets = [
+        "aloha_sim_insertion_human",
+        "aloha_sim_insertion_scripted",
+        "aloha_sim_transfer_cube_human",
+        "aloha_sim_transfer_cube_scripted",
+    ]
+    fps = 50
+    image_keys = ["observation.images.top"]
 
-    gdown.download_folder(FOLDER_URLS[dataset_id], output=str(data_dir))
-
-    # because of the 50 files limit per directory, two files episode 48 and 49 were missing
-    gdown.download(EP48_URLS[dataset_id], output=str(data_dir / "episode_48.hdf5"), fuzzy=True)
-    gdown.download(EP49_URLS[dataset_id], output=str(data_dir / "episode_49.hdf5"), fuzzy=True)
-
-
-class AlohaExperienceReplay(AbstractExperienceReplay):
     def __init__(
         self,
         dataset_id: str,
-        batch_size: int = None,
-        *,
-        shuffle: bool = True,
+        version: str | None = "v1.1",
         root: Path | None = None,
-        pin_memory: bool = False,
-        prefetch: int = None,
-        sampler: SliceSampler = None,
-        collate_fn: Callable = None,
-        writer: Writer = None,
-        transform: "torchrl.envs.Transform" = None,
+        split: str = "train",
+        transform: callable = None,
+        delta_timestamps: dict[list[float]] | None = None,
     ):
-        assert dataset_id in DATASET_IDS
-
-        super().__init__(
-            dataset_id,
-            batch_size,
-            shuffle=shuffle,
-            root=root,
-            pin_memory=pin_memory,
-            prefetch=prefetch,
-            sampler=sampler,
-            collate_fn=collate_fn,
-            writer=writer,
-            transform=transform,
-        )
+        super().__init__()
+        self.dataset_id = dataset_id
+        self.version = version
+        self.root = root
+        self.split = split
+        self.transform = transform
+        self.delta_timestamps = delta_timestamps
+        # load data from hub or locally when root is provided
+        self.hf_dataset = load_hf_dataset(dataset_id, version, root, split)
+        self.episode_data_index = load_episode_data_index(dataset_id, version, root)
+        self.stats = load_stats(dataset_id, version, root)
 
     @property
-    def stats_patterns(self) -> dict:
-        d = {
-            ("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 -> 1 c 1 1"
-        return d
+    def num_samples(self) -> int:
+        return len(self.hf_dataset)
 
     @property
-    def image_keys(self) -> list:
-        return [("observation", "image", cam) for cam in CAMERAS[self.dataset_id]]
+    def num_episodes(self) -> int:
+        return len(self.hf_dataset.unique("episode_index"))
 
-    def _download_and_preproc_obsolete(self):
-        assert self.root is not None
-        raw_dir = self.root / f"{self.dataset_id}_raw"
-        if not raw_dir.is_dir():
-            download(raw_dir, self.dataset_id)
+    def __len__(self):
+        return self.num_samples
 
-        total_num_frames = 0
-        logging.info("Compute total number of frames to initialize offline buffer")
-        for ep_id in range(NUM_EPISODES[self.dataset_id]):
-            ep_path = raw_dir / f"episode_{ep_id}.hdf5"
-            with h5py.File(ep_path, "r") as ep:
-                total_num_frames += ep["/action"].shape[0] - 1
-        logging.info(f"{total_num_frames=}")
+    def __getitem__(self, idx):
+        item = self.hf_dataset[idx]
 
-        logging.info("Initialize and feed offline buffer")
-        idxtd = 0
-        for ep_id in tqdm.tqdm(range(NUM_EPISODES[self.dataset_id])):
-            ep_path = raw_dir / f"episode_{ep_id}.hdf5"
-            with h5py.File(ep_path, "r") as ep:
-                ep_num_frames = ep["/action"].shape[0]
+        if self.delta_timestamps is not None:
+            item = load_previous_and_future_frames(
+                item,
+                self.hf_dataset,
+                self.episode_data_index,
+                self.delta_timestamps,
+                tol=1 / self.fps - 1e-4,  # 1e-4 to account for possible numerical error
+            )
 
-                # last step of demonstration is considered done
-                done = torch.zeros(ep_num_frames, 1, dtype=torch.bool)
-                done[-1] = True
+        if self.transform is not None:
+            item = self.transform(item)
 
-                state = torch.from_numpy(ep["/observations/qpos"][:])
-                action = torch.from_numpy(ep["/action"][:])
-
-                ep_td = TensorDict(
-                    {
-                        ("observation", "state"): state[:-1],
-                        "action": action[:-1],
-                        "episode": torch.tensor([ep_id] * (ep_num_frames - 1)),
-                        "frame_id": torch.arange(0, ep_num_frames - 1, 1),
-                        ("next", "observation", "state"): state[1:],
-                        # TODO: compute reward and success
-                        # ("next", "reward"): reward[1:],
-                        ("next", "done"): done[1:],
-                        # ("next", "success"): success[1:],
-                    },
-                    batch_size=ep_num_frames - 1,
-                )
-
-                for cam in CAMERAS[self.dataset_id]:
-                    image = torch.from_numpy(ep[f"/observations/images/{cam}"][:])
-                    image = einops.rearrange(image, "b h w c -> b c h w").contiguous()
-                    ep_td["observation", "image", cam] = image[:-1]
-                    ep_td["next", "observation", "image", cam] = image[1:]
-
-                if ep_id == 0:
-                    # hack to initialize tensordict data structure to store episodes
-                    td_data = ep_td[0].expand(total_num_frames).memmap_like(self.root / f"{self.dataset_id}")
-
-                td_data[idxtd : idxtd + len(ep_td)] = ep_td
-                idxtd = idxtd + len(ep_td)
-
-        return TensorStorage(td_data.lock_())
+        return item

lerobot/common/datasets/factory.py

@@ -1,131 +1,90 @@
-import logging
 import os
 from pathlib import Path
 
 import torch
-from torchrl.data.replay_buffers import PrioritizedSliceSampler, SliceSampler
+from torchvision.transforms import v2
 
-from lerobot.common.envs.transforms import NormalizeTransform, Prod
+from lerobot.common.transforms import NormalizeTransform
 
-# DATA_DIR specifies to location where datasets are loaded. By default, DATA_DIR is None and
-# we load from `$HOME/.cache/huggingface/hub/datasets`. For our unit tests, we set `DATA_DIR=tests/data`
-# to load a subset of our datasets for faster continuous integration.
 DATA_DIR = Path(os.environ["DATA_DIR"]) if "DATA_DIR" in os.environ else None
 
 
-def make_offline_buffer(
-    cfg, overwrite_sampler=None, normalize=True, overwrite_batch_size=None, overwrite_prefetch=None
+def make_dataset(
+    cfg,
+    # set normalize=False to remove all transformations and keep images unnormalized in [0,255]
+    normalize=True,
+    stats_path=None,
+    split="train",
 ):
-    if cfg.policy.balanced_sampling:
-        assert cfg.online_steps > 0
-        batch_size = None
-        pin_memory = False
-        prefetch = None
-    else:
-        assert cfg.online_steps == 0
-        num_slices = cfg.policy.batch_size
-        batch_size = cfg.policy.horizon * num_slices
-        pin_memory = cfg.device == "cuda"
-        prefetch = cfg.prefetch
+    if cfg.env.name == "xarm":
+        from lerobot.common.datasets.xarm import XarmDataset
 
-    if overwrite_batch_size is not None:
-        batch_size = overwrite_batch_size
-
-    if overwrite_prefetch is not None:
-        prefetch = overwrite_prefetch
-
-    if overwrite_sampler is None:
-        # TODO(rcadene): move batch_size outside
-        num_traj_per_batch = cfg.policy.batch_size  # // cfg.horizon
-        # TODO(rcadene): Sampler outputs a batch_size <= cfg.batch_size.
-        # We would need to add a transform to pad the tensordict to ensure batch_size == cfg.batch_size.
-
-        if cfg.offline_prioritized_sampler:
-            logging.info("use prioritized sampler for offline dataset")
-            sampler = PrioritizedSliceSampler(
-                max_capacity=100_000,
-                alpha=cfg.policy.per_alpha,
-                beta=cfg.policy.per_beta,
-                num_slices=num_traj_per_batch,
-                strict_length=False,
-            )
-        else:
-            logging.info("use simple sampler for offline dataset")
-            sampler = SliceSampler(
-                num_slices=num_traj_per_batch,
-                strict_length=False,
-            )
-    else:
-        sampler = overwrite_sampler
-
-    if cfg.env.name == "simxarm":
-        from lerobot.common.datasets.simxarm import SimxarmExperienceReplay
-
-        clsfunc = SimxarmExperienceReplay
-        dataset_id = f"xarm_{cfg.env.task}_medium"
+        clsfunc = XarmDataset
 
     elif cfg.env.name == "pusht":
-        from lerobot.common.datasets.pusht import PushtExperienceReplay
+        from lerobot.common.datasets.pusht import PushtDataset
 
-        clsfunc = PushtExperienceReplay
-        dataset_id = "pusht"
+        clsfunc = PushtDataset
 
     elif cfg.env.name == "aloha":
-        from lerobot.common.datasets.aloha import AlohaExperienceReplay
+        from lerobot.common.datasets.aloha import AlohaDataset
 
-        clsfunc = AlohaExperienceReplay
-        dataset_id = f"aloha_{cfg.env.task}"
+        clsfunc = AlohaDataset
     else:
         raise ValueError(cfg.env.name)
 
-    offline_buffer = clsfunc(
-        dataset_id=dataset_id,
-        sampler=sampler,
-        batch_size=batch_size,
-        root=DATA_DIR,
-        pin_memory=pin_memory,
-        prefetch=prefetch if isinstance(prefetch, int) else None,
-    )
-
-    if cfg.policy.name == "tdmpc":
-        img_keys = []
-        for key in offline_buffer.image_keys:
-            img_keys.append(("next", *key))
-        img_keys += offline_buffer.image_keys
-    else:
-        img_keys = offline_buffer.image_keys
-
-    transforms = [Prod(in_keys=img_keys, prod=1 / 255)]
-
+    transforms = None
     if normalize:
-        # TODO(rcadene): make normalization strategy configurable between mean_std, min_max, manual_min_max, min_max_from_spec
-        stats = offline_buffer.compute_or_load_stats()
-
-        # we only normalize the state and action, since the images are usually normalized inside the model for now (except for tdmpc: see the following)
-        in_keys = [("observation", "state"), ("action")]
-
-        if cfg.policy.name == "tdmpc":
-            # TODO(rcadene): we add img_keys to the keys to normalize for tdmpc only, since diffusion and act policies normalize the image inside the model for now
-            in_keys += img_keys
-            # TODO(racdene): since we use next observations in tdmpc, we also add them to the normalization. We are wasting a bit of compute on this for now.
-            in_keys += [("next", *key) for key in img_keys]
-            in_keys.append(("next", "observation", "state"))
-
-        if cfg.policy.name == "diffusion" and cfg.env.name == "pusht":
-            # TODO(rcadene): we overwrite stats to have the same as pretrained model, but we should remove this
-            stats["observation", "state", "min"] = torch.tensor([13.456424, 32.938293], dtype=torch.float32)
-            stats["observation", "state", "max"] = torch.tensor([496.14618, 510.9579], dtype=torch.float32)
-            stats["action", "min"] = torch.tensor([12.0, 25.0], dtype=torch.float32)
-            stats["action", "max"] = torch.tensor([511.0, 511.0], dtype=torch.float32)
-
+        # TODO(rcadene): make normalization strategy configurable between mean_std, min_max, manual_min_max,
+        # min_max_from_spec
         # TODO(rcadene): remove this and put it in config. Ideally we want to reproduce SOTA results just with mean_std
         normalization_mode = "mean_std" if cfg.env.name == "aloha" else "min_max"
-        transforms.append(NormalizeTransform(stats, in_keys, mode=normalization_mode))
 
-    offline_buffer.set_transform(transforms)
+        if cfg.policy.name == "diffusion" and cfg.env.name == "pusht":
+            stats = {}
+            # TODO(rcadene): we overwrite stats to have the same as pretrained model, but we should remove this
+            stats["observation.state"] = {}
+            stats["observation.state"]["min"] = torch.tensor([13.456424, 32.938293], dtype=torch.float32)
+            stats["observation.state"]["max"] = torch.tensor([496.14618, 510.9579], dtype=torch.float32)
+            stats["action"] = {}
+            stats["action"]["min"] = torch.tensor([12.0, 25.0], dtype=torch.float32)
+            stats["action"]["max"] = torch.tensor([511.0, 511.0], dtype=torch.float32)
+        elif stats_path is None:
+            # load a first dataset to access precomputed stats
+            stats_dataset = clsfunc(
+                dataset_id=cfg.dataset_id,
+                split="train",
+                root=DATA_DIR,
+            )
+            stats = stats_dataset.stats
+        else:
+            stats = torch.load(stats_path)
 
-    if not overwrite_sampler:
-        index = torch.arange(0, offline_buffer.num_samples, 1)
-        sampler.extend(index)
+        transforms = v2.Compose(
+            [
+                NormalizeTransform(
+                    stats,
+                    in_keys=[
+                        "observation.state",
+                        "action",
+                    ],
+                    mode=normalization_mode,
+                ),
+            ]
+        )
 
-    return offline_buffer
+    delta_timestamps = cfg.policy.get("delta_timestamps")
+    if delta_timestamps is not None:
+        for key in delta_timestamps:
+            if isinstance(delta_timestamps[key], str):
+                delta_timestamps[key] = eval(delta_timestamps[key])
+
+    dataset = clsfunc(
+        dataset_id=cfg.dataset_id,
+        split=split,
+        root=DATA_DIR,
+        delta_timestamps=delta_timestamps,
+        transform=transforms,
+    )
+
+    return dataset

lerobot/common/datasets/pusht.py

@@ -1,219 +1,76 @@
 from pathlib import Path
-from typing import Callable
 
-import einops
-import numpy as np
-import pygame
-import pymunk
 import torch
-import torchrl
-import tqdm
-from tensordict import TensorDict
-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 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
-
-# as define in env
-SUCCESS_THRESHOLD = 0.95  # 95% coverage,
-
-PUSHT_URL = "https://diffusion-policy.cs.columbia.edu/data/training/pusht.zip"
-PUSHT_ZARR = Path("pusht/pusht_cchi_v7_replay.zarr")
+from lerobot.common.datasets.utils import (
+    load_episode_data_index,
+    load_hf_dataset,
+    load_previous_and_future_frames,
+    load_stats,
+)
 
 
-def get_goal_pose_body(pose):
-    mass = 1
-    inertia = pymunk.moment_for_box(mass, (50, 100))
-    body = pymunk.Body(mass, inertia)
-    # preserving the legacy assignment order for compatibility
-    # the order here doesn't matter somehow, maybe because CoM is aligned with body origin
-    body.position = pose[:2].tolist()
-    body.angle = pose[2]
-    return body
+class PushtDataset(torch.utils.data.Dataset):
+    """
+    https://huggingface.co/datasets/lerobot/pusht
 
+    Arguments
+    ----------
+    delta_timestamps : dict[list[float]] | None, optional
+        Loads data from frames with a shift in timestamps with a different strategy for each data key (e.g. state, action or image)
+        If `None`, no shift is applied to current timestamp and the data from the current frame is loaded.
+    """
 
-def add_segment(space, a, b, radius):
-    shape = pymunk.Segment(space.static_body, a, b, radius)
-    shape.color = pygame.Color("LightGray")  # https://htmlcolorcodes.com/color-names
-    return shape
+    # Copied from lerobot/__init__.py
+    available_datasets = ["pusht"]
+    fps = 10
+    image_keys = ["observation.image"]
 
-
-def add_tee(
-    space,
-    position,
-    angle,
-    scale=30,
-    color="LightSlateGray",
-    mask=None,
-):
-    if mask is None:
-        mask = pymunk.ShapeFilter.ALL_MASKS()
-    mass = 1
-    length = 4
-    vertices1 = [
-        (-length * scale / 2, scale),
-        (length * scale / 2, scale),
-        (length * scale / 2, 0),
-        (-length * scale / 2, 0),
-    ]
-    inertia1 = pymunk.moment_for_poly(mass, vertices=vertices1)
-    vertices2 = [
-        (-scale / 2, scale),
-        (-scale / 2, length * scale),
-        (scale / 2, length * scale),
-        (scale / 2, scale),
-    ]
-    inertia2 = pymunk.moment_for_poly(mass, vertices=vertices1)
-    body = pymunk.Body(mass, inertia1 + inertia2)
-    shape1 = pymunk.Poly(body, vertices1)
-    shape2 = pymunk.Poly(body, vertices2)
-    shape1.color = pygame.Color(color)
-    shape2.color = pygame.Color(color)
-    shape1.filter = pymunk.ShapeFilter(mask=mask)
-    shape2.filter = pymunk.ShapeFilter(mask=mask)
-    body.center_of_gravity = (shape1.center_of_gravity + shape2.center_of_gravity) / 2
-    body.position = position
-    body.angle = angle
-    body.friction = 1
-    space.add(body, shape1, shape2)
-    return body
-
-
-class PushtExperienceReplay(AbstractExperienceReplay):
     def __init__(
         self,
-        dataset_id: str,
-        batch_size: int = None,
-        *,
-        shuffle: bool = True,
+        dataset_id: str = "pusht",
+        version: str | None = "v1.1",
         root: Path | None = None,
-        pin_memory: bool = False,
-        prefetch: int = None,
-        sampler: SliceSampler = None,
-        collate_fn: Callable = None,
-        writer: Writer = None,
-        transform: "torchrl.envs.Transform" = None,
+        split: str = "train",
+        transform: callable = None,
+        delta_timestamps: dict[list[float]] | None = None,
     ):
-        super().__init__(
-            dataset_id,
-            batch_size,
-            shuffle=shuffle,
-            root=root,
-            pin_memory=pin_memory,
-            prefetch=prefetch,
-            sampler=sampler,
-            collate_fn=collate_fn,
-            writer=writer,
-            transform=transform,
-        )
+        super().__init__()
+        self.dataset_id = dataset_id
+        self.version = version
+        self.root = root
+        self.split = split
+        self.transform = transform
+        self.delta_timestamps = delta_timestamps
+        # load data from hub or locally when root is provided
+        self.hf_dataset = load_hf_dataset(dataset_id, version, root, split)
+        self.episode_data_index = load_episode_data_index(dataset_id, version, root)
+        self.stats = load_stats(dataset_id, version, root)
 
-    def _download_and_preproc_obsolete(self):
-        assert self.root is not None
-        raw_dir = self.root / f"{self.dataset_id}_raw"
-        zarr_path = (raw_dir / PUSHT_ZARR).resolve()
-        if not zarr_path.is_dir():
-            raw_dir.mkdir(parents=True, exist_ok=True)
-            download_and_extract_zip(PUSHT_URL, raw_dir)
+    @property
+    def num_samples(self) -> int:
+        return len(self.hf_dataset)
 
-        # load
-        dataset_dict = DiffusionPolicyReplayBuffer.copy_from_path(
-            zarr_path
-        )  # , keys=['img', 'state', 'action'])
+    @property
+    def num_episodes(self) -> int:
+        return len(self.episode_data_index["from"])
 
-        episode_ids = torch.from_numpy(dataset_dict.get_episode_idxs())
-        num_episodes = dataset_dict.meta["episode_ends"].shape[0]
-        total_frames = dataset_dict["action"].shape[0]
-        # to create test artifact
-        # num_episodes = 1
-        # total_frames = 50
-        assert len(
-            {dataset_dict[key].shape[0] for key in dataset_dict.keys()}  # noqa: SIM118
-        ), "Some data type dont have the same number of total frames."
+    def __len__(self):
+        return self.num_samples
 
-        # TODO: verify that goal pose is expected to be fixed
-        goal_pos_angle = np.array([256, 256, np.pi / 4])  # x, y, theta (in radians)
-        goal_body = get_goal_pose_body(goal_pos_angle)
+    def __getitem__(self, idx):
+        item = self.hf_dataset[idx]
 
-        imgs = torch.from_numpy(dataset_dict["img"])
-        imgs = einops.rearrange(imgs, "b h w c -> b c h w")
-        states = torch.from_numpy(dataset_dict["state"])
-        actions = torch.from_numpy(dataset_dict["action"])
-
-        idx0 = 0
-        idxtd = 0
-        for episode_id in tqdm.tqdm(range(num_episodes)):
-            idx1 = dataset_dict.meta["episode_ends"][episode_id]
-            # to create test artifact
-            # idx1 = 51
-
-            num_frames = idx1 - idx0
-
-            assert (episode_ids[idx0:idx1] == episode_id).all()
-
-            image = imgs[idx0:idx1]
-
-            state = states[idx0:idx1]
-            agent_pos = state[:, :2]
-            block_pos = state[:, 2:4]
-            block_angle = state[:, 4]
-
-            reward = torch.zeros(num_frames, 1)
-            success = torch.zeros(num_frames, 1, dtype=torch.bool)
-            done = torch.zeros(num_frames, 1, dtype=torch.bool)
-            for i in range(num_frames):
-                space = pymunk.Space()
-                space.gravity = 0, 0
-                space.damping = 0
-
-                # Add walls.
-                walls = [
-                    add_segment(space, (5, 506), (5, 5), 2),
-                    add_segment(space, (5, 5), (506, 5), 2),
-                    add_segment(space, (506, 5), (506, 506), 2),
-                    add_segment(space, (5, 506), (506, 506), 2),
-                ]
-                space.add(*walls)
-
-                block_body = add_tee(space, block_pos[i].tolist(), block_angle[i].item())
-                goal_geom = pymunk_to_shapely(goal_body, block_body.shapes)
-                block_geom = pymunk_to_shapely(block_body, block_body.shapes)
-                intersection_area = goal_geom.intersection(block_geom).area
-                goal_area = goal_geom.area
-                coverage = intersection_area / goal_area
-                reward[i] = np.clip(coverage / SUCCESS_THRESHOLD, 0, 1)
-                success[i] = coverage > SUCCESS_THRESHOLD
-
-            # last step of demonstration is considered done
-            done[-1] = True
-
-            ep_td = TensorDict(
-                {
-                    ("observation", "image"): image[:-1],
-                    ("observation", "state"): agent_pos[:-1],
-                    "action": actions[idx0:idx1][:-1],
-                    "episode": episode_ids[idx0:idx1][:-1],
-                    "frame_id": torch.arange(0, num_frames - 1, 1),
-                    ("next", "observation", "image"): image[1:],
-                    ("next", "observation", "state"): agent_pos[1:],
-                    # TODO: verify that reward and done are aligned with image and agent_pos
-                    ("next", "reward"): reward[1:],
-                    ("next", "done"): done[1:],
-                    ("next", "success"): success[1:],
-                },
-                batch_size=num_frames - 1,
+        if self.delta_timestamps is not None:
+            item = load_previous_and_future_frames(
+                item,
+                self.hf_dataset,
+                self.episode_data_index,
+                self.delta_timestamps,
+                tol=1 / self.fps - 1e-4,  # 1e-4 to account for possible numerical error
             )
 
-            if episode_id == 0:
-                # hack to initialize tensordict data structure to store episodes
-                td_data = ep_td[0].expand(total_frames).memmap_like(self.root / f"{self.dataset_id}")
+        if self.transform is not None:
+            item = self.transform(item)
 
-            td_data[idxtd : idxtd + len(ep_td)] = ep_td
-
-            idx0 = idx1
-            idxtd = idxtd + len(ep_td)
-
-        return TensorStorage(td_data.lock_())
+        return item

lerobot/common/datasets/simxarm.py

@@ -1,121 +0,0 @@
-import pickle
-import zipfile
-from pathlib import Path
-from typing import Callable
-
-import torch
-import torchrl
-import tqdm
-from tensordict import TensorDict
-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 AbstractExperienceReplay
-
-
-def download():
-    raise NotImplementedError()
-    import gdown
-
-    url = "https://drive.google.com/uc?id=1nhxpykGtPDhmQKm-_B8zBSywVRdgeVya"
-    download_path = "data.zip"
-    gdown.download(url, download_path, quiet=False)
-    print("Extracting...")
-    with zipfile.ZipFile(download_path, "r") as zip_f:
-        for member in zip_f.namelist():
-            if member.startswith("data/xarm") and member.endswith(".pkl"):
-                print(member)
-                zip_f.extract(member=member)
-    Path(download_path).unlink()
-
-
-class SimxarmExperienceReplay(AbstractExperienceReplay):
-    available_datasets = [
-        "xarm_lift_medium",
-    ]
-
-    def __init__(
-        self,
-        dataset_id: str,
-        batch_size: int = None,
-        *,
-        shuffle: bool = True,
-        root: Path | None = None,
-        pin_memory: bool = False,
-        prefetch: int = None,
-        sampler: SliceSampler = None,
-        collate_fn: Callable = None,
-        writer: Writer = None,
-        transform: "torchrl.envs.Transform" = None,
-    ):
-        super().__init__(
-            dataset_id,
-            batch_size,
-            shuffle=shuffle,
-            root=root,
-            pin_memory=pin_memory,
-            prefetch=prefetch,
-            sampler=sampler,
-            collate_fn=collate_fn,
-            writer=writer,
-            transform=transform,
-        )
-
-    def _download_and_preproc_obsolete(self):
-        assert self.root is not None
-        # TODO(rcadene): finish download
-        download()
-
-        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)
-
-        total_frames = dataset_dict["actions"].shape[0]
-
-        idx0 = 0
-        idx1 = 0
-        episode_id = 0
-        for i in tqdm.tqdm(range(total_frames)):
-            idx1 += 1
-
-            if not dataset_dict["dones"][i]:
-                continue
-
-            num_frames = idx1 - idx0
-
-            image = torch.tensor(dataset_dict["observations"]["rgb"][idx0:idx1])
-            state = torch.tensor(dataset_dict["observations"]["state"][idx0:idx1])
-            next_image = torch.tensor(dataset_dict["next_observations"]["rgb"][idx0:idx1])
-            next_state = torch.tensor(dataset_dict["next_observations"]["state"][idx0:idx1])
-            next_reward = torch.tensor(dataset_dict["rewards"][idx0:idx1])
-            next_done = torch.tensor(dataset_dict["dones"][idx0:idx1])
-
-            episode = TensorDict(
-                {
-                    ("observation", "image"): image,
-                    ("observation", "state"): state,
-                    "action": torch.tensor(dataset_dict["actions"][idx0:idx1]),
-                    "episode": torch.tensor([episode_id] * num_frames, dtype=torch.int),
-                    "frame_id": torch.arange(0, num_frames, 1),
-                    ("next", "observation", "image"): next_image,
-                    ("next", "observation", "state"): next_state,
-                    ("next", "observation", "reward"): next_reward,
-                    ("next", "observation", "done"): next_done,
-                },
-                batch_size=num_frames,
-            )
-
-            if episode_id == 0:
-                # hack to initialize tensordict data structure to store episodes
-                td_data = episode[0].expand(total_frames).memmap_like(self.root / f"{self.dataset_id}")
-
-            td_data[idx0:idx1] = episode
-
-            episode_id += 1
-            idx0 = idx1
-
-        return TensorStorage(td_data.lock_())

lerobot/common/datasets/utils.py

@@ -1,30 +1,336 @@
-import io
-import zipfile
+from copy import deepcopy
+from math import ceil
 from pathlib import Path
 
-import requests
+import datasets
+import einops
+import torch
 import tqdm
+from datasets import Image, load_dataset, load_from_disk
+from huggingface_hub import hf_hub_download
+from PIL import Image as PILImage
+from safetensors.torch import load_file
+from torchvision import transforms
+
+from lerobot.common.utils.utils import set_global_seed
 
 
-def download_and_extract_zip(url: str, destination_folder: Path) -> bool:
-    print(f"downloading from {url}")
-    response = requests.get(url, stream=True)
-    if response.status_code == 200:
-        total_size = int(response.headers.get("content-length", 0))
-        progress_bar = tqdm.tqdm(total=total_size, unit="B", unit_scale=True)
+def flatten_dict(d, parent_key="", sep="/"):
+    items = []
+    for k, v in d.items():
+        new_key = f"{parent_key}{sep}{k}" if parent_key else k
+        if isinstance(v, dict):
+            items.extend(flatten_dict(v, new_key, sep=sep).items())
+        else:
+            items.append((new_key, v))
+    return dict(items)
 
-        zip_file = io.BytesIO()
-        for chunk in response.iter_content(chunk_size=1024):
-            if chunk:
-                zip_file.write(chunk)
-                progress_bar.update(len(chunk))
 
-        progress_bar.close()
+def unflatten_dict(d, sep="/"):
+    outdict = {}
+    for key, value in d.items():
+        parts = key.split(sep)
+        d = outdict
+        for part in parts[:-1]:
+            if part not in d:
+                d[part] = {}
+            d = d[part]
+        d[parts[-1]] = value
+    return outdict
 
-        zip_file.seek(0)
 
-        with zipfile.ZipFile(zip_file, "r") as zip_ref:
-            zip_ref.extractall(destination_folder)
-        return True
+def hf_transform_to_torch(items_dict):
+    """Get a transform function that convert items from Hugging Face dataset (pyarrow)
+    to torch tensors. Importantly, images are converted from PIL, which corresponds to
+    a channel last representation (h w c) of uint8 type, to a torch image representation
+    with channel first (c h w) of float32 type in range [0,1].
+    """
+    for key in items_dict:
+        first_item = items_dict[key][0]
+        if isinstance(first_item, PILImage.Image):
+            to_tensor = transforms.ToTensor()
+            items_dict[key] = [to_tensor(img) for img in items_dict[key]]
+        else:
+            items_dict[key] = [torch.tensor(x) for x in items_dict[key]]
+    return items_dict
+
+
+def load_hf_dataset(dataset_id, version, root, split) -> datasets.Dataset:
+    """hf_dataset contains all the observations, states, actions, rewards, etc."""
+    if root is not None:
+        hf_dataset = load_from_disk(Path(root) / dataset_id / split)
     else:
-        return False
+        # TODO(rcadene): remove dataset_id everywhere and use repo_id instead
+        repo_id = f"lerobot/{dataset_id}"
+        hf_dataset = load_dataset(repo_id, revision=version, split=split)
+    hf_dataset.set_transform(hf_transform_to_torch)
+    return hf_dataset
+
+
+def load_episode_data_index(dataset_id, version, root) -> dict[str, torch.Tensor]:
+    """episode_data_index contains the range of indices for each episode
+
+    Example:
+    ```python
+    from_id = episode_data_index["from"][episode_id].item()
+    to_id = episode_data_index["to"][episode_id].item()
+    episode_frames = [dataset[i] for i in range(from_id, to_id)]
+    ```
+    """
+    if root is not None:
+        path = Path(root) / dataset_id / "meta_data" / "episode_data_index.safetensors"
+    else:
+        repo_id = f"lerobot/{dataset_id}"
+        path = hf_hub_download(
+            repo_id, "meta_data/episode_data_index.safetensors", repo_type="dataset", revision=version
+        )
+
+    return load_file(path)
+
+
+def load_stats(dataset_id, version, root) -> dict[str, dict[str, torch.Tensor]]:
+    """stats contains the statistics per modality computed over the full dataset, such as max, min, mean, std
+
+    Example:
+    ```python
+    normalized_action = (action - stats["action"]["mean"]) / stats["action"]["std"]
+    ```
+    """
+    if root is not None:
+        path = Path(root) / dataset_id / "meta_data" / "stats.safetensors"
+    else:
+        repo_id = f"lerobot/{dataset_id}"
+        path = hf_hub_download(repo_id, "meta_data/stats.safetensors", repo_type="dataset", revision=version)
+
+    stats = load_file(path)
+    return unflatten_dict(stats)
+
+
+def load_previous_and_future_frames(
+    item: dict[str, torch.Tensor],
+    hf_dataset: datasets.Dataset,
+    episode_data_index: dict[str, torch.Tensor],
+    delta_timestamps: dict[str, list[float]],
+    tol: float,
+) -> dict[torch.Tensor]:
+    """
+    Given a current item in the dataset containing a timestamp (e.g. 0.6 seconds), and a list of time differences of
+    some modalities (e.g. delta_timestamps={"observation.image": [-0.8, -0.2, 0, 0.2]}), this function computes for each
+    given modality a list of query timestamps (e.g. [-0.2, 0.4, 0.6, 0.8]) and loads the closest frames in the dataset.
+
+    Importantly, when no frame can be found around a query timestamp within a specified tolerance window, this function
+    raises an AssertionError. When a timestamp is queried before the first available timestamp of the episode or after
+    the last available timestamp, the violation of the tolerance doesnt raise an AssertionError, and the function
+    populates a boolean array indicating which frames are outside of the episode range. For instance, this boolean array
+    is useful during batched training to not supervise actions associated to timestamps coming after the end of the
+    episode, or to pad the observations in a specific way. Note that by default the observation frames before the start
+    of the episode are the same as the first frame of the episode.
+
+    Parameters:
+    - item (dict): A dictionary containing all the data related to a frame. It is the result of `dataset[idx]`. Each key
+      corresponds to a different modality (e.g., "timestamp", "observation.image", "action").
+    - hf_dataset (datasets.Dataset): A dictionary containing the full dataset. Each key corresponds to a different
+      modality (e.g., "timestamp", "observation.image", "action").
+    - episode_data_index (dict): A dictionary containing two keys ("from" and "to") associated to dataset indices.
+      They indicate the start index and end index of each episode in the dataset.
+    - delta_timestamps (dict): A dictionary containing lists of delta timestamps for each possible modality to be
+      retrieved. These deltas are added to the item timestamp to form the query timestamps.
+    - tol (float, optional): The tolerance level used to determine if a data point is close enough to the query
+      timestamp by asserting `tol > difference`. It is suggested to set `tol` to a smaller value than the
+      smallest expected inter-frame period, but large enough to account for jitter.
+
+    Returns:
+    - The same item with the queried frames for each modality specified in delta_timestamps, with an additional key for
+      each modality (e.g. "observation.image_is_pad").
+
+    Raises:
+    - AssertionError: If any of the frames unexpectedly violate the tolerance level. This could indicate synchronization
+      issues with timestamps during data collection.
+    """
+    # get indices of the frames associated to the episode, and their timestamps
+    ep_id = item["episode_index"].item()
+    ep_data_id_from = episode_data_index["from"][ep_id].item()
+    ep_data_id_to = episode_data_index["to"][ep_id].item()
+    ep_data_ids = torch.arange(ep_data_id_from, ep_data_id_to, 1)
+
+    # load timestamps
+    ep_timestamps = hf_dataset.select_columns("timestamp")[ep_data_id_from:ep_data_id_to]["timestamp"]
+    ep_timestamps = torch.stack(ep_timestamps)
+
+    # we make the assumption that the timestamps are sorted
+    ep_first_ts = ep_timestamps[0]
+    ep_last_ts = ep_timestamps[-1]
+    current_ts = item["timestamp"].item()
+
+    for key in delta_timestamps:
+        # get timestamps used as query to retrieve data of previous/future frames
+        delta_ts = delta_timestamps[key]
+        query_ts = current_ts + torch.tensor(delta_ts)
+
+        # compute distances between each query timestamp and all timestamps of all the frames belonging to the episode
+        dist = torch.cdist(query_ts[:, None], ep_timestamps[:, None], p=1)
+        min_, argmin_ = dist.min(1)
+
+        # TODO(rcadene): synchronize timestamps + interpolation if needed
+
+        is_pad = min_ > tol
+
+        # check violated query timestamps are all outside the episode range
+        assert ((query_ts[is_pad] < ep_first_ts) | (ep_last_ts < query_ts[is_pad])).all(), (
+            f"One or several timestamps unexpectedly violate the tolerance ({min_} > {tol=}) inside episode range."
+            "This might be due to synchronization issues with timestamps during data collection."
+        )
+
+        # get dataset indices corresponding to frames to be loaded
+        data_ids = ep_data_ids[argmin_]
+
+        # load frames modality
+        item[key] = hf_dataset.select_columns(key)[data_ids][key]
+        item[key] = torch.stack(item[key])
+        item[f"{key}_is_pad"] = is_pad
+
+    return item
+
+
+def get_stats_einops_patterns(hf_dataset):
+    """These einops patterns will be used to aggregate batches and compute statistics.
+
+    Note: We assume the images are returned in channel first format
+    """
+
+    dataloader = torch.utils.data.DataLoader(
+        hf_dataset,
+        num_workers=0,
+        batch_size=2,
+        shuffle=False,
+    )
+    batch = next(iter(dataloader))
+
+    stats_patterns = {}
+    for key, feats_type in hf_dataset.features.items():
+        # sanity check that tensors are not float64
+        assert batch[key].dtype != torch.float64
+
+        if isinstance(feats_type, Image):
+            # sanity check that images are channel first
+            _, c, h, w = batch[key].shape
+            assert c < h and c < w, f"expect channel first images, but instead {batch[key].shape}"
+
+            # sanity check that images are float32 in range [0,1]
+            assert batch[key].dtype == torch.float32, f"expect torch.float32, but instead {batch[key].dtype=}"
+            assert batch[key].max() <= 1, f"expect pixels lower than 1, but instead {batch[key].max()=}"
+            assert batch[key].min() >= 0, f"expect pixels greater than 1, but instead {batch[key].min()=}"
+
+            stats_patterns[key] = "b c h w -> c 1 1"
+        elif batch[key].ndim == 2:
+            stats_patterns[key] = "b c -> c "
+        elif batch[key].ndim == 1:
+            stats_patterns[key] = "b -> 1"
+        else:
+            raise ValueError(f"{key}, {feats_type}, {batch[key].shape}")
+
+    return stats_patterns
+
+
+def compute_stats(hf_dataset, batch_size=32, max_num_samples=None):
+    if max_num_samples is None:
+        max_num_samples = len(hf_dataset)
+
+    stats_patterns = get_stats_einops_patterns(hf_dataset)
+
+    # mean and std will be computed incrementally while max and min will track the running value.
+    mean, std, max, min = {}, {}, {}, {}
+    for key in stats_patterns:
+        mean[key] = torch.tensor(0.0).float()
+        std[key] = torch.tensor(0.0).float()
+        max[key] = torch.tensor(-float("inf")).float()
+        min[key] = torch.tensor(float("inf")).float()
+
+    def create_seeded_dataloader(hf_dataset, batch_size, seed):
+        set_global_seed(seed)
+        dataloader = torch.utils.data.DataLoader(
+            hf_dataset,
+            num_workers=4,
+            batch_size=batch_size,
+            shuffle=True,
+            drop_last=False,
+        )
+        return dataloader
+
+    # Note: Due to be refactored soon. The point of storing `first_batch` is to make sure we don't get
+    # surprises when rerunning the sampler.
+    first_batch = None
+    running_item_count = 0  # for online mean computation
+    dataloader = create_seeded_dataloader(hf_dataset, batch_size, seed=1337)
+    for i, batch in enumerate(
+        tqdm.tqdm(dataloader, total=ceil(max_num_samples / batch_size), desc="Compute mean, min, max")
+    ):
+        this_batch_size = len(batch["index"])
+        running_item_count += this_batch_size
+        if first_batch is None:
+            first_batch = deepcopy(batch)
+        for key, pattern in stats_patterns.items():
+            batch[key] = batch[key].float()
+            # Numerically stable update step for mean computation.
+            batch_mean = einops.reduce(batch[key], pattern, "mean")
+            # Hint: to update the mean we need x̄ₙ = (Nₙ₋₁x̄ₙ₋₁ + Bₙxₙ) / Nₙ, where the subscript represents
+            # the update step, N is the running item count, B is this batch size, x̄ is the running mean,
+            # and x is the current batch mean. Some rearrangement is then required to avoid risking
+            # numerical overflow. Another hint: Nₙ₋₁ = Nₙ - Bₙ. Rearrangement yields
+            # x̄ₙ = x̄ₙ₋₁ + Bₙ * (xₙ - x̄ₙ₋₁) / Nₙ
+            mean[key] = mean[key] + this_batch_size * (batch_mean - mean[key]) / running_item_count
+            max[key] = torch.maximum(max[key], einops.reduce(batch[key], pattern, "max"))
+            min[key] = torch.minimum(min[key], einops.reduce(batch[key], pattern, "min"))
+
+        if i == ceil(max_num_samples / batch_size) - 1:
+            break
+
+    first_batch_ = None
+    running_item_count = 0  # for online std computation
+    dataloader = create_seeded_dataloader(hf_dataset, batch_size, seed=1337)
+    for i, batch in enumerate(
+        tqdm.tqdm(dataloader, total=ceil(max_num_samples / batch_size), desc="Compute std")
+    ):
+        this_batch_size = len(batch["index"])
+        running_item_count += this_batch_size
+        # Sanity check to make sure the batches are still in the same order as before.
+        if first_batch_ is None:
+            first_batch_ = deepcopy(batch)
+            for key in stats_patterns:
+                assert torch.equal(first_batch_[key], first_batch[key])
+        for key, pattern in stats_patterns.items():
+            batch[key] = batch[key].float()
+            # Numerically stable update step for mean computation (where the mean is over squared
+            # residuals).See notes in the mean computation loop above.
+            batch_std = einops.reduce((batch[key] - mean[key]) ** 2, pattern, "mean")
+            std[key] = std[key] + this_batch_size * (batch_std - std[key]) / running_item_count
+
+        if i == ceil(max_num_samples / batch_size) - 1:
+            break
+
+    for key in stats_patterns:
+        std[key] = torch.sqrt(std[key])
+
+    stats = {}
+    for key in stats_patterns:
+        stats[key] = {
+            "mean": mean[key],
+            "std": std[key],
+            "max": max[key],
+            "min": min[key],
+        }
+
+    return stats
+
+
+def cycle(iterable):
+    """The equivalent of itertools.cycle, but safe for Pytorch dataloaders.
+
+    See https://github.com/pytorch/pytorch/issues/23900 for information on why itertools.cycle is not safe.
+    """
+    iterator = iter(iterable)
+    while True:
+        try:
+            yield next(iterator)
+        except StopIteration:
+            iterator = iter(iterable)

lerobot/common/datasets/xarm.py

@@ -0,0 +1,78 @@
+from pathlib import Path
+
+import torch
+
+from lerobot.common.datasets.utils import (
+    load_episode_data_index,
+    load_hf_dataset,
+    load_previous_and_future_frames,
+    load_stats,
+)
+
+
+class XarmDataset(torch.utils.data.Dataset):
+    """
+    https://huggingface.co/datasets/lerobot/xarm_lift_medium
+    https://huggingface.co/datasets/lerobot/xarm_lift_medium_replay
+    https://huggingface.co/datasets/lerobot/xarm_push_medium
+    https://huggingface.co/datasets/lerobot/xarm_push_medium_replay
+    """
+
+    # Copied from lerobot/__init__.py
+    available_datasets = [
+        "xarm_lift_medium",
+        "xarm_lift_medium_replay",
+        "xarm_push_medium",
+        "xarm_push_medium_replay",
+    ]
+    fps = 15
+    image_keys = ["observation.image"]
+
+    def __init__(
+        self,
+        dataset_id: str,
+        version: str | None = "v1.1",
+        root: Path | None = None,
+        split: str = "train",
+        transform: callable = None,
+        delta_timestamps: dict[list[float]] | None = None,
+    ):
+        super().__init__()
+        self.dataset_id = dataset_id
+        self.version = version
+        self.root = root
+        self.split = split
+        self.transform = transform
+        self.delta_timestamps = delta_timestamps
+        # load data from hub or locally when root is provided
+        self.hf_dataset = load_hf_dataset(dataset_id, version, root, split)
+        self.episode_data_index = load_episode_data_index(dataset_id, version, root)
+        self.stats = load_stats(dataset_id, version, root)
+
+    @property
+    def num_samples(self) -> int:
+        return len(self.hf_dataset)
+
+    @property
+    def num_episodes(self) -> int:
+        return len(self.hf_dataset.unique("episode_index"))
+
+    def __len__(self):
+        return self.num_samples
+
+    def __getitem__(self, idx):
+        item = self.hf_dataset[idx]
+
+        if self.delta_timestamps is not None:
+            item = load_previous_and_future_frames(
+                item,
+                self.hf_dataset,
+                self.episode_data_index,
+                self.delta_timestamps,
+                tol=1 / self.fps - 1e-4,  # 1e-4 to account for possible numerical error
+            )
+
+        if self.transform is not None:
+            item = self.transform(item)
+
+        return item

lerobot/common/envs/abstract.py

@@ -1,80 +0,0 @@
-import abc
-from collections import deque
-from typing import Optional
-
-from tensordict import TensorDict
-from torchrl.envs import EnvBase
-
-
-class AbstractEnv(EnvBase):
-    def __init__(
-        self,
-        task,
-        frame_skip: int = 1,
-        from_pixels: bool = False,
-        pixels_only: bool = False,
-        image_size=None,
-        seed=1337,
-        device="cpu",
-        num_prev_obs=1,
-        num_prev_action=0,
-    ):
-        super().__init__(device=device, batch_size=[])
-        self.task = task
-        self.frame_skip = frame_skip
-        self.from_pixels = from_pixels
-        self.pixels_only = pixels_only
-        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
-        if from_pixels:
-            assert image_size
-
-        self._make_env()
-        self._make_spec()
-        self._current_seed = self.set_seed(seed)
-
-        if self.num_prev_obs > 0:
-            self._prev_obs_image_queue = deque(maxlen=self.num_prev_obs)
-            self._prev_obs_state_queue = deque(maxlen=self.num_prev_obs)
-        if self.num_prev_action > 0:
-            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()
-
-    @abc.abstractmethod
-    def _reset(self, tensordict: Optional[TensorDict] = None):
-        raise NotImplementedError()
-
-    @abc.abstractmethod
-    def _step(self, tensordict: TensorDict):
-        raise NotImplementedError()
-
-    @abc.abstractmethod
-    def _make_env(self):
-        raise NotImplementedError()
-
-    @abc.abstractmethod
-    def _make_spec(self):
-        raise NotImplementedError()
-
-    @abc.abstractmethod
-    def _set_seed(self, seed: Optional[int]):
-        raise NotImplementedError()

lerobot/common/envs/aloha/assets/bimanual_viperx_end_effector_insertion.xml

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

lerobot/common/envs/aloha/assets/bimanual_viperx_end_effector_transfer_cube.xml

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

lerobot/common/envs/aloha/assets/bimanual_viperx_insertion.xml

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

lerobot/common/envs/aloha/assets/bimanual_viperx_transfer_cube.xml

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

lerobot/common/envs/aloha/assets/scene.xml

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

lerobot/common/envs/aloha/assets/vx300s_dependencies.xml

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

lerobot/common/envs/aloha/assets/vx300s_left.xml

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

lerobot/common/envs/aloha/assets/vx300s_right.xml

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

lerobot/common/envs/aloha/constants.py

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

lerobot/common/envs/aloha/env.py

@@ -1,311 +0,0 @@
-import importlib
-import logging
-from collections import deque
-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,
-    CompositeSpec,
-    DiscreteTensorSpec,
-    UnboundedContinuousTensorSpec,
-)
-
-from lerobot.common.envs.abstract import AbstractEnv
-from lerobot.common.envs.aloha.constants import (
-    ACTIONS,
-    ASSETS_DIR,
-    DT,
-    JOINTS,
-)
-from lerobot.common.envs.aloha.tasks.sim import BOX_POSE, InsertionTask, TransferCubeTask
-from lerobot.common.envs.aloha.tasks.sim_end_effector import (
-    InsertionEndEffectorTask,
-    TransferCubeEndEffectorTask,
-)
-from lerobot.common.envs.aloha.utils import sample_box_pose, sample_insertion_pose
-from lerobot.common.utils import set_seed
-
-_has_gym = importlib.util.find_spec("gym") is not None
-
-
-class AlohaEnv(AbstractEnv):
-    def __init__(
-        self,
-        task,
-        frame_skip: int = 1,
-        from_pixels: bool = False,
-        pixels_only: bool = False,
-        image_size=None,
-        seed=1337,
-        device="cpu",
-        num_prev_obs=1,
-        num_prev_action=0,
-    ):
-        super().__init__(
-            task=task,
-            frame_skip=frame_skip,
-            from_pixels=from_pixels,
-            pixels_only=pixels_only,
-            image_size=image_size,
-            seed=seed,
-            device=device,
-            num_prev_obs=num_prev_obs,
-            num_prev_action=num_prev_action,
-        )
-
-    def _make_env(self):
-        if not _has_gym:
-            raise ImportError("Cannot import gym.")
-
-        if not self.from_pixels:
-            raise NotImplementedError()
-
-        self._env = self._make_env_task(self.task)
-
-    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())
-            image = einops.rearrange(image, "h w c -> c h w")
-            assert image.dtype == torch.uint8
-            obs = {"image": {"top": image}}
-
-            if not self.pixels_only:
-                obs["state"] = torch.from_numpy(raw_obs["qpos"]).type(torch.float32)
-        else:
-            # TODO(rcadene):
-            raise NotImplementedError()
-            # obs = {"state": torch.from_numpy(raw_obs["observation"]).type(torch.float32)}
-
-        return obs
-
-    def _reset(self, tensordict: Optional[TensorDict] = None):
-        td = tensordict
-        if td is None or td.is_empty():
-            # we need to handle seed iteration, since self._env.reset() rely an internal _seed.
-            self._current_seed += 1
-            self.set_seed(self._current_seed)
-
-            # 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
-
-            raw_obs = self._env.reset()
-            # TODO(rcadene): add assert
-            # assert self._current_seed == self._env._seed
-
-            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=[],
-            )
-        else:
-            raise NotImplementedError()
-
-        self.call_rendering_hooks()
-        return td
-
-    def _step(self, tensordict: TensorDict):
-        td = tensordict
-        action = td["action"].numpy()
-        # step expects shape=(4,) so we pad if necessary
-        # TODO(rcadene): add info["is_success"] and info["success"] ?
-        sum_reward = 0
-
-        if action.ndim == 1:
-            action = einops.repeat(action, "c -> t c", t=self.frame_skip)
-        else:
-            if self.frame_skip > 1:
-                raise NotImplementedError()
-
-        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
-
-            # 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([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),
-            },
-            batch_size=[],
-        )
-        return td
-
-    def _make_spec(self):
-        obs = {}
-        from omegaconf import OmegaConf
-
-        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):
-                assert len(self.image_size) == 3  # c h w
-                assert self.image_size[0] == 3  # c is RGB
-                image_shape = tuple(self.image_size)
-            else:
-                raise ValueError(self.image_size)
-            if self.num_prev_obs > 0:
-                image_shape = (self.num_prev_obs + 1, *image_shape)
-
-            obs["image"] = {
-                "top": BoundedTensorSpec(
-                    low=0,
-                    high=255,
-                    shape=image_shape,
-                    dtype=torch.uint8,
-                    device=self.device,
-                )
-            }
-            if not self.pixels_only:
-                state_shape = (len(JOINTS),)
-                if self.num_prev_obs > 0:
-                    state_shape = (self.num_prev_obs + 1, *state_shape)
-
-                obs["state"] = UnboundedContinuousTensorSpec(
-                    # TODO: add low and high bounds
-                    shape=state_shape,
-                    dtype=torch.float32,
-                    device=self.device,
-                )
-        else:
-            # TODO(rcadene): add observation_space achieved_goal and desired_goal?
-            state_shape = (len(JOINTS),)
-            if self.num_prev_obs > 0:
-                state_shape = (self.num_prev_obs + 1, *state_shape)
-
-            obs["state"] = UnboundedContinuousTensorSpec(
-                # TODO: add low and high bounds
-                shape=state_shape,
-                dtype=torch.float32,
-                device=self.device,
-            )
-        self.observation_spec = CompositeSpec({"observation": obs})
-
-        # TODO(rcadene): valid when controling end effector?
-        # action_space = self._env.action_spec()
-        # self.action_spec = BoundedTensorSpec(
-        #     low=action_space.minimum,
-        #     high=action_space.maximum,
-        #     shape=action_space.shape,
-        #     dtype=torch.float32,
-        #     device=self.device,
-        # )
-
-        # TODO(rcaene): add bounds (where are they????)
-        self.action_spec = BoundedTensorSpec(
-            shape=(len(ACTIONS)),
-            low=-1,
-            high=1,
-            dtype=torch.float32,
-            device=self.device,
-        )
-
-        self.reward_spec = UnboundedContinuousTensorSpec(
-            shape=(1,),
-            dtype=torch.float32,
-            device=self.device,
-        )
-
-        self.done_spec = CompositeSpec(
-            {
-                "done": DiscreteTensorSpec(
-                    2,
-                    shape=(1,),
-                    dtype=torch.bool,
-                    device=self.device,
-                ),
-                "success": DiscreteTensorSpec(
-                    2,
-                    shape=(1,),
-                    dtype=torch.bool,
-                    device=self.device,
-                ),
-            }
-        )
-
-    def _set_seed(self, seed: Optional[int]):
-        set_seed(seed)
-        # TODO(rcadene): seed the env
-        # self._env.seed(seed)
-        logging.warning("Aloha env is not seeded")

lerobot/common/envs/aloha/tasks/sim.py

@@ -1,219 +0,0 @@
-import collections
-
-import numpy as np
-from dm_control.suite import base
-
-from lerobot.common.envs.aloha.constants import (
-    START_ARM_POSE,
-    normalize_puppet_gripper_position,
-    normalize_puppet_gripper_velocity,
-    unnormalize_puppet_gripper_position,
-)
-
-BOX_POSE = [None]  # to be changed from outside
-
-"""
-Environment for simulated robot bi-manual manipulation, with joint position control
-Action space:      [left_arm_qpos (6),             # absolute joint position
-                    left_gripper_positions (1),    # normalized gripper position (0: close, 1: open)
-                    right_arm_qpos (6),            # absolute joint position
-                    right_gripper_positions (1),]  # normalized gripper position (0: close, 1: open)
-
-Observation space: {"qpos": Concat[ left_arm_qpos (6),         # absolute joint position
-                                    left_gripper_position (1),  # normalized gripper position (0: close, 1: open)
-                                    right_arm_qpos (6),         # absolute joint position
-                                    right_gripper_qpos (1)]     # normalized gripper position (0: close, 1: open)
-                    "qvel": Concat[ left_arm_qvel (6),         # absolute joint velocity (rad)
-                                    left_gripper_velocity (1),  # normalized gripper velocity (pos: opening, neg: closing)
-                                    right_arm_qvel (6),         # absolute joint velocity (rad)
-                                    right_gripper_qvel (1)]     # normalized gripper velocity (pos: opening, neg: closing)
-                    "images": {"main": (480x640x3)}        # h, w, c, dtype='uint8'
-"""
-
-
-class BimanualViperXTask(base.Task):
-    def __init__(self, random=None):
-        super().__init__(random=random)
-
-    def before_step(self, action, physics):
-        left_arm_action = action[:6]
-        right_arm_action = action[7 : 7 + 6]
-        normalized_left_gripper_action = action[6]
-        normalized_right_gripper_action = action[7 + 6]
-
-        left_gripper_action = unnormalize_puppet_gripper_position(normalized_left_gripper_action)
-        right_gripper_action = unnormalize_puppet_gripper_position(normalized_right_gripper_action)
-
-        full_left_gripper_action = [left_gripper_action, -left_gripper_action]
-        full_right_gripper_action = [right_gripper_action, -right_gripper_action]
-
-        env_action = np.concatenate(
-            [left_arm_action, full_left_gripper_action, right_arm_action, full_right_gripper_action]
-        )
-        super().before_step(env_action, physics)
-        return
-
-    def initialize_episode(self, physics):
-        """Sets the state of the environment at the start of each episode."""
-        super().initialize_episode(physics)
-
-    @staticmethod
-    def get_qpos(physics):
-        qpos_raw = physics.data.qpos.copy()
-        left_qpos_raw = qpos_raw[:8]
-        right_qpos_raw = qpos_raw[8:16]
-        left_arm_qpos = left_qpos_raw[:6]
-        right_arm_qpos = right_qpos_raw[:6]
-        left_gripper_qpos = [normalize_puppet_gripper_position(left_qpos_raw[6])]
-        right_gripper_qpos = [normalize_puppet_gripper_position(right_qpos_raw[6])]
-        return np.concatenate([left_arm_qpos, left_gripper_qpos, right_arm_qpos, right_gripper_qpos])
-
-    @staticmethod
-    def get_qvel(physics):
-        qvel_raw = physics.data.qvel.copy()
-        left_qvel_raw = qvel_raw[:8]
-        right_qvel_raw = qvel_raw[8:16]
-        left_arm_qvel = left_qvel_raw[:6]
-        right_arm_qvel = right_qvel_raw[:6]
-        left_gripper_qvel = [normalize_puppet_gripper_velocity(left_qvel_raw[6])]
-        right_gripper_qvel = [normalize_puppet_gripper_velocity(right_qvel_raw[6])]
-        return np.concatenate([left_arm_qvel, left_gripper_qvel, right_arm_qvel, right_gripper_qvel])
-
-    @staticmethod
-    def get_env_state(physics):
-        raise NotImplementedError
-
-    def get_observation(self, physics):
-        obs = collections.OrderedDict()
-        obs["qpos"] = self.get_qpos(physics)
-        obs["qvel"] = self.get_qvel(physics)
-        obs["env_state"] = self.get_env_state(physics)
-        obs["images"] = {}
-        obs["images"]["top"] = physics.render(height=480, width=640, camera_id="top")
-        obs["images"]["angle"] = physics.render(height=480, width=640, camera_id="angle")
-        obs["images"]["vis"] = physics.render(height=480, width=640, camera_id="front_close")
-
-        return obs
-
-    def get_reward(self, physics):
-        # return whether left gripper is holding the box
-        raise NotImplementedError
-
-
-class TransferCubeTask(BimanualViperXTask):
-    def __init__(self, random=None):
-        super().__init__(random=random)
-        self.max_reward = 4
-
-    def initialize_episode(self, physics):
-        """Sets the state of the environment at the start of each episode."""
-        # TODO Notice: this function does not randomize the env configuration. Instead, set BOX_POSE from outside
-        # reset qpos, control and box position
-        with physics.reset_context():
-            physics.named.data.qpos[:16] = START_ARM_POSE
-            np.copyto(physics.data.ctrl, START_ARM_POSE)
-            assert BOX_POSE[0] is not None
-            physics.named.data.qpos[-7:] = BOX_POSE[0]
-            # print(f"{BOX_POSE=}")
-        super().initialize_episode(physics)
-
-    @staticmethod
-    def get_env_state(physics):
-        env_state = physics.data.qpos.copy()[16:]
-        return env_state
-
-    def get_reward(self, physics):
-        # return whether left gripper is holding the box
-        all_contact_pairs = []
-        for i_contact in range(physics.data.ncon):
-            id_geom_1 = physics.data.contact[i_contact].geom1
-            id_geom_2 = physics.data.contact[i_contact].geom2
-            name_geom_1 = physics.model.id2name(id_geom_1, "geom")
-            name_geom_2 = physics.model.id2name(id_geom_2, "geom")
-            contact_pair = (name_geom_1, name_geom_2)
-            all_contact_pairs.append(contact_pair)
-
-        touch_left_gripper = ("red_box", "vx300s_left/10_left_gripper_finger") in all_contact_pairs
-        touch_right_gripper = ("red_box", "vx300s_right/10_right_gripper_finger") in all_contact_pairs
-        touch_table = ("red_box", "table") in all_contact_pairs
-
-        reward = 0
-        if touch_right_gripper:
-            reward = 1
-        if touch_right_gripper and not touch_table:  # lifted
-            reward = 2
-        if touch_left_gripper:  # attempted transfer
-            reward = 3
-        if touch_left_gripper and not touch_table:  # successful transfer
-            reward = 4
-        return reward
-
-
-class InsertionTask(BimanualViperXTask):
-    def __init__(self, random=None):
-        super().__init__(random=random)
-        self.max_reward = 4
-
-    def initialize_episode(self, physics):
-        """Sets the state of the environment at the start of each episode."""
-        # TODO Notice: this function does not randomize the env configuration. Instead, set BOX_POSE from outside
-        # reset qpos, control and box position
-        with physics.reset_context():
-            physics.named.data.qpos[:16] = START_ARM_POSE
-            np.copyto(physics.data.ctrl, START_ARM_POSE)
-            assert BOX_POSE[0] is not None
-            physics.named.data.qpos[-7 * 2 :] = BOX_POSE[0]  # two objects
-            # print(f"{BOX_POSE=}")
-        super().initialize_episode(physics)
-
-    @staticmethod
-    def get_env_state(physics):
-        env_state = physics.data.qpos.copy()[16:]
-        return env_state
-
-    def get_reward(self, physics):
-        # return whether peg touches the pin
-        all_contact_pairs = []
-        for i_contact in range(physics.data.ncon):
-            id_geom_1 = physics.data.contact[i_contact].geom1
-            id_geom_2 = physics.data.contact[i_contact].geom2
-            name_geom_1 = physics.model.id2name(id_geom_1, "geom")
-            name_geom_2 = physics.model.id2name(id_geom_2, "geom")
-            contact_pair = (name_geom_1, name_geom_2)
-            all_contact_pairs.append(contact_pair)
-
-        touch_right_gripper = ("red_peg", "vx300s_right/10_right_gripper_finger") in all_contact_pairs
-        touch_left_gripper = (
-            ("socket-1", "vx300s_left/10_left_gripper_finger") in all_contact_pairs
-            or ("socket-2", "vx300s_left/10_left_gripper_finger") in all_contact_pairs
-            or ("socket-3", "vx300s_left/10_left_gripper_finger") in all_contact_pairs
-            or ("socket-4", "vx300s_left/10_left_gripper_finger") in all_contact_pairs
-        )
-
-        peg_touch_table = ("red_peg", "table") in all_contact_pairs
-        socket_touch_table = (
-            ("socket-1", "table") in all_contact_pairs
-            or ("socket-2", "table") in all_contact_pairs
-            or ("socket-3", "table") in all_contact_pairs
-            or ("socket-4", "table") in all_contact_pairs
-        )
-        peg_touch_socket = (
-            ("red_peg", "socket-1") in all_contact_pairs
-            or ("red_peg", "socket-2") in all_contact_pairs
-            or ("red_peg", "socket-3") in all_contact_pairs
-            or ("red_peg", "socket-4") in all_contact_pairs
-        )
-        pin_touched = ("red_peg", "pin") in all_contact_pairs
-
-        reward = 0
-        if touch_left_gripper and touch_right_gripper:  # touch both
-            reward = 1
-        if (
-            touch_left_gripper and touch_right_gripper and (not peg_touch_table) and (not socket_touch_table)
-        ):  # grasp both
-            reward = 2
-        if peg_touch_socket and (not peg_touch_table) and (not socket_touch_table):  # peg and socket touching
-            reward = 3
-        if pin_touched:  # successful insertion
-            reward = 4
-        return reward

lerobot/common/envs/aloha/tasks/sim_end_effector.py

@@ -1,263 +0,0 @@
-import collections
-
-import numpy as np
-from dm_control.suite import base
-
-from lerobot.common.envs.aloha.constants import (
-    PUPPET_GRIPPER_POSITION_CLOSE,
-    START_ARM_POSE,
-    normalize_puppet_gripper_position,
-    normalize_puppet_gripper_velocity,
-    unnormalize_puppet_gripper_position,
-)
-from lerobot.common.envs.aloha.utils import sample_box_pose, sample_insertion_pose
-
-"""
-Environment for simulated robot bi-manual manipulation, with end-effector control.
-Action space:      [left_arm_pose (7),             # position and quaternion for end effector
-                    left_gripper_positions (1),    # normalized gripper position (0: close, 1: open)
-                    right_arm_pose (7),            # position and quaternion for end effector
-                    right_gripper_positions (1),]  # normalized gripper position (0: close, 1: open)
-
-Observation space: {"qpos": Concat[ left_arm_qpos (6),         # absolute joint position
-                                    left_gripper_position (1),  # normalized gripper position (0: close, 1: open)
-                                    right_arm_qpos (6),         # absolute joint position
-                                    right_gripper_qpos (1)]     # normalized gripper position (0: close, 1: open)
-                    "qvel": Concat[ left_arm_qvel (6),         # absolute joint velocity (rad)
-                                    left_gripper_velocity (1),  # normalized gripper velocity (pos: opening, neg: closing)
-                                    right_arm_qvel (6),         # absolute joint velocity (rad)
-                                    right_gripper_qvel (1)]     # normalized gripper velocity (pos: opening, neg: closing)
-                    "images": {"main": (480x640x3)}        # h, w, c, dtype='uint8'
-"""
-
-
-class BimanualViperXEndEffectorTask(base.Task):
-    def __init__(self, random=None):
-        super().__init__(random=random)
-
-    def before_step(self, action, physics):
-        a_len = len(action) // 2
-        action_left = action[:a_len]
-        action_right = action[a_len:]
-
-        # set mocap position and quat
-        # left
-        np.copyto(physics.data.mocap_pos[0], action_left[:3])
-        np.copyto(physics.data.mocap_quat[0], action_left[3:7])
-        # right
-        np.copyto(physics.data.mocap_pos[1], action_right[:3])
-        np.copyto(physics.data.mocap_quat[1], action_right[3:7])
-
-        # set gripper
-        g_left_ctrl = unnormalize_puppet_gripper_position(action_left[7])
-        g_right_ctrl = unnormalize_puppet_gripper_position(action_right[7])
-        np.copyto(physics.data.ctrl, np.array([g_left_ctrl, -g_left_ctrl, g_right_ctrl, -g_right_ctrl]))
-
-    def initialize_robots(self, physics):
-        # reset joint position
-        physics.named.data.qpos[:16] = START_ARM_POSE
-
-        # reset mocap to align with end effector
-        # to obtain these numbers:
-        # (1) make an ee_sim env and reset to the same start_pose
-        # (2) get env._physics.named.data.xpos['vx300s_left/gripper_link']
-        #     get env._physics.named.data.xquat['vx300s_left/gripper_link']
-        #     repeat the same for right side
-        np.copyto(physics.data.mocap_pos[0], [-0.31718881, 0.5, 0.29525084])
-        np.copyto(physics.data.mocap_quat[0], [1, 0, 0, 0])
-        # right
-        np.copyto(physics.data.mocap_pos[1], np.array([0.31718881, 0.49999888, 0.29525084]))
-        np.copyto(physics.data.mocap_quat[1], [1, 0, 0, 0])
-
-        # reset gripper control
-        close_gripper_control = np.array(
-            [
-                PUPPET_GRIPPER_POSITION_CLOSE,
-                -PUPPET_GRIPPER_POSITION_CLOSE,
-                PUPPET_GRIPPER_POSITION_CLOSE,
-                -PUPPET_GRIPPER_POSITION_CLOSE,
-            ]
-        )
-        np.copyto(physics.data.ctrl, close_gripper_control)
-
-    def initialize_episode(self, physics):
-        """Sets the state of the environment at the start of each episode."""
-        super().initialize_episode(physics)
-
-    @staticmethod
-    def get_qpos(physics):
-        qpos_raw = physics.data.qpos.copy()
-        left_qpos_raw = qpos_raw[:8]
-        right_qpos_raw = qpos_raw[8:16]
-        left_arm_qpos = left_qpos_raw[:6]
-        right_arm_qpos = right_qpos_raw[:6]
-        left_gripper_qpos = [normalize_puppet_gripper_position(left_qpos_raw[6])]
-        right_gripper_qpos = [normalize_puppet_gripper_position(right_qpos_raw[6])]
-        return np.concatenate([left_arm_qpos, left_gripper_qpos, right_arm_qpos, right_gripper_qpos])
-
-    @staticmethod
-    def get_qvel(physics):
-        qvel_raw = physics.data.qvel.copy()
-        left_qvel_raw = qvel_raw[:8]
-        right_qvel_raw = qvel_raw[8:16]
-        left_arm_qvel = left_qvel_raw[:6]
-        right_arm_qvel = right_qvel_raw[:6]
-        left_gripper_qvel = [normalize_puppet_gripper_velocity(left_qvel_raw[6])]
-        right_gripper_qvel = [normalize_puppet_gripper_velocity(right_qvel_raw[6])]
-        return np.concatenate([left_arm_qvel, left_gripper_qvel, right_arm_qvel, right_gripper_qvel])
-
-    @staticmethod
-    def get_env_state(physics):
-        raise NotImplementedError
-
-    def get_observation(self, physics):
-        # note: it is important to do .copy()
-        obs = collections.OrderedDict()
-        obs["qpos"] = self.get_qpos(physics)
-        obs["qvel"] = self.get_qvel(physics)
-        obs["env_state"] = self.get_env_state(physics)
-        obs["images"] = {}
-        obs["images"]["top"] = physics.render(height=480, width=640, camera_id="top")
-        obs["images"]["angle"] = physics.render(height=480, width=640, camera_id="angle")
-        obs["images"]["vis"] = physics.render(height=480, width=640, camera_id="front_close")
-        # used in scripted policy to obtain starting pose
-        obs["mocap_pose_left"] = np.concatenate(
-            [physics.data.mocap_pos[0], physics.data.mocap_quat[0]]
-        ).copy()
-        obs["mocap_pose_right"] = np.concatenate(
-            [physics.data.mocap_pos[1], physics.data.mocap_quat[1]]
-        ).copy()
-
-        # used when replaying joint trajectory
-        obs["gripper_ctrl"] = physics.data.ctrl.copy()
-        return obs
-
-    def get_reward(self, physics):
-        raise NotImplementedError
-
-
-class TransferCubeEndEffectorTask(BimanualViperXEndEffectorTask):
-    def __init__(self, random=None):
-        super().__init__(random=random)
-        self.max_reward = 4
-
-    def initialize_episode(self, physics):
-        """Sets the state of the environment at the start of each episode."""
-        self.initialize_robots(physics)
-        # randomize box position
-        cube_pose = sample_box_pose()
-        box_start_idx = physics.model.name2id("red_box_joint", "joint")
-        np.copyto(physics.data.qpos[box_start_idx : box_start_idx + 7], cube_pose)
-        # print(f"randomized cube position to {cube_position}")
-
-        super().initialize_episode(physics)
-
-    @staticmethod
-    def get_env_state(physics):
-        env_state = physics.data.qpos.copy()[16:]
-        return env_state
-
-    def get_reward(self, physics):
-        # return whether left gripper is holding the box
-        all_contact_pairs = []
-        for i_contact in range(physics.data.ncon):
-            id_geom_1 = physics.data.contact[i_contact].geom1
-            id_geom_2 = physics.data.contact[i_contact].geom2
-            name_geom_1 = physics.model.id2name(id_geom_1, "geom")
-            name_geom_2 = physics.model.id2name(id_geom_2, "geom")
-            contact_pair = (name_geom_1, name_geom_2)
-            all_contact_pairs.append(contact_pair)
-
-        touch_left_gripper = ("red_box", "vx300s_left/10_left_gripper_finger") in all_contact_pairs
-        touch_right_gripper = ("red_box", "vx300s_right/10_right_gripper_finger") in all_contact_pairs
-        touch_table = ("red_box", "table") in all_contact_pairs
-
-        reward = 0
-        if touch_right_gripper:
-            reward = 1
-        if touch_right_gripper and not touch_table:  # lifted
-            reward = 2
-        if touch_left_gripper:  # attempted transfer
-            reward = 3
-        if touch_left_gripper and not touch_table:  # successful transfer
-            reward = 4
-        return reward
-
-
-class InsertionEndEffectorTask(BimanualViperXEndEffectorTask):
-    def __init__(self, random=None):
-        super().__init__(random=random)
-        self.max_reward = 4
-
-    def initialize_episode(self, physics):
-        """Sets the state of the environment at the start of each episode."""
-        self.initialize_robots(physics)
-        # randomize peg and socket position
-        peg_pose, socket_pose = sample_insertion_pose()
-
-        def id2index(j_id):
-            return 16 + (j_id - 16) * 7  # first 16 is robot qpos, 7 is pose dim # hacky
-
-        peg_start_id = physics.model.name2id("red_peg_joint", "joint")
-        peg_start_idx = id2index(peg_start_id)
-        np.copyto(physics.data.qpos[peg_start_idx : peg_start_idx + 7], peg_pose)
-        # print(f"randomized cube position to {cube_position}")
-
-        socket_start_id = physics.model.name2id("blue_socket_joint", "joint")
-        socket_start_idx = id2index(socket_start_id)
-        np.copyto(physics.data.qpos[socket_start_idx : socket_start_idx + 7], socket_pose)
-        # print(f"randomized cube position to {cube_position}")
-
-        super().initialize_episode(physics)
-
-    @staticmethod
-    def get_env_state(physics):
-        env_state = physics.data.qpos.copy()[16:]
-        return env_state
-
-    def get_reward(self, physics):
-        # return whether peg touches the pin
-        all_contact_pairs = []
-        for i_contact in range(physics.data.ncon):
-            id_geom_1 = physics.data.contact[i_contact].geom1
-            id_geom_2 = physics.data.contact[i_contact].geom2
-            name_geom_1 = physics.model.id2name(id_geom_1, "geom")
-            name_geom_2 = physics.model.id2name(id_geom_2, "geom")
-            contact_pair = (name_geom_1, name_geom_2)
-            all_contact_pairs.append(contact_pair)
-
-        touch_right_gripper = ("red_peg", "vx300s_right/10_right_gripper_finger") in all_contact_pairs
-        touch_left_gripper = (
-            ("socket-1", "vx300s_left/10_left_gripper_finger") in all_contact_pairs
-            or ("socket-2", "vx300s_left/10_left_gripper_finger") in all_contact_pairs
-            or ("socket-3", "vx300s_left/10_left_gripper_finger") in all_contact_pairs
-            or ("socket-4", "vx300s_left/10_left_gripper_finger") in all_contact_pairs
-        )
-
-        peg_touch_table = ("red_peg", "table") in all_contact_pairs
-        socket_touch_table = (
-            ("socket-1", "table") in all_contact_pairs
-            or ("socket-2", "table") in all_contact_pairs
-            or ("socket-3", "table") in all_contact_pairs
-            or ("socket-4", "table") in all_contact_pairs
-        )
-        peg_touch_socket = (
-            ("red_peg", "socket-1") in all_contact_pairs
-            or ("red_peg", "socket-2") in all_contact_pairs
-            or ("red_peg", "socket-3") in all_contact_pairs
-            or ("red_peg", "socket-4") in all_contact_pairs
-        )
-        pin_touched = ("red_peg", "pin") in all_contact_pairs
-
-        reward = 0
-        if touch_left_gripper and touch_right_gripper:  # touch both
-            reward = 1
-        if (
-            touch_left_gripper and touch_right_gripper and (not peg_touch_table) and (not socket_touch_table)
-        ):  # grasp both
-            reward = 2
-        if peg_touch_socket and (not peg_touch_table) and (not socket_touch_table):  # peg and socket touching
-            reward = 3
-        if pin_touched:  # successful insertion
-            reward = 4
-        return reward

lerobot/common/envs/aloha/utils.py

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

lerobot/common/envs/factory.py

@@ -1,73 +1,42 @@
-from torchrl.envs.transforms import Compose, StepCounter, Transform, TransformedEnv
+import importlib
+
+import gymnasium as gym
 
 
-def make_env(cfg, transform=None):
+def make_env(cfg, num_parallel_envs=0) -> gym.Env | gym.vector.SyncVectorEnv:
+    """
+    Note: When `num_parallel_envs > 0`, this function returns a `SyncVectorEnv` which takes batched action as input and
+    returns batched observation, reward, terminated, truncated of `num_parallel_envs` items.
+    """
     kwargs = {
-        "frame_skip": cfg.env.action_repeat,
-        "from_pixels": cfg.env.from_pixels,
-        "pixels_only": cfg.env.pixels_only,
-        "image_size": cfg.env.image_size,
-        # TODO(rcadene): do we want a specific eval_env_seed?
-        "seed": cfg.seed,
-        "num_prev_obs": cfg.n_obs_steps - 1,
+        "obs_type": "pixels_agent_pos",
+        "render_mode": "rgb_array",
+        "max_episode_steps": cfg.env.episode_length,
+        "visualization_width": 384,
+        "visualization_height": 384,
     }
 
-    if cfg.env.name == "simxarm":
-        from lerobot.common.envs.simxarm import SimxarmEnv
+    package_name = f"gym_{cfg.env.name}"
 
-        kwargs["task"] = cfg.env.task
-        clsfunc = SimxarmEnv
-    elif cfg.env.name == "pusht":
-        from lerobot.common.envs.pusht.env import PushtEnv
+    try:
+        importlib.import_module(package_name)
+    except ModuleNotFoundError as e:
+        print(
+            f"{package_name} is not installed. Please install it with `pip install 'lerobot[{cfg.env.name}]'`"
+        )
+        raise e
 
-        # assert kwargs["seed"] > 200, "Seed 0-200 are used for the demonstration dataset, so we don't want to seed the eval env with this range."
+    gym_handle = f"{package_name}/{cfg.env.task}"
 
-        clsfunc = PushtEnv
-    elif cfg.env.name == "aloha":
-        from lerobot.common.envs.aloha.env import AlohaEnv
-
-        kwargs["task"] = cfg.env.task
-        clsfunc = AlohaEnv
+    if num_parallel_envs == 0:
+        # non-batched version of the env that returns an observation of shape (c)
+        env = gym.make(gym_handle, disable_env_checker=True, **kwargs)
     else:
-        raise ValueError(cfg.env.name)
-
-    env = clsfunc(**kwargs)
-
-    # limit rollout to max_steps
-    env = TransformedEnv(env, StepCounter(max_steps=cfg.env.episode_length))
-
-    if transform is not None:
-        # useful to add normalization
-        if isinstance(transform, Compose):
-            for tf in transform:
-                env.append_transform(tf.clone())
-        elif isinstance(transform, Transform):
-            env.append_transform(transform.clone())
-        else:
-            raise NotImplementedError()
-
+        # batched version of the env that returns an observation of shape (b, c)
+        env = gym.vector.SyncVectorEnv(
+            [
+                lambda: gym.make(gym_handle, disable_env_checker=True, **kwargs)
+                for _ in range(num_parallel_envs)
+            ]
+        )
     return env
-
-
-# def make_env(env_name, frame_skip, device, is_test=False):
-#     env = GymEnv(
-#         env_name,
-#         frame_skip=frame_skip,
-#         from_pixels=True,
-#         pixels_only=False,
-#         device=device,
-#     )
-#     env = TransformedEnv(env)
-#     env.append_transform(NoopResetEnv(noops=30, random=True))
-#     if not is_test:
-#         env.append_transform(EndOfLifeTransform())
-#         env.append_transform(RewardClipping(-1, 1))
-#     env.append_transform(ToTensorImage())
-#     env.append_transform(GrayScale())
-#     env.append_transform(Resize(84, 84))
-#     env.append_transform(CatFrames(N=4, dim=-3))
-#     env.append_transform(RewardSum())
-#     env.append_transform(StepCounter(max_steps=4500))
-#     env.append_transform(DoubleToFloat())
-#     env.append_transform(VecNorm(in_keys=["pixels"]))
-#     return env

lerobot/common/envs/pusht/env.py

@@ -1,234 +0,0 @@
-import importlib
-from collections import deque
-from typing import Optional
-
-import einops
-import torch
-from tensordict import TensorDict
-from torchrl.data.tensor_specs import (
-    BoundedTensorSpec,
-    CompositeSpec,
-    DiscreteTensorSpec,
-    UnboundedContinuousTensorSpec,
-)
-from torchrl.envs.libs.gym import _gym_to_torchrl_spec_transform
-
-from lerobot.common.envs.abstract import AbstractEnv
-from lerobot.common.utils import set_seed
-
-_has_gym = importlib.util.find_spec("gym") is not None
-
-
-class PushtEnv(AbstractEnv):
-    def __init__(
-        self,
-        task="pusht",
-        frame_skip: int = 1,
-        from_pixels: bool = False,
-        pixels_only: bool = False,
-        image_size=None,
-        seed=1337,
-        device="cpu",
-        num_prev_obs=1,
-        num_prev_action=0,
-    ):
-        super().__init__(
-            task=task,
-            frame_skip=frame_skip,
-            from_pixels=from_pixels,
-            pixels_only=pixels_only,
-            image_size=image_size,
-            seed=seed,
-            device=device,
-            num_prev_obs=num_prev_obs,
-            num_prev_action=num_prev_action,
-        )
-
-    def _make_env(self):
-        if not _has_gym:
-            raise ImportError("Cannot import gym.")
-
-        # TODO(rcadene) (PushTEnv is similar to PushTImageEnv, but without the image rendering, it's faster to iterate on)
-        # from lerobot.common.envs.pusht.pusht_env import PushTEnv
-
-        if not self.from_pixels:
-            raise NotImplementedError("Use PushTEnv, instead of PushTImageEnv")
-        from lerobot.common.envs.pusht.pusht_image_env import PushTImageEnv
-
-        self._env = PushTImageEnv(render_size=self.image_size)
-
-    def render(self, mode="rgb_array", width=384, height=384):
-        if width != height:
-            raise NotImplementedError()
-        tmp = self._env.render_size
-        self._env.render_size = width
-        out = self._env.render(mode)
-        self._env.render_size = tmp
-        return out
-
-    def _format_raw_obs(self, raw_obs):
-        if self.from_pixels:
-            image = torch.from_numpy(raw_obs["image"])
-            obs = {"image": image}
-
-            if not self.pixels_only:
-                obs["state"] = torch.from_numpy(raw_obs["agent_pos"]).type(torch.float32)
-        else:
-            # TODO:
-            obs = {"state": torch.from_numpy(raw_obs["observation"]).type(torch.float32)}
-
-        return obs
-
-    def _reset(self, tensordict: Optional[TensorDict] = None):
-        td = tensordict
-        if td is None or td.is_empty():
-            # we need to handle seed iteration, since self._env.reset() rely an internal _seed.
-            self._current_seed += 1
-            self.set_seed(self._current_seed)
-            raw_obs = self._env.reset()
-            assert self._current_seed == self._env._seed
-
-            obs = self._format_raw_obs(raw_obs)
-
-            if self.num_prev_obs > 0:
-                stacked_obs = {}
-                if "image" in obs:
-                    self._prev_obs_image_queue = deque(
-                        [obs["image"]] * (self.num_prev_obs + 1), maxlen=(self.num_prev_obs + 1)
-                    )
-                    stacked_obs["image"] = 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=[],
-            )
-        else:
-            raise NotImplementedError()
-
-        self.call_rendering_hooks()
-        return td
-
-    def _step(self, tensordict: TensorDict):
-        td = tensordict
-        action = td["action"].numpy()
-        # step expects shape=(4,) so we pad if necessary
-        # TODO(rcadene): add info["is_success"] and info["success"] ?
-        sum_reward = 0
-
-        if action.ndim == 1:
-            action = einops.repeat(action, "c -> t c", t=self.frame_skip)
-        else:
-            if self.frame_skip > 1:
-                raise NotImplementedError()
-
-        num_action_steps = action.shape[0]
-        for i in range(num_action_steps):
-            raw_obs, reward, done, info = self._env.step(action[i])
-            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"])
-                    stacked_obs["image"] = 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([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([done], dtype=torch.bool),
-            },
-            batch_size=[],
-        )
-        return td
-
-    def _make_spec(self):
-        obs = {}
-        if self.from_pixels:
-            image_shape = (3, self.image_size, self.image_size)
-            if self.num_prev_obs > 0:
-                image_shape = (self.num_prev_obs + 1, *image_shape)
-
-            obs["image"] = BoundedTensorSpec(
-                low=0,
-                high=255,
-                shape=image_shape,
-                dtype=torch.uint8,
-                device=self.device,
-            )
-            if not self.pixels_only:
-                state_shape = self._env.observation_space["agent_pos"].shape
-                if self.num_prev_obs > 0:
-                    state_shape = (self.num_prev_obs + 1, *state_shape)
-
-                obs["state"] = BoundedTensorSpec(
-                    low=0,
-                    high=512,
-                    shape=state_shape,
-                    dtype=torch.float32,
-                    device=self.device,
-                )
-        else:
-            # TODO(rcadene): add observation_space achieved_goal and desired_goal?
-            state_shape = self._env.observation_space["observation"].shape
-            if self.num_prev_obs > 0:
-                state_shape = (self.num_prev_obs + 1, *state_shape)
-
-            obs["state"] = UnboundedContinuousTensorSpec(
-                # TODO:
-                shape=state_shape,
-                dtype=torch.float32,
-                device=self.device,
-            )
-        self.observation_spec = CompositeSpec({"observation": obs})
-
-        self.action_spec = _gym_to_torchrl_spec_transform(
-            self._env.action_space,
-            device=self.device,
-        )
-
-        self.reward_spec = UnboundedContinuousTensorSpec(
-            shape=(1,),
-            dtype=torch.float32,
-            device=self.device,
-        )
-
-        self.done_spec = CompositeSpec(
-            {
-                "done": DiscreteTensorSpec(
-                    2,
-                    shape=(1,),
-                    dtype=torch.bool,
-                    device=self.device,
-                ),
-                "success": DiscreteTensorSpec(
-                    2,
-                    shape=(1,),
-                    dtype=torch.bool,
-                    device=self.device,
-                ),
-            }
-        )
-
-    def _set_seed(self, seed: Optional[int]):
-        set_seed(seed)
-        self._env.seed(seed)

lerobot/common/envs/pusht/pusht_env.py

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

lerobot/common/envs/pusht/pusht_image_env.py

@@ -1,55 +0,0 @@
-import cv2
-import numpy as np
-from gym import spaces
-
-from lerobot.common.envs.pusht.pusht_env import PushTEnv
-
-
-class PushTImageEnv(PushTEnv):
-    metadata = {"render.modes": ["rgb_array"], "video.frames_per_second": 10}
-
-    def __init__(self, legacy=False, block_cog=None, damping=None, render_size=96):
-        super().__init__(
-            legacy=legacy, block_cog=block_cog, damping=damping, render_size=render_size, render_action=False
-        )
-        ws = self.window_size
-        self.observation_space = spaces.Dict(
-            {
-                "image": spaces.Box(low=0, high=1, shape=(3, render_size, render_size), dtype=np.float32),
-                "agent_pos": spaces.Box(low=0, high=ws, shape=(2,), dtype=np.float32),
-            }
-        )
-        self.render_cache = None
-
-    def _get_obs(self):
-        img = super()._render_frame(mode="rgb_array")
-
-        agent_pos = np.array(self.agent.position)
-        img_obs = np.moveaxis(img, -1, 0)
-        obs = {"image": img_obs, "agent_pos": agent_pos}
-
-        # draw action
-        if self.latest_action is not None:
-            action = np.array(self.latest_action)
-            coord = (action / 512 * 96).astype(np.int32)
-            marker_size = int(8 / 96 * self.render_size)
-            thickness = int(1 / 96 * self.render_size)
-            cv2.drawMarker(
-                img,
-                coord,
-                color=(255, 0, 0),
-                markerType=cv2.MARKER_CROSS,
-                markerSize=marker_size,
-                thickness=thickness,
-            )
-        self.render_cache = img
-
-        return obs
-
-    def render(self, mode):
-        assert mode == "rgb_array"
-
-        if self.render_cache is None:
-            self._get_obs()
-
-        return self.render_cache

lerobot/common/envs/pusht/pymunk_override.py

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

lerobot/common/envs/simxarm.py

@@ -1,236 +0,0 @@
-import importlib
-from collections import deque
-from typing import Optional
-
-import einops
-import numpy as np
-import torch
-from tensordict import TensorDict
-from torchrl.data.tensor_specs import (
-    BoundedTensorSpec,
-    CompositeSpec,
-    DiscreteTensorSpec,
-    UnboundedContinuousTensorSpec,
-)
-from torchrl.envs.libs.gym import _gym_to_torchrl_spec_transform
-
-from lerobot.common.envs.abstract import AbstractEnv
-from lerobot.common.utils import set_seed
-
-MAX_NUM_ACTIONS = 4
-
-_has_gym = importlib.util.find_spec("gym") is not None
-_has_simxarm = importlib.util.find_spec("simxarm") is not None and _has_gym
-
-
-class SimxarmEnv(AbstractEnv):
-    def __init__(
-        self,
-        task,
-        frame_skip: int = 1,
-        from_pixels: bool = False,
-        pixels_only: bool = False,
-        image_size=None,
-        seed=1337,
-        device="cpu",
-        num_prev_obs=0,
-        num_prev_action=0,
-    ):
-        super().__init__(
-            task=task,
-            frame_skip=frame_skip,
-            from_pixels=from_pixels,
-            pixels_only=pixels_only,
-            image_size=image_size,
-            seed=seed,
-            device=device,
-            num_prev_obs=num_prev_obs,
-            num_prev_action=num_prev_action,
-        )
-
-    def _make_env(self):
-        if not _has_simxarm:
-            raise ImportError("Cannot import simxarm.")
-        if not _has_gym:
-            raise ImportError("Cannot import gym.")
-
-        import gym
-        from simxarm import TASKS
-
-        if self.task not in TASKS:
-            raise ValueError(f"Unknown task {self.task}. Must be one of {list(TASKS.keys())}")
-
-        self._env = TASKS[self.task]["env"]()
-
-        num_actions = len(TASKS[self.task]["action_space"])
-        self._action_space = gym.spaces.Box(low=-1.0, high=1.0, shape=(num_actions,))
-        self._action_padding = np.zeros((MAX_NUM_ACTIONS - num_actions), dtype=np.float32)
-        if "w" not in TASKS[self.task]["action_space"]:
-            self._action_padding[-1] = 1.0
-
-    def render(self, mode="rgb_array", width=384, height=384):
-        return self._env.render(mode, width=width, height=height)
-
-    def _format_raw_obs(self, raw_obs):
-        if self.from_pixels:
-            image = self.render(mode="rgb_array", width=self.image_size, height=self.image_size)
-            image = image.transpose(2, 0, 1)  # (H, W, C) -> (C, H, W)
-            image = torch.tensor(image.copy(), dtype=torch.uint8)
-
-            obs = {"image": image}
-
-            if not self.pixels_only:
-                obs["state"] = torch.tensor(self._env.robot_state, dtype=torch.float32)
-        else:
-            obs = {"state": torch.tensor(raw_obs["observation"], dtype=torch.float32)}
-
-        obs = TensorDict(obs, batch_size=[])
-        return obs
-
-    def _reset(self, tensordict: Optional[TensorDict] = None):
-        td = tensordict
-        if td is None or td.is_empty():
-            raw_obs = self._env.reset()
-
-            obs = self._format_raw_obs(raw_obs)
-
-            if self.num_prev_obs > 0:
-                stacked_obs = {}
-                if "image" in obs:
-                    self._prev_obs_image_queue = deque(
-                        [obs["image"]] * (self.num_prev_obs + 1), maxlen=(self.num_prev_obs + 1)
-                    )
-                    stacked_obs["image"] = 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=[],
-            )
-        else:
-            raise NotImplementedError()
-
-        self.call_rendering_hooks()
-        return td
-
-    def _step(self, tensordict: TensorDict):
-        td = tensordict
-        action = td["action"].numpy()
-        # step expects shape=(4,) so we pad if necessary
-        action = np.concatenate([action, self._action_padding])
-        # TODO(rcadene): add info["is_success"] and info["success"] ?
-        sum_reward = 0
-
-        if action.ndim == 1:
-            action = einops.repeat(action, "c -> t c", t=self.frame_skip)
-        else:
-            if self.frame_skip > 1:
-                raise NotImplementedError()
-
-        num_action_steps = action.shape[0]
-        for i in range(num_action_steps):
-            raw_obs, reward, done, info = self._env.step(action[i])
-            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"])
-                    stacked_obs["image"] = 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": self._format_raw_obs(raw_obs),
-                "reward": torch.tensor([sum_reward], dtype=torch.float32),
-                "done": torch.tensor([done], dtype=torch.bool),
-                "success": torch.tensor([info["success"]], dtype=torch.bool),
-            },
-            batch_size=[],
-        )
-        return td
-
-    def _make_spec(self):
-        obs = {}
-        if self.from_pixels:
-            image_shape = (3, self.image_size, self.image_size)
-            if self.num_prev_obs > 0:
-                image_shape = (self.num_prev_obs + 1, *image_shape)
-
-            obs["image"] = BoundedTensorSpec(
-                low=0,
-                high=255,
-                shape=image_shape,
-                dtype=torch.uint8,
-                device=self.device,
-            )
-            if not self.pixels_only:
-                state_shape = (len(self._env.robot_state),)
-                if self.num_prev_obs > 0:
-                    state_shape = (self.num_prev_obs + 1, *state_shape)
-
-                obs["state"] = UnboundedContinuousTensorSpec(
-                    shape=state_shape,
-                    dtype=torch.float32,
-                    device=self.device,
-                )
-        else:
-            # TODO(rcadene): add observation_space achieved_goal and desired_goal?
-            state_shape = self._env.observation_space["observation"].shape
-            if self.num_prev_obs > 0:
-                state_shape = (self.num_prev_obs + 1, *state_shape)
-
-            obs["state"] = UnboundedContinuousTensorSpec(
-                # TODO:
-                shape=state_shape,
-                dtype=torch.float32,
-                device=self.device,
-            )
-        self.observation_spec = CompositeSpec({"observation": obs})
-
-        self.action_spec = _gym_to_torchrl_spec_transform(
-            self._action_space,
-            device=self.device,
-        )
-
-        self.reward_spec = UnboundedContinuousTensorSpec(
-            shape=(1,),
-            dtype=torch.float32,
-            device=self.device,
-        )
-
-        self.done_spec = CompositeSpec(
-            {
-                "done": DiscreteTensorSpec(
-                    2,
-                    shape=(1,),
-                    dtype=torch.bool,
-                    device=self.device,
-                ),
-                "success": DiscreteTensorSpec(
-                    2,
-                    shape=(1,),
-                    dtype=torch.bool,
-                    device=self.device,
-                ),
-            }
-        )
-
-    def _set_seed(self, seed: Optional[int]):
-        set_seed(seed)
-        self._env.seed(seed)

lerobot/common/envs/transforms.py

@@ -1,119 +0,0 @@
-from typing import Sequence
-
-import torch
-from tensordict import TensorDictBase
-from tensordict.nn import dispatch
-from tensordict.utils import NestedKey
-from torchrl.envs.transforms import ObservationTransform, Transform
-
-
-class Prod(ObservationTransform):
-    invertible = True
-
-    def __init__(self, in_keys: Sequence[NestedKey], prod: float):
-        super().__init__()
-        self.in_keys = in_keys
-        self.prod = prod
-        self.original_dtypes = {}
-
-    def _reset(self, tensordict: TensorDictBase, tensordict_reset: TensorDictBase) -> TensorDictBase:
-        # _reset is called once when the environment reset to normalize the first observation
-        tensordict_reset = self._call(tensordict_reset)
-        return tensordict_reset
-
-    @dispatch(source="in_keys", dest="out_keys")
-    def forward(self, tensordict: TensorDictBase) -> TensorDictBase:
-        return self._call(tensordict)
-
-    def _call(self, td):
-        for key in self.in_keys:
-            if td.get(key, None) is None:
-                continue
-            self.original_dtypes[key] = td[key].dtype
-            td[key] = td[key].type(torch.float32) * self.prod
-        return td
-
-    def _inv_call(self, td: TensorDictBase) -> TensorDictBase:
-        for key in self.in_keys:
-            if td.get(key, None) is None:
-                continue
-            td[key] = (td[key] / self.prod).type(self.original_dtypes[key])
-        return td
-
-    def transform_observation_spec(self, obs_spec):
-        for key in self.in_keys:
-            if obs_spec.get(key, None) is None:
-                continue
-            obs_spec[key].space.high = obs_spec[key].space.high.type(torch.float32) * self.prod
-            obs_spec[key].space.low = obs_spec[key].space.low.type(torch.float32) * self.prod
-            obs_spec[key].dtype = torch.float32
-        return obs_spec
-
-
-class NormalizeTransform(Transform):
-    invertible = True
-
-    def __init__(
-        self,
-        stats: TensorDictBase,
-        in_keys: Sequence[NestedKey] = None,
-        out_keys: Sequence[NestedKey] | None = None,
-        in_keys_inv: Sequence[NestedKey] | None = None,
-        out_keys_inv: Sequence[NestedKey] | None = None,
-        mode="mean_std",
-    ):
-        if out_keys is None:
-            out_keys = in_keys
-        if in_keys_inv is None:
-            in_keys_inv = out_keys
-        if out_keys_inv is None:
-            out_keys_inv = in_keys
-        super().__init__(
-            in_keys=in_keys, out_keys=out_keys, in_keys_inv=in_keys_inv, out_keys_inv=out_keys_inv
-        )
-        self.stats = stats
-        assert mode in ["mean_std", "min_max"]
-        self.mode = mode
-
-    def _reset(self, tensordict: TensorDictBase, tensordict_reset: TensorDictBase) -> TensorDictBase:
-        # _reset is called once when the environment reset to normalize the first observation
-        tensordict_reset = self._call(tensordict_reset)
-        return tensordict_reset
-
-    @dispatch(source="in_keys", dest="out_keys")
-    def forward(self, tensordict: TensorDictBase) -> TensorDictBase:
-        return self._call(tensordict)
-
-    def _call(self, td: TensorDictBase) -> TensorDictBase:
-        for inkey, outkey in zip(self.in_keys, self.out_keys, strict=False):
-            # TODO(rcadene): don't know how to do `inkey not in td`
-            if td.get(inkey, None) is None:
-                continue
-            if self.mode == "mean_std":
-                mean = self.stats[inkey]["mean"]
-                std = self.stats[inkey]["std"]
-                td[outkey] = (td[inkey] - mean) / (std + 1e-8)
-            else:
-                min = self.stats[inkey]["min"]
-                max = self.stats[inkey]["max"]
-                # normalize to [0,1]
-                td[outkey] = (td[inkey] - min) / (max - min)
-                # normalize to [-1, 1]
-                td[outkey] = td[outkey] * 2 - 1
-        return td
-
-    def _inv_call(self, td: TensorDictBase) -> TensorDictBase:
-        for inkey, outkey in zip(self.in_keys_inv, self.out_keys_inv, strict=False):
-            # TODO(rcadene): don't know how to do `inkey not in td`
-            if td.get(inkey, None) is None:
-                continue
-            if self.mode == "mean_std":
-                mean = self.stats[inkey]["mean"]
-                std = self.stats[inkey]["std"]
-                td[outkey] = td[inkey] * std + mean
-            else:
-                min = self.stats[inkey]["min"]
-                max = self.stats[inkey]["max"]
-                td[outkey] = (td[inkey] + 1) / 2
-                td[outkey] = td[outkey] * (max - min) + min
-        return td

lerobot/common/envs/utils.py

@@ -0,0 +1,52 @@
+import einops
+import torch
+
+from lerobot.common.transforms import apply_inverse_transform
+
+
+def preprocess_observation(observation, transform=None):
+    # map to expected inputs for the policy
+    obs = {}
+
+    if isinstance(observation["pixels"], dict):
+        imgs = {f"observation.images.{key}": img for key, img in observation["pixels"].items()}
+    else:
+        imgs = {"observation.image": observation["pixels"]}
+
+    for imgkey, img in imgs.items():
+        img = torch.from_numpy(img)
+
+        # sanity check that images are channel last
+        _, h, w, c = img.shape
+        assert c < h and c < w, f"expect channel first images, but instead {img.shape}"
+
+        # sanity check that images are uint8
+        assert img.dtype == torch.uint8, f"expect torch.uint8, but instead {img.dtype=}"
+
+        # convert to channel first of type float32 in range [0,1]
+        img = einops.rearrange(img, "b h w c -> b c h w")
+        img = img.type(torch.float32)
+        img /= 255
+
+        obs[imgkey] = img
+
+    # TODO(rcadene): enable pixels only baseline with `obs_type="pixels"` in environment by removing requirement for "agent_pos"
+    obs["observation.state"] = torch.from_numpy(observation["agent_pos"]).float()
+
+    # apply same transforms as in training
+    if transform is not None:
+        for key in obs:
+            obs[key] = torch.stack([transform({key: item})[key] for item in obs[key]])
+
+    return obs
+
+
+def postprocess_action(action, transform=None):
+    action = action.to("cpu")
+    # action is a batch (num_env,action_dim) instead of an item (action_dim),
+    # we assume applying inverse transform on a batch works the same
+    action = apply_inverse_transform({"action": action}, transform)["action"].numpy()
+    assert (
+        action.ndim == 2
+    ), "we assume dimensions are respectively the number of parallel envs, action dimensions"
+    return action

lerobot/common/logger.py

@@ -30,6 +30,7 @@ class Logger:
         self._model_dir = self._log_dir / "models"
         self._buffer_dir = self._log_dir / "buffers"
         self._save_model = cfg.save_model
+        self._disable_wandb_artifact = cfg.wandb.disable_artifact
         self._save_buffer = cfg.save_buffer
         self._group = cfg_to_group(cfg)
         self._seed = cfg.seed
@@ -71,9 +72,10 @@ class Logger:
             self._model_dir.mkdir(parents=True, exist_ok=True)
             fp = self._model_dir / f"{str(identifier)}.pt"
             policy.save(fp)
-            if self._wandb:
+            if self._wandb and not self._disable_wandb_artifact:
+                # note wandb artifact does not accept ":" in its name
                 artifact = self._wandb.Artifact(
-                    self._group + "-" + str(self._seed) + "-" + str(identifier),
+                    self._group.replace(":", "_") + "-" + str(self._seed) + "-" + str(identifier),
                     type="model",
                 )
                 artifact.add_file(fp)

lerobot/common/policies/act/backbone.py

@@ -1,115 +0,0 @@
-from typing import List
-
-import torch
-import torchvision
-from torch import nn
-from torchvision.models._utils import IntermediateLayerGetter
-
-from .position_encoding import build_position_encoding
-from .utils import NestedTensor, is_main_process
-
-
-class FrozenBatchNorm2d(torch.nn.Module):
-    """
-    BatchNorm2d where the batch statistics and the affine parameters are fixed.
-
-    Copy-paste from torchvision.misc.ops with added eps before rqsrt,
-    without which any other policy_models than torchvision.policy_models.resnet[18,34,50,101]
-    produce nans.
-    """
-
-    def __init__(self, n):
-        super().__init__()
-        self.register_buffer("weight", torch.ones(n))
-        self.register_buffer("bias", torch.zeros(n))
-        self.register_buffer("running_mean", torch.zeros(n))
-        self.register_buffer("running_var", torch.ones(n))
-
-    def _load_from_state_dict(
-        self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
-    ):
-        num_batches_tracked_key = prefix + "num_batches_tracked"
-        if num_batches_tracked_key in state_dict:
-            del state_dict[num_batches_tracked_key]
-
-        super()._load_from_state_dict(
-            state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
-        )
-
-    def forward(self, x):
-        # move reshapes to the beginning
-        # to make it fuser-friendly
-        w = self.weight.reshape(1, -1, 1, 1)
-        b = self.bias.reshape(1, -1, 1, 1)
-        rv = self.running_var.reshape(1, -1, 1, 1)
-        rm = self.running_mean.reshape(1, -1, 1, 1)
-        eps = 1e-5
-        scale = w * (rv + eps).rsqrt()
-        bias = b - rm * scale
-        return x * scale + bias
-
-
-class BackboneBase(nn.Module):
-    def __init__(
-        self, backbone: nn.Module, train_backbone: bool, num_channels: int, return_interm_layers: bool
-    ):
-        super().__init__()
-        # for name, parameter in backbone.named_parameters(): # only train later layers # TODO do we want this?
-        #     if not train_backbone or 'layer2' not in name and 'layer3' not in name and 'layer4' not in name:
-        #         parameter.requires_grad_(False)
-        if return_interm_layers:
-            return_layers = {"layer1": "0", "layer2": "1", "layer3": "2", "layer4": "3"}
-        else:
-            return_layers = {"layer4": "0"}
-        self.body = IntermediateLayerGetter(backbone, return_layers=return_layers)
-        self.num_channels = num_channels
-
-    def forward(self, tensor):
-        xs = self.body(tensor)
-        return xs
-        # out: Dict[str, NestedTensor] = {}
-        # for name, x in xs.items():
-        #     m = tensor_list.mask
-        #     assert m is not None
-        #     mask = F.interpolate(m[None].float(), size=x.shape[-2:]).to(torch.bool)[0]
-        #     out[name] = NestedTensor(x, mask)
-        # return out
-
-
-class Backbone(BackboneBase):
-    """ResNet backbone with frozen BatchNorm."""
-
-    def __init__(self, name: str, train_backbone: bool, return_interm_layers: bool, dilation: bool):
-        backbone = getattr(torchvision.models, name)(
-            replace_stride_with_dilation=[False, False, dilation],
-            pretrained=is_main_process(),
-            norm_layer=FrozenBatchNorm2d,
-        )  # pretrained # TODO do we want frozen batch_norm??
-        num_channels = 512 if name in ("resnet18", "resnet34") else 2048
-        super().__init__(backbone, train_backbone, num_channels, return_interm_layers)
-
-
-class Joiner(nn.Sequential):
-    def __init__(self, backbone, position_embedding):
-        super().__init__(backbone, position_embedding)
-
-    def forward(self, tensor_list: NestedTensor):
-        xs = self[0](tensor_list)
-        out: List[NestedTensor] = []
-        pos = []
-        for _, x in xs.items():
-            out.append(x)
-            # position encoding
-            pos.append(self[1](x).to(x.dtype))
-
-        return out, pos
-
-
-def build_backbone(args):
-    position_embedding = build_position_encoding(args)
-    train_backbone = args.lr_backbone > 0
-    return_interm_layers = args.masks
-    backbone = Backbone(args.backbone, train_backbone, return_interm_layers, args.dilation)
-    model = Joiner(backbone, position_embedding)
-    model.num_channels = backbone.num_channels
-    return model

lerobot/common/policies/act/configuration_act.py

@@ -0,0 +1,123 @@
+from dataclasses import dataclass, field
+
+
+@dataclass
+class ActionChunkingTransformerConfig:
+    """Configuration class for the Action Chunking Transformers policy.
+
+    Defaults are configured for training on bimanual Aloha tasks like "insertion" or "transfer".
+
+    The parameters you will most likely need to change are the ones which depend on the environment / sensors.
+    Those are: `state_dim`, `action_dim` and `camera_names`.
+
+    Args:
+        state_dim: Dimensionality of the observation state space (excluding images).
+        action_dim: Dimensionality of the action space.
+        n_obs_steps: Number of environment steps worth of observations to pass to the policy (takes the
+            current step and additional steps going back).
+        camera_names: The (unique) set of names for the cameras.
+        chunk_size: The size of the action prediction "chunks" in units of environment steps.
+        n_action_steps: The number of action steps to run in the environment for one invocation of the policy.
+            This should be no greater than the chunk size. For example, if the chunk size size 100, you may
+            set this to 50. This would mean that the model predicts 100 steps worth of actions, runs 50 in the
+            environment, and throws the other 50 out.
+        image_normalization_mean: Value to subtract from the input image pixels (inputs are assumed to be in
+            [0, 1]) for normalization.
+        image_normalization_std: Value by which to divide the input image pixels (after the mean has been
+            subtracted).
+        vision_backbone: Name of the torchvision resnet backbone to use for encoding images.
+        use_pretrained_backbone: Whether the backbone should be initialized with pretrained weights from
+            torchvision.
+        replace_final_stride_with_dilation: Whether to replace the ResNet's final 2x2 stride with a dilated
+            convolution.
+        pre_norm: Whether to use "pre-norm" in the transformer blocks.
+        d_model: The transformer blocks' main hidden dimension.
+        n_heads: The number of heads to use in the transformer blocks' multi-head attention.
+        dim_feedforward: The dimension to expand the transformer's hidden dimension to in the feed-forward
+            layers.
+        feedforward_activation: The activation to use in the transformer block's feed-forward layers.
+        n_encoder_layers: The number of transformer layers to use for the transformer encoder.
+        n_decoder_layers: The number of transformer layers to use for the transformer decoder.
+        use_vae: Whether to use a variational objective during training. This introduces another transformer
+            which is used as the VAE's encoder (not to be confused with the transformer encoder - see
+            documentation in the policy class).
+        latent_dim: The VAE's latent dimension.
+        n_vae_encoder_layers: The number of transformer layers to use for the VAE's encoder.
+        use_temporal_aggregation: Whether to blend the actions of multiple policy invocations for any given
+            environment step.
+        dropout: Dropout to use in the transformer layers (see code for details).
+        kl_weight: The weight to use for the KL-divergence component of the loss if the variational objective
+            is enabled. Loss is then calculated as: `reconstruction_loss + kl_weight * kld_loss`.
+    """
+
+    # Environment.
+    state_dim: int = 14
+    action_dim: int = 14
+
+    # Inputs / output structure.
+    n_obs_steps: int = 1
+    camera_names: tuple[str] = ("top",)
+    chunk_size: int = 100
+    n_action_steps: int = 100
+
+    # Vision preprocessing.
+    image_normalization_mean: tuple[float, float, float] = field(
+        default_factory=lambda: [0.485, 0.456, 0.406]
+    )
+    image_normalization_std: tuple[float, float, float] = field(default_factory=lambda: [0.229, 0.224, 0.225])
+
+    # Architecture.
+    # Vision backbone.
+    vision_backbone: str = "resnet18"
+    use_pretrained_backbone: bool = True
+    replace_final_stride_with_dilation: int = False
+    # Transformer layers.
+    pre_norm: bool = False
+    d_model: int = 512
+    n_heads: int = 8
+    dim_feedforward: int = 3200
+    feedforward_activation: str = "relu"
+    n_encoder_layers: int = 4
+    n_decoder_layers: int = 1
+    # VAE.
+    use_vae: bool = True
+    latent_dim: int = 32
+    n_vae_encoder_layers: int = 4
+
+    # Inference.
+    use_temporal_aggregation: bool = False
+
+    # Training and loss computation.
+    dropout: float = 0.1
+    kl_weight: float = 10.0
+
+    # ---
+    # TODO(alexander-soare): Remove these from the policy config.
+    batch_size: int = 8
+    lr: float = 1e-5
+    lr_backbone: float = 1e-5
+    weight_decay: float = 1e-4
+    grad_clip_norm: float = 10
+    utd: int = 1
+
+    def __post_init__(self):
+        """Input validation (not exhaustive)."""
+        if not self.vision_backbone.startswith("resnet"):
+            raise ValueError(
+                f"`vision_backbone` must be one of the ResNet variants. Got {self.vision_backbone}."
+            )
+        if self.use_temporal_aggregation:
+            raise NotImplementedError("Temporal aggregation is not yet implemented.")
+        if self.n_action_steps > self.chunk_size:
+            raise ValueError(
+                f"The chunk size is the upper bound for the number of action steps per model invocation. Got "
+                f"{self.n_action_steps} for `n_action_steps` and {self.chunk_size} for `chunk_size`."
+            )
+        if self.n_obs_steps != 1:
+            raise ValueError(
+                f"Multiple observation steps not handled yet. Got `nobs_steps={self.n_obs_steps}`"
+            )
+        if self.camera_names != ["top"]:
+            raise ValueError(f"For now, `camera_names` can only be ['top']. Got {self.camera_names}.")
+        if len(set(self.camera_names)) != len(self.camera_names):
+            raise ValueError(f"`camera_names` should not have any repeated entries. Got {self.camera_names}.")

lerobot/common/policies/act/detr_vae.py

@@ -1,212 +0,0 @@
-import numpy as np
-import torch
-from torch import nn
-from torch.autograd import Variable
-
-from .backbone import build_backbone
-from .transformer import TransformerEncoder, TransformerEncoderLayer, build_transformer
-
-
-def reparametrize(mu, logvar):
-    std = logvar.div(2).exp()
-    eps = Variable(std.data.new(std.size()).normal_())
-    return mu + std * eps
-
-
-def get_sinusoid_encoding_table(n_position, d_hid):
-    def get_position_angle_vec(position):
-        return [position / np.power(10000, 2 * (hid_j // 2) / d_hid) for hid_j in range(d_hid)]
-
-    sinusoid_table = np.array([get_position_angle_vec(pos_i) for pos_i in range(n_position)])
-    sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2])  # dim 2i
-    sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2])  # dim 2i+1
-
-    return torch.FloatTensor(sinusoid_table).unsqueeze(0)
-
-
-class DETRVAE(nn.Module):
-    """This is the DETR module that performs object detection"""
-
-    def __init__(
-        self, backbones, transformer, encoder, state_dim, action_dim, num_queries, camera_names, vae
-    ):
-        """Initializes the model.
-        Parameters:
-            backbones: torch module of the backbone to be used. See backbone.py
-            transformer: torch module of the transformer architecture. See transformer.py
-            state_dim: robot state dimension of the environment
-            num_queries: number of object queries, ie detection slot. This is the maximal number of objects
-                         DETR can detect in a single image. For COCO, we recommend 100 queries.
-            aux_loss: True if auxiliary decoding losses (loss at each decoder layer) are to be used.
-        """
-        super().__init__()
-        self.num_queries = num_queries
-        self.camera_names = camera_names
-        self.transformer = transformer
-        self.encoder = encoder
-        self.vae = vae
-        hidden_dim = transformer.d_model
-        self.action_head = nn.Linear(hidden_dim, action_dim)
-        self.is_pad_head = nn.Linear(hidden_dim, 1)
-        self.query_embed = nn.Embedding(num_queries, hidden_dim)
-        if backbones is not None:
-            self.input_proj = nn.Conv2d(backbones[0].num_channels, hidden_dim, kernel_size=1)
-            self.backbones = nn.ModuleList(backbones)
-            self.input_proj_robot_state = nn.Linear(state_dim, hidden_dim)
-        else:
-            # input_dim = 14 + 7 # robot_state + env_state
-            self.input_proj_robot_state = nn.Linear(state_dim, hidden_dim)
-            # TODO(rcadene): understand what is env_state, and why it needs to be 7
-            self.input_proj_env_state = nn.Linear(state_dim // 2, hidden_dim)
-            self.pos = torch.nn.Embedding(2, hidden_dim)
-            self.backbones = None
-
-        # encoder extra parameters
-        self.latent_dim = 32  # final size of latent z # TODO tune
-        self.cls_embed = nn.Embedding(1, hidden_dim)  # extra cls token embedding
-        self.encoder_action_proj = nn.Linear(14, hidden_dim)  # project action to embedding
-        self.encoder_joint_proj = nn.Linear(14, hidden_dim)  # project qpos to embedding
-        self.latent_proj = nn.Linear(
-            hidden_dim, self.latent_dim * 2
-        )  # project hidden state to latent std, var
-        self.register_buffer(
-            "pos_table", get_sinusoid_encoding_table(1 + 1 + num_queries, hidden_dim)
-        )  # [CLS], qpos, a_seq
-
-        # decoder extra parameters
-        self.latent_out_proj = nn.Linear(self.latent_dim, hidden_dim)  # project latent sample to embedding
-        self.additional_pos_embed = nn.Embedding(
-            2, hidden_dim
-        )  # learned position embedding for proprio and latent
-
-    def forward(self, qpos, image, env_state, actions=None, is_pad=None):
-        """
-        qpos: batch, qpos_dim
-        image: batch, num_cam, channel, height, width
-        env_state: None
-        actions: batch, seq, action_dim
-        """
-        is_training = actions is not None  # train or val
-        bs, _ = qpos.shape
-        ### Obtain latent z from action sequence
-        if self.vae and is_training:
-            # project action sequence to embedding dim, and concat with a CLS token
-            action_embed = self.encoder_action_proj(actions)  # (bs, seq, hidden_dim)
-            qpos_embed = self.encoder_joint_proj(qpos)  # (bs, hidden_dim)
-            qpos_embed = torch.unsqueeze(qpos_embed, axis=1)  # (bs, 1, hidden_dim)
-            cls_embed = self.cls_embed.weight  # (1, hidden_dim)
-            cls_embed = torch.unsqueeze(cls_embed, axis=0).repeat(bs, 1, 1)  # (bs, 1, hidden_dim)
-            encoder_input = torch.cat(
-                [cls_embed, qpos_embed, action_embed], axis=1
-            )  # (bs, seq+1, hidden_dim)
-            encoder_input = encoder_input.permute(1, 0, 2)  # (seq+1, bs, hidden_dim)
-            # do not mask cls token
-            # cls_joint_is_pad = torch.full((bs, 2), False).to(qpos.device)  # False: not a padding
-            # is_pad = torch.cat([cls_joint_is_pad, is_pad], axis=1)  # (bs, seq+1)
-            # obtain position embedding
-            pos_embed = self.pos_table.clone().detach()
-            pos_embed = pos_embed.permute(1, 0, 2)  # (seq+1, 1, hidden_dim)
-            # query model
-            encoder_output = self.encoder(encoder_input, pos=pos_embed)  # , src_key_padding_mask=is_pad)
-            encoder_output = encoder_output[0]  # take cls output only
-            latent_info = self.latent_proj(encoder_output)
-            mu = latent_info[:, : self.latent_dim]
-            logvar = latent_info[:, self.latent_dim :]
-            latent_sample = reparametrize(mu, logvar)
-            latent_input = self.latent_out_proj(latent_sample)
-        else:
-            mu = logvar = None
-            latent_sample = torch.zeros([bs, self.latent_dim], dtype=torch.float32).to(qpos.device)
-            latent_input = self.latent_out_proj(latent_sample)
-
-        if self.backbones is not None:
-            # Image observation features and position embeddings
-            all_cam_features = []
-            all_cam_pos = []
-            for cam_id, _ in enumerate(self.camera_names):
-                features, pos = self.backbones[0](image[:, cam_id])  # HARDCODED
-                features = features[0]  # take the last layer feature
-                pos = pos[0]
-                all_cam_features.append(self.input_proj(features))
-                all_cam_pos.append(pos)
-            # proprioception features
-            proprio_input = self.input_proj_robot_state(qpos)
-            # fold camera dimension into width dimension
-            src = torch.cat(all_cam_features, axis=3)
-            pos = torch.cat(all_cam_pos, axis=3)
-            hs = self.transformer(
-                src,
-                None,
-                self.query_embed.weight,
-                pos,
-                latent_input,
-                proprio_input,
-                self.additional_pos_embed.weight,
-            )[0]
-        else:
-            qpos = self.input_proj_robot_state(qpos)
-            env_state = self.input_proj_env_state(env_state)
-            transformer_input = torch.cat([qpos, env_state], axis=1)  # seq length = 2
-            hs = self.transformer(transformer_input, None, self.query_embed.weight, self.pos.weight)[0]
-        a_hat = self.action_head(hs)
-        is_pad_hat = self.is_pad_head(hs)
-        return a_hat, is_pad_hat, [mu, logvar]
-
-
-def mlp(input_dim, hidden_dim, output_dim, hidden_depth):
-    if hidden_depth == 0:
-        mods = [nn.Linear(input_dim, output_dim)]
-    else:
-        mods = [nn.Linear(input_dim, hidden_dim), nn.ReLU(inplace=True)]
-        for _ in range(hidden_depth - 1):
-            mods += [nn.Linear(hidden_dim, hidden_dim), nn.ReLU(inplace=True)]
-        mods.append(nn.Linear(hidden_dim, output_dim))
-    trunk = nn.Sequential(*mods)
-    return trunk
-
-
-def build_encoder(args):
-    d_model = args.hidden_dim  # 256
-    dropout = args.dropout  # 0.1
-    nhead = args.nheads  # 8
-    dim_feedforward = args.dim_feedforward  # 2048
-    num_encoder_layers = args.enc_layers  # 4 # TODO shared with VAE decoder
-    normalize_before = args.pre_norm  # False
-    activation = "relu"
-
-    encoder_layer = TransformerEncoderLayer(
-        d_model, nhead, dim_feedforward, dropout, activation, normalize_before
-    )
-    encoder_norm = nn.LayerNorm(d_model) if normalize_before else None
-    encoder = TransformerEncoder(encoder_layer, num_encoder_layers, encoder_norm)
-
-    return encoder
-
-
-def build(args):
-    # From state
-    # backbone = None # from state for now, no need for conv nets
-    # From image
-    backbones = []
-    backbone = build_backbone(args)
-    backbones.append(backbone)
-
-    transformer = build_transformer(args)
-
-    encoder = build_encoder(args)
-
-    model = DETRVAE(
-        backbones,
-        transformer,
-        encoder,
-        state_dim=args.state_dim,
-        action_dim=args.action_dim,
-        num_queries=args.num_queries,
-        camera_names=args.camera_names,
-        vae=args.vae,
-    )
-
-    n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad)
-    print("number of parameters: {:.2f}M".format(n_parameters / 1e6))
-
-    return model

lerobot/common/policies/act/modeling_act.py

@@ -0,0 +1,599 @@
+"""Action Chunking Transformer Policy
+
+As per Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware (https://arxiv.org/abs/2304.13705).
+The majority of changes here involve removing unused code, unifying naming, and adding helpful comments.
+"""
+
+import math
+import time
+from collections import deque
+from itertools import chain
+from typing import Callable
+
+import einops
+import numpy as np
+import torch
+import torch.nn.functional as F  # noqa: N812
+import torchvision
+import torchvision.transforms as transforms
+from torch import Tensor, nn
+from torchvision.models._utils import IntermediateLayerGetter
+from torchvision.ops.misc import FrozenBatchNorm2d
+
+from lerobot.common.policies.act.configuration_act import ActionChunkingTransformerConfig
+
+
+class ActionChunkingTransformerPolicy(nn.Module):
+    """
+    Action Chunking Transformer Policy as per Learning Fine-Grained Bimanual Manipulation with Low-Cost
+    Hardware (paper: https://arxiv.org/abs/2304.13705, code: https://github.com/tonyzhaozh/act)
+
+    Note: In this code we use the terms `vae_encoder`, 'encoder', `decoder`. The meanings are as follows.
+        - The `vae_encoder` is, as per the literature around variational auto-encoders (VAE), the part of the
+          model that encodes the target data (a sequence of actions), and the condition (the robot
+          joint-space).
+        - A transformer with an `encoder` (not the VAE encoder) and `decoder` (not the VAE decoder) with
+          cross-attention is used as the VAE decoder. For these terms, we drop the `vae_` prefix because we
+          have an option to train this model without the variational objective (in which case we drop the
+          `vae_encoder` altogether, and nothing about this model has anything to do with a VAE).
+
+                                 Transformer
+                                 Used alone for inference
+                                 (acts as VAE decoder
+                                  during training)
+                                ┌───────────────────────┐
+                                │             Outputs   │
+                                │                ▲      │
+                                │     ┌─────►┌───────┐  │
+                   ┌──────┐     │     │      │Transf.│  │
+                   │      │     │     ├─────►│decoder│  │
+              ┌────┴────┐ │     │     │      │       │  │
+              │         │ │     │ ┌───┴───┬─►│       │  │
+              │ VAE     │ │     │ │       │  └───────┘  │
+              │ encoder │ │     │ │Transf.│             │
+              │         │ │     │ │encoder│             │
+              └───▲─────┘ │     │ │       │             │
+                  │       │     │ └───▲───┘             │
+                  │       │     │     │                 │
+                inputs    └─────┼─────┘                 │
+                                │                       │
+                                └───────────────────────┘
+    """
+
+    name = "act"
+
+    def __init__(self, cfg: ActionChunkingTransformerConfig | None = None):
+        """
+        Args:
+            cfg: Policy configuration class instance or None, in which case the default instantiation of the
+                 configuration class is used.
+        """
+        super().__init__()
+        if cfg is None:
+            cfg = ActionChunkingTransformerConfig()
+        self.cfg = cfg
+
+        # BERT style VAE encoder with input [cls, *joint_space_configuration, *action_sequence].
+        # The cls token forms parameters of the latent's distribution (like this [*means, *log_variances]).
+        if self.cfg.use_vae:
+            self.vae_encoder = _TransformerEncoder(cfg)
+            self.vae_encoder_cls_embed = nn.Embedding(1, cfg.d_model)
+            # Projection layer for joint-space configuration to hidden dimension.
+            self.vae_encoder_robot_state_input_proj = nn.Linear(cfg.state_dim, cfg.d_model)
+            # Projection layer for action (joint-space target) to hidden dimension.
+            self.vae_encoder_action_input_proj = nn.Linear(cfg.state_dim, cfg.d_model)
+            self.latent_dim = cfg.latent_dim
+            # Projection layer from the VAE encoder's output to the latent distribution's parameter space.
+            self.vae_encoder_latent_output_proj = nn.Linear(cfg.d_model, self.latent_dim * 2)
+            # Fixed sinusoidal positional embedding the whole input to the VAE encoder. Unsqueeze for batch
+            # dimension.
+            self.register_buffer(
+                "vae_encoder_pos_enc",
+                _create_sinusoidal_position_embedding(1 + 1 + cfg.chunk_size, cfg.d_model).unsqueeze(0),
+            )
+
+        # Backbone for image feature extraction.
+        self.image_normalizer = transforms.Normalize(
+            mean=cfg.image_normalization_mean, std=cfg.image_normalization_std
+        )
+        backbone_model = getattr(torchvision.models, cfg.vision_backbone)(
+            replace_stride_with_dilation=[False, False, cfg.replace_final_stride_with_dilation],
+            pretrained=cfg.use_pretrained_backbone,
+            norm_layer=FrozenBatchNorm2d,
+        )
+        # Note: The assumption here is that we are using a ResNet model (and hence layer4 is the final feature
+        # map).
+        # Note: The forward method of this returns a dict: {"feature_map": output}.
+        self.backbone = IntermediateLayerGetter(backbone_model, return_layers={"layer4": "feature_map"})
+
+        # Transformer (acts as VAE decoder when training with the variational objective).
+        self.encoder = _TransformerEncoder(cfg)
+        self.decoder = _TransformerDecoder(cfg)
+
+        # Transformer encoder input projections. The tokens will be structured like
+        # [latent, robot_state, image_feature_map_pixels].
+        self.encoder_robot_state_input_proj = nn.Linear(cfg.state_dim, cfg.d_model)
+        self.encoder_latent_input_proj = nn.Linear(self.latent_dim, cfg.d_model)
+        self.encoder_img_feat_input_proj = nn.Conv2d(
+            backbone_model.fc.in_features, cfg.d_model, kernel_size=1
+        )
+        # Transformer encoder positional embeddings.
+        self.encoder_robot_and_latent_pos_embed = nn.Embedding(2, cfg.d_model)
+        self.encoder_cam_feat_pos_embed = _SinusoidalPositionEmbedding2D(cfg.d_model // 2)
+
+        # Transformer decoder.
+        # Learnable positional embedding for the transformer's decoder (in the style of DETR object queries).
+        self.decoder_pos_embed = nn.Embedding(cfg.chunk_size, cfg.d_model)
+
+        # Final action regression head on the output of the transformer's decoder.
+        self.action_head = nn.Linear(cfg.d_model, cfg.action_dim)
+
+        self._reset_parameters()
+        self._create_optimizer()
+
+    def _create_optimizer(self):
+        optimizer_params_dicts = [
+            {
+                "params": [
+                    p for n, p in self.named_parameters() if not n.startswith("backbone") and p.requires_grad
+                ]
+            },
+            {
+                "params": [
+                    p for n, p in self.named_parameters() if n.startswith("backbone") and p.requires_grad
+                ],
+                "lr": self.cfg.lr_backbone,
+            },
+        ]
+        self.optimizer = torch.optim.AdamW(
+            optimizer_params_dicts, lr=self.cfg.lr, weight_decay=self.cfg.weight_decay
+        )
+
+    def _reset_parameters(self):
+        """Xavier-uniform initialization of the transformer parameters as in the original code."""
+        for p in chain(self.encoder.parameters(), self.decoder.parameters()):
+            if p.dim() > 1:
+                nn.init.xavier_uniform_(p)
+
+    def reset(self):
+        """This should be called whenever the environment is reset."""
+        if self.cfg.n_action_steps is not None:
+            self._action_queue = deque([], maxlen=self.cfg.n_action_steps)
+
+    @torch.no_grad
+    def select_action(self, batch: dict[str, Tensor], **_) -> Tensor:
+        """Select a single action given environment observations.
+
+        This method wraps `select_actions` in order to return one action at a time for execution in the
+        environment. It works by managing the actions in a queue and only calling `select_actions` when the
+        queue is empty.
+        """
+        self.eval()
+        if len(self._action_queue) == 0:
+            # `_forward` returns a (batch_size, n_action_steps, action_dim) tensor, but the queue effectively
+            # has shape (n_action_steps, batch_size, *), hence the transpose.
+            self._action_queue.extend(self._forward(batch)[0][: self.cfg.n_action_steps].transpose(0, 1))
+        return self._action_queue.popleft()
+
+    def forward(self, batch, **_) -> dict[str, Tensor]:
+        """Run the batch through the model and compute the loss for training or validation."""
+        actions_hat, (mu_hat, log_sigma_x2_hat) = self._forward(batch)
+
+        l1_loss = (
+            F.l1_loss(batch["action"], actions_hat, reduction="none") * ~batch["action_is_pad"].unsqueeze(-1)
+        ).mean()
+
+        loss_dict = {"l1_loss": l1_loss}
+        if self.cfg.use_vae:
+            # Calculate Dₖₗ(latent_pdf || standard_normal). Note: After computing the KL-divergence for
+            # each dimension independently, we sum over the latent dimension to get the total
+            # KL-divergence per batch element, then take the mean over the batch.
+            # (See App. B of https://arxiv.org/abs/1312.6114 for more details).
+            mean_kld = (
+                (-0.5 * (1 + log_sigma_x2_hat - mu_hat.pow(2) - (log_sigma_x2_hat).exp())).sum(-1).mean()
+            )
+            loss_dict["kld_loss"] = mean_kld
+            loss_dict["loss"] = l1_loss + mean_kld * self.cfg.kl_weight
+        else:
+            loss_dict["loss"] = l1_loss
+
+        return loss_dict
+
+    def update(self, batch, **_) -> dict:
+        """Run the model in train mode, compute the loss, and do an optimization step."""
+        start_time = time.time()
+        self.train()
+        loss_dict = self.forward(batch)
+        loss = loss_dict["loss"]
+        loss.backward()
+
+        grad_norm = torch.nn.utils.clip_grad_norm_(
+            self.parameters(), self.cfg.grad_clip_norm, error_if_nonfinite=False
+        )
+
+        self.optimizer.step()
+        self.optimizer.zero_grad()
+
+        info = {
+            "loss": loss.item(),
+            "grad_norm": float(grad_norm),
+            "lr": self.cfg.lr,
+            "update_s": time.time() - start_time,
+        }
+
+        return info
+
+    def _stack_images(self, batch: dict[str, Tensor]) -> dict[str, Tensor]:
+        """Stacks all the images in a batch and puts them in a new key: "observation.images".
+
+        This function expects `batch` to have (at least):
+        {
+            "observation.state": (B, state_dim) batch of robot states.
+            "observation.images.{name}": (B, C, H, W) tensor of images.
+        }
+        """
+        # Check that there is only one image.
+        # TODO(alexander-soare): generalize this to multiple images.
+        provided_cameras = {k.rsplit(".", 1)[-1] for k in batch if k.startswith("observation.images.")}
+        if len(missing := set(self.cfg.camera_names).difference(provided_cameras)) > 0:
+            raise ValueError(
+                f"The following camera images are missing from the provided batch: {missing}. Check the "
+                "configuration parameter: `camera_names`."
+            )
+        # Stack images in the order dictated by the camera names.
+        batch["observation.images"] = torch.stack(
+            [batch[f"observation.images.{name}"] for name in self.cfg.camera_names],
+            dim=-4,
+        )
+
+    def _forward(self, batch: dict[str, Tensor]) -> tuple[Tensor, tuple[Tensor, Tensor] | tuple[None, None]]:
+        """A forward pass through the Action Chunking Transformer (with optional VAE encoder).
+
+        `batch` should have the following structure:
+
+        {
+            "observation.state": (B, state_dim) batch of robot states.
+            "observation.images": (B, n_cameras, C, H, W) batch of images.
+            "action" (optional, only if training with VAE): (B, chunk_size, action dim) batch of actions.
+        }
+
+        Returns:
+            (B, chunk_size, action_dim) batch of action sequences
+            Tuple containing the latent PDF's parameters (mean, log(σ²)) both as (B, L) tensors where L is the
+            latent dimension.
+        """
+        if self.cfg.use_vae and self.training:
+            assert (
+                "action" in batch
+            ), "actions must be provided when using the variational objective in training mode."
+
+        self._stack_images(batch)
+
+        batch_size = batch["observation.state"].shape[0]
+
+        # Prepare the latent for input to the transformer encoder.
+        if self.cfg.use_vae and "action" in batch:
+            # Prepare the input to the VAE encoder: [cls, *joint_space_configuration, *action_sequence].
+            cls_embed = einops.repeat(
+                self.vae_encoder_cls_embed.weight, "1 d -> b 1 d", b=batch_size
+            )  # (B, 1, D)
+            robot_state_embed = self.vae_encoder_robot_state_input_proj(batch["observation.state"]).unsqueeze(
+                1
+            )  # (B, 1, D)
+            action_embed = self.vae_encoder_action_input_proj(batch["action"])  # (B, S, D)
+            vae_encoder_input = torch.cat([cls_embed, robot_state_embed, action_embed], axis=1)  # (B, S+2, D)
+
+            # Prepare fixed positional embedding.
+            # Note: detach() shouldn't be necessary but leaving it the same as the original code just in case.
+            pos_embed = self.vae_encoder_pos_enc.clone().detach()  # (1, S+2, D)
+
+            # Forward pass through VAE encoder to get the latent PDF parameters.
+            cls_token_out = self.vae_encoder(
+                vae_encoder_input.permute(1, 0, 2), pos_embed=pos_embed.permute(1, 0, 2)
+            )[0]  # select the class token, with shape (B, D)
+            latent_pdf_params = self.vae_encoder_latent_output_proj(cls_token_out)
+            mu = latent_pdf_params[:, : self.latent_dim]
+            # This is 2log(sigma). Done this way to match the original implementation.
+            log_sigma_x2 = latent_pdf_params[:, self.latent_dim :]
+
+            # Sample the latent with the reparameterization trick.
+            latent_sample = mu + log_sigma_x2.div(2).exp() * torch.randn_like(mu)
+        else:
+            # When not using the VAE encoder, we set the latent to be all zeros.
+            mu = log_sigma_x2 = None
+            latent_sample = torch.zeros([batch_size, self.latent_dim], dtype=torch.float32).to(
+                batch["observation.state"].device
+            )
+
+        # Prepare all other transformer encoder inputs.
+        # Camera observation features and positional embeddings.
+        all_cam_features = []
+        all_cam_pos_embeds = []
+        images = self.image_normalizer(batch["observation.images"])
+        for cam_index in range(len(self.cfg.camera_names)):
+            cam_features = self.backbone(images[:, cam_index])["feature_map"]
+            cam_pos_embed = self.encoder_cam_feat_pos_embed(cam_features).to(dtype=cam_features.dtype)
+            cam_features = self.encoder_img_feat_input_proj(cam_features)  # (B, C, h, w)
+            all_cam_features.append(cam_features)
+            all_cam_pos_embeds.append(cam_pos_embed)
+        # Concatenate camera observation feature maps and positional embeddings along the width dimension.
+        encoder_in = torch.cat(all_cam_features, axis=3)
+        cam_pos_embed = torch.cat(all_cam_pos_embeds, axis=3)
+
+        # Get positional embeddings for robot state and latent.
+        robot_state_embed = self.encoder_robot_state_input_proj(batch["observation.state"])
+        latent_embed = self.encoder_latent_input_proj(latent_sample)
+
+        # Stack encoder input and positional embeddings moving to (S, B, C).
+        encoder_in = torch.cat(
+            [
+                torch.stack([latent_embed, robot_state_embed], axis=0),
+                encoder_in.flatten(2).permute(2, 0, 1),
+            ]
+        )
+        pos_embed = torch.cat(
+            [
+                self.encoder_robot_and_latent_pos_embed.weight.unsqueeze(1),
+                cam_pos_embed.flatten(2).permute(2, 0, 1),
+            ],
+            axis=0,
+        )
+
+        # Forward pass through the transformer modules.
+        encoder_out = self.encoder(encoder_in, pos_embed=pos_embed)
+        decoder_in = torch.zeros(
+            (self.cfg.chunk_size, batch_size, self.cfg.d_model),
+            dtype=pos_embed.dtype,
+            device=pos_embed.device,
+        )
+        decoder_out = self.decoder(
+            decoder_in,
+            encoder_out,
+            encoder_pos_embed=pos_embed,
+            decoder_pos_embed=self.decoder_pos_embed.weight.unsqueeze(1),
+        )
+
+        # Move back to (B, S, C).
+        decoder_out = decoder_out.transpose(0, 1)
+
+        actions = self.action_head(decoder_out)
+
+        return actions, (mu, log_sigma_x2)
+
+    def save(self, fp):
+        torch.save(self.state_dict(), fp)
+
+    def load(self, fp):
+        d = torch.load(fp)
+        self.load_state_dict(d)
+
+
+class _TransformerEncoder(nn.Module):
+    """Convenience module for running multiple encoder layers, maybe followed by normalization."""
+
+    def __init__(self, cfg: ActionChunkingTransformerConfig):
+        super().__init__()
+        self.layers = nn.ModuleList([_TransformerEncoderLayer(cfg) for _ in range(cfg.n_encoder_layers)])
+        self.norm = nn.LayerNorm(cfg.d_model) if cfg.pre_norm else nn.Identity()
+
+    def forward(self, x: Tensor, pos_embed: Tensor | None = None) -> Tensor:
+        for layer in self.layers:
+            x = layer(x, pos_embed=pos_embed)
+        x = self.norm(x)
+        return x
+
+
+class _TransformerEncoderLayer(nn.Module):
+    def __init__(self, cfg: ActionChunkingTransformerConfig):
+        super().__init__()
+        self.self_attn = nn.MultiheadAttention(cfg.d_model, cfg.n_heads, dropout=cfg.dropout)
+
+        # Feed forward layers.
+        self.linear1 = nn.Linear(cfg.d_model, cfg.dim_feedforward)
+        self.dropout = nn.Dropout(cfg.dropout)
+        self.linear2 = nn.Linear(cfg.dim_feedforward, cfg.d_model)
+
+        self.norm1 = nn.LayerNorm(cfg.d_model)
+        self.norm2 = nn.LayerNorm(cfg.d_model)
+        self.dropout1 = nn.Dropout(cfg.dropout)
+        self.dropout2 = nn.Dropout(cfg.dropout)
+
+        self.activation = _get_activation_fn(cfg.feedforward_activation)
+        self.pre_norm = cfg.pre_norm
+
+    def forward(self, x, pos_embed: Tensor | None = None) -> Tensor:
+        skip = x
+        if self.pre_norm:
+            x = self.norm1(x)
+        q = k = x if pos_embed is None else x + pos_embed
+        x = self.self_attn(q, k, value=x)[0]  # select just the output, not the attention weights
+        x = skip + self.dropout1(x)
+        if self.pre_norm:
+            skip = x
+            x = self.norm2(x)
+        else:
+            x = self.norm1(x)
+            skip = x
+        x = self.linear2(self.dropout(self.activation(self.linear1(x))))
+        x = skip + self.dropout2(x)
+        if not self.pre_norm:
+            x = self.norm2(x)
+        return x
+
+
+class _TransformerDecoder(nn.Module):
+    def __init__(self, cfg: ActionChunkingTransformerConfig):
+        """Convenience module for running multiple decoder layers followed by normalization."""
+        super().__init__()
+        self.layers = nn.ModuleList([_TransformerDecoderLayer(cfg) for _ in range(cfg.n_decoder_layers)])
+        self.norm = nn.LayerNorm(cfg.d_model)
+
+    def forward(
+        self,
+        x: Tensor,
+        encoder_out: Tensor,
+        decoder_pos_embed: Tensor | None = None,
+        encoder_pos_embed: Tensor | None = None,
+    ) -> Tensor:
+        for layer in self.layers:
+            x = layer(
+                x, encoder_out, decoder_pos_embed=decoder_pos_embed, encoder_pos_embed=encoder_pos_embed
+            )
+        if self.norm is not None:
+            x = self.norm(x)
+        return x
+
+
+class _TransformerDecoderLayer(nn.Module):
+    def __init__(self, cfg: ActionChunkingTransformerConfig):
+        super().__init__()
+        self.self_attn = nn.MultiheadAttention(cfg.d_model, cfg.n_heads, dropout=cfg.dropout)
+        self.multihead_attn = nn.MultiheadAttention(cfg.d_model, cfg.n_heads, dropout=cfg.dropout)
+
+        # Feed forward layers.
+        self.linear1 = nn.Linear(cfg.d_model, cfg.dim_feedforward)
+        self.dropout = nn.Dropout(cfg.dropout)
+        self.linear2 = nn.Linear(cfg.dim_feedforward, cfg.d_model)
+
+        self.norm1 = nn.LayerNorm(cfg.d_model)
+        self.norm2 = nn.LayerNorm(cfg.d_model)
+        self.norm3 = nn.LayerNorm(cfg.d_model)
+        self.dropout1 = nn.Dropout(cfg.dropout)
+        self.dropout2 = nn.Dropout(cfg.dropout)
+        self.dropout3 = nn.Dropout(cfg.dropout)
+
+        self.activation = _get_activation_fn(cfg.feedforward_activation)
+        self.pre_norm = cfg.pre_norm
+
+    def maybe_add_pos_embed(self, tensor: Tensor, pos_embed: Tensor | None) -> Tensor:
+        return tensor if pos_embed is None else tensor + pos_embed
+
+    def forward(
+        self,
+        x: Tensor,
+        encoder_out: Tensor,
+        decoder_pos_embed: Tensor | None = None,
+        encoder_pos_embed: Tensor | None = None,
+    ) -> Tensor:
+        """
+        Args:
+            x: (Decoder Sequence, Batch, Channel) tensor of input tokens.
+            encoder_out: (Encoder Sequence, B, C) output features from the last layer of the encoder we are
+                cross-attending with.
+            decoder_pos_embed: (ES, 1, C) positional embedding for keys (from the encoder).
+            encoder_pos_embed: (DS, 1, C) Positional_embedding for the queries (from the decoder).
+        Returns:
+            (DS, B, C) tensor of decoder output features.
+        """
+        skip = x
+        if self.pre_norm:
+            x = self.norm1(x)
+        q = k = self.maybe_add_pos_embed(x, decoder_pos_embed)
+        x = self.self_attn(q, k, value=x)[0]  # select just the output, not the attention weights
+        x = skip + self.dropout1(x)
+        if self.pre_norm:
+            skip = x
+            x = self.norm2(x)
+        else:
+            x = self.norm1(x)
+            skip = x
+        x = self.multihead_attn(
+            query=self.maybe_add_pos_embed(x, decoder_pos_embed),
+            key=self.maybe_add_pos_embed(encoder_out, encoder_pos_embed),
+            value=encoder_out,
+        )[0]  # select just the output, not the attention weights
+        x = skip + self.dropout2(x)
+        if self.pre_norm:
+            skip = x
+            x = self.norm3(x)
+        else:
+            x = self.norm2(x)
+            skip = x
+        x = self.linear2(self.dropout(self.activation(self.linear1(x))))
+        x = skip + self.dropout3(x)
+        if not self.pre_norm:
+            x = self.norm3(x)
+        return x
+
+
+def _create_sinusoidal_position_embedding(num_positions: int, dimension: int) -> Tensor:
+    """1D sinusoidal positional embeddings as in Attention is All You Need.
+
+    Args:
+        num_positions: Number of token positions required.
+    Returns: (num_positions, dimension) position embeddings (the first dimension is the batch dimension).
+
+    """
+
+    def get_position_angle_vec(position):
+        return [position / np.power(10000, 2 * (hid_j // 2) / dimension) for hid_j in range(dimension)]
+
+    sinusoid_table = np.array([get_position_angle_vec(pos_i) for pos_i in range(num_positions)])
+    sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2])  # dim 2i
+    sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2])  # dim 2i+1
+    return torch.from_numpy(sinusoid_table).float()
+
+
+class _SinusoidalPositionEmbedding2D(nn.Module):
+    """2D sinusoidal positional embeddings similar to what's presented in Attention Is All You Need.
+
+    The variation is that the position indices are normalized in [0, 2π] (not quite: the lower bound is 1/H
+    for the vertical direction, and 1/W for the horizontal direction.
+    """
+
+    def __init__(self, dimension: int):
+        """
+        Args:
+            dimension: The desired dimension of the embeddings.
+        """
+        super().__init__()
+        self.dimension = dimension
+        self._two_pi = 2 * math.pi
+        self._eps = 1e-6
+        # Inverse "common ratio" for the geometric progression in sinusoid frequencies.
+        self._temperature = 10000
+
+    def forward(self, x: Tensor) -> Tensor:
+        """
+        Args:
+            x: A (B, C, H, W) batch of 2D feature map to generate the embeddings for.
+        Returns:
+            A (1, C, H, W) batch of corresponding sinusoidal positional embeddings.
+        """
+        not_mask = torch.ones_like(x[0, :1])  # (1, H, W)
+        # Note: These are like range(1, H+1) and range(1, W+1) respectively, but in most implementations
+        # they would be range(0, H) and range(0, W). Keeping it at as is to match the original code.
+        y_range = not_mask.cumsum(1, dtype=torch.float32)
+        x_range = not_mask.cumsum(2, dtype=torch.float32)
+
+        # "Normalize" the position index such that it ranges in [0, 2π].
+        # Note: Adding epsilon on the denominator should not be needed as all values of y_embed and x_range
+        # are non-zero by construction. This is an artifact of the original code.
+        y_range = y_range / (y_range[:, -1:, :] + self._eps) * self._two_pi
+        x_range = x_range / (x_range[:, :, -1:] + self._eps) * self._two_pi
+
+        inverse_frequency = self._temperature ** (
+            2 * (torch.arange(self.dimension, dtype=torch.float32, device=x.device) // 2) / self.dimension
+        )
+
+        x_range = x_range.unsqueeze(-1) / inverse_frequency  # (1, H, W, 1)
+        y_range = y_range.unsqueeze(-1) / inverse_frequency  # (1, H, W, 1)
+
+        # Note: this stack then flatten operation results in interleaved sine and cosine terms.
+        # pos_embed_x and pos_embed_y are (1, H, W, C // 2).
+        pos_embed_x = torch.stack((x_range[..., 0::2].sin(), x_range[..., 1::2].cos()), dim=-1).flatten(3)
+        pos_embed_y = torch.stack((y_range[..., 0::2].sin(), y_range[..., 1::2].cos()), dim=-1).flatten(3)
+        pos_embed = torch.cat((pos_embed_y, pos_embed_x), dim=3).permute(0, 3, 1, 2)  # (1, C, H, W)
+
+        return pos_embed
+
+
+def _get_activation_fn(activation: str) -> Callable:
+    """Return an activation function given a string."""
+    if activation == "relu":
+        return F.relu
+    if activation == "gelu":
+        return F.gelu
+    if activation == "glu":
+        return F.glu
+    raise RuntimeError(f"activation should be relu/gelu/glu, not {activation}.")

lerobot/common/policies/act/policy.py

@@ -1,217 +0,0 @@
-import logging
-import time
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F  # noqa: N812
-import torchvision.transforms as transforms
-
-from lerobot.common.policies.act.detr_vae import build
-
-
-def build_act_model_and_optimizer(cfg):
-    model = build(cfg)
-
-    param_dicts = [
-        {"params": [p for n, p in model.named_parameters() if "backbone" not in n and p.requires_grad]},
-        {
-            "params": [p for n, p in model.named_parameters() if "backbone" in n and p.requires_grad],
-            "lr": cfg.lr_backbone,
-        },
-    ]
-    optimizer = torch.optim.AdamW(param_dicts, lr=cfg.lr, weight_decay=cfg.weight_decay)
-
-    return model, optimizer
-
-
-def kl_divergence(mu, logvar):
-    batch_size = mu.size(0)
-    assert batch_size != 0
-    if mu.data.ndimension() == 4:
-        mu = mu.view(mu.size(0), mu.size(1))
-    if logvar.data.ndimension() == 4:
-        logvar = logvar.view(logvar.size(0), logvar.size(1))
-
-    klds = -0.5 * (1 + logvar - mu.pow(2) - logvar.exp())
-    total_kld = klds.sum(1).mean(0, True)
-    dimension_wise_kld = klds.mean(0)
-    mean_kld = klds.mean(1).mean(0, True)
-
-    return total_kld, dimension_wise_kld, mean_kld
-
-
-class ActionChunkingTransformerPolicy(nn.Module):
-    def __init__(self, cfg, device, n_action_steps=1):
-        super().__init__()
-        self.cfg = cfg
-        self.n_action_steps = n_action_steps
-        self.device = device
-        self.model, self.optimizer = build_act_model_and_optimizer(cfg)
-        self.kl_weight = self.cfg.kl_weight
-        logging.info(f"KL Weight {self.kl_weight}")
-        self.to(self.device)
-
-    def update(self, replay_buffer, step):
-        del step
-
-        start_time = time.time()
-
-        self.train()
-
-        num_slices = self.cfg.batch_size
-        batch_size = self.cfg.horizon * num_slices
-
-        assert batch_size % self.cfg.horizon == 0
-        assert batch_size % num_slices == 0
-
-        def process_batch(batch, horizon, num_slices):
-            # trajectory t = 64, horizon h = 16
-            # (t h) ... -> t h ...
-            batch = batch.reshape(num_slices, horizon)
-
-            image = batch["observation", "image", "top"]
-            image = image[:, 0]  # first observation t=0
-            # batch, num_cam, channel, height, width
-            image = image.unsqueeze(1)
-            assert image.ndim == 5
-            image = image.float()
-
-            state = batch["observation", "state"]
-            state = state[:, 0]  # first observation t=0
-            # batch, qpos_dim
-            assert state.ndim == 2
-
-            action = batch["action"]
-            # batch, seq, action_dim
-            assert action.ndim == 3
-            assert action.shape[1] == horizon
-
-            if self.cfg.n_obs_steps > 1:
-                raise NotImplementedError()
-                # # keep first n observations of the slice corresponding to t=[-1,0]
-                # image = image[:, : self.cfg.n_obs_steps]
-                # state = state[:, : self.cfg.n_obs_steps]
-
-            out = {
-                "obs": {
-                    "image": image.to(self.device, non_blocking=True),
-                    "agent_pos": state.to(self.device, non_blocking=True),
-                },
-                "action": action.to(self.device, non_blocking=True),
-            }
-            return out
-
-        batch = replay_buffer.sample(batch_size)
-        batch = process_batch(batch, self.cfg.horizon, num_slices)
-
-        data_s = time.time() - start_time
-
-        loss = self.compute_loss(batch)
-        loss.backward()
-
-        grad_norm = torch.nn.utils.clip_grad_norm_(
-            self.model.parameters(),
-            self.cfg.grad_clip_norm,
-            error_if_nonfinite=False,
-        )
-
-        self.optimizer.step()
-        self.optimizer.zero_grad()
-        # self.lr_scheduler.step()
-
-        info = {
-            "loss": loss.item(),
-            "grad_norm": float(grad_norm),
-            # "lr": self.lr_scheduler.get_last_lr()[0],
-            "lr": self.cfg.lr,
-            "data_s": data_s,
-            "update_s": time.time() - start_time,
-        }
-
-        return info
-
-    def save(self, fp):
-        torch.save(self.state_dict(), fp)
-
-    def load(self, fp):
-        d = torch.load(fp)
-        self.load_state_dict(d)
-
-    def compute_loss(self, batch):
-        loss_dict = self._forward(
-            qpos=batch["obs"]["agent_pos"],
-            image=batch["obs"]["image"],
-            actions=batch["action"],
-        )
-        loss = loss_dict["loss"]
-        return loss
-
-    @torch.no_grad()
-    def forward(self, observation, step_count):
-        # TODO(rcadene): remove unused step_count
-        del step_count
-
-        self.eval()
-
-        # TODO(rcadene): remove unsqueeze hack to add bsize=1
-        observation["image", "top"] = observation["image", "top"].unsqueeze(0)
-        # observation["state"] = observation["state"].unsqueeze(0)
-
-        # TODO(rcadene): remove hack
-        # add 1 camera dimension
-        observation["image", "top"] = observation["image", "top"].unsqueeze(1)
-
-        obs_dict = {
-            "image": observation["image", "top"],
-            "agent_pos": observation["state"],
-        }
-        action = self._forward(qpos=obs_dict["agent_pos"], image=obs_dict["image"])
-
-        if self.cfg.temporal_agg:
-            # TODO(rcadene): implement temporal aggregation
-            raise NotImplementedError()
-            # all_time_actions[[t], t:t+num_queries] = action
-            # actions_for_curr_step = all_time_actions[:, t]
-            # actions_populated = torch.all(actions_for_curr_step != 0, axis=1)
-            # actions_for_curr_step = actions_for_curr_step[actions_populated]
-            # k = 0.01
-            # exp_weights = np.exp(-k * np.arange(len(actions_for_curr_step)))
-            # exp_weights = exp_weights / exp_weights.sum()
-            # exp_weights = torch.from_numpy(exp_weights).cuda().unsqueeze(dim=1)
-            # raw_action = (actions_for_curr_step * exp_weights).sum(dim=0, keepdim=True)
-
-        # remove bsize=1
-        action = action.squeeze(0)
-
-        # take first predicted action or n first actions
-        action = action[0] if self.n_action_steps == 1 else action[: self.n_action_steps]
-        return action
-
-    def _forward(self, qpos, image, actions=None, is_pad=None):
-        env_state = None
-        normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
-        image = normalize(image)
-
-        is_training = actions is not None
-        if is_training:  # training time
-            actions = actions[:, : self.model.num_queries]
-            if is_pad is not None:
-                is_pad = is_pad[:, : self.model.num_queries]
-
-            a_hat, is_pad_hat, (mu, logvar) = self.model(qpos, image, env_state, actions, is_pad)
-
-            all_l1 = F.l1_loss(actions, a_hat, reduction="none")
-            l1 = all_l1.mean() if is_pad is None else (all_l1 * ~is_pad.unsqueeze(-1)).mean()
-
-            loss_dict = {}
-            loss_dict["l1"] = l1
-            if self.cfg.vae:
-                total_kld, dim_wise_kld, mean_kld = kl_divergence(mu, logvar)
-                loss_dict["kl"] = total_kld[0]
-                loss_dict["loss"] = loss_dict["l1"] + loss_dict["kl"] * self.kl_weight
-            else:
-                loss_dict["loss"] = loss_dict["l1"]
-            return loss_dict
-        else:
-            action, _, (_, _) = self.model(qpos, image, env_state)  # no action, sample from prior
-            return action

lerobot/common/policies/act/position_encoding.py

@@ -1,101 +0,0 @@
-"""
-Various positional encodings for the transformer.
-"""
-import math
-
-import torch
-from torch import nn
-
-from .utils import NestedTensor
-
-
-class PositionEmbeddingSine(nn.Module):
-    """
-    This is a more standard version of the position embedding, very similar to the one
-    used by the Attention is all you need paper, generalized to work on images.
-    """
-
-    def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None):
-        super().__init__()
-        self.num_pos_feats = num_pos_feats
-        self.temperature = temperature
-        self.normalize = normalize
-        if scale is not None and normalize is False:
-            raise ValueError("normalize should be True if scale is passed")
-        if scale is None:
-            scale = 2 * math.pi
-        self.scale = scale
-
-    def forward(self, tensor):
-        x = tensor
-        # mask = tensor_list.mask
-        # assert mask is not None
-        # not_mask = ~mask
-
-        not_mask = torch.ones_like(x[0, [0]])
-        y_embed = not_mask.cumsum(1, dtype=torch.float32)
-        x_embed = not_mask.cumsum(2, dtype=torch.float32)
-        if self.normalize:
-            eps = 1e-6
-            y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
-            x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
-
-        dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
-        dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)
-
-        pos_x = x_embed[:, :, :, None] / dim_t
-        pos_y = y_embed[:, :, :, None] / dim_t
-        pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3)
-        pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3)
-        pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
-        return pos
-
-
-class PositionEmbeddingLearned(nn.Module):
-    """
-    Absolute pos embedding, learned.
-    """
-
-    def __init__(self, num_pos_feats=256):
-        super().__init__()
-        self.row_embed = nn.Embedding(50, num_pos_feats)
-        self.col_embed = nn.Embedding(50, num_pos_feats)
-        self.reset_parameters()
-
-    def reset_parameters(self):
-        nn.init.uniform_(self.row_embed.weight)
-        nn.init.uniform_(self.col_embed.weight)
-
-    def forward(self, tensor_list: NestedTensor):
-        x = tensor_list.tensors
-        h, w = x.shape[-2:]
-        i = torch.arange(w, device=x.device)
-        j = torch.arange(h, device=x.device)
-        x_emb = self.col_embed(i)
-        y_emb = self.row_embed(j)
-        pos = (
-            torch.cat(
-                [
-                    x_emb.unsqueeze(0).repeat(h, 1, 1),
-                    y_emb.unsqueeze(1).repeat(1, w, 1),
-                ],
-                dim=-1,
-            )
-            .permute(2, 0, 1)
-            .unsqueeze(0)
-            .repeat(x.shape[0], 1, 1, 1)
-        )
-        return pos
-
-
-def build_position_encoding(args):
-    n_steps = args.hidden_dim // 2
-    if args.position_embedding in ("v2", "sine"):
-        # TODO find a better way of exposing other arguments
-        position_embedding = PositionEmbeddingSine(n_steps, normalize=True)
-    elif args.position_embedding in ("v3", "learned"):
-        position_embedding = PositionEmbeddingLearned(n_steps)
-    else:
-        raise ValueError(f"not supported {args.position_embedding}")
-
-    return position_embedding

lerobot/common/policies/act/transformer.py

@@ -1,370 +0,0 @@
-"""
-DETR Transformer class.
-
-Copy-paste from torch.nn.Transformer with modifications:
-    * positional encodings are passed in MHattention
-    * extra LN at the end of encoder is removed
-    * decoder returns a stack of activations from all decoding layers
-"""
-import copy
-from typing import Optional
-
-import torch
-import torch.nn.functional as F  # noqa: N812
-from torch import Tensor, nn
-
-
-class Transformer(nn.Module):
-    def __init__(
-        self,
-        d_model=512,
-        nhead=8,
-        num_encoder_layers=6,
-        num_decoder_layers=6,
-        dim_feedforward=2048,
-        dropout=0.1,
-        activation="relu",
-        normalize_before=False,
-        return_intermediate_dec=False,
-    ):
-        super().__init__()
-
-        encoder_layer = TransformerEncoderLayer(
-            d_model, nhead, dim_feedforward, dropout, activation, normalize_before
-        )
-        encoder_norm = nn.LayerNorm(d_model) if normalize_before else None
-        self.encoder = TransformerEncoder(encoder_layer, num_encoder_layers, encoder_norm)
-
-        decoder_layer = TransformerDecoderLayer(
-            d_model, nhead, dim_feedforward, dropout, activation, normalize_before
-        )
-        decoder_norm = nn.LayerNorm(d_model)
-        self.decoder = TransformerDecoder(
-            decoder_layer, num_decoder_layers, decoder_norm, return_intermediate=return_intermediate_dec
-        )
-
-        self._reset_parameters()
-
-        self.d_model = d_model
-        self.nhead = nhead
-
-    def _reset_parameters(self):
-        for p in self.parameters():
-            if p.dim() > 1:
-                nn.init.xavier_uniform_(p)
-
-    def forward(
-        self,
-        src,
-        mask,
-        query_embed,
-        pos_embed,
-        latent_input=None,
-        proprio_input=None,
-        additional_pos_embed=None,
-    ):
-        # TODO flatten only when input has H and W
-        if len(src.shape) == 4:  # has H and W
-            # flatten NxCxHxW to HWxNxC
-            bs, c, h, w = src.shape
-            src = src.flatten(2).permute(2, 0, 1)
-            pos_embed = pos_embed.flatten(2).permute(2, 0, 1).repeat(1, bs, 1)
-            query_embed = query_embed.unsqueeze(1).repeat(1, bs, 1)
-            # mask = mask.flatten(1)
-
-            additional_pos_embed = additional_pos_embed.unsqueeze(1).repeat(1, bs, 1)  # seq, bs, dim
-            pos_embed = torch.cat([additional_pos_embed, pos_embed], axis=0)
-
-            addition_input = torch.stack([latent_input, proprio_input], axis=0)
-            src = torch.cat([addition_input, src], axis=0)
-        else:
-            assert len(src.shape) == 3
-            # flatten NxHWxC to HWxNxC
-            bs, hw, c = src.shape
-            src = src.permute(1, 0, 2)
-            pos_embed = pos_embed.unsqueeze(1).repeat(1, bs, 1)
-            query_embed = query_embed.unsqueeze(1).repeat(1, bs, 1)
-
-        tgt = torch.zeros_like(query_embed)
-        memory = self.encoder(src, src_key_padding_mask=mask, pos=pos_embed)
-        hs = self.decoder(tgt, memory, memory_key_padding_mask=mask, pos=pos_embed, query_pos=query_embed)
-        hs = hs.transpose(1, 2)
-        return hs
-
-
-class TransformerEncoder(nn.Module):
-    def __init__(self, encoder_layer, num_layers, norm=None):
-        super().__init__()
-        self.layers = _get_clones(encoder_layer, num_layers)
-        self.num_layers = num_layers
-        self.norm = norm
-
-    def forward(
-        self,
-        src,
-        mask: Optional[Tensor] = None,
-        src_key_padding_mask: Optional[Tensor] = None,
-        pos: Optional[Tensor] = None,
-    ):
-        output = src
-
-        for layer in self.layers:
-            output = layer(output, src_mask=mask, src_key_padding_mask=src_key_padding_mask, pos=pos)
-
-        if self.norm is not None:
-            output = self.norm(output)
-
-        return output
-
-
-class TransformerDecoder(nn.Module):
-    def __init__(self, decoder_layer, num_layers, norm=None, return_intermediate=False):
-        super().__init__()
-        self.layers = _get_clones(decoder_layer, num_layers)
-        self.num_layers = num_layers
-        self.norm = norm
-        self.return_intermediate = return_intermediate
-
-    def forward(
-        self,
-        tgt,
-        memory,
-        tgt_mask: Optional[Tensor] = None,
-        memory_mask: Optional[Tensor] = None,
-        tgt_key_padding_mask: Optional[Tensor] = None,
-        memory_key_padding_mask: Optional[Tensor] = None,
-        pos: Optional[Tensor] = None,
-        query_pos: Optional[Tensor] = None,
-    ):
-        output = tgt
-
-        intermediate = []
-
-        for layer in self.layers:
-            output = layer(
-                output,
-                memory,
-                tgt_mask=tgt_mask,
-                memory_mask=memory_mask,
-                tgt_key_padding_mask=tgt_key_padding_mask,
-                memory_key_padding_mask=memory_key_padding_mask,
-                pos=pos,
-                query_pos=query_pos,
-            )
-            if self.return_intermediate:
-                intermediate.append(self.norm(output))
-
-        if self.norm is not None:
-            output = self.norm(output)
-            if self.return_intermediate:
-                intermediate.pop()
-                intermediate.append(output)
-
-        if self.return_intermediate:
-            return torch.stack(intermediate)
-
-        return output.unsqueeze(0)
-
-
-class TransformerEncoderLayer(nn.Module):
-    def __init__(
-        self, d_model, nhead, dim_feedforward=2048, dropout=0.1, activation="relu", normalize_before=False
-    ):
-        super().__init__()
-        self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
-        # Implementation of Feedforward model
-        self.linear1 = nn.Linear(d_model, dim_feedforward)
-        self.dropout = nn.Dropout(dropout)
-        self.linear2 = nn.Linear(dim_feedforward, d_model)
-
-        self.norm1 = nn.LayerNorm(d_model)
-        self.norm2 = nn.LayerNorm(d_model)
-        self.dropout1 = nn.Dropout(dropout)
-        self.dropout2 = nn.Dropout(dropout)
-
-        self.activation = _get_activation_fn(activation)
-        self.normalize_before = normalize_before
-
-    def with_pos_embed(self, tensor, pos: Optional[Tensor]):
-        return tensor if pos is None else tensor + pos
-
-    def forward_post(
-        self,
-        src,
-        src_mask: Optional[Tensor] = None,
-        src_key_padding_mask: Optional[Tensor] = None,
-        pos: Optional[Tensor] = None,
-    ):
-        q = k = self.with_pos_embed(src, pos)
-        src2 = self.self_attn(q, k, value=src, attn_mask=src_mask, key_padding_mask=src_key_padding_mask)[0]
-        src = src + self.dropout1(src2)
-        src = self.norm1(src)
-        src2 = self.linear2(self.dropout(self.activation(self.linear1(src))))
-        src = src + self.dropout2(src2)
-        src = self.norm2(src)
-        return src
-
-    def forward_pre(
-        self,
-        src,
-        src_mask: Optional[Tensor] = None,
-        src_key_padding_mask: Optional[Tensor] = None,
-        pos: Optional[Tensor] = None,
-    ):
-        src2 = self.norm1(src)
-        q = k = self.with_pos_embed(src2, pos)
-        src2 = self.self_attn(q, k, value=src2, attn_mask=src_mask, key_padding_mask=src_key_padding_mask)[0]
-        src = src + self.dropout1(src2)
-        src2 = self.norm2(src)
-        src2 = self.linear2(self.dropout(self.activation(self.linear1(src2))))
-        src = src + self.dropout2(src2)
-        return src
-
-    def forward(
-        self,
-        src,
-        src_mask: Optional[Tensor] = None,
-        src_key_padding_mask: Optional[Tensor] = None,
-        pos: Optional[Tensor] = None,
-    ):
-        if self.normalize_before:
-            return self.forward_pre(src, src_mask, src_key_padding_mask, pos)
-        return self.forward_post(src, src_mask, src_key_padding_mask, pos)
-
-
-class TransformerDecoderLayer(nn.Module):
-    def __init__(
-        self, d_model, nhead, dim_feedforward=2048, dropout=0.1, activation="relu", normalize_before=False
-    ):
-        super().__init__()
-        self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
-        self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
-        # Implementation of Feedforward model
-        self.linear1 = nn.Linear(d_model, dim_feedforward)
-        self.dropout = nn.Dropout(dropout)
-        self.linear2 = nn.Linear(dim_feedforward, d_model)
-
-        self.norm1 = nn.LayerNorm(d_model)
-        self.norm2 = nn.LayerNorm(d_model)
-        self.norm3 = nn.LayerNorm(d_model)
-        self.dropout1 = nn.Dropout(dropout)
-        self.dropout2 = nn.Dropout(dropout)
-        self.dropout3 = nn.Dropout(dropout)
-
-        self.activation = _get_activation_fn(activation)
-        self.normalize_before = normalize_before
-
-    def with_pos_embed(self, tensor, pos: Optional[Tensor]):
-        return tensor if pos is None else tensor + pos
-
-    def forward_post(
-        self,
-        tgt,
-        memory,
-        tgt_mask: Optional[Tensor] = None,
-        memory_mask: Optional[Tensor] = None,
-        tgt_key_padding_mask: Optional[Tensor] = None,
-        memory_key_padding_mask: Optional[Tensor] = None,
-        pos: Optional[Tensor] = None,
-        query_pos: Optional[Tensor] = None,
-    ):
-        q = k = self.with_pos_embed(tgt, query_pos)
-        tgt2 = self.self_attn(q, k, value=tgt, attn_mask=tgt_mask, key_padding_mask=tgt_key_padding_mask)[0]
-        tgt = tgt + self.dropout1(tgt2)
-        tgt = self.norm1(tgt)
-        tgt2 = self.multihead_attn(
-            query=self.with_pos_embed(tgt, query_pos),
-            key=self.with_pos_embed(memory, pos),
-            value=memory,
-            attn_mask=memory_mask,
-            key_padding_mask=memory_key_padding_mask,
-        )[0]
-        tgt = tgt + self.dropout2(tgt2)
-        tgt = self.norm2(tgt)
-        tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt))))
-        tgt = tgt + self.dropout3(tgt2)
-        tgt = self.norm3(tgt)
-        return tgt
-
-    def forward_pre(
-        self,
-        tgt,
-        memory,
-        tgt_mask: Optional[Tensor] = None,
-        memory_mask: Optional[Tensor] = None,
-        tgt_key_padding_mask: Optional[Tensor] = None,
-        memory_key_padding_mask: Optional[Tensor] = None,
-        pos: Optional[Tensor] = None,
-        query_pos: Optional[Tensor] = None,
-    ):
-        tgt2 = self.norm1(tgt)
-        q = k = self.with_pos_embed(tgt2, query_pos)
-        tgt2 = self.self_attn(q, k, value=tgt2, attn_mask=tgt_mask, key_padding_mask=tgt_key_padding_mask)[0]
-        tgt = tgt + self.dropout1(tgt2)
-        tgt2 = self.norm2(tgt)
-        tgt2 = self.multihead_attn(
-            query=self.with_pos_embed(tgt2, query_pos),
-            key=self.with_pos_embed(memory, pos),
-            value=memory,
-            attn_mask=memory_mask,
-            key_padding_mask=memory_key_padding_mask,
-        )[0]
-        tgt = tgt + self.dropout2(tgt2)
-        tgt2 = self.norm3(tgt)
-        tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))
-        tgt = tgt + self.dropout3(tgt2)
-        return tgt
-
-    def forward(
-        self,
-        tgt,
-        memory,
-        tgt_mask: Optional[Tensor] = None,
-        memory_mask: Optional[Tensor] = None,
-        tgt_key_padding_mask: Optional[Tensor] = None,
-        memory_key_padding_mask: Optional[Tensor] = None,
-        pos: Optional[Tensor] = None,
-        query_pos: Optional[Tensor] = None,
-    ):
-        if self.normalize_before:
-            return self.forward_pre(
-                tgt,
-                memory,
-                tgt_mask,
-                memory_mask,
-                tgt_key_padding_mask,
-                memory_key_padding_mask,
-                pos,
-                query_pos,
-            )
-        return self.forward_post(
-            tgt, memory, tgt_mask, memory_mask, tgt_key_padding_mask, memory_key_padding_mask, pos, query_pos
-        )
-
-
-def _get_clones(module, n):
-    return nn.ModuleList([copy.deepcopy(module) for _ in range(n)])
-
-
-def build_transformer(args):
-    return Transformer(
-        d_model=args.hidden_dim,
-        dropout=args.dropout,
-        nhead=args.nheads,
-        dim_feedforward=args.dim_feedforward,
-        num_encoder_layers=args.enc_layers,
-        num_decoder_layers=args.dec_layers,
-        normalize_before=args.pre_norm,
-        return_intermediate_dec=True,
-    )
-
-
-def _get_activation_fn(activation):
-    """Return an activation function given a string"""
-    if activation == "relu":
-        return F.relu
-    if activation == "gelu":
-        return F.gelu
-    if activation == "glu":
-        return F.glu
-    raise RuntimeError(f"activation should be relu/gelu, not {activation}.")

lerobot/common/policies/act/utils.py

@@ -1,477 +0,0 @@
-"""
-Misc functions, including distributed helpers.
-
-Mostly copy-paste from torchvision references.
-"""
-import datetime
-import os
-import pickle
-import subprocess
-import time
-from collections import defaultdict, deque
-from typing import List, Optional
-
-import torch
-import torch.distributed as dist
-
-# needed due to empty tensor bug in pytorch and torchvision 0.5
-import torchvision
-from packaging import version
-from torch import Tensor
-
-if version.parse(torchvision.__version__) < version.parse("0.7"):
-    from torchvision.ops import _new_empty_tensor
-    from torchvision.ops.misc import _output_size
-
-
-class SmoothedValue:
-    """Track a series of values and provide access to smoothed values over a
-    window or the global series average.
-    """
-
-    def __init__(self, window_size=20, fmt=None):
-        if fmt is None:
-            fmt = "{median:.4f} ({global_avg:.4f})"
-        self.deque = deque(maxlen=window_size)
-        self.total = 0.0
-        self.count = 0
-        self.fmt = fmt
-
-    def update(self, value, n=1):
-        self.deque.append(value)
-        self.count += n
-        self.total += value * n
-
-    def synchronize_between_processes(self):
-        """
-        Warning: does not synchronize the deque!
-        """
-        if not is_dist_avail_and_initialized():
-            return
-        t = torch.tensor([self.count, self.total], dtype=torch.float64, device="cuda")
-        dist.barrier()
-        dist.all_reduce(t)
-        t = t.tolist()
-        self.count = int(t[0])
-        self.total = t[1]
-
-    @property
-    def median(self):
-        d = torch.tensor(list(self.deque))
-        return d.median().item()
-
-    @property
-    def avg(self):
-        d = torch.tensor(list(self.deque), dtype=torch.float32)
-        return d.mean().item()
-
-    @property
-    def global_avg(self):
-        return self.total / self.count
-
-    @property
-    def max(self):
-        return max(self.deque)
-
-    @property
-    def value(self):
-        return self.deque[-1]
-
-    def __str__(self):
-        return self.fmt.format(
-            median=self.median, avg=self.avg, global_avg=self.global_avg, max=self.max, value=self.value
-        )
-
-
-def all_gather(data):
-    """
-    Run all_gather on arbitrary picklable data (not necessarily tensors)
-    Args:
-        data: any picklable object
-    Returns:
-        list[data]: list of data gathered from each rank
-    """
-    world_size = get_world_size()
-    if world_size == 1:
-        return [data]
-
-    # serialized to a Tensor
-    buffer = pickle.dumps(data)
-    storage = torch.ByteStorage.from_buffer(buffer)
-    tensor = torch.ByteTensor(storage).to("cuda")
-
-    # obtain Tensor size of each rank
-    local_size = torch.tensor([tensor.numel()], device="cuda")
-    size_list = [torch.tensor([0], device="cuda") for _ in range(world_size)]
-    dist.all_gather(size_list, local_size)
-    size_list = [int(size.item()) for size in size_list]
-    max_size = max(size_list)
-
-    # receiving Tensor from all ranks
-    # we pad the tensor because torch all_gather does not support
-    # gathering tensors of different shapes
-    tensor_list = []
-    for _ in size_list:
-        tensor_list.append(torch.empty((max_size,), dtype=torch.uint8, device="cuda"))
-    if local_size != max_size:
-        padding = torch.empty(size=(max_size - local_size,), dtype=torch.uint8, device="cuda")
-        tensor = torch.cat((tensor, padding), dim=0)
-    dist.all_gather(tensor_list, tensor)
-
-    data_list = []
-    for size, tensor in zip(size_list, tensor_list, strict=False):
-        buffer = tensor.cpu().numpy().tobytes()[:size]
-        data_list.append(pickle.loads(buffer))
-
-    return data_list
-
-
-def reduce_dict(input_dict, average=True):
-    """
-    Args:
-        input_dict (dict): all the values will be reduced
-        average (bool): whether to do average or sum
-    Reduce the values in the dictionary from all processes so that all processes
-    have the averaged results. Returns a dict with the same fields as
-    input_dict, after reduction.
-    """
-    world_size = get_world_size()
-    if world_size < 2:
-        return input_dict
-    with torch.no_grad():
-        names = []
-        values = []
-        # sort the keys so that they are consistent across processes
-        for k in sorted(input_dict.keys()):
-            names.append(k)
-            values.append(input_dict[k])
-        values = torch.stack(values, dim=0)
-        dist.all_reduce(values)
-        if average:
-            values /= world_size
-        reduced_dict = {k: v for k, v in zip(names, values, strict=False)}  # noqa: C416
-    return reduced_dict
-
-
-class MetricLogger:
-    def __init__(self, delimiter="\t"):
-        self.meters = defaultdict(SmoothedValue)
-        self.delimiter = delimiter
-
-    def update(self, **kwargs):
-        for k, v in kwargs.items():
-            if isinstance(v, torch.Tensor):
-                v = v.item()
-            assert isinstance(v, (float, int))
-            self.meters[k].update(v)
-
-    def __getattr__(self, attr):
-        if attr in self.meters:
-            return self.meters[attr]
-        if attr in self.__dict__:
-            return self.__dict__[attr]
-        raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, attr))
-
-    def __str__(self):
-        loss_str = []
-        for name, meter in self.meters.items():
-            loss_str.append("{}: {}".format(name, str(meter)))
-        return self.delimiter.join(loss_str)
-
-    def synchronize_between_processes(self):
-        for meter in self.meters.values():
-            meter.synchronize_between_processes()
-
-    def add_meter(self, name, meter):
-        self.meters[name] = meter
-
-    def log_every(self, iterable, print_freq, header=None):
-        if not header:
-            header = ""
-        start_time = time.time()
-        end = time.time()
-        iter_time = SmoothedValue(fmt="{avg:.4f}")
-        data_time = SmoothedValue(fmt="{avg:.4f}")
-        space_fmt = ":" + str(len(str(len(iterable)))) + "d"
-        if torch.cuda.is_available():
-            log_msg = self.delimiter.join(
-                [
-                    header,
-                    "[{0" + space_fmt + "}/{1}]",
-                    "eta: {eta}",
-                    "{meters}",
-                    "time: {time}",
-                    "data: {data}",
-                    "max mem: {memory:.0f}",
-                ]
-            )
-        else:
-            log_msg = self.delimiter.join(
-                [
-                    header,
-                    "[{0" + space_fmt + "}/{1}]",
-                    "eta: {eta}",
-                    "{meters}",
-                    "time: {time}",
-                    "data: {data}",
-                ]
-            )
-        mega_b = 1024.0 * 1024.0
-        for i, obj in enumerate(iterable):
-            data_time.update(time.time() - end)
-            yield obj
-            iter_time.update(time.time() - end)
-            if i % print_freq == 0 or i == len(iterable) - 1:
-                eta_seconds = iter_time.global_avg * (len(iterable) - i)
-                eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))
-                if torch.cuda.is_available():
-                    print(
-                        log_msg.format(
-                            i,
-                            len(iterable),
-                            eta=eta_string,
-                            meters=str(self),
-                            time=str(iter_time),
-                            data=str(data_time),
-                            memory=torch.cuda.max_memory_allocated() / mega_b,
-                        )
-                    )
-                else:
-                    print(
-                        log_msg.format(
-                            i,
-                            len(iterable),
-                            eta=eta_string,
-                            meters=str(self),
-                            time=str(iter_time),
-                            data=str(data_time),
-                        )
-                    )
-            end = time.time()
-        total_time = time.time() - start_time
-        total_time_str = str(datetime.timedelta(seconds=int(total_time)))
-        print("{} Total time: {} ({:.4f} s / it)".format(header, total_time_str, total_time / len(iterable)))
-
-
-def get_sha():
-    cwd = os.path.dirname(os.path.abspath(__file__))
-
-    def _run(command):
-        return subprocess.check_output(command, cwd=cwd).decode("ascii").strip()
-
-    sha = "N/A"
-    diff = "clean"
-    branch = "N/A"
-    try:
-        sha = _run(["git", "rev-parse", "HEAD"])
-        subprocess.check_output(["git", "diff"], cwd=cwd)
-        diff = _run(["git", "diff-index", "HEAD"])
-        diff = "has uncommited changes" if diff else "clean"
-        branch = _run(["git", "rev-parse", "--abbrev-ref", "HEAD"])
-    except Exception:
-        pass
-    message = f"sha: {sha}, status: {diff}, branch: {branch}"
-    return message
-
-
-def collate_fn(batch):
-    batch = list(zip(*batch, strict=False))
-    batch[0] = nested_tensor_from_tensor_list(batch[0])
-    return tuple(batch)
-
-
-def _max_by_axis(the_list):
-    # type: (List[List[int]]) -> List[int]
-    maxes = the_list[0]
-    for sublist in the_list[1:]:
-        for index, item in enumerate(sublist):
-            maxes[index] = max(maxes[index], item)
-    return maxes
-
-
-class NestedTensor:
-    def __init__(self, tensors, mask: Optional[Tensor]):
-        self.tensors = tensors
-        self.mask = mask
-
-    def to(self, device):
-        # type: (Device) -> NestedTensor # noqa
-        cast_tensor = self.tensors.to(device)
-        mask = self.mask
-        if mask is not None:
-            assert mask is not None
-            cast_mask = mask.to(device)
-        else:
-            cast_mask = None
-        return NestedTensor(cast_tensor, cast_mask)
-
-    def decompose(self):
-        return self.tensors, self.mask
-
-    def __repr__(self):
-        return str(self.tensors)
-
-
-def nested_tensor_from_tensor_list(tensor_list: List[Tensor]):
-    # TODO make this more general
-    if tensor_list[0].ndim == 3:
-        if torchvision._is_tracing():
-            # nested_tensor_from_tensor_list() does not export well to ONNX
-            # call _onnx_nested_tensor_from_tensor_list() instead
-            return _onnx_nested_tensor_from_tensor_list(tensor_list)
-
-        # TODO make it support different-sized images
-        max_size = _max_by_axis([list(img.shape) for img in tensor_list])
-        # min_size = tuple(min(s) for s in zip(*[img.shape for img in tensor_list]))
-        batch_shape = [len(tensor_list)] + max_size
-        b, c, h, w = batch_shape
-        dtype = tensor_list[0].dtype
-        device = tensor_list[0].device
-        tensor = torch.zeros(batch_shape, dtype=dtype, device=device)
-        mask = torch.ones((b, h, w), dtype=torch.bool, device=device)
-        for img, pad_img, m in zip(tensor_list, tensor, mask, strict=False):
-            pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img)
-            m[: img.shape[1], : img.shape[2]] = False
-    else:
-        raise ValueError("not supported")
-    return NestedTensor(tensor, mask)
-
-
-# _onnx_nested_tensor_from_tensor_list() is an implementation of
-# nested_tensor_from_tensor_list() that is supported by ONNX tracing.
-@torch.jit.unused
-def _onnx_nested_tensor_from_tensor_list(tensor_list: List[Tensor]) -> NestedTensor:
-    max_size = []
-    for i in range(tensor_list[0].dim()):
-        max_size_i = torch.max(torch.stack([img.shape[i] for img in tensor_list]).to(torch.float32)).to(
-            torch.int64
-        )
-        max_size.append(max_size_i)
-    max_size = tuple(max_size)
-
-    # work around for
-    # pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img)
-    # m[: img.shape[1], :img.shape[2]] = False
-    # which is not yet supported in onnx
-    padded_imgs = []
-    padded_masks = []
-    for img in tensor_list:
-        padding = [(s1 - s2) for s1, s2 in zip(max_size, tuple(img.shape), strict=False)]
-        padded_img = torch.nn.functional.pad(img, (0, padding[2], 0, padding[1], 0, padding[0]))
-        padded_imgs.append(padded_img)
-
-        m = torch.zeros_like(img[0], dtype=torch.int, device=img.device)
-        padded_mask = torch.nn.functional.pad(m, (0, padding[2], 0, padding[1]), "constant", 1)
-        padded_masks.append(padded_mask.to(torch.bool))
-
-    tensor = torch.stack(padded_imgs)
-    mask = torch.stack(padded_masks)
-
-    return NestedTensor(tensor, mask=mask)
-
-
-def setup_for_distributed(is_master):
-    """
-    This function disables printing when not in master process
-    """
-    import builtins as __builtin__
-
-    builtin_print = __builtin__.print
-
-    def print(*args, **kwargs):
-        force = kwargs.pop("force", False)
-        if is_master or force:
-            builtin_print(*args, **kwargs)
-
-    __builtin__.print = print
-
-
-def is_dist_avail_and_initialized():
-    if not dist.is_available():
-        return False
-    if not dist.is_initialized():
-        return False
-    return True
-
-
-def get_world_size():
-    if not is_dist_avail_and_initialized():
-        return 1
-    return dist.get_world_size()
-
-
-def get_rank():
-    if not is_dist_avail_and_initialized():
-        return 0
-    return dist.get_rank()
-
-
-def is_main_process():
-    return get_rank() == 0
-
-
-def save_on_master(*args, **kwargs):
-    if is_main_process():
-        torch.save(*args, **kwargs)
-
-
-def init_distributed_mode(args):
-    if "RANK" in os.environ and "WORLD_SIZE" in os.environ:
-        args.rank = int(os.environ["RANK"])
-        args.world_size = int(os.environ["WORLD_SIZE"])
-        args.gpu = int(os.environ["LOCAL_RANK"])
-    elif "SLURM_PROCID" in os.environ:
-        args.rank = int(os.environ["SLURM_PROCID"])
-        args.gpu = args.rank % torch.cuda.device_count()
-    else:
-        print("Not using distributed mode")
-        args.distributed = False
-        return
-
-    args.distributed = True
-
-    torch.cuda.set_device(args.gpu)
-    args.dist_backend = "nccl"
-    print("| distributed init (rank {}): {}".format(args.rank, args.dist_url), flush=True)
-    torch.distributed.init_process_group(
-        backend=args.dist_backend, init_method=args.dist_url, world_size=args.world_size, rank=args.rank
-    )
-    torch.distributed.barrier()
-    setup_for_distributed(args.rank == 0)
-
-
-@torch.no_grad()
-def accuracy(output, target, topk=(1,)):
-    """Computes the precision@k for the specified values of k"""
-    if target.numel() == 0:
-        return [torch.zeros([], device=output.device)]
-    maxk = max(topk)
-    batch_size = target.size(0)
-
-    _, pred = output.topk(maxk, 1, True, True)
-    pred = pred.t()
-    correct = pred.eq(target.view(1, -1).expand_as(pred))
-
-    res = []
-    for k in topk:
-        correct_k = correct[:k].view(-1).float().sum(0)
-        res.append(correct_k.mul_(100.0 / batch_size))
-    return res
-
-
-def interpolate(input, size=None, scale_factor=None, mode="nearest", align_corners=None):
-    # type: (Tensor, Optional[List[int]], Optional[float], str, Optional[bool]) -> Tensor
-    """
-    Equivalent to nn.functional.interpolate, but with support for empty batch sizes.
-    This will eventually be supported natively by PyTorch, and this
-    class can go away.
-    """
-    if version.parse(torchvision.__version__) < version.parse("0.7"):
-        if input.numel() > 0:
-            return torch.nn.functional.interpolate(input, size, scale_factor, mode, align_corners)
-
-        output_shape = _output_size(2, input, size, scale_factor)
-        output_shape = list(input.shape[:-2]) + list(output_shape)
-        return _new_empty_tensor(input, output_shape)
-    else:
-        return torchvision.ops.misc.interpolate(input, size, scale_factor, mode, align_corners)

lerobot/common/policies/diffusion/configuration_diffusion.py

@@ -0,0 +1,135 @@
+from dataclasses import dataclass
+
+
+@dataclass
+class DiffusionConfig:
+    """Configuration class for Diffusion Policy.
+
+    Defaults are configured for training with PushT providing proprioceptive and single camera observations.
+
+    The parameters you will most likely need to change are the ones which depend on the environment / sensors.
+    Those are: `state_dim`, `action_dim` and `image_size`.
+
+    Args:
+        state_dim: Dimensionality of the observation state space (excluding images).
+        action_dim: Dimensionality of the action space.
+        image_size: (H, W) size of the input images.
+        n_obs_steps: Number of environment steps worth of observations to pass to the policy (takes the
+            current step and additional steps going back).
+        horizon: Diffusion model action prediction size as detailed in `DiffusionPolicy.select_action`.
+        n_action_steps: The number of action steps to run in the environment for one invocation of the policy.
+            See `DiffusionPolicy.select_action` for more details.
+        image_normalization_mean: Value to subtract from the input image pixels (inputs are assumed to be in
+            [0, 1]) for normalization.
+        image_normalization_std: Value by which to divide the input image pixels (after the mean has been
+            subtracted).
+        vision_backbone: Name of the torchvision resnet backbone to use for encoding images.
+        crop_shape: (H, W) shape to crop images to as a preprocessing step for the vision backbone. Must fit
+            within the image size. If None, no cropping is done.
+        crop_is_random: Whether the crop should be random at training time (it's always a center crop in eval
+            mode).
+        use_pretrained_backbone: Whether the backbone should be initialized with pretrained weights from
+            torchvision.
+        use_group_norm: Whether to replace batch normalization with group normalization in the backbone.
+            The group sizes are set to be about 16 (to be precise, feature_dim // 16).
+        spatial_softmax_num_keypoints: Number of keypoints for SpatialSoftmax.
+        down_dims: Feature dimension for each stage of temporal downsampling in the diffusion modeling Unet.
+            You may provide a variable number of dimensions, therefore also controlling the degree of
+            downsampling.
+        kernel_size: The convolutional kernel size of the diffusion modeling Unet.
+        n_groups: Number of groups used in the group norm of the Unet's convolutional blocks.
+        diffusion_step_embed_dim: The Unet is conditioned on the diffusion timestep via a small non-linear
+            network. This is the output dimension of that network, i.e., the embedding dimension.
+        use_film_scale_modulation: FiLM (https://arxiv.org/abs/1709.07871) is used for the Unet conditioning.
+            Bias modulation is used be default, while this parameter indicates whether to also use scale
+            modulation.
+        num_train_timesteps: Number of diffusion steps for the forward diffusion schedule.
+        beta_schedule: Name of the diffusion beta schedule as per DDPMScheduler from Hugging Face diffusers.
+        beta_start: Beta value for the first forward-diffusion step.
+        beta_end: Beta value for the last forward-diffusion step.
+        prediction_type: The type of prediction that the diffusion modeling Unet makes. Choose from "epsilon"
+            or "sample". These have equivalent outcomes from a latent variable modeling perspective, but
+            "epsilon" has been shown to work better in many deep neural network settings.
+        clip_sample: Whether to clip the sample to [-`clip_sample_range`, +`clip_sample_range`] for each
+            denoising step at inference time. WARNING: you will need to make sure your action-space is
+            normalized to fit within this range.
+        clip_sample_range: The magnitude of the clipping range as described above.
+        num_inference_steps: Number of reverse diffusion steps to use at inference time (steps are evenly
+            spaced). If not provided, this defaults to be the same as `num_train_timesteps`.
+    """
+
+    # Environment.
+    # Inherit these from the environment config.
+    state_dim: int = 2
+    action_dim: int = 2
+    image_size: tuple[int, int] = (96, 96)
+
+    # Inputs / output structure.
+    n_obs_steps: int = 2
+    horizon: int = 16
+    n_action_steps: int = 8
+
+    # Vision preprocessing.
+    image_normalization_mean: tuple[float, float, float] = (0.5, 0.5, 0.5)
+    image_normalization_std: tuple[float, float, float] = (0.5, 0.5, 0.5)
+
+    # Architecture / modeling.
+    # Vision backbone.
+    vision_backbone: str = "resnet18"
+    crop_shape: tuple[int, int] | None = (84, 84)
+    crop_is_random: bool = True
+    use_pretrained_backbone: bool = False
+    use_group_norm: bool = True
+    spatial_softmax_num_keypoints: int = 32
+    # Unet.
+    down_dims: tuple[int, ...] = (512, 1024, 2048)
+    kernel_size: int = 5
+    n_groups: int = 8
+    diffusion_step_embed_dim: int = 128
+    use_film_scale_modulation: bool = True
+    # Noise scheduler.
+    num_train_timesteps: int = 100
+    beta_schedule: str = "squaredcos_cap_v2"
+    beta_start: float = 0.0001
+    beta_end: float = 0.02
+    prediction_type: str = "epsilon"
+    clip_sample: bool = True
+    clip_sample_range: float = 1.0
+
+    # Inference
+    num_inference_steps: int | None = None
+
+    # ---
+    # TODO(alexander-soare): Remove these from the policy config.
+    batch_size: int = 64
+    grad_clip_norm: int = 10
+    lr: float = 1.0e-4
+    lr_scheduler: str = "cosine"
+    lr_warmup_steps: int = 500
+    adam_betas: tuple[float, float] = (0.95, 0.999)
+    adam_eps: float = 1.0e-8
+    adam_weight_decay: float = 1.0e-6
+    utd: int = 1
+    use_ema: bool = True
+    ema_update_after_step: int = 0
+    ema_min_alpha: float = 0.0
+    ema_max_alpha: float = 0.9999
+    ema_inv_gamma: float = 1.0
+    ema_power: float = 0.75
+
+    def __post_init__(self):
+        """Input validation (not exhaustive)."""
+        if not self.vision_backbone.startswith("resnet"):
+            raise ValueError(
+                f"`vision_backbone` must be one of the ResNet variants. Got {self.vision_backbone}."
+            )
+        if self.crop_shape[0] > self.image_size[0] or self.crop_shape[1] > self.image_size[1]:
+            raise ValueError(
+                f"`crop_shape` should fit within `image_size`. Got {self.crop_shape} for `crop_shape` and "
+                f"{self.image_size} for `image_size`."
+            )
+        supported_prediction_types = ["epsilon", "sample"]
+        if self.prediction_type not in supported_prediction_types:
+            raise ValueError(
+                f"`prediction_type` must be one of {supported_prediction_types}. Got {self.prediction_type}."
+            )

lerobot/common/policies/diffusion/diffusion_unet_image_policy.py

@@ -1,268 +0,0 @@
-from typing import Dict
-
-import torch
-import torch.nn.functional as F  # noqa: N812
-from diffusers.schedulers.scheduling_ddpm import DDPMScheduler
-from einops import reduce
-
-from lerobot.common.policies.diffusion.model.conditional_unet1d import ConditionalUnet1D
-from lerobot.common.policies.diffusion.model.mask_generator import LowdimMaskGenerator
-from lerobot.common.policies.diffusion.model.module_attr_mixin import ModuleAttrMixin
-from lerobot.common.policies.diffusion.model.multi_image_obs_encoder import MultiImageObsEncoder
-from lerobot.common.policies.diffusion.model.normalizer import LinearNormalizer
-from lerobot.common.policies.diffusion.pytorch_utils import dict_apply
-
-
-class BaseImagePolicy(ModuleAttrMixin):
-    # init accepts keyword argument shape_meta, see config/task/*_image.yaml
-
-    def predict_action(self, obs_dict: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
-        """
-        obs_dict:
-            str: B,To,*
-        return: B,Ta,Da
-        """
-        raise NotImplementedError()
-
-    # reset state for stateful policies
-    def reset(self):
-        pass
-
-    # ========== training ===========
-    # no standard training interface except setting normalizer
-    def set_normalizer(self, normalizer: LinearNormalizer):
-        raise NotImplementedError()
-
-
-class DiffusionUnetImagePolicy(BaseImagePolicy):
-    def __init__(
-        self,
-        shape_meta: dict,
-        noise_scheduler: DDPMScheduler,
-        obs_encoder: MultiImageObsEncoder,
-        horizon,
-        n_action_steps,
-        n_obs_steps,
-        num_inference_steps=None,
-        obs_as_global_cond=True,
-        diffusion_step_embed_dim=256,
-        down_dims=(256, 512, 1024),
-        kernel_size=5,
-        n_groups=8,
-        cond_predict_scale=True,
-        # parameters passed to step
-        **kwargs,
-    ):
-        super().__init__()
-
-        # parse shapes
-        action_shape = shape_meta["action"]["shape"]
-        assert len(action_shape) == 1
-        action_dim = action_shape[0]
-        # get feature dim
-        obs_feature_dim = obs_encoder.output_shape()[0]
-
-        # create diffusion model
-        input_dim = action_dim + obs_feature_dim
-        global_cond_dim = None
-        if obs_as_global_cond:
-            input_dim = action_dim
-            global_cond_dim = obs_feature_dim * n_obs_steps
-
-        model = ConditionalUnet1D(
-            input_dim=input_dim,
-            local_cond_dim=None,
-            global_cond_dim=global_cond_dim,
-            diffusion_step_embed_dim=diffusion_step_embed_dim,
-            down_dims=down_dims,
-            kernel_size=kernel_size,
-            n_groups=n_groups,
-            cond_predict_scale=cond_predict_scale,
-        )
-
-        self.obs_encoder = obs_encoder
-        self.model = model
-        self.noise_scheduler = noise_scheduler
-        self.mask_generator = LowdimMaskGenerator(
-            action_dim=action_dim,
-            obs_dim=0 if obs_as_global_cond else obs_feature_dim,
-            max_n_obs_steps=n_obs_steps,
-            fix_obs_steps=True,
-            action_visible=False,
-        )
-        self.horizon = horizon
-        self.obs_feature_dim = obs_feature_dim
-        self.action_dim = action_dim
-        self.n_action_steps = n_action_steps
-        self.n_obs_steps = n_obs_steps
-        self.obs_as_global_cond = obs_as_global_cond
-        self.kwargs = kwargs
-
-        if num_inference_steps is None:
-            num_inference_steps = noise_scheduler.config.num_train_timesteps
-        self.num_inference_steps = num_inference_steps
-
-    # ========= inference  ============
-    def conditional_sample(
-        self,
-        condition_data,
-        condition_mask,
-        local_cond=None,
-        global_cond=None,
-        generator=None,
-        # keyword arguments to scheduler.step
-        **kwargs,
-    ):
-        model = self.model
-        scheduler = self.noise_scheduler
-
-        trajectory = torch.randn(
-            size=condition_data.shape,
-            dtype=condition_data.dtype,
-            device=condition_data.device,
-            generator=generator,
-        )
-
-        # set step values
-        scheduler.set_timesteps(self.num_inference_steps)
-
-        for t in scheduler.timesteps:
-            # 1. apply conditioning
-            trajectory[condition_mask] = condition_data[condition_mask]
-
-            # 2. predict model output
-            model_output = model(trajectory, t, local_cond=local_cond, global_cond=global_cond)
-
-            # 3. compute previous image: x_t -> x_t-1
-            trajectory = scheduler.step(
-                model_output,
-                t,
-                trajectory,
-                generator=generator,
-                # **kwargs  # TODO(rcadene): in diffusion_policy, expected to be {}
-            ).prev_sample
-
-        # finally make sure conditioning is enforced
-        trajectory[condition_mask] = condition_data[condition_mask]
-
-        return trajectory
-
-    def predict_action(self, obs_dict: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
-        """
-        obs_dict: must include "obs" key
-        result: must include "action" key
-        """
-        assert "past_action" not in obs_dict  # not implemented yet
-        nobs = obs_dict
-        value = next(iter(nobs.values()))
-        bsize, n_obs_steps = value.shape[:2]
-        horizon = self.horizon
-        action_dim = self.action_dim
-        obs_dim = self.obs_feature_dim
-        assert self.n_obs_steps == n_obs_steps
-
-        # build input
-        device = self.device
-        dtype = self.dtype
-
-        # handle different ways of passing observation
-        local_cond = None
-        global_cond = None
-        if self.obs_as_global_cond:
-            # condition through global feature
-            this_nobs = dict_apply(nobs, lambda x: x[:, :n_obs_steps, ...].reshape(-1, *x.shape[2:]))
-            nobs_features = self.obs_encoder(this_nobs)
-            # reshape back to B, Do
-            global_cond = nobs_features.reshape(bsize, -1)
-            # empty data for action
-            cond_data = torch.zeros(size=(bsize, horizon, action_dim), device=device, dtype=dtype)
-            cond_mask = torch.zeros_like(cond_data, dtype=torch.bool)
-        else:
-            # condition through impainting
-            this_nobs = dict_apply(nobs, lambda x: x[:, :n_obs_steps, ...].reshape(-1, *x.shape[2:]))
-            nobs_features = self.obs_encoder(this_nobs)
-            # reshape back to B, T, Do
-            nobs_features = nobs_features.reshape(bsize, n_obs_steps, -1)
-            cond_data = torch.zeros(size=(bsize, horizon, action_dim + obs_dim), device=device, dtype=dtype)
-            cond_mask = torch.zeros_like(cond_data, dtype=torch.bool)
-            cond_data[:, :n_obs_steps, action_dim:] = nobs_features
-            cond_mask[:, :n_obs_steps, action_dim:] = True
-
-        # run sampling
-        nsample = self.conditional_sample(
-            cond_data, cond_mask, local_cond=local_cond, global_cond=global_cond, **self.kwargs
-        )
-
-        action_pred = nsample[..., :action_dim]
-
-        # get action
-        start = n_obs_steps - 1
-        end = start + self.n_action_steps
-        action = action_pred[:, start:end]
-
-        result = {"action": action, "action_pred": action_pred}
-        return result
-
-    def compute_loss(self, batch):
-        assert "valid_mask" not in batch
-        nobs = batch["obs"]
-        nactions = batch["action"]
-        batch_size = nactions.shape[0]
-        horizon = nactions.shape[1]
-
-        # handle different ways of passing observation
-        local_cond = None
-        global_cond = None
-        trajectory = nactions
-        cond_data = trajectory
-        if self.obs_as_global_cond:
-            # reshape B, T, ... to B*T
-            this_nobs = dict_apply(nobs, lambda x: x[:, : self.n_obs_steps, ...].reshape(-1, *x.shape[2:]))
-            nobs_features = self.obs_encoder(this_nobs)
-            # reshape back to B, Do
-            global_cond = nobs_features.reshape(batch_size, -1)
-        else:
-            # reshape B, T, ... to B*T
-            this_nobs = dict_apply(nobs, lambda x: x.reshape(-1, *x.shape[2:]))
-            nobs_features = self.obs_encoder(this_nobs)
-            # reshape back to B, T, Do
-            nobs_features = nobs_features.reshape(batch_size, horizon, -1)
-            cond_data = torch.cat([nactions, nobs_features], dim=-1)
-            trajectory = cond_data.detach()
-
-        # generate impainting mask
-        condition_mask = self.mask_generator(trajectory.shape)
-
-        # Sample noise that we'll add to the images
-        noise = torch.randn(trajectory.shape, device=trajectory.device)
-        bsz = trajectory.shape[0]
-        # Sample a random timestep for each image
-        timesteps = torch.randint(
-            0, self.noise_scheduler.config.num_train_timesteps, (bsz,), device=trajectory.device
-        ).long()
-        # Add noise to the clean images according to the noise magnitude at each timestep
-        # (this is the forward diffusion process)
-        noisy_trajectory = self.noise_scheduler.add_noise(trajectory, noise, timesteps)
-
-        # compute loss mask
-        loss_mask = ~condition_mask
-
-        # apply conditioning
-        noisy_trajectory[condition_mask] = cond_data[condition_mask]
-
-        # Predict the noise residual
-        pred = self.model(noisy_trajectory, timesteps, local_cond=local_cond, global_cond=global_cond)
-
-        pred_type = self.noise_scheduler.config.prediction_type
-        if pred_type == "epsilon":
-            target = noise
-        elif pred_type == "sample":
-            target = trajectory
-        else:
-            raise ValueError(f"Unsupported prediction type {pred_type}")
-
-        loss = F.mse_loss(pred, target, reduction="none")
-        loss = loss * loss_mask.type(loss.dtype)
-        loss = reduce(loss, "b ... -> b (...)", "mean")
-        loss = loss.mean()
-        return loss

lerobot/common/policies/diffusion/model/conditional_unet1d.py

@@ -1,286 +0,0 @@
-import logging
-from typing import Union
-
-import einops
-import torch
-import torch.nn as nn
-from einops.layers.torch import Rearrange
-
-from lerobot.common.policies.diffusion.model.conv1d_components import Conv1dBlock, Downsample1d, Upsample1d
-from lerobot.common.policies.diffusion.model.positional_embedding import SinusoidalPosEmb
-
-logger = logging.getLogger(__name__)
-
-
-class ConditionalResidualBlock1D(nn.Module):
-    def __init__(
-        self, in_channels, out_channels, cond_dim, kernel_size=3, n_groups=8, cond_predict_scale=False
-    ):
-        super().__init__()
-
-        self.blocks = nn.ModuleList(
-            [
-                Conv1dBlock(in_channels, out_channels, kernel_size, n_groups=n_groups),
-                Conv1dBlock(out_channels, out_channels, kernel_size, n_groups=n_groups),
-            ]
-        )
-
-        # FiLM modulation https://arxiv.org/abs/1709.07871
-        # predicts per-channel scale and bias
-        cond_channels = out_channels
-        if cond_predict_scale:
-            cond_channels = out_channels * 2
-        self.cond_predict_scale = cond_predict_scale
-        self.out_channels = out_channels
-        self.cond_encoder = nn.Sequential(
-            nn.Mish(),
-            nn.Linear(cond_dim, cond_channels),
-            Rearrange("batch t -> batch t 1"),
-        )
-
-        # make sure dimensions compatible
-        self.residual_conv = (
-            nn.Conv1d(in_channels, out_channels, 1) if in_channels != out_channels else nn.Identity()
-        )
-
-    def forward(self, x, cond):
-        """
-        x : [ batch_size x in_channels x horizon ]
-        cond : [ batch_size x cond_dim]
-
-        returns:
-        out : [ batch_size x out_channels x horizon ]
-        """
-        out = self.blocks[0](x)
-        embed = self.cond_encoder(cond)
-        if self.cond_predict_scale:
-            embed = embed.reshape(embed.shape[0], 2, self.out_channels, 1)
-            scale = embed[:, 0, ...]
-            bias = embed[:, 1, ...]
-            out = scale * out + bias
-        else:
-            out = out + embed
-        out = self.blocks[1](out)
-        out = out + self.residual_conv(x)
-        return out
-
-
-class ConditionalUnet1D(nn.Module):
-    def __init__(
-        self,
-        input_dim,
-        local_cond_dim=None,
-        global_cond_dim=None,
-        diffusion_step_embed_dim=256,
-        down_dims=None,
-        kernel_size=3,
-        n_groups=8,
-        cond_predict_scale=False,
-    ):
-        super().__init__()
-        if down_dims is None:
-            down_dims = [256, 512, 1024]
-
-        all_dims = [input_dim] + list(down_dims)
-        start_dim = down_dims[0]
-
-        dsed = diffusion_step_embed_dim
-        diffusion_step_encoder = nn.Sequential(
-            SinusoidalPosEmb(dsed),
-            nn.Linear(dsed, dsed * 4),
-            nn.Mish(),
-            nn.Linear(dsed * 4, dsed),
-        )
-        cond_dim = dsed
-        if global_cond_dim is not None:
-            cond_dim += global_cond_dim
-
-        in_out = list(zip(all_dims[:-1], all_dims[1:], strict=False))
-
-        local_cond_encoder = None
-        if local_cond_dim is not None:
-            _, dim_out = in_out[0]
-            dim_in = local_cond_dim
-            local_cond_encoder = nn.ModuleList(
-                [
-                    # down encoder
-                    ConditionalResidualBlock1D(
-                        dim_in,
-                        dim_out,
-                        cond_dim=cond_dim,
-                        kernel_size=kernel_size,
-                        n_groups=n_groups,
-                        cond_predict_scale=cond_predict_scale,
-                    ),
-                    # up encoder
-                    ConditionalResidualBlock1D(
-                        dim_in,
-                        dim_out,
-                        cond_dim=cond_dim,
-                        kernel_size=kernel_size,
-                        n_groups=n_groups,
-                        cond_predict_scale=cond_predict_scale,
-                    ),
-                ]
-            )
-
-        mid_dim = all_dims[-1]
-        self.mid_modules = nn.ModuleList(
-            [
-                ConditionalResidualBlock1D(
-                    mid_dim,
-                    mid_dim,
-                    cond_dim=cond_dim,
-                    kernel_size=kernel_size,
-                    n_groups=n_groups,
-                    cond_predict_scale=cond_predict_scale,
-                ),
-                ConditionalResidualBlock1D(
-                    mid_dim,
-                    mid_dim,
-                    cond_dim=cond_dim,
-                    kernel_size=kernel_size,
-                    n_groups=n_groups,
-                    cond_predict_scale=cond_predict_scale,
-                ),
-            ]
-        )
-
-        down_modules = nn.ModuleList([])
-        for ind, (dim_in, dim_out) in enumerate(in_out):
-            is_last = ind >= (len(in_out) - 1)
-            down_modules.append(
-                nn.ModuleList(
-                    [
-                        ConditionalResidualBlock1D(
-                            dim_in,
-                            dim_out,
-                            cond_dim=cond_dim,
-                            kernel_size=kernel_size,
-                            n_groups=n_groups,
-                            cond_predict_scale=cond_predict_scale,
-                        ),
-                        ConditionalResidualBlock1D(
-                            dim_out,
-                            dim_out,
-                            cond_dim=cond_dim,
-                            kernel_size=kernel_size,
-                            n_groups=n_groups,
-                            cond_predict_scale=cond_predict_scale,
-                        ),
-                        Downsample1d(dim_out) if not is_last else nn.Identity(),
-                    ]
-                )
-            )
-
-        up_modules = nn.ModuleList([])
-        for ind, (dim_in, dim_out) in enumerate(reversed(in_out[1:])):
-            is_last = ind >= (len(in_out) - 1)
-            up_modules.append(
-                nn.ModuleList(
-                    [
-                        ConditionalResidualBlock1D(
-                            dim_out * 2,
-                            dim_in,
-                            cond_dim=cond_dim,
-                            kernel_size=kernel_size,
-                            n_groups=n_groups,
-                            cond_predict_scale=cond_predict_scale,
-                        ),
-                        ConditionalResidualBlock1D(
-                            dim_in,
-                            dim_in,
-                            cond_dim=cond_dim,
-                            kernel_size=kernel_size,
-                            n_groups=n_groups,
-                            cond_predict_scale=cond_predict_scale,
-                        ),
-                        Upsample1d(dim_in) if not is_last else nn.Identity(),
-                    ]
-                )
-            )
-
-        final_conv = nn.Sequential(
-            Conv1dBlock(start_dim, start_dim, kernel_size=kernel_size),
-            nn.Conv1d(start_dim, input_dim, 1),
-        )
-
-        self.diffusion_step_encoder = diffusion_step_encoder
-        self.local_cond_encoder = local_cond_encoder
-        self.up_modules = up_modules
-        self.down_modules = down_modules
-        self.final_conv = final_conv
-
-        logger.info("number of parameters: %e", sum(p.numel() for p in self.parameters()))
-
-    def forward(
-        self,
-        sample: torch.Tensor,
-        timestep: Union[torch.Tensor, float, int],
-        local_cond=None,
-        global_cond=None,
-        **kwargs,
-    ):
-        """
-        x: (B,T,input_dim)
-        timestep: (B,) or int, diffusion step
-        local_cond: (B,T,local_cond_dim)
-        global_cond: (B,global_cond_dim)
-        output: (B,T,input_dim)
-        """
-        sample = einops.rearrange(sample, "b h t -> b t h")
-
-        # 1. time
-        timesteps = timestep
-        if not torch.is_tensor(timesteps):
-            # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
-            timesteps = torch.tensor([timesteps], dtype=torch.long, device=sample.device)
-        elif torch.is_tensor(timesteps) and len(timesteps.shape) == 0:
-            timesteps = timesteps[None].to(sample.device)
-        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
-        timesteps = timesteps.expand(sample.shape[0])
-
-        global_feature = self.diffusion_step_encoder(timesteps)
-
-        if global_cond is not None:
-            global_feature = torch.cat([global_feature, global_cond], axis=-1)
-
-        # encode local features
-        h_local = []
-        if local_cond is not None:
-            local_cond = einops.rearrange(local_cond, "b h t -> b t h")
-            resnet, resnet2 = self.local_cond_encoder
-            x = resnet(local_cond, global_feature)
-            h_local.append(x)
-            x = resnet2(local_cond, global_feature)
-            h_local.append(x)
-
-        x = sample
-        h = []
-        for idx, (resnet, resnet2, downsample) in enumerate(self.down_modules):
-            x = resnet(x, global_feature)
-            if idx == 0 and len(h_local) > 0:
-                x = x + h_local[0]
-            x = resnet2(x, global_feature)
-            h.append(x)
-            x = downsample(x)
-
-        for mid_module in self.mid_modules:
-            x = mid_module(x, global_feature)
-
-        for idx, (resnet, resnet2, upsample) in enumerate(self.up_modules):
-            x = torch.cat((x, h.pop()), dim=1)
-            x = resnet(x, global_feature)
-            # The correct condition should be:
-            # if idx == (len(self.up_modules)-1) and len(h_local) > 0:
-            # However this change will break compatibility with published checkpoints.
-            # Therefore it is left as a comment.
-            if idx == len(self.up_modules) and len(h_local) > 0:
-                x = x + h_local[1]
-            x = resnet2(x, global_feature)
-            x = upsample(x)
-
-        x = self.final_conv(x)
-
-        x = einops.rearrange(x, "b t h -> b h t")
-        return x

lerobot/common/policies/diffusion/model/conv1d_components.py

@@ -1,47 +0,0 @@
-import torch.nn as nn
-
-# from einops.layers.torch import Rearrange
-
-
-class Downsample1d(nn.Module):
-    def __init__(self, dim):
-        super().__init__()
-        self.conv = nn.Conv1d(dim, dim, 3, 2, 1)
-
-    def forward(self, x):
-        return self.conv(x)
-
-
-class Upsample1d(nn.Module):
-    def __init__(self, dim):
-        super().__init__()
-        self.conv = nn.ConvTranspose1d(dim, dim, 4, 2, 1)
-
-    def forward(self, x):
-        return self.conv(x)
-
-
-class Conv1dBlock(nn.Module):
-    """
-    Conv1d --> GroupNorm --> Mish
-    """
-
-    def __init__(self, inp_channels, out_channels, kernel_size, n_groups=8):
-        super().__init__()
-
-        self.block = nn.Sequential(
-            nn.Conv1d(inp_channels, out_channels, kernel_size, padding=kernel_size // 2),
-            # Rearrange('batch channels horizon -> batch channels 1 horizon'),
-            nn.GroupNorm(n_groups, out_channels),
-            # Rearrange('batch channels 1 horizon -> batch channels horizon'),
-            nn.Mish(),
-        )
-
-    def forward(self, x):
-        return self.block(x)
-
-
-# def test():
-#     cb = Conv1dBlock(256, 128, kernel_size=3)
-#     x = torch.zeros((1,256,16))
-#     o = cb(x)

lerobot/common/policies/diffusion/model/crop_randomizer.py

@@ -1,294 +0,0 @@
-import torch
-import torch.nn as nn
-import torchvision.transforms.functional as ttf
-
-import lerobot.common.policies.diffusion.model.tensor_utils as tu
-
-
-class CropRandomizer(nn.Module):
-    """
-    Randomly sample crops at input, and then average across crop features at output.
-    """
-
-    def __init__(
-        self,
-        input_shape,
-        crop_height,
-        crop_width,
-        num_crops=1,
-        pos_enc=False,
-    ):
-        """
-        Args:
-            input_shape (tuple, list): shape of input (not including batch dimension)
-            crop_height (int): crop height
-            crop_width (int): crop width
-            num_crops (int): number of random crops to take
-            pos_enc (bool): if True, add 2 channels to the output to encode the spatial
-                location of the cropped pixels in the source image
-        """
-        super().__init__()
-
-        assert len(input_shape) == 3  # (C, H, W)
-        assert crop_height < input_shape[1]
-        assert crop_width < input_shape[2]
-
-        self.input_shape = input_shape
-        self.crop_height = crop_height
-        self.crop_width = crop_width
-        self.num_crops = num_crops
-        self.pos_enc = pos_enc
-
-    def output_shape_in(self, input_shape=None):
-        """
-        Function to compute output shape from inputs to this module. Corresponds to
-        the @forward_in operation, where raw inputs (usually observation modalities)
-        are passed in.
-
-        Args:
-            input_shape (iterable of int): shape of input. Does not include batch dimension.
-                Some modules may not need this argument, if their output does not depend
-                on the size of the input, or if they assume fixed size input.
-
-        Returns:
-            out_shape ([int]): list of integers corresponding to output shape
-        """
-
-        # outputs are shape (C, CH, CW), or maybe C + 2 if using position encoding, because
-        # the number of crops are reshaped into the batch dimension, increasing the batch
-        # size from B to B * N
-        out_c = self.input_shape[0] + 2 if self.pos_enc else self.input_shape[0]
-        return [out_c, self.crop_height, self.crop_width]
-
-    def output_shape_out(self, input_shape=None):
-        """
-        Function to compute output shape from inputs to this module. Corresponds to
-        the @forward_out operation, where processed inputs (usually encoded observation
-        modalities) are passed in.
-
-        Args:
-            input_shape (iterable of int): shape of input. Does not include batch dimension.
-                Some modules may not need this argument, if their output does not depend
-                on the size of the input, or if they assume fixed size input.
-
-        Returns:
-            out_shape ([int]): list of integers corresponding to output shape
-        """
-
-        # since the forward_out operation splits [B * N, ...] -> [B, N, ...]
-        # and then pools to result in [B, ...], only the batch dimension changes,
-        # and so the other dimensions retain their shape.
-        return list(input_shape)
-
-    def forward_in(self, inputs):
-        """
-        Samples N random crops for each input in the batch, and then reshapes
-        inputs to [B * N, ...].
-        """
-        assert len(inputs.shape) >= 3  # must have at least (C, H, W) dimensions
-        if self.training:
-            # generate random crops
-            out, _ = sample_random_image_crops(
-                images=inputs,
-                crop_height=self.crop_height,
-                crop_width=self.crop_width,
-                num_crops=self.num_crops,
-                pos_enc=self.pos_enc,
-            )
-            # [B, N, ...] -> [B * N, ...]
-            return tu.join_dimensions(out, 0, 1)
-        else:
-            # take center crop during eval
-            out = ttf.center_crop(img=inputs, output_size=(self.crop_height, self.crop_width))
-            if self.num_crops > 1:
-                B, C, H, W = out.shape  # noqa: N806
-                out = out.unsqueeze(1).expand(B, self.num_crops, C, H, W).reshape(-1, C, H, W)
-                # [B * N, ...]
-            return out
-
-    def forward_out(self, inputs):
-        """
-        Splits the outputs from shape [B * N, ...] -> [B, N, ...] and then average across N
-        to result in shape [B, ...] to make sure the network output is consistent with
-        what would have happened if there were no randomization.
-        """
-        if self.num_crops <= 1:
-            return inputs
-        else:
-            batch_size = inputs.shape[0] // self.num_crops
-            out = tu.reshape_dimensions(
-                inputs, begin_axis=0, end_axis=0, target_dims=(batch_size, self.num_crops)
-            )
-            return out.mean(dim=1)
-
-    def forward(self, inputs):
-        return self.forward_in(inputs)
-
-    def __repr__(self):
-        """Pretty print network."""
-        header = "{}".format(str(self.__class__.__name__))
-        msg = header + "(input_shape={}, crop_size=[{}, {}], num_crops={})".format(
-            self.input_shape, self.crop_height, self.crop_width, self.num_crops
-        )
-        return msg
-
-
-def crop_image_from_indices(images, crop_indices, crop_height, crop_width):
-    """
-    Crops images at the locations specified by @crop_indices. Crops will be
-    taken across all channels.
-
-    Args:
-        images (torch.Tensor): batch of images of shape [..., C, H, W]
-
-        crop_indices (torch.Tensor): batch of indices of shape [..., N, 2] where
-            N is the number of crops to take per image and each entry corresponds
-            to the pixel height and width of where to take the crop. Note that
-            the indices can also be of shape [..., 2] if only 1 crop should
-            be taken per image. Leading dimensions must be consistent with
-            @images argument. Each index specifies the top left of the crop.
-            Values must be in range [0, H - CH - 1] x [0, W - CW - 1] where
-            H and W are the height and width of @images and CH and CW are
-            @crop_height and @crop_width.
-
-        crop_height (int): height of crop to take
-
-        crop_width (int): width of crop to take
-
-    Returns:
-        crops (torch.Tesnor): cropped images of shape [..., C, @crop_height, @crop_width]
-    """
-
-    # make sure length of input shapes is consistent
-    assert crop_indices.shape[-1] == 2
-    ndim_im_shape = len(images.shape)
-    ndim_indices_shape = len(crop_indices.shape)
-    assert (ndim_im_shape == ndim_indices_shape + 1) or (ndim_im_shape == ndim_indices_shape + 2)
-
-    # maybe pad so that @crop_indices is shape [..., N, 2]
-    is_padded = False
-    if ndim_im_shape == ndim_indices_shape + 2:
-        crop_indices = crop_indices.unsqueeze(-2)
-        is_padded = True
-
-    # make sure leading dimensions between images and indices are consistent
-    assert images.shape[:-3] == crop_indices.shape[:-2]
-
-    device = images.device
-    image_c, image_h, image_w = images.shape[-3:]
-    num_crops = crop_indices.shape[-2]
-
-    # make sure @crop_indices are in valid range
-    assert (crop_indices[..., 0] >= 0).all().item()
-    assert (crop_indices[..., 0] < (image_h - crop_height)).all().item()
-    assert (crop_indices[..., 1] >= 0).all().item()
-    assert (crop_indices[..., 1] < (image_w - crop_width)).all().item()
-
-    # convert each crop index (ch, cw) into a list of pixel indices that correspond to the entire window.
-
-    # 2D index array with columns [0, 1, ..., CH - 1] and shape [CH, CW]
-    crop_ind_grid_h = torch.arange(crop_height).to(device)
-    crop_ind_grid_h = tu.unsqueeze_expand_at(crop_ind_grid_h, size=crop_width, dim=-1)
-    # 2D index array with rows [0, 1, ..., CW - 1] and shape [CH, CW]
-    crop_ind_grid_w = torch.arange(crop_width).to(device)
-    crop_ind_grid_w = tu.unsqueeze_expand_at(crop_ind_grid_w, size=crop_height, dim=0)
-    # combine into shape [CH, CW, 2]
-    crop_in_grid = torch.cat((crop_ind_grid_h.unsqueeze(-1), crop_ind_grid_w.unsqueeze(-1)), dim=-1)
-
-    # Add above grid with the offset index of each sampled crop to get 2d indices for each crop.
-    # After broadcasting, this will be shape [..., N, CH, CW, 2] and each crop has a [CH, CW, 2]
-    # shape array that tells us which pixels from the corresponding source image to grab.
-    grid_reshape = [1] * len(crop_indices.shape[:-1]) + [crop_height, crop_width, 2]
-    all_crop_inds = crop_indices.unsqueeze(-2).unsqueeze(-2) + crop_in_grid.reshape(grid_reshape)
-
-    # For using @torch.gather, convert to flat indices from 2D indices, and also
-    # repeat across the channel dimension. To get flat index of each pixel to grab for
-    # each sampled crop, we just use the mapping: ind = h_ind * @image_w + w_ind
-    all_crop_inds = all_crop_inds[..., 0] * image_w + all_crop_inds[..., 1]  # shape [..., N, CH, CW]
-    all_crop_inds = tu.unsqueeze_expand_at(all_crop_inds, size=image_c, dim=-3)  # shape [..., N, C, CH, CW]
-    all_crop_inds = tu.flatten(all_crop_inds, begin_axis=-2)  # shape [..., N, C, CH * CW]
-
-    # Repeat and flatten the source images -> [..., N, C, H * W] and then use gather to index with crop pixel inds
-    images_to_crop = tu.unsqueeze_expand_at(images, size=num_crops, dim=-4)
-    images_to_crop = tu.flatten(images_to_crop, begin_axis=-2)
-    crops = torch.gather(images_to_crop, dim=-1, index=all_crop_inds)
-    # [..., N, C, CH * CW] -> [..., N, C, CH, CW]
-    reshape_axis = len(crops.shape) - 1
-    crops = tu.reshape_dimensions(
-        crops, begin_axis=reshape_axis, end_axis=reshape_axis, target_dims=(crop_height, crop_width)
-    )
-
-    if is_padded:
-        # undo padding -> [..., C, CH, CW]
-        crops = crops.squeeze(-4)
-    return crops
-
-
-def sample_random_image_crops(images, crop_height, crop_width, num_crops, pos_enc=False):
-    """
-    For each image, randomly sample @num_crops crops of size (@crop_height, @crop_width), from
-    @images.
-
-    Args:
-        images (torch.Tensor): batch of images of shape [..., C, H, W]
-
-        crop_height (int): height of crop to take
-
-        crop_width (int): width of crop to take
-
-        num_crops (n): number of crops to sample
-
-        pos_enc (bool): if True, also add 2 channels to the outputs that gives a spatial
-            encoding of the original source pixel locations. This means that the
-            output crops will contain information about where in the source image
-            it was sampled from.
-
-    Returns:
-        crops (torch.Tensor): crops of shape (..., @num_crops, C, @crop_height, @crop_width)
-            if @pos_enc is False, otherwise (..., @num_crops, C + 2, @crop_height, @crop_width)
-
-        crop_inds (torch.Tensor): sampled crop indices of shape (..., N, 2)
-    """
-    device = images.device
-
-    # maybe add 2 channels of spatial encoding to the source image
-    source_im = images
-    if pos_enc:
-        # spatial encoding [y, x] in [0, 1]
-        h, w = source_im.shape[-2:]
-        pos_y, pos_x = torch.meshgrid(torch.arange(h), torch.arange(w))
-        pos_y = pos_y.float().to(device) / float(h)
-        pos_x = pos_x.float().to(device) / float(w)
-        position_enc = torch.stack((pos_y, pos_x))  # shape [C, H, W]
-
-        # unsqueeze and expand to match leading dimensions -> shape [..., C, H, W]
-        leading_shape = source_im.shape[:-3]
-        position_enc = position_enc[(None,) * len(leading_shape)]
-        position_enc = position_enc.expand(*leading_shape, -1, -1, -1)
-
-        # concat across channel dimension with input
-        source_im = torch.cat((source_im, position_enc), dim=-3)
-
-    # make sure sample boundaries ensure crops are fully within the images
-    image_c, image_h, image_w = source_im.shape[-3:]
-    max_sample_h = image_h - crop_height
-    max_sample_w = image_w - crop_width
-
-    # Sample crop locations for all tensor dimensions up to the last 3, which are [C, H, W].
-    # Each gets @num_crops samples - typically this will just be the batch dimension (B), so
-    # we will sample [B, N] indices, but this supports having more than one leading dimension,
-    # or possibly no leading dimension.
-    #
-    # Trick: sample in [0, 1) with rand, then re-scale to [0, M) and convert to long to get sampled ints
-    crop_inds_h = (max_sample_h * torch.rand(*source_im.shape[:-3], num_crops).to(device)).long()
-    crop_inds_w = (max_sample_w * torch.rand(*source_im.shape[:-3], num_crops).to(device)).long()
-    crop_inds = torch.cat((crop_inds_h.unsqueeze(-1), crop_inds_w.unsqueeze(-1)), dim=-1)  # shape [..., N, 2]
-
-    crops = crop_image_from_indices(
-        images=source_im,
-        crop_indices=crop_inds,
-        crop_height=crop_height,
-        crop_width=crop_width,
-    )
-
-    return crops, crop_inds

lerobot/common/policies/diffusion/model/dict_of_tensor_mixin.py

@@ -1,41 +0,0 @@
-import torch
-import torch.nn as nn
-
-
-class DictOfTensorMixin(nn.Module):
-    def __init__(self, params_dict=None):
-        super().__init__()
-        if params_dict is None:
-            params_dict = nn.ParameterDict()
-        self.params_dict = params_dict
-
-    @property
-    def device(self):
-        return next(iter(self.parameters())).device
-
-    def _load_from_state_dict(
-        self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
-    ):
-        def dfs_add(dest, keys, value: torch.Tensor):
-            if len(keys) == 1:
-                dest[keys[0]] = value
-                return
-
-            if keys[0] not in dest:
-                dest[keys[0]] = nn.ParameterDict()
-            dfs_add(dest[keys[0]], keys[1:], value)
-
-        def load_dict(state_dict, prefix):
-            out_dict = nn.ParameterDict()
-            for key, value in state_dict.items():
-                value: torch.Tensor
-                if key.startswith(prefix):
-                    param_keys = key[len(prefix) :].split(".")[1:]
-                    # if len(param_keys) == 0:
-                    #     import pdb; pdb.set_trace()
-                    dfs_add(out_dict, param_keys, value.clone())
-            return out_dict
-
-        self.params_dict = load_dict(state_dict, prefix + "params_dict")
-        self.params_dict.requires_grad_(False)
-        return

lerobot/common/policies/diffusion/model/ema_model.py

@@ -1,84 +0,0 @@
-import torch
-from torch.nn.modules.batchnorm import _BatchNorm
-
-
-class EMAModel:
-    """
-    Exponential Moving Average of models weights
-    """
-
-    def __init__(
-        self, model, update_after_step=0, inv_gamma=1.0, power=2 / 3, min_value=0.0, max_value=0.9999
-    ):
-        """
-        @crowsonkb's notes on EMA Warmup:
-            If gamma=1 and power=1, implements a simple average. gamma=1, power=2/3 are good values for models you plan
-            to train for a million or more steps (reaches decay factor 0.999 at 31.6K steps, 0.9999 at 1M steps),
-            gamma=1, power=3/4 for models you plan to train for less (reaches decay factor 0.999 at 10K steps, 0.9999
-            at 215.4k steps).
-        Args:
-            inv_gamma (float): Inverse multiplicative factor of EMA warmup. Default: 1.
-            power (float): Exponential factor of EMA warmup. Default: 2/3.
-            min_value (float): The minimum EMA decay rate. Default: 0.
-        """
-
-        self.averaged_model = model
-        self.averaged_model.eval()
-        self.averaged_model.requires_grad_(False)
-
-        self.update_after_step = update_after_step
-        self.inv_gamma = inv_gamma
-        self.power = power
-        self.min_value = min_value
-        self.max_value = max_value
-
-        self.decay = 0.0
-        self.optimization_step = 0
-
-    def get_decay(self, optimization_step):
-        """
-        Compute the decay factor for the exponential moving average.
-        """
-        step = max(0, optimization_step - self.update_after_step - 1)
-        value = 1 - (1 + step / self.inv_gamma) ** -self.power
-
-        if step <= 0:
-            return 0.0
-
-        return max(self.min_value, min(value, self.max_value))
-
-    @torch.no_grad()
-    def step(self, new_model):
-        self.decay = self.get_decay(self.optimization_step)
-
-        # old_all_dataptrs = set()
-        # for param in new_model.parameters():
-        #     data_ptr = param.data_ptr()
-        #     if data_ptr != 0:
-        #         old_all_dataptrs.add(data_ptr)
-
-        # all_dataptrs = set()
-        for module, ema_module in zip(new_model.modules(), self.averaged_model.modules(), strict=False):
-            for param, ema_param in zip(
-                module.parameters(recurse=False), ema_module.parameters(recurse=False), strict=False
-            ):
-                # iterative over immediate parameters only.
-                if isinstance(param, dict):
-                    raise RuntimeError("Dict parameter not supported")
-
-                # data_ptr = param.data_ptr()
-                # if data_ptr != 0:
-                #     all_dataptrs.add(data_ptr)
-
-                if isinstance(module, _BatchNorm):
-                    # skip batchnorms
-                    ema_param.copy_(param.to(dtype=ema_param.dtype).data)
-                elif not param.requires_grad:
-                    ema_param.copy_(param.to(dtype=ema_param.dtype).data)
-                else:
-                    ema_param.mul_(self.decay)
-                    ema_param.add_(param.data.to(dtype=ema_param.dtype), alpha=1 - self.decay)
-
-        # verify that iterating over module and then parameters is identical to parameters recursively.
-        # assert old_all_dataptrs == all_dataptrs
-        self.optimization_step += 1

lerobot/common/policies/diffusion/model/lr_scheduler.py

@@ -1,46 +0,0 @@
-from diffusers.optimization import TYPE_TO_SCHEDULER_FUNCTION, Optimizer, Optional, SchedulerType, Union
-
-
-def get_scheduler(
-    name: Union[str, SchedulerType],
-    optimizer: Optimizer,
-    num_warmup_steps: Optional[int] = None,
-    num_training_steps: Optional[int] = None,
-    **kwargs,
-):
-    """
-    Added kwargs vs diffuser's original implementation
-
-    Unified API to get any scheduler from its name.
-
-    Args:
-        name (`str` or `SchedulerType`):
-            The name of the scheduler to use.
-        optimizer (`torch.optim.Optimizer`):
-            The optimizer that will be used during training.
-        num_warmup_steps (`int`, *optional*):
-            The number of warmup steps to do. This is not required by all schedulers (hence the argument being
-            optional), the function will raise an error if it's unset and the scheduler type requires it.
-        num_training_steps (`int``, *optional*):
-            The number of training steps to do. This is not required by all schedulers (hence the argument being
-            optional), the function will raise an error if it's unset and the scheduler type requires it.
-    """
-    name = SchedulerType(name)
-    schedule_func = TYPE_TO_SCHEDULER_FUNCTION[name]
-    if name == SchedulerType.CONSTANT:
-        return schedule_func(optimizer, **kwargs)
-
-    # All other schedulers require `num_warmup_steps`
-    if num_warmup_steps is None:
-        raise ValueError(f"{name} requires `num_warmup_steps`, please provide that argument.")
-
-    if name == SchedulerType.CONSTANT_WITH_WARMUP:
-        return schedule_func(optimizer, num_warmup_steps=num_warmup_steps, **kwargs)
-
-    # All other schedulers require `num_training_steps`
-    if num_training_steps is None:
-        raise ValueError(f"{name} requires `num_training_steps`, please provide that argument.")
-
-    return schedule_func(
-        optimizer, num_warmup_steps=num_warmup_steps, num_training_steps=num_training_steps, **kwargs
-    )

lerobot/common/policies/diffusion/model/mask_generator.py

@@ -1,65 +0,0 @@
-import torch
-
-from lerobot.common.policies.diffusion.model.module_attr_mixin import ModuleAttrMixin
-
-
-class LowdimMaskGenerator(ModuleAttrMixin):
-    def __init__(
-        self,
-        action_dim,
-        obs_dim,
-        # obs mask setup
-        max_n_obs_steps=2,
-        fix_obs_steps=True,
-        # action mask
-        action_visible=False,
-    ):
-        super().__init__()
-        self.action_dim = action_dim
-        self.obs_dim = obs_dim
-        self.max_n_obs_steps = max_n_obs_steps
-        self.fix_obs_steps = fix_obs_steps
-        self.action_visible = action_visible
-
-    @torch.no_grad()
-    def forward(self, shape, seed=None):
-        device = self.device
-        B, T, D = shape  # noqa: N806
-        assert (self.action_dim + self.obs_dim) == D
-
-        # create all tensors on this device
-        rng = torch.Generator(device=device)
-        if seed is not None:
-            rng = rng.manual_seed(seed)
-
-        # generate dim mask
-        dim_mask = torch.zeros(size=shape, dtype=torch.bool, device=device)
-        is_action_dim = dim_mask.clone()
-        is_action_dim[..., : self.action_dim] = True
-        is_obs_dim = ~is_action_dim
-
-        # generate obs mask
-        if self.fix_obs_steps:
-            obs_steps = torch.full((B,), fill_value=self.max_n_obs_steps, device=device)
-        else:
-            obs_steps = torch.randint(
-                low=1, high=self.max_n_obs_steps + 1, size=(B,), generator=rng, device=device
-            )
-
-        steps = torch.arange(0, T, device=device).reshape(1, T).expand(B, T)
-        obs_mask = (obs_steps > steps.T).T.reshape(B, T, 1).expand(B, T, D)
-        obs_mask = obs_mask & is_obs_dim
-
-        # generate action mask
-        if self.action_visible:
-            action_steps = torch.maximum(
-                obs_steps - 1, torch.tensor(0, dtype=obs_steps.dtype, device=obs_steps.device)
-            )
-            action_mask = (action_steps > steps.T).T.reshape(B, T, 1).expand(B, T, D)
-            action_mask = action_mask & is_action_dim
-
-        mask = obs_mask
-        if self.action_visible:
-            mask = mask | action_mask
-
-        return mask

lerobot/common/policies/diffusion/model/module_attr_mixin.py

@@ -1,15 +0,0 @@
-import torch.nn as nn
-
-
-class ModuleAttrMixin(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self._dummy_variable = nn.Parameter()
-
-    @property
-    def device(self):
-        return next(iter(self.parameters())).device
-
-    @property
-    def dtype(self):
-        return next(iter(self.parameters())).dtype

lerobot/common/policies/diffusion/model/multi_image_obs_encoder.py

@@ -1,189 +0,0 @@
-import copy
-from typing import Dict, Tuple, Union
-
-import torch
-import torch.nn as nn
-import torchvision
-
-from lerobot.common.policies.diffusion.model.crop_randomizer import CropRandomizer
-from lerobot.common.policies.diffusion.model.module_attr_mixin import ModuleAttrMixin
-from lerobot.common.policies.diffusion.pytorch_utils import replace_submodules
-
-
-class MultiImageObsEncoder(ModuleAttrMixin):
-    def __init__(
-        self,
-        shape_meta: dict,
-        rgb_model: Union[nn.Module, Dict[str, nn.Module]],
-        resize_shape: Union[Tuple[int, int], Dict[str, tuple], None] = None,
-        crop_shape: Union[Tuple[int, int], Dict[str, tuple], None] = None,
-        random_crop: bool = True,
-        # replace BatchNorm with GroupNorm
-        use_group_norm: bool = False,
-        # use single rgb model for all rgb inputs
-        share_rgb_model: bool = False,
-        # renormalize rgb input with imagenet normalization
-        # assuming input in [0,1]
-        imagenet_norm: bool = False,
-    ):
-        """
-        Assumes rgb input: B,C,H,W
-        Assumes low_dim input: B,D
-        """
-        super().__init__()
-
-        rgb_keys = []
-        low_dim_keys = []
-        key_model_map = nn.ModuleDict()
-        key_transform_map = nn.ModuleDict()
-        key_shape_map = {}
-
-        # handle sharing vision backbone
-        if share_rgb_model:
-            assert isinstance(rgb_model, nn.Module)
-            key_model_map["rgb"] = rgb_model
-
-        obs_shape_meta = shape_meta["obs"]
-        for key, attr in obs_shape_meta.items():
-            shape = tuple(attr["shape"])
-            type = attr.get("type", "low_dim")
-            key_shape_map[key] = shape
-            if type == "rgb":
-                rgb_keys.append(key)
-                # configure model for this key
-                this_model = None
-                if not share_rgb_model:
-                    if isinstance(rgb_model, dict):
-                        # have provided model for each key
-                        this_model = rgb_model[key]
-                    else:
-                        assert isinstance(rgb_model, nn.Module)
-                        # have a copy of the rgb model
-                        this_model = copy.deepcopy(rgb_model)
-
-                if this_model is not None:
-                    if use_group_norm:
-                        this_model = replace_submodules(
-                            root_module=this_model,
-                            predicate=lambda x: isinstance(x, nn.BatchNorm2d),
-                            func=lambda x: nn.GroupNorm(
-                                num_groups=x.num_features // 16, num_channels=x.num_features
-                            ),
-                        )
-                    key_model_map[key] = this_model
-
-                # configure resize
-                input_shape = shape
-                this_resizer = nn.Identity()
-                if resize_shape is not None:
-                    if isinstance(resize_shape, dict):
-                        h, w = resize_shape[key]
-                    else:
-                        h, w = resize_shape
-                    this_resizer = torchvision.transforms.Resize(size=(h, w))
-                    input_shape = (shape[0], h, w)
-
-                # configure randomizer
-                this_randomizer = nn.Identity()
-                if crop_shape is not None:
-                    if isinstance(crop_shape, dict):
-                        h, w = crop_shape[key]
-                    else:
-                        h, w = crop_shape
-                    if random_crop:
-                        this_randomizer = CropRandomizer(
-                            input_shape=input_shape, crop_height=h, crop_width=w, num_crops=1, pos_enc=False
-                        )
-                    else:
-                        this_normalizer = torchvision.transforms.CenterCrop(size=(h, w))
-                # configure normalizer
-                this_normalizer = nn.Identity()
-                if imagenet_norm:
-                    # TODO(rcadene): move normalizer to dataset and env
-                    this_normalizer = torchvision.transforms.Normalize(
-                        mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
-                    )
-
-                this_transform = nn.Sequential(this_resizer, this_randomizer, this_normalizer)
-                key_transform_map[key] = this_transform
-            elif type == "low_dim":
-                low_dim_keys.append(key)
-            else:
-                raise RuntimeError(f"Unsupported obs type: {type}")
-        rgb_keys = sorted(rgb_keys)
-        low_dim_keys = sorted(low_dim_keys)
-
-        self.shape_meta = shape_meta
-        self.key_model_map = key_model_map
-        self.key_transform_map = key_transform_map
-        self.share_rgb_model = share_rgb_model
-        self.rgb_keys = rgb_keys
-        self.low_dim_keys = low_dim_keys
-        self.key_shape_map = key_shape_map
-
-    def forward(self, obs_dict):
-        batch_size = None
-        features = []
-        # process rgb input
-        if self.share_rgb_model:
-            # pass all rgb obs to rgb model
-            imgs = []
-            for key in self.rgb_keys:
-                img = obs_dict[key]
-                if batch_size is None:
-                    batch_size = img.shape[0]
-                else:
-                    assert batch_size == img.shape[0]
-                assert img.shape[1:] == self.key_shape_map[key]
-                img = self.key_transform_map[key](img)
-                imgs.append(img)
-            # (N*B,C,H,W)
-            imgs = torch.cat(imgs, dim=0)
-            # (N*B,D)
-            feature = self.key_model_map["rgb"](imgs)
-            # (N,B,D)
-            feature = feature.reshape(-1, batch_size, *feature.shape[1:])
-            # (B,N,D)
-            feature = torch.moveaxis(feature, 0, 1)
-            # (B,N*D)
-            feature = feature.reshape(batch_size, -1)
-            features.append(feature)
-        else:
-            # run each rgb obs to independent models
-            for key in self.rgb_keys:
-                img = obs_dict[key]
-                if batch_size is None:
-                    batch_size = img.shape[0]
-                else:
-                    assert batch_size == img.shape[0]
-                assert img.shape[1:] == self.key_shape_map[key]
-                img = self.key_transform_map[key](img)
-                feature = self.key_model_map[key](img)
-                features.append(feature)
-
-        # process lowdim input
-        for key in self.low_dim_keys:
-            data = obs_dict[key]
-            if batch_size is None:
-                batch_size = data.shape[0]
-            else:
-                assert batch_size == data.shape[0]
-            assert data.shape[1:] == self.key_shape_map[key]
-            features.append(data)
-
-        # concatenate all features
-        result = torch.cat(features, dim=-1)
-        return result
-
-    @torch.no_grad()
-    def output_shape(self):
-        example_obs_dict = {}
-        obs_shape_meta = self.shape_meta["obs"]
-        batch_size = 1
-        for key, attr in obs_shape_meta.items():
-            shape = tuple(attr["shape"])
-            this_obs = torch.zeros((batch_size,) + shape, dtype=self.dtype, device=self.device)
-            example_obs_dict[key] = this_obs
-        example_output = self.forward(example_obs_dict)
-        output_shape = example_output.shape[1:]
-        return output_shape

lerobot/common/policies/diffusion/model/normalizer.py

@@ -1,358 +0,0 @@
-from typing import Dict, Union
-
-import numpy as np
-import torch
-import torch.nn as nn
-import zarr
-
-from lerobot.common.policies.diffusion.model.dict_of_tensor_mixin import DictOfTensorMixin
-from lerobot.common.policies.diffusion.pytorch_utils import dict_apply
-
-
-class LinearNormalizer(DictOfTensorMixin):
-    avaliable_modes = ["limits", "gaussian"]
-
-    @torch.no_grad()
-    def fit(
-        self,
-        data: Union[Dict, torch.Tensor, np.ndarray, zarr.Array],
-        last_n_dims=1,
-        dtype=torch.float32,
-        mode="limits",
-        output_max=1.0,
-        output_min=-1.0,
-        range_eps=1e-4,
-        fit_offset=True,
-    ):
-        if isinstance(data, dict):
-            for key, value in data.items():
-                self.params_dict[key] = _fit(
-                    value,
-                    last_n_dims=last_n_dims,
-                    dtype=dtype,
-                    mode=mode,
-                    output_max=output_max,
-                    output_min=output_min,
-                    range_eps=range_eps,
-                    fit_offset=fit_offset,
-                )
-        else:
-            self.params_dict["_default"] = _fit(
-                data,
-                last_n_dims=last_n_dims,
-                dtype=dtype,
-                mode=mode,
-                output_max=output_max,
-                output_min=output_min,
-                range_eps=range_eps,
-                fit_offset=fit_offset,
-            )
-
-    def __call__(self, x: Union[Dict, torch.Tensor, np.ndarray]) -> torch.Tensor:
-        return self.normalize(x)
-
-    def __getitem__(self, key: str):
-        return SingleFieldLinearNormalizer(self.params_dict[key])
-
-    def __setitem__(self, key: str, value: "SingleFieldLinearNormalizer"):
-        self.params_dict[key] = value.params_dict
-
-    def _normalize_impl(self, x, forward=True):
-        if isinstance(x, dict):
-            result = {}
-            for key, value in x.items():
-                params = self.params_dict[key]
-                result[key] = _normalize(value, params, forward=forward)
-            return result
-        else:
-            if "_default" not in self.params_dict:
-                raise RuntimeError("Not initialized")
-            params = self.params_dict["_default"]
-            return _normalize(x, params, forward=forward)
-
-    def normalize(self, x: Union[Dict, torch.Tensor, np.ndarray]) -> torch.Tensor:
-        return self._normalize_impl(x, forward=True)
-
-    def unnormalize(self, x: Union[Dict, torch.Tensor, np.ndarray]) -> torch.Tensor:
-        return self._normalize_impl(x, forward=False)
-
-    def get_input_stats(self) -> Dict:
-        if len(self.params_dict) == 0:
-            raise RuntimeError("Not initialized")
-        if len(self.params_dict) == 1 and "_default" in self.params_dict:
-            return self.params_dict["_default"]["input_stats"]
-
-        result = {}
-        for key, value in self.params_dict.items():
-            if key != "_default":
-                result[key] = value["input_stats"]
-        return result
-
-    def get_output_stats(self, key="_default"):
-        input_stats = self.get_input_stats()
-        if "min" in input_stats:
-            # no dict
-            return dict_apply(input_stats, self.normalize)
-
-        result = {}
-        for key, group in input_stats.items():
-            this_dict = {}
-            for name, value in group.items():
-                this_dict[name] = self.normalize({key: value})[key]
-            result[key] = this_dict
-        return result
-
-
-class SingleFieldLinearNormalizer(DictOfTensorMixin):
-    avaliable_modes = ["limits", "gaussian"]
-
-    @torch.no_grad()
-    def fit(
-        self,
-        data: Union[torch.Tensor, np.ndarray, zarr.Array],
-        last_n_dims=1,
-        dtype=torch.float32,
-        mode="limits",
-        output_max=1.0,
-        output_min=-1.0,
-        range_eps=1e-4,
-        fit_offset=True,
-    ):
-        self.params_dict = _fit(
-            data,
-            last_n_dims=last_n_dims,
-            dtype=dtype,
-            mode=mode,
-            output_max=output_max,
-            output_min=output_min,
-            range_eps=range_eps,
-            fit_offset=fit_offset,
-        )
-
-    @classmethod
-    def create_fit(cls, data: Union[torch.Tensor, np.ndarray, zarr.Array], **kwargs):
-        obj = cls()
-        obj.fit(data, **kwargs)
-        return obj
-
-    @classmethod
-    def create_manual(
-        cls,
-        scale: Union[torch.Tensor, np.ndarray],
-        offset: Union[torch.Tensor, np.ndarray],
-        input_stats_dict: Dict[str, Union[torch.Tensor, np.ndarray]],
-    ):
-        def to_tensor(x):
-            if not isinstance(x, torch.Tensor):
-                x = torch.from_numpy(x)
-            x = x.flatten()
-            return x
-
-        # check
-        for x in [offset] + list(input_stats_dict.values()):
-            assert x.shape == scale.shape
-            assert x.dtype == scale.dtype
-
-        params_dict = nn.ParameterDict(
-            {
-                "scale": to_tensor(scale),
-                "offset": to_tensor(offset),
-                "input_stats": nn.ParameterDict(dict_apply(input_stats_dict, to_tensor)),
-            }
-        )
-        return cls(params_dict)
-
-    @classmethod
-    def create_identity(cls, dtype=torch.float32):
-        scale = torch.tensor([1], dtype=dtype)
-        offset = torch.tensor([0], dtype=dtype)
-        input_stats_dict = {
-            "min": torch.tensor([-1], dtype=dtype),
-            "max": torch.tensor([1], dtype=dtype),
-            "mean": torch.tensor([0], dtype=dtype),
-            "std": torch.tensor([1], dtype=dtype),
-        }
-        return cls.create_manual(scale, offset, input_stats_dict)
-
-    def normalize(self, x: Union[torch.Tensor, np.ndarray]) -> torch.Tensor:
-        return _normalize(x, self.params_dict, forward=True)
-
-    def unnormalize(self, x: Union[torch.Tensor, np.ndarray]) -> torch.Tensor:
-        return _normalize(x, self.params_dict, forward=False)
-
-    def get_input_stats(self):
-        return self.params_dict["input_stats"]
-
-    def get_output_stats(self):
-        return dict_apply(self.params_dict["input_stats"], self.normalize)
-
-    def __call__(self, x: Union[torch.Tensor, np.ndarray]) -> torch.Tensor:
-        return self.normalize(x)
-
-
-def _fit(
-    data: Union[torch.Tensor, np.ndarray, zarr.Array],
-    last_n_dims=1,
-    dtype=torch.float32,
-    mode="limits",
-    output_max=1.0,
-    output_min=-1.0,
-    range_eps=1e-4,
-    fit_offset=True,
-):
-    assert mode in ["limits", "gaussian"]
-    assert last_n_dims >= 0
-    assert output_max > output_min
-
-    # convert data to torch and type
-    if isinstance(data, zarr.Array):
-        data = data[:]
-    if isinstance(data, np.ndarray):
-        data = torch.from_numpy(data)
-    if dtype is not None:
-        data = data.type(dtype)
-
-    # convert shape
-    dim = 1
-    if last_n_dims > 0:
-        dim = np.prod(data.shape[-last_n_dims:])
-    data = data.reshape(-1, dim)
-
-    # compute input stats min max mean std
-    input_min, _ = data.min(axis=0)
-    input_max, _ = data.max(axis=0)
-    input_mean = data.mean(axis=0)
-    input_std = data.std(axis=0)
-
-    # compute scale and offset
-    if mode == "limits":
-        if fit_offset:
-            # unit scale
-            input_range = input_max - input_min
-            ignore_dim = input_range < range_eps
-            input_range[ignore_dim] = output_max - output_min
-            scale = (output_max - output_min) / input_range
-            offset = output_min - scale * input_min
-            offset[ignore_dim] = (output_max + output_min) / 2 - input_min[ignore_dim]
-            # ignore dims scaled to mean of output max and min
-        else:
-            # use this when data is pre-zero-centered.
-            assert output_max > 0
-            assert output_min < 0
-            # unit abs
-            output_abs = min(abs(output_min), abs(output_max))
-            input_abs = torch.maximum(torch.abs(input_min), torch.abs(input_max))
-            ignore_dim = input_abs < range_eps
-            input_abs[ignore_dim] = output_abs
-            # don't scale constant channels
-            scale = output_abs / input_abs
-            offset = torch.zeros_like(input_mean)
-    elif mode == "gaussian":
-        ignore_dim = input_std < range_eps
-        scale = input_std.clone()
-        scale[ignore_dim] = 1
-        scale = 1 / scale
-
-        offset = -input_mean * scale if fit_offset else torch.zeros_like(input_mean)
-
-    # save
-    this_params = nn.ParameterDict(
-        {
-            "scale": scale,
-            "offset": offset,
-            "input_stats": nn.ParameterDict(
-                {"min": input_min, "max": input_max, "mean": input_mean, "std": input_std}
-            ),
-        }
-    )
-    for p in this_params.parameters():
-        p.requires_grad_(False)
-    return this_params
-
-
-def _normalize(x, params, forward=True):
-    assert "scale" in params
-    if isinstance(x, np.ndarray):
-        x = torch.from_numpy(x)
-    scale = params["scale"]
-    offset = params["offset"]
-    x = x.to(device=scale.device, dtype=scale.dtype)
-    src_shape = x.shape
-    x = x.reshape(-1, scale.shape[0])
-    x = x * scale + offset if forward else (x - offset) / scale
-    x = x.reshape(src_shape)
-    return x
-
-
-def test():
-    data = torch.zeros((100, 10, 9, 2)).uniform_()
-    data[..., 0, 0] = 0
-
-    normalizer = SingleFieldLinearNormalizer()
-    normalizer.fit(data, mode="limits", last_n_dims=2)
-    datan = normalizer.normalize(data)
-    assert datan.shape == data.shape
-    assert np.allclose(datan.max(), 1.0)
-    assert np.allclose(datan.min(), -1.0)
-    dataun = normalizer.unnormalize(datan)
-    assert torch.allclose(data, dataun, atol=1e-7)
-
-    _ = normalizer.get_input_stats()
-    _ = normalizer.get_output_stats()
-
-    normalizer = SingleFieldLinearNormalizer()
-    normalizer.fit(data, mode="limits", last_n_dims=1, fit_offset=False)
-    datan = normalizer.normalize(data)
-    assert datan.shape == data.shape
-    assert np.allclose(datan.max(), 1.0, atol=1e-3)
-    assert np.allclose(datan.min(), 0.0, atol=1e-3)
-    dataun = normalizer.unnormalize(datan)
-    assert torch.allclose(data, dataun, atol=1e-7)
-
-    data = torch.zeros((100, 10, 9, 2)).uniform_()
-    normalizer = SingleFieldLinearNormalizer()
-    normalizer.fit(data, mode="gaussian", last_n_dims=0)
-    datan = normalizer.normalize(data)
-    assert datan.shape == data.shape
-    assert np.allclose(datan.mean(), 0.0, atol=1e-3)
-    assert np.allclose(datan.std(), 1.0, atol=1e-3)
-    dataun = normalizer.unnormalize(datan)
-    assert torch.allclose(data, dataun, atol=1e-7)
-
-    # dict
-    data = torch.zeros((100, 10, 9, 2)).uniform_()
-    data[..., 0, 0] = 0
-
-    normalizer = LinearNormalizer()
-    normalizer.fit(data, mode="limits", last_n_dims=2)
-    datan = normalizer.normalize(data)
-    assert datan.shape == data.shape
-    assert np.allclose(datan.max(), 1.0)
-    assert np.allclose(datan.min(), -1.0)
-    dataun = normalizer.unnormalize(datan)
-    assert torch.allclose(data, dataun, atol=1e-7)
-
-    _ = normalizer.get_input_stats()
-    _ = normalizer.get_output_stats()
-
-    data = {
-        "obs": torch.zeros((1000, 128, 9, 2)).uniform_() * 512,
-        "action": torch.zeros((1000, 128, 2)).uniform_() * 512,
-    }
-    normalizer = LinearNormalizer()
-    normalizer.fit(data)
-    datan = normalizer.normalize(data)
-    dataun = normalizer.unnormalize(datan)
-    for key in data:
-        assert torch.allclose(data[key], dataun[key], atol=1e-4)
-
-    _ = normalizer.get_input_stats()
-    _ = normalizer.get_output_stats()
-
-    state_dict = normalizer.state_dict()
-    n = LinearNormalizer()
-    n.load_state_dict(state_dict)
-    datan = n.normalize(data)
-    dataun = n.unnormalize(datan)
-    for key in data:
-        assert torch.allclose(data[key], dataun[key], atol=1e-4)

lerobot/common/policies/diffusion/model/positional_embedding.py

@@ -1,19 +0,0 @@
-import math
-
-import torch
-import torch.nn as nn
-
-
-class SinusoidalPosEmb(nn.Module):
-    def __init__(self, dim):
-        super().__init__()
-        self.dim = dim
-
-    def forward(self, x):
-        device = x.device
-        half_dim = self.dim // 2
-        emb = math.log(10000) / (half_dim - 1)
-        emb = torch.exp(torch.arange(half_dim, device=device) * -emb)
-        emb = x[:, None] * emb[None, :]
-        emb = torch.cat((emb.sin(), emb.cos()), dim=-1)
-        return emb

lerobot/common/policies/diffusion/model/tensor_utils.py

@@ -1,971 +0,0 @@
-"""
-A collection of utilities for working with nested tensor structures consisting
-of numpy arrays and torch tensors.
-"""
-import collections
-
-import numpy as np
-import torch
-
-
-def recursive_dict_list_tuple_apply(x, type_func_dict):
-    """
-    Recursively apply functions to a nested dictionary or list or tuple, given a dictionary of
-    {data_type: function_to_apply}.
-
-    Args:
-        x (dict or list or tuple): a possibly nested dictionary or list or tuple
-        type_func_dict (dict): a mapping from data types to the functions to be
-            applied for each data type.
-
-    Returns:
-        y (dict or list or tuple): new nested dict-list-tuple
-    """
-    assert list not in type_func_dict
-    assert tuple not in type_func_dict
-    assert dict not in type_func_dict
-
-    if isinstance(x, (dict, collections.OrderedDict)):
-        new_x = collections.OrderedDict() if isinstance(x, collections.OrderedDict) else {}
-        for k, v in x.items():
-            new_x[k] = recursive_dict_list_tuple_apply(v, type_func_dict)
-        return new_x
-    elif isinstance(x, (list, tuple)):
-        ret = [recursive_dict_list_tuple_apply(v, type_func_dict) for v in x]
-        if isinstance(x, tuple):
-            ret = tuple(ret)
-        return ret
-    else:
-        for t, f in type_func_dict.items():
-            if isinstance(x, t):
-                return f(x)
-        else:
-            raise NotImplementedError("Cannot handle data type %s" % str(type(x)))
-
-
-def map_tensor(x, func):
-    """
-    Apply function @func to torch.Tensor objects in a nested dictionary or
-    list or tuple.
-
-    Args:
-        x (dict or list or tuple): a possibly nested dictionary or list or tuple
-        func (function): function to apply to each tensor
-
-    Returns:
-        y (dict or list or tuple): new nested dict-list-tuple
-    """
-    return recursive_dict_list_tuple_apply(
-        x,
-        {
-            torch.Tensor: func,
-            type(None): lambda x: x,
-        },
-    )
-
-
-def map_ndarray(x, func):
-    """
-    Apply function @func to np.ndarray objects in a nested dictionary or
-    list or tuple.
-
-    Args:
-        x (dict or list or tuple): a possibly nested dictionary or list or tuple
-        func (function): function to apply to each array
-
-    Returns:
-        y (dict or list or tuple): new nested dict-list-tuple
-    """
-    return recursive_dict_list_tuple_apply(
-        x,
-        {
-            np.ndarray: func,
-            type(None): lambda x: x,
-        },
-    )
-
-
-def map_tensor_ndarray(x, tensor_func, ndarray_func):
-    """
-    Apply function @tensor_func to torch.Tensor objects and @ndarray_func to
-    np.ndarray objects in a nested dictionary or list or tuple.
-
-    Args:
-        x (dict or list or tuple): a possibly nested dictionary or list or tuple
-        tensor_func (function): function to apply to each tensor
-        ndarray_Func (function): function to apply to each array
-
-    Returns:
-        y (dict or list or tuple): new nested dict-list-tuple
-    """
-    return recursive_dict_list_tuple_apply(
-        x,
-        {
-            torch.Tensor: tensor_func,
-            np.ndarray: ndarray_func,
-            type(None): lambda x: x,
-        },
-    )
-
-
-def clone(x):
-    """
-    Clones all torch tensors and numpy arrays in nested dictionary or list
-    or tuple and returns a new nested structure.
-
-    Args:
-        x (dict or list or tuple): a possibly nested dictionary or list or tuple
-
-    Returns:
-        y (dict or list or tuple): new nested dict-list-tuple
-    """
-    return recursive_dict_list_tuple_apply(
-        x,
-        {
-            torch.Tensor: lambda x: x.clone(),
-            np.ndarray: lambda x: x.copy(),
-            type(None): lambda x: x,
-        },
-    )
-
-
-def detach(x):
-    """
-    Detaches all torch tensors in nested dictionary or list
-    or tuple and returns a new nested structure.
-
-    Args:
-        x (dict or list or tuple): a possibly nested dictionary or list or tuple
-
-    Returns:
-        y (dict or list or tuple): new nested dict-list-tuple
-    """
-    return recursive_dict_list_tuple_apply(
-        x,
-        {
-            torch.Tensor: lambda x: x.detach(),
-        },
-    )
-
-
-def to_batch(x):
-    """
-    Introduces a leading batch dimension of 1 for all torch tensors and numpy
-    arrays in nested dictionary or list or tuple and returns a new nested structure.
-
-    Args:
-        x (dict or list or tuple): a possibly nested dictionary or list or tuple
-
-    Returns:
-        y (dict or list or tuple): new nested dict-list-tuple
-    """
-    return recursive_dict_list_tuple_apply(
-        x,
-        {
-            torch.Tensor: lambda x: x[None, ...],
-            np.ndarray: lambda x: x[None, ...],
-            type(None): lambda x: x,
-        },
-    )
-
-
-def to_sequence(x):
-    """
-    Introduces a time dimension of 1 at dimension 1 for all torch tensors and numpy
-    arrays in nested dictionary or list or tuple and returns a new nested structure.
-
-    Args:
-        x (dict or list or tuple): a possibly nested dictionary or list or tuple
-
-    Returns:
-        y (dict or list or tuple): new nested dict-list-tuple
-    """
-    return recursive_dict_list_tuple_apply(
-        x,
-        {
-            torch.Tensor: lambda x: x[:, None, ...],
-            np.ndarray: lambda x: x[:, None, ...],
-            type(None): lambda x: x,
-        },
-    )
-
-
-def index_at_time(x, ind):
-    """
-    Indexes all torch tensors and numpy arrays in dimension 1 with index @ind in
-    nested dictionary or list or tuple and returns a new nested structure.
-
-    Args:
-        x (dict or list or tuple): a possibly nested dictionary or list or tuple
-        ind (int): index
-
-    Returns:
-        y (dict or list or tuple): new nested dict-list-tuple
-    """
-    return recursive_dict_list_tuple_apply(
-        x,
-        {
-            torch.Tensor: lambda x: x[:, ind, ...],
-            np.ndarray: lambda x: x[:, ind, ...],
-            type(None): lambda x: x,
-        },
-    )
-
-
-def unsqueeze(x, dim):
-    """
-    Adds dimension of size 1 at dimension @dim in all torch tensors and numpy arrays
-    in nested dictionary or list or tuple and returns a new nested structure.
-
-    Args:
-        x (dict or list or tuple): a possibly nested dictionary or list or tuple
-        dim (int): dimension
-
-    Returns:
-        y (dict or list or tuple): new nested dict-list-tuple
-    """
-    return recursive_dict_list_tuple_apply(
-        x,
-        {
-            torch.Tensor: lambda x: x.unsqueeze(dim=dim),
-            np.ndarray: lambda x: np.expand_dims(x, axis=dim),
-            type(None): lambda x: x,
-        },
-    )
-
-
-def contiguous(x):
-    """
-    Makes all torch tensors and numpy arrays contiguous in nested dictionary or
-    list or tuple and returns a new nested structure.
-
-    Args:
-        x (dict or list or tuple): a possibly nested dictionary or list or tuple
-
-    Returns:
-        y (dict or list or tuple): new nested dict-list-tuple
-    """
-    return recursive_dict_list_tuple_apply(
-        x,
-        {
-            torch.Tensor: lambda x: x.contiguous(),
-            np.ndarray: lambda x: np.ascontiguousarray(x),
-            type(None): lambda x: x,
-        },
-    )
-
-
-def to_device(x, device):
-    """
-    Sends all torch tensors in nested dictionary or list or tuple to device
-    @device, and returns a new nested structure.
-
-    Args:
-        x (dict or list or tuple): a possibly nested dictionary or list or tuple
-        device (torch.Device): device to send tensors to
-
-    Returns:
-        y (dict or list or tuple): new nested dict-list-tuple
-    """
-    return recursive_dict_list_tuple_apply(
-        x,
-        {
-            torch.Tensor: lambda x, d=device: x.to(d),
-            type(None): lambda x: x,
-        },
-    )
-
-
-def to_tensor(x):
-    """
-    Converts all numpy arrays in nested dictionary or list or tuple to
-    torch tensors (and leaves existing torch Tensors as-is), and returns
-    a new nested structure.
-
-    Args:
-        x (dict or list or tuple): a possibly nested dictionary or list or tuple
-
-    Returns:
-        y (dict or list or tuple): new nested dict-list-tuple
-    """
-    return recursive_dict_list_tuple_apply(
-        x,
-        {
-            torch.Tensor: lambda x: x,
-            np.ndarray: lambda x: torch.from_numpy(x),
-            type(None): lambda x: x,
-        },
-    )
-
-
-def to_numpy(x):
-    """
-    Converts all torch tensors in nested dictionary or list or tuple to
-    numpy (and leaves existing numpy arrays as-is), and returns
-    a new nested structure.
-
-    Args:
-        x (dict or list or tuple): a possibly nested dictionary or list or tuple
-
-    Returns:
-        y (dict or list or tuple): new nested dict-list-tuple
-    """
-
-    def f(tensor):
-        if tensor.is_cuda:
-            return tensor.detach().cpu().numpy()
-        else:
-            return tensor.detach().numpy()
-
-    return recursive_dict_list_tuple_apply(
-        x,
-        {
-            torch.Tensor: f,
-            np.ndarray: lambda x: x,
-            type(None): lambda x: x,
-        },
-    )
-
-
-def to_list(x):
-    """
-    Converts all torch tensors and numpy arrays in nested dictionary or list
-    or tuple to a list, and returns a new nested structure. Useful for
-    json encoding.
-
-    Args:
-        x (dict or list or tuple): a possibly nested dictionary or list or tuple
-
-    Returns:
-        y (dict or list or tuple): new nested dict-list-tuple
-    """
-
-    def f(tensor):
-        if tensor.is_cuda:
-            return tensor.detach().cpu().numpy().tolist()
-        else:
-            return tensor.detach().numpy().tolist()
-
-    return recursive_dict_list_tuple_apply(
-        x,
-        {
-            torch.Tensor: f,
-            np.ndarray: lambda x: x.tolist(),
-            type(None): lambda x: x,
-        },
-    )
-
-
-def to_float(x):
-    """
-    Converts all torch tensors and numpy arrays in nested dictionary or list
-    or tuple to float type entries, and returns a new nested structure.
-
-    Args:
-        x (dict or list or tuple): a possibly nested dictionary or list or tuple
-
-    Returns:
-        y (dict or list or tuple): new nested dict-list-tuple
-    """
-    return recursive_dict_list_tuple_apply(
-        x,
-        {
-            torch.Tensor: lambda x: x.float(),
-            np.ndarray: lambda x: x.astype(np.float32),
-            type(None): lambda x: x,
-        },
-    )
-
-
-def to_uint8(x):
-    """
-    Converts all torch tensors and numpy arrays in nested dictionary or list
-    or tuple to uint8 type entries, and returns a new nested structure.
-
-    Args:
-        x (dict or list or tuple): a possibly nested dictionary or list or tuple
-
-    Returns:
-        y (dict or list or tuple): new nested dict-list-tuple
-    """
-    return recursive_dict_list_tuple_apply(
-        x,
-        {
-            torch.Tensor: lambda x: x.byte(),
-            np.ndarray: lambda x: x.astype(np.uint8),
-            type(None): lambda x: x,
-        },
-    )
-
-
-def to_torch(x, device):
-    """
-    Converts all numpy arrays and torch tensors in nested dictionary or list or tuple to
-    torch tensors on device @device and returns a new nested structure.
-
-    Args:
-        x (dict or list or tuple): a possibly nested dictionary or list or tuple
-        device (torch.Device): device to send tensors to
-
-    Returns:
-        y (dict or list or tuple): new nested dict-list-tuple
-    """
-    return to_device(to_float(to_tensor(x)), device)
-
-
-def to_one_hot_single(tensor, num_class):
-    """
-    Convert tensor to one-hot representation, assuming a certain number of total class labels.
-
-    Args:
-        tensor (torch.Tensor): tensor containing integer labels
-        num_class (int): number of classes
-
-    Returns:
-        x (torch.Tensor): tensor containing one-hot representation of labels
-    """
-    x = torch.zeros(tensor.size() + (num_class,)).to(tensor.device)
-    x.scatter_(-1, tensor.unsqueeze(-1), 1)
-    return x
-
-
-def to_one_hot(tensor, num_class):
-    """
-    Convert all tensors in nested dictionary or list or tuple to one-hot representation,
-    assuming a certain number of total class labels.
-
-    Args:
-        tensor (dict or list or tuple): a possibly nested dictionary or list or tuple
-        num_class (int): number of classes
-
-    Returns:
-        y (dict or list or tuple): new nested dict-list-tuple
-    """
-    return map_tensor(tensor, func=lambda x, nc=num_class: to_one_hot_single(x, nc))
-
-
-def flatten_single(x, begin_axis=1):
-    """
-    Flatten a tensor in all dimensions from @begin_axis onwards.
-
-    Args:
-        x (torch.Tensor): tensor to flatten
-        begin_axis (int): which axis to flatten from
-
-    Returns:
-        y (torch.Tensor): flattened tensor
-    """
-    fixed_size = x.size()[:begin_axis]
-    _s = list(fixed_size) + [-1]
-    return x.reshape(*_s)
-
-
-def flatten(x, begin_axis=1):
-    """
-    Flatten all tensors in nested dictionary or list or tuple, from @begin_axis onwards.
-
-    Args:
-        x (dict or list or tuple): a possibly nested dictionary or list or tuple
-        begin_axis (int): which axis to flatten from
-
-    Returns:
-        y (dict or list or tuple): new nested dict-list-tuple
-    """
-    return recursive_dict_list_tuple_apply(
-        x,
-        {
-            torch.Tensor: lambda x, b=begin_axis: flatten_single(x, begin_axis=b),
-        },
-    )
-
-
-def reshape_dimensions_single(x, begin_axis, end_axis, target_dims):
-    """
-    Reshape selected dimensions in a tensor to a target dimension.
-
-    Args:
-        x (torch.Tensor): tensor to reshape
-        begin_axis (int): begin dimension
-        end_axis (int): end dimension
-        target_dims (tuple or list): target shape for the range of dimensions
-            (@begin_axis, @end_axis)
-
-    Returns:
-        y (torch.Tensor): reshaped tensor
-    """
-    assert begin_axis <= end_axis
-    assert begin_axis >= 0
-    assert end_axis < len(x.shape)
-    assert isinstance(target_dims, (tuple, list))
-    s = x.shape
-    final_s = []
-    for i in range(len(s)):
-        if i == begin_axis:
-            final_s.extend(target_dims)
-        elif i < begin_axis or i > end_axis:
-            final_s.append(s[i])
-    return x.reshape(*final_s)
-
-
-def reshape_dimensions(x, begin_axis, end_axis, target_dims):
-    """
-    Reshape selected dimensions for all tensors in nested dictionary or list or tuple
-    to a target dimension.
-
-    Args:
-        x (dict or list or tuple): a possibly nested dictionary or list or tuple
-        begin_axis (int): begin dimension
-        end_axis (int): end dimension
-        target_dims (tuple or list): target shape for the range of dimensions
-            (@begin_axis, @end_axis)
-
-    Returns:
-        y (dict or list or tuple): new nested dict-list-tuple
-    """
-    return recursive_dict_list_tuple_apply(
-        x,
-        {
-            torch.Tensor: lambda x, b=begin_axis, e=end_axis, t=target_dims: reshape_dimensions_single(
-                x, begin_axis=b, end_axis=e, target_dims=t
-            ),
-            np.ndarray: lambda x, b=begin_axis, e=end_axis, t=target_dims: reshape_dimensions_single(
-                x, begin_axis=b, end_axis=e, target_dims=t
-            ),
-            type(None): lambda x: x,
-        },
-    )
-
-
-def join_dimensions(x, begin_axis, end_axis):
-    """
-    Joins all dimensions between dimensions (@begin_axis, @end_axis) into a flat dimension, for
-    all tensors in nested dictionary or list or tuple.
-
-    Args:
-        x (dict or list or tuple): a possibly nested dictionary or list or tuple
-        begin_axis (int): begin dimension
-        end_axis (int): end dimension
-
-    Returns:
-        y (dict or list or tuple): new nested dict-list-tuple
-    """
-    return recursive_dict_list_tuple_apply(
-        x,
-        {
-            torch.Tensor: lambda x, b=begin_axis, e=end_axis: reshape_dimensions_single(
-                x, begin_axis=b, end_axis=e, target_dims=[-1]
-            ),
-            np.ndarray: lambda x, b=begin_axis, e=end_axis: reshape_dimensions_single(
-                x, begin_axis=b, end_axis=e, target_dims=[-1]
-            ),
-            type(None): lambda x: x,
-        },
-    )
-
-
-def expand_at_single(x, size, dim):
-    """
-    Expand a tensor at a single dimension @dim by @size
-
-    Args:
-        x (torch.Tensor): input tensor
-        size (int): size to expand
-        dim (int): dimension to expand
-
-    Returns:
-        y (torch.Tensor): expanded tensor
-    """
-    assert dim < x.ndimension()
-    assert x.shape[dim] == 1
-    expand_dims = [-1] * x.ndimension()
-    expand_dims[dim] = size
-    return x.expand(*expand_dims)
-
-
-def expand_at(x, size, dim):
-    """
-    Expand all tensors in nested dictionary or list or tuple at a single
-    dimension @dim by @size.
-
-    Args:
-        x (dict or list or tuple): a possibly nested dictionary or list or tuple
-        size (int): size to expand
-        dim (int): dimension to expand
-
-    Returns:
-        y (dict or list or tuple): new nested dict-list-tuple
-    """
-    return map_tensor(x, lambda t, s=size, d=dim: expand_at_single(t, s, d))
-
-
-def unsqueeze_expand_at(x, size, dim):
-    """
-    Unsqueeze and expand a tensor at a dimension @dim by @size.
-
-    Args:
-        x (dict or list or tuple): a possibly nested dictionary or list or tuple
-        size (int): size to expand
-        dim (int): dimension to unsqueeze and expand
-
-    Returns:
-        y (dict or list or tuple): new nested dict-list-tuple
-    """
-    x = unsqueeze(x, dim)
-    return expand_at(x, size, dim)
-
-
-def repeat_by_expand_at(x, repeats, dim):
-    """
-    Repeat a dimension by combining expand and reshape operations.
-
-    Args:
-        x (dict or list or tuple): a possibly nested dictionary or list or tuple
-        repeats (int): number of times to repeat the target dimension
-        dim (int): dimension to repeat on
-
-    Returns:
-        y (dict or list or tuple): new nested dict-list-tuple
-    """
-    x = unsqueeze_expand_at(x, repeats, dim + 1)
-    return join_dimensions(x, dim, dim + 1)
-
-
-def named_reduce_single(x, reduction, dim):
-    """
-    Reduce tensor at a dimension by named reduction functions.
-
-    Args:
-        x (torch.Tensor): tensor to be reduced
-        reduction (str): one of ["sum", "max", "mean", "flatten"]
-        dim (int): dimension to be reduced (or begin axis for flatten)
-
-    Returns:
-        y (torch.Tensor): reduced tensor
-    """
-    assert x.ndimension() > dim
-    assert reduction in ["sum", "max", "mean", "flatten"]
-    if reduction == "flatten":
-        x = flatten(x, begin_axis=dim)
-    elif reduction == "max":
-        x = torch.max(x, dim=dim)[0]  # [B, D]
-    elif reduction == "sum":
-        x = torch.sum(x, dim=dim)
-    else:
-        x = torch.mean(x, dim=dim)
-    return x
-
-
-def named_reduce(x, reduction, dim):
-    """
-    Reduces all tensors in nested dictionary or list or tuple at a dimension
-    using a named reduction function.
-
-    Args:
-        x (dict or list or tuple): a possibly nested dictionary or list or tuple
-        reduction (str): one of ["sum", "max", "mean", "flatten"]
-        dim (int): dimension to be reduced (or begin axis for flatten)
-
-    Returns:
-        y (dict or list or tuple): new nested dict-list-tuple
-    """
-    return map_tensor(x, func=lambda t, r=reduction, d=dim: named_reduce_single(t, r, d))
-
-
-def gather_along_dim_with_dim_single(x, target_dim, source_dim, indices):
-    """
-    This function indexes out a target dimension of a tensor in a structured way,
-    by allowing a different value to be selected for each member of a flat index
-    tensor (@indices) corresponding to a source dimension. This can be interpreted
-    as moving along the source dimension, using the corresponding index value
-    in @indices to select values for all other dimensions outside of the
-    source and target dimensions. A common use case is to gather values
-    in target dimension 1 for each batch member (target dimension 0).
-
-    Args:
-        x (torch.Tensor): tensor to gather values for
-        target_dim (int): dimension to gather values along
-        source_dim (int): dimension to hold constant and use for gathering values
-            from the other dimensions
-        indices (torch.Tensor): flat index tensor with same shape as tensor @x along
-            @source_dim
-
-    Returns:
-        y (torch.Tensor): gathered tensor, with dimension @target_dim indexed out
-    """
-    assert len(indices.shape) == 1
-    assert x.shape[source_dim] == indices.shape[0]
-
-    # unsqueeze in all dimensions except the source dimension
-    new_shape = [1] * x.ndimension()
-    new_shape[source_dim] = -1
-    indices = indices.reshape(*new_shape)
-
-    # repeat in all dimensions - but preserve shape of source dimension,
-    # and make sure target_dimension has singleton dimension
-    expand_shape = list(x.shape)
-    expand_shape[source_dim] = -1
-    expand_shape[target_dim] = 1
-    indices = indices.expand(*expand_shape)
-
-    out = x.gather(dim=target_dim, index=indices)
-    return out.squeeze(target_dim)
-
-
-def gather_along_dim_with_dim(x, target_dim, source_dim, indices):
-    """
-    Apply @gather_along_dim_with_dim_single to all tensors in a nested
-    dictionary or list or tuple.
-
-    Args:
-        x (dict or list or tuple): a possibly nested dictionary or list or tuple
-        target_dim (int): dimension to gather values along
-        source_dim (int): dimension to hold constant and use for gathering values
-            from the other dimensions
-        indices (torch.Tensor): flat index tensor with same shape as tensor @x along
-            @source_dim
-
-    Returns:
-        y (dict or list or tuple): new nested dict-list-tuple
-    """
-    return map_tensor(
-        x, lambda y, t=target_dim, s=source_dim, i=indices: gather_along_dim_with_dim_single(y, t, s, i)
-    )
-
-
-def gather_sequence_single(seq, indices):
-    """
-    Given a tensor with leading dimensions [B, T, ...], gather an element from each sequence in
-    the batch given an index for each sequence.
-
-    Args:
-        seq (torch.Tensor): tensor with leading dimensions [B, T, ...]
-        indices (torch.Tensor): tensor indices of shape [B]
-
-    Return:
-        y (torch.Tensor): indexed tensor of shape [B, ....]
-    """
-    return gather_along_dim_with_dim_single(seq, target_dim=1, source_dim=0, indices=indices)
-
-
-def gather_sequence(seq, indices):
-    """
-    Given a nested dictionary or list or tuple, gathers an element from each sequence of the batch
-    for tensors with leading dimensions [B, T, ...].
-
-    Args:
-        seq (dict or list or tuple): a possibly nested dictionary or list or tuple with tensors
-            of leading dimensions [B, T, ...]
-        indices (torch.Tensor): tensor indices of shape [B]
-
-    Returns:
-        y (dict or list or tuple): new nested dict-list-tuple with tensors of shape [B, ...]
-    """
-    return gather_along_dim_with_dim(seq, target_dim=1, source_dim=0, indices=indices)
-
-
-def pad_sequence_single(seq, padding, batched=False, pad_same=True, pad_values=None):
-    """
-    Pad input tensor or array @seq in the time dimension (dimension 1).
-
-    Args:
-        seq (np.ndarray or torch.Tensor): sequence to be padded
-        padding (tuple): begin and end padding, e.g. [1, 1] pads both begin and end of the sequence by 1
-        batched (bool): if sequence has the batch dimension
-        pad_same (bool): if pad by duplicating
-        pad_values (scalar or (ndarray, Tensor)): values to be padded if not pad_same
-
-    Returns:
-        padded sequence (np.ndarray or torch.Tensor)
-    """
-    assert isinstance(seq, (np.ndarray, torch.Tensor))
-    assert pad_same or pad_values is not None
-    if pad_values is not None:
-        assert isinstance(pad_values, float)
-    repeat_func = np.repeat if isinstance(seq, np.ndarray) else torch.repeat_interleave
-    concat_func = np.concatenate if isinstance(seq, np.ndarray) else torch.cat
-    ones_like_func = np.ones_like if isinstance(seq, np.ndarray) else torch.ones_like
-    seq_dim = 1 if batched else 0
-
-    begin_pad = []
-    end_pad = []
-
-    if padding[0] > 0:
-        pad = seq[[0]] if pad_same else ones_like_func(seq[[0]]) * pad_values
-        begin_pad.append(repeat_func(pad, padding[0], seq_dim))
-    if padding[1] > 0:
-        pad = seq[[-1]] if pad_same else ones_like_func(seq[[-1]]) * pad_values
-        end_pad.append(repeat_func(pad, padding[1], seq_dim))
-
-    return concat_func(begin_pad + [seq] + end_pad, seq_dim)
-
-
-def pad_sequence(seq, padding, batched=False, pad_same=True, pad_values=None):
-    """
-    Pad a nested dictionary or list or tuple of sequence tensors in the time dimension (dimension 1).
-
-    Args:
-        seq (dict or list or tuple): a possibly nested dictionary or list or tuple with tensors
-            of leading dimensions [B, T, ...]
-        padding (tuple): begin and end padding, e.g. [1, 1] pads both begin and end of the sequence by 1
-        batched (bool): if sequence has the batch dimension
-        pad_same (bool): if pad by duplicating
-        pad_values (scalar or (ndarray, Tensor)): values to be padded if not pad_same
-
-    Returns:
-        padded sequence (dict or list or tuple)
-    """
-    return recursive_dict_list_tuple_apply(
-        seq,
-        {
-            torch.Tensor: lambda x, p=padding, b=batched, ps=pad_same, pv=pad_values: pad_sequence_single(
-                x, p, b, ps, pv
-            ),
-            np.ndarray: lambda x, p=padding, b=batched, ps=pad_same, pv=pad_values: pad_sequence_single(
-                x, p, b, ps, pv
-            ),
-            type(None): lambda x: x,
-        },
-    )
-
-
-def assert_size_at_dim_single(x, size, dim, msg):
-    """
-    Ensure that array or tensor @x has size @size in dim @dim.
-
-    Args:
-        x (np.ndarray or torch.Tensor): input array or tensor
-        size (int): size that tensors should have at @dim
-        dim (int): dimension to check
-        msg (str): text to display if assertion fails
-    """
-    assert x.shape[dim] == size, msg
-
-
-def assert_size_at_dim(x, size, dim, msg):
-    """
-    Ensure that arrays and tensors in nested dictionary or list or tuple have
-    size @size in dim @dim.
-
-    Args:
-        x (dict or list or tuple): a possibly nested dictionary or list or tuple
-        size (int): size that tensors should have at @dim
-        dim (int): dimension to check
-    """
-    map_tensor(x, lambda t, s=size, d=dim, m=msg: assert_size_at_dim_single(t, s, d, m))
-
-
-def get_shape(x):
-    """
-    Get all shapes of arrays and tensors in nested dictionary or list or tuple.
-
-    Args:
-        x (dict or list or tuple): a possibly nested dictionary or list or tuple
-
-    Returns:
-        y (dict or list or tuple): new nested dict-list-tuple that contains each array or
-            tensor's shape
-    """
-    return recursive_dict_list_tuple_apply(
-        x,
-        {
-            torch.Tensor: lambda x: x.shape,
-            np.ndarray: lambda x: x.shape,
-            type(None): lambda x: x,
-        },
-    )
-
-
-def list_of_flat_dict_to_dict_of_list(list_of_dict):
-    """
-    Helper function to go from a list of flat dictionaries to a dictionary of lists.
-    By "flat" we mean that none of the values are dictionaries, but are numpy arrays,
-    floats, etc.
-
-    Args:
-        list_of_dict (list): list of flat dictionaries
-
-    Returns:
-        dict_of_list (dict): dictionary of lists
-    """
-    assert isinstance(list_of_dict, list)
-    dic = collections.OrderedDict()
-    for i in range(len(list_of_dict)):
-        for k in list_of_dict[i]:
-            if k not in dic:
-                dic[k] = []
-            dic[k].append(list_of_dict[i][k])
-    return dic
-
-
-def flatten_nested_dict_list(d, parent_key="", sep="_", item_key=""):
-    """
-    Flatten a nested dict or list to a list.
-
-    For example, given a dict
-    {
-        a: 1
-        b: {
-            c: 2
-        }
-        c: 3
-    }
-
-    the function would return [(a, 1), (b_c, 2), (c, 3)]
-
-    Args:
-        d (dict, list): a nested dict or list to be flattened
-        parent_key (str): recursion helper
-        sep (str): separator for nesting keys
-        item_key (str): recursion helper
-    Returns:
-        list: a list of (key, value) tuples
-    """
-    items = []
-    if isinstance(d, (tuple, list)):
-        new_key = parent_key + sep + item_key if len(parent_key) > 0 else item_key
-        for i, v in enumerate(d):
-            items.extend(flatten_nested_dict_list(v, new_key, sep=sep, item_key=str(i)))
-        return items
-    elif isinstance(d, dict):
-        new_key = parent_key + sep + item_key if len(parent_key) > 0 else item_key
-        for k, v in d.items():
-            assert isinstance(k, str)
-            items.extend(flatten_nested_dict_list(v, new_key, sep=sep, item_key=k))
-        return items
-    else:
-        new_key = parent_key + sep + item_key if len(parent_key) > 0 else item_key
-        return [(new_key, d)]
-
-
-def time_distributed(inputs, op, activation=None, inputs_as_kwargs=False, inputs_as_args=False, **kwargs):
-    """
-    Apply function @op to all tensors in nested dictionary or list or tuple @inputs in both the
-    batch (B) and time (T) dimension, where the tensors are expected to have shape [B, T, ...].
-    Will do this by reshaping tensors to [B * T, ...], passing through the op, and then reshaping
-    outputs to [B, T, ...].
-
-    Args:
-        inputs (list or tuple or dict): a possibly nested dictionary or list or tuple with tensors
-            of leading dimensions [B, T, ...]
-        op: a layer op that accepts inputs
-        activation: activation to apply at the output
-        inputs_as_kwargs (bool): whether to feed input as a kwargs dict to the op
-        inputs_as_args (bool) whether to feed input as a args list to the op
-        kwargs (dict): other kwargs to supply to the op
-
-    Returns:
-        outputs (dict or list or tuple): new nested dict-list-tuple with tensors of leading dimension [B, T].
-    """
-    batch_size, seq_len = flatten_nested_dict_list(inputs)[0][1].shape[:2]
-    inputs = join_dimensions(inputs, 0, 1)
-    if inputs_as_kwargs:
-        outputs = op(**inputs, **kwargs)
-    elif inputs_as_args:
-        outputs = op(*inputs, **kwargs)
-    else:
-        outputs = op(inputs, **kwargs)
-
-    if activation is not None:
-        outputs = map_tensor(outputs, activation)
-    outputs = reshape_dimensions(outputs, begin_axis=0, end_axis=0, target_dims=(batch_size, seq_len))
-    return outputs

lerobot/common/policies/diffusion/modeling_diffusion.py

@@ -0,0 +1,723 @@
+"""Diffusion Policy as per "Diffusion Policy: Visuomotor Policy Learning via Action Diffusion"
+
+TODO(alexander-soare):
+  - Remove reliance on Robomimic for SpatialSoftmax.
+  - Remove reliance on diffusers for DDPMScheduler and LR scheduler.
+  - Move EMA out of policy.
+  - Consolidate _DiffusionUnetImagePolicy into DiffusionPolicy.
+  - One more pass on comments and documentation.
+"""
+
+import copy
+import logging
+import math
+import time
+from collections import deque
+from itertools import chain
+from typing import Callable
+
+import einops
+import torch
+import torch.nn.functional as F  # noqa: N812
+import torchvision
+from diffusers.optimization import get_scheduler
+from diffusers.schedulers.scheduling_ddpm import DDPMScheduler
+from robomimic.models.base_nets import SpatialSoftmax
+from torch import Tensor, nn
+from torch.nn.modules.batchnorm import _BatchNorm
+
+from lerobot.common.policies.diffusion.configuration_diffusion import DiffusionConfig
+from lerobot.common.policies.utils import (
+    get_device_from_parameters,
+    get_dtype_from_parameters,
+    populate_queues,
+)
+
+
+class DiffusionPolicy(nn.Module):
+    """
+    Diffusion Policy as per "Diffusion Policy: Visuomotor Policy Learning via Action Diffusion"
+    (paper: https://arxiv.org/abs/2303.04137, code: https://github.com/real-stanford/diffusion_policy).
+    """
+
+    name = "diffusion"
+
+    def __init__(self, cfg: DiffusionConfig | None, lr_scheduler_num_training_steps: int = 0):
+        """
+        Args:
+            cfg: Policy configuration class instance or None, in which case the default instantiation of the
+                 configuration class is used.
+        """
+        super().__init__()
+        # TODO(alexander-soare): LR scheduler will be removed.
+        assert lr_scheduler_num_training_steps > 0
+        if cfg is None:
+            cfg = DiffusionConfig()
+        self.cfg = cfg
+
+        # queues are populated during rollout of the policy, they contain the n latest observations and actions
+        self._queues = None
+
+        self.diffusion = _DiffusionUnetImagePolicy(cfg)
+
+        # TODO(alexander-soare): This should probably be managed outside of the policy class.
+        self.ema_diffusion = None
+        self.ema = None
+        if self.cfg.use_ema:
+            self.ema_diffusion = copy.deepcopy(self.diffusion)
+            self.ema = _EMA(cfg, model=self.ema_diffusion)
+
+        # TODO(alexander-soare): Move optimizer out of policy.
+        self.optimizer = torch.optim.Adam(
+            self.diffusion.parameters(), cfg.lr, cfg.adam_betas, cfg.adam_eps, cfg.adam_weight_decay
+        )
+
+        # TODO(alexander-soare): Move LR scheduler out of policy.
+        # TODO(rcadene): modify lr scheduler so that it doesn't depend on epochs but steps
+        self.global_step = 0
+
+        # configure lr scheduler
+        self.lr_scheduler = get_scheduler(
+            cfg.lr_scheduler,
+            optimizer=self.optimizer,
+            num_warmup_steps=cfg.lr_warmup_steps,
+            num_training_steps=lr_scheduler_num_training_steps,
+            # pytorch assumes stepping LRScheduler every epoch
+            # however huggingface diffusers steps it every batch
+            last_epoch=self.global_step - 1,
+        )
+
+    def reset(self):
+        """
+        Clear observation and action queues. Should be called on `env.reset()`
+        """
+        self._queues = {
+            "observation.image": deque(maxlen=self.cfg.n_obs_steps),
+            "observation.state": deque(maxlen=self.cfg.n_obs_steps),
+            "action": deque(maxlen=self.cfg.n_action_steps),
+        }
+
+    @torch.no_grad
+    def select_action(self, batch: dict[str, Tensor], **_) -> Tensor:
+        """Select a single action given environment observations.
+
+        This method handles caching a history of observations and an action trajectory generated by the
+        underlying diffusion model. Here's how it works:
+          - `n_obs_steps` steps worth of observations are cached (for the first steps, the observation is
+            copied `n_obs_steps` times to fill the cache).
+          - The diffusion model generates `horizon` steps worth of actions.
+          - `n_action_steps` worth of actions are actually kept for execution, starting from the current step.
+        Schematically this looks like:
+            ----------------------------------------------------------------------------------------------
+            (legend: o = n_obs_steps, h = horizon, a = n_action_steps)
+            |timestep            | n-o+1 | n-o+2 | ..... | n     | ..... | n+a-1 | n+a   | ..... |n-o+1+h|
+            |observation is used | YES   | YES   | YES   | NO    | NO    | NO    | NO    | NO    | NO    |
+            |action is generated | YES   | YES   | YES   | YES   | YES   | YES   | YES   | YES   | YES   |
+            |action is used      | NO    | NO    | NO    | YES   | YES   | YES   | NO    | NO    | NO    |
+            ----------------------------------------------------------------------------------------------
+        Note that this means we require: `n_action_steps < horizon - n_obs_steps + 1`. Also, note that
+        "horizon" may not the best name to describe what the variable actually means, because this period is
+        actually measured from the first observation which (if `n_obs_steps` > 1) happened in the past.
+
+        Note: this method uses the ema model weights if self.training == False, otherwise the non-ema model
+        weights.
+        """
+        assert "observation.image" in batch
+        assert "observation.state" in batch
+        assert len(batch) == 2
+
+        self._queues = populate_queues(self._queues, batch)
+
+        if len(self._queues["action"]) == 0:
+            # stack n latest observations from the queue
+            batch = {key: torch.stack(list(self._queues[key]), dim=1) for key in batch}
+            if not self.training and self.ema_diffusion is not None:
+                actions = self.ema_diffusion.generate_actions(batch)
+            else:
+                actions = self.diffusion.generate_actions(batch)
+            self._queues["action"].extend(actions.transpose(0, 1))
+
+        action = self._queues["action"].popleft()
+        return action
+
+    def forward(self, batch: dict[str, Tensor], **_) -> dict[str, Tensor]:
+        """Run the batch through the model and compute the loss for training or validation."""
+        loss = self.diffusion.compute_loss(batch)
+        return {"loss": loss}
+
+    def update(self, batch: dict[str, Tensor], **_) -> dict:
+        """Run the model in train mode, compute the loss, and do an optimization step."""
+        start_time = time.time()
+
+        self.diffusion.train()
+
+        loss = self.forward(batch)["loss"]
+        loss.backward()
+
+        grad_norm = torch.nn.utils.clip_grad_norm_(
+            self.diffusion.parameters(),
+            self.cfg.grad_clip_norm,
+            error_if_nonfinite=False,
+        )
+
+        self.optimizer.step()
+        self.optimizer.zero_grad()
+        self.lr_scheduler.step()
+
+        if self.ema is not None:
+            self.ema.step(self.diffusion)
+
+        info = {
+            "loss": loss.item(),
+            "grad_norm": float(grad_norm),
+            "lr": self.lr_scheduler.get_last_lr()[0],
+            "update_s": time.time() - start_time,
+        }
+
+        return info
+
+    def save(self, fp):
+        torch.save(self.state_dict(), fp)
+
+    def load(self, fp):
+        d = torch.load(fp)
+        missing_keys, unexpected_keys = self.load_state_dict(d, strict=False)
+        if len(missing_keys) > 0:
+            assert all(k.startswith("ema_diffusion.") for k in missing_keys)
+            logging.warning(
+                "DiffusionPolicy.load expected ema parameters in loaded state dict but none were found."
+            )
+        assert len(unexpected_keys) == 0
+
+
+class _DiffusionUnetImagePolicy(nn.Module):
+    def __init__(self, cfg: DiffusionConfig):
+        super().__init__()
+        self.cfg = cfg
+
+        self.rgb_encoder = _RgbEncoder(cfg)
+        self.unet = _ConditionalUnet1D(
+            cfg, global_cond_dim=(cfg.action_dim + self.rgb_encoder.feature_dim) * cfg.n_obs_steps
+        )
+
+        self.noise_scheduler = DDPMScheduler(
+            num_train_timesteps=cfg.num_train_timesteps,
+            beta_start=cfg.beta_start,
+            beta_end=cfg.beta_end,
+            beta_schedule=cfg.beta_schedule,
+            variance_type="fixed_small",
+            clip_sample=cfg.clip_sample,
+            clip_sample_range=cfg.clip_sample_range,
+            prediction_type=cfg.prediction_type,
+        )
+
+        if cfg.num_inference_steps is None:
+            self.num_inference_steps = self.noise_scheduler.config.num_train_timesteps
+        else:
+            self.num_inference_steps = cfg.num_inference_steps
+
+    # ========= inference  ============
+    def conditional_sample(
+        self, batch_size: int, global_cond: Tensor | None = None, generator: torch.Generator | None = None
+    ) -> Tensor:
+        device = get_device_from_parameters(self)
+        dtype = get_dtype_from_parameters(self)
+
+        # Sample prior.
+        sample = torch.randn(
+            size=(batch_size, self.cfg.horizon, self.cfg.action_dim),
+            dtype=dtype,
+            device=device,
+            generator=generator,
+        )
+
+        self.noise_scheduler.set_timesteps(self.num_inference_steps)
+
+        for t in self.noise_scheduler.timesteps:
+            # Predict model output.
+            model_output = self.unet(
+                sample,
+                torch.full(sample.shape[:1], t, dtype=torch.long, device=sample.device),
+                global_cond=global_cond,
+            )
+            # Compute previous image: x_t -> x_t-1
+            sample = self.noise_scheduler.step(model_output, t, sample, generator=generator).prev_sample
+
+        return sample
+
+    def generate_actions(self, batch: dict[str, Tensor]) -> Tensor:
+        """
+        This function expects `batch` to have (at least):
+        {
+            "observation.state": (B, n_obs_steps, state_dim)
+            "observation.image": (B, n_obs_steps, C, H, W)
+        }
+        """
+        assert set(batch).issuperset({"observation.state", "observation.image"})
+        batch_size, n_obs_steps = batch["observation.state"].shape[:2]
+        assert n_obs_steps == self.cfg.n_obs_steps
+
+        # Extract image feature (first combine batch and sequence dims).
+        img_features = self.rgb_encoder(einops.rearrange(batch["observation.image"], "b n ... -> (b n) ..."))
+        # Separate batch and sequence dims.
+        img_features = einops.rearrange(img_features, "(b n) ... -> b n ...", b=batch_size)
+        # Concatenate state and image features then flatten to (B, global_cond_dim).
+        global_cond = torch.cat([batch["observation.state"], img_features], dim=-1).flatten(start_dim=1)
+
+        # run sampling
+        sample = self.conditional_sample(batch_size, global_cond=global_cond)
+
+        # `horizon` steps worth of actions (from the first observation).
+        actions = sample[..., : self.cfg.action_dim]
+        # Extract `n_action_steps` steps worth of actions (from the current observation).
+        start = n_obs_steps - 1
+        end = start + self.cfg.n_action_steps
+        actions = actions[:, start:end]
+
+        return actions
+
+    def compute_loss(self, batch: dict[str, Tensor]) -> Tensor:
+        """
+        This function expects `batch` to have (at least):
+        {
+            "observation.state": (B, n_obs_steps, state_dim)
+            "observation.image": (B, n_obs_steps, C, H, W)
+            "action": (B, horizon, action_dim)
+            "action_is_pad": (B, horizon)
+        }
+        """
+        # Input validation.
+        assert set(batch).issuperset({"observation.state", "observation.image", "action", "action_is_pad"})
+        batch_size, n_obs_steps = batch["observation.state"].shape[:2]
+        horizon = batch["action"].shape[1]
+        assert horizon == self.cfg.horizon
+        assert n_obs_steps == self.cfg.n_obs_steps
+
+        # Extract image feature (first combine batch and sequence dims).
+        img_features = self.rgb_encoder(einops.rearrange(batch["observation.image"], "b n ... -> (b n) ..."))
+        # Separate batch and sequence dims.
+        img_features = einops.rearrange(img_features, "(b n) ... -> b n ...", b=batch_size)
+        # Concatenate state and image features then flatten to (B, global_cond_dim).
+        global_cond = torch.cat([batch["observation.state"], img_features], dim=-1).flatten(start_dim=1)
+
+        trajectory = batch["action"]
+
+        # Forward diffusion.
+        # Sample noise to add to the trajectory.
+        eps = torch.randn(trajectory.shape, device=trajectory.device)
+        # Sample a random noising timestep for each item in the batch.
+        timesteps = torch.randint(
+            low=0,
+            high=self.noise_scheduler.config.num_train_timesteps,
+            size=(trajectory.shape[0],),
+            device=trajectory.device,
+        ).long()
+        # Add noise to the clean trajectories according to the noise magnitude at each timestep.
+        noisy_trajectory = self.noise_scheduler.add_noise(trajectory, eps, timesteps)
+
+        # Run the denoising network (that might denoise the trajectory, or attempt to predict the noise).
+        pred = self.unet(noisy_trajectory, timesteps, global_cond=global_cond)
+
+        # Compute the loss.
+        # The target is either the original trajectory, or the noise.
+        if self.cfg.prediction_type == "epsilon":
+            target = eps
+        elif self.cfg.prediction_type == "sample":
+            target = batch["action"]
+        else:
+            raise ValueError(f"Unsupported prediction type {self.cfg.prediction_type}")
+
+        loss = F.mse_loss(pred, target, reduction="none")
+
+        # Mask loss wherever the action is padded with copies (edges of the dataset trajectory).
+        if "action_is_pad" in batch:
+            in_episode_bound = ~batch["action_is_pad"]
+            loss = loss * in_episode_bound.unsqueeze(-1)
+
+        return loss.mean()
+
+
+class _RgbEncoder(nn.Module):
+    """Encoder an RGB image into a 1D feature vector.
+
+    Includes the ability to normalize and crop the image first.
+    """
+
+    def __init__(self, cfg: DiffusionConfig):
+        super().__init__()
+        # Set up optional preprocessing.
+        if all(v == 1.0 for v in chain(cfg.image_normalization_mean, cfg.image_normalization_std)):
+            self.normalizer = nn.Identity()
+        else:
+            self.normalizer = torchvision.transforms.Normalize(
+                mean=cfg.image_normalization_mean, std=cfg.image_normalization_std
+            )
+        if cfg.crop_shape is not None:
+            self.do_crop = True
+            # Always use center crop for eval
+            self.center_crop = torchvision.transforms.CenterCrop(cfg.crop_shape)
+            if cfg.crop_is_random:
+                self.maybe_random_crop = torchvision.transforms.RandomCrop(cfg.crop_shape)
+            else:
+                self.maybe_random_crop = self.center_crop
+        else:
+            self.do_crop = False
+
+        # Set up backbone.
+        backbone_model = getattr(torchvision.models, cfg.vision_backbone)(
+            pretrained=cfg.use_pretrained_backbone
+        )
+        # Note: This assumes that the layer4 feature map is children()[-3]
+        # TODO(alexander-soare): Use a safer alternative.
+        self.backbone = nn.Sequential(*(list(backbone_model.children())[:-2]))
+        if cfg.use_group_norm:
+            if cfg.use_pretrained_backbone:
+                raise ValueError(
+                    "You can't replace BatchNorm in a pretrained model without ruining the weights!"
+                )
+            self.backbone = _replace_submodules(
+                root_module=self.backbone,
+                predicate=lambda x: isinstance(x, nn.BatchNorm2d),
+                func=lambda x: nn.GroupNorm(num_groups=x.num_features // 16, num_channels=x.num_features),
+            )
+
+        # Set up pooling and final layers.
+        # Use a dry run to get the feature map shape.
+        with torch.inference_mode():
+            feat_map_shape = tuple(self.backbone(torch.zeros(size=(1, 3, *cfg.image_size))).shape[1:])
+        self.pool = SpatialSoftmax(feat_map_shape, num_kp=cfg.spatial_softmax_num_keypoints)
+        self.feature_dim = cfg.spatial_softmax_num_keypoints * 2
+        self.out = nn.Linear(cfg.spatial_softmax_num_keypoints * 2, self.feature_dim)
+        self.relu = nn.ReLU()
+
+    def forward(self, x: Tensor) -> Tensor:
+        """
+        Args:
+            x: (B, C, H, W) image tensor with pixel values in [0, 1].
+        Returns:
+            (B, D) image feature.
+        """
+        # Preprocess: normalize and maybe crop (if it was set up in the __init__).
+        x = self.normalizer(x)
+        if self.do_crop:
+            if self.training:  # noqa: SIM108
+                x = self.maybe_random_crop(x)
+            else:
+                # Always use center crop for eval.
+                x = self.center_crop(x)
+        # Extract backbone feature.
+        x = torch.flatten(self.pool(self.backbone(x)), start_dim=1)
+        # Final linear layer with non-linearity.
+        x = self.relu(self.out(x))
+        return x
+
+
+def _replace_submodules(
+    root_module: nn.Module, predicate: Callable[[nn.Module], bool], func: Callable[[nn.Module], nn.Module]
+) -> nn.Module:
+    """
+    Args:
+        root_module: The module for which the submodules need to be replaced
+        predicate: Takes a module as an argument and must return True if the that module is to be replaced.
+        func: Takes a module as an argument and returns a new module to replace it with.
+    Returns:
+        The root module with its submodules replaced.
+    """
+    if predicate(root_module):
+        return func(root_module)
+
+    replace_list = [k.split(".") for k, m in root_module.named_modules(remove_duplicate=True) if predicate(m)]
+    for *parents, k in replace_list:
+        parent_module = root_module
+        if len(parents) > 0:
+            parent_module = root_module.get_submodule(".".join(parents))
+        if isinstance(parent_module, nn.Sequential):
+            src_module = parent_module[int(k)]
+        else:
+            src_module = getattr(parent_module, k)
+        tgt_module = func(src_module)
+        if isinstance(parent_module, nn.Sequential):
+            parent_module[int(k)] = tgt_module
+        else:
+            setattr(parent_module, k, tgt_module)
+    # verify that all BN are replaced
+    assert not any(predicate(m) for _, m in root_module.named_modules(remove_duplicate=True))
+    return root_module
+
+
+class _SinusoidalPosEmb(nn.Module):
+    """1D sinusoidal positional embeddings as in Attention is All You Need."""
+
+    def __init__(self, dim: int):
+        super().__init__()
+        self.dim = dim
+
+    def forward(self, x: Tensor) -> Tensor:
+        device = x.device
+        half_dim = self.dim // 2
+        emb = math.log(10000) / (half_dim - 1)
+        emb = torch.exp(torch.arange(half_dim, device=device) * -emb)
+        emb = x.unsqueeze(-1) * emb.unsqueeze(0)
+        emb = torch.cat((emb.sin(), emb.cos()), dim=-1)
+        return emb
+
+
+class _Conv1dBlock(nn.Module):
+    """Conv1d --> GroupNorm --> Mish"""
+
+    def __init__(self, inp_channels, out_channels, kernel_size, n_groups=8):
+        super().__init__()
+
+        self.block = nn.Sequential(
+            nn.Conv1d(inp_channels, out_channels, kernel_size, padding=kernel_size // 2),
+            nn.GroupNorm(n_groups, out_channels),
+            nn.Mish(),
+        )
+
+    def forward(self, x):
+        return self.block(x)
+
+
+class _ConditionalUnet1D(nn.Module):
+    """A 1D convolutional UNet with FiLM modulation for conditioning.
+
+    Note: this removes local conditioning as compared to the original diffusion policy code.
+    """
+
+    def __init__(self, cfg: DiffusionConfig, global_cond_dim: int):
+        super().__init__()
+
+        self.cfg = cfg
+
+        # Encoder for the diffusion timestep.
+        self.diffusion_step_encoder = nn.Sequential(
+            _SinusoidalPosEmb(cfg.diffusion_step_embed_dim),
+            nn.Linear(cfg.diffusion_step_embed_dim, cfg.diffusion_step_embed_dim * 4),
+            nn.Mish(),
+            nn.Linear(cfg.diffusion_step_embed_dim * 4, cfg.diffusion_step_embed_dim),
+        )
+
+        # The FiLM conditioning dimension.
+        cond_dim = cfg.diffusion_step_embed_dim + global_cond_dim
+
+        # In channels / out channels for each downsampling block in the Unet's encoder. For the decoder, we
+        # just reverse these.
+        in_out = [(cfg.action_dim, cfg.down_dims[0])] + list(
+            zip(cfg.down_dims[:-1], cfg.down_dims[1:], strict=True)
+        )
+
+        # Unet encoder.
+        common_res_block_kwargs = {
+            "cond_dim": cond_dim,
+            "kernel_size": cfg.kernel_size,
+            "n_groups": cfg.n_groups,
+            "use_film_scale_modulation": cfg.use_film_scale_modulation,
+        }
+        self.down_modules = nn.ModuleList([])
+        for ind, (dim_in, dim_out) in enumerate(in_out):
+            is_last = ind >= (len(in_out) - 1)
+            self.down_modules.append(
+                nn.ModuleList(
+                    [
+                        _ConditionalResidualBlock1D(dim_in, dim_out, **common_res_block_kwargs),
+                        _ConditionalResidualBlock1D(dim_out, dim_out, **common_res_block_kwargs),
+                        # Downsample as long as it is not the last block.
+                        nn.Conv1d(dim_out, dim_out, 3, 2, 1) if not is_last else nn.Identity(),
+                    ]
+                )
+            )
+
+        # Processing in the middle of the auto-encoder.
+        self.mid_modules = nn.ModuleList(
+            [
+                _ConditionalResidualBlock1D(cfg.down_dims[-1], cfg.down_dims[-1], **common_res_block_kwargs),
+                _ConditionalResidualBlock1D(cfg.down_dims[-1], cfg.down_dims[-1], **common_res_block_kwargs),
+            ]
+        )
+
+        # Unet decoder.
+        self.up_modules = nn.ModuleList([])
+        for ind, (dim_out, dim_in) in enumerate(reversed(in_out[1:])):
+            is_last = ind >= (len(in_out) - 1)
+            self.up_modules.append(
+                nn.ModuleList(
+                    [
+                        # dim_in * 2, because it takes the encoder's skip connection as well
+                        _ConditionalResidualBlock1D(dim_in * 2, dim_out, **common_res_block_kwargs),
+                        _ConditionalResidualBlock1D(dim_out, dim_out, **common_res_block_kwargs),
+                        # Upsample as long as it is not the last block.
+                        nn.ConvTranspose1d(dim_out, dim_out, 4, 2, 1) if not is_last else nn.Identity(),
+                    ]
+                )
+            )
+
+        self.final_conv = nn.Sequential(
+            _Conv1dBlock(cfg.down_dims[0], cfg.down_dims[0], kernel_size=cfg.kernel_size),
+            nn.Conv1d(cfg.down_dims[0], cfg.action_dim, 1),
+        )
+
+    def forward(self, x: Tensor, timestep: Tensor | int, global_cond=None) -> Tensor:
+        """
+        Args:
+            x: (B, T, input_dim) tensor for input to the Unet.
+            timestep: (B,) tensor of (timestep_we_are_denoising_from - 1).
+            global_cond: (B, global_cond_dim)
+            output: (B, T, input_dim)
+        Returns:
+            (B, T, input_dim) diffusion model prediction.
+        """
+        # For 1D convolutions we'll need feature dimension first.
+        x = einops.rearrange(x, "b t d -> b d t")
+
+        timesteps_embed = self.diffusion_step_encoder(timestep)
+
+        # If there is a global conditioning feature, concatenate it to the timestep embedding.
+        if global_cond is not None:
+            global_feature = torch.cat([timesteps_embed, global_cond], axis=-1)
+        else:
+            global_feature = timesteps_embed
+
+        # Run encoder, keeping track of skip features to pass to the decoder.
+        encoder_skip_features: list[Tensor] = []
+        for resnet, resnet2, downsample in self.down_modules:
+            x = resnet(x, global_feature)
+            x = resnet2(x, global_feature)
+            encoder_skip_features.append(x)
+            x = downsample(x)
+
+        for mid_module in self.mid_modules:
+            x = mid_module(x, global_feature)
+
+        # Run decoder, using the skip features from the encoder.
+        for resnet, resnet2, upsample in self.up_modules:
+            x = torch.cat((x, encoder_skip_features.pop()), dim=1)
+            x = resnet(x, global_feature)
+            x = resnet2(x, global_feature)
+            x = upsample(x)
+
+        x = self.final_conv(x)
+
+        x = einops.rearrange(x, "b d t -> b t d")
+        return x
+
+
+class _ConditionalResidualBlock1D(nn.Module):
+    """ResNet style 1D convolutional block with FiLM modulation for conditioning."""
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        cond_dim: int,
+        kernel_size: int = 3,
+        n_groups: int = 8,
+        # Set to True to do scale modulation with FiLM as well as bias modulation (defaults to False meaning
+        # FiLM just modulates bias).
+        use_film_scale_modulation: bool = False,
+    ):
+        super().__init__()
+
+        self.use_film_scale_modulation = use_film_scale_modulation
+        self.out_channels = out_channels
+
+        self.conv1 = _Conv1dBlock(in_channels, out_channels, kernel_size, n_groups=n_groups)
+
+        # FiLM modulation (https://arxiv.org/abs/1709.07871) outputs per-channel bias and (maybe) scale.
+        cond_channels = out_channels * 2 if use_film_scale_modulation else out_channels
+        self.cond_encoder = nn.Sequential(nn.Mish(), nn.Linear(cond_dim, cond_channels))
+
+        self.conv2 = _Conv1dBlock(out_channels, out_channels, kernel_size, n_groups=n_groups)
+
+        # A final convolution for dimension matching the residual (if needed).
+        self.residual_conv = (
+            nn.Conv1d(in_channels, out_channels, 1) if in_channels != out_channels else nn.Identity()
+        )
+
+    def forward(self, x: Tensor, cond: Tensor) -> Tensor:
+        """
+        Args:
+            x: (B, in_channels, T)
+            cond: (B, cond_dim)
+        Returns:
+            (B, out_channels, T)
+        """
+        out = self.conv1(x)
+
+        # Get condition embedding. Unsqueeze for broadcasting to `out`, resulting in (B, out_channels, 1).
+        cond_embed = self.cond_encoder(cond).unsqueeze(-1)
+        if self.use_film_scale_modulation:
+            # Treat the embedding as a list of scales and biases.
+            scale = cond_embed[:, : self.out_channels]
+            bias = cond_embed[:, self.out_channels :]
+            out = scale * out + bias
+        else:
+            # Treat the embedding as biases.
+            out = out + cond_embed
+
+        out = self.conv2(out)
+        out = out + self.residual_conv(x)
+        return out
+
+
+class _EMA:
+    """
+    Exponential Moving Average of models weights
+    """
+
+    def __init__(self, cfg: DiffusionConfig, model: nn.Module):
+        """
+        @crowsonkb's notes on EMA Warmup:
+            If gamma=1 and power=1, implements a simple average. gamma=1, power=2/3 are good values for models you plan
+            to train for a million or more steps (reaches decay factor 0.999 at 31.6K steps, 0.9999 at 1M steps),
+            gamma=1, power=3/4 for models you plan to train for less (reaches decay factor 0.999 at 10K steps, 0.9999
+            at 215.4k steps).
+        Args:
+            inv_gamma (float): Inverse multiplicative factor of EMA warmup. Default: 1.
+            power (float): Exponential factor of EMA warmup. Default: 2/3.
+            min_alpha (float): The minimum EMA decay rate. Default: 0.
+        """
+
+        self.averaged_model = model
+        self.averaged_model.eval()
+        self.averaged_model.requires_grad_(False)
+
+        self.update_after_step = cfg.ema_update_after_step
+        self.inv_gamma = cfg.ema_inv_gamma
+        self.power = cfg.ema_power
+        self.min_alpha = cfg.ema_min_alpha
+        self.max_alpha = cfg.ema_max_alpha
+
+        self.alpha = 0.0
+        self.optimization_step = 0
+
+    def get_decay(self, optimization_step):
+        """
+        Compute the decay factor for the exponential moving average.
+        """
+        step = max(0, optimization_step - self.update_after_step - 1)
+        value = 1 - (1 + step / self.inv_gamma) ** -self.power
+
+        if step <= 0:
+            return 0.0
+
+        return max(self.min_alpha, min(value, self.max_alpha))
+
+    @torch.no_grad()
+    def step(self, new_model):
+        self.alpha = self.get_decay(self.optimization_step)
+
+        for module, ema_module in zip(new_model.modules(), self.averaged_model.modules(), strict=True):
+            # Iterate over immediate parameters only.
+            for param, ema_param in zip(
+                module.parameters(recurse=False), ema_module.parameters(recurse=False), strict=True
+            ):
+                if isinstance(param, dict):
+                    raise RuntimeError("Dict parameter not supported")
+                if isinstance(module, _BatchNorm) or not param.requires_grad:
+                    # Copy BatchNorm parameters, and non-trainable parameters directly.
+                    ema_param.copy_(param.to(dtype=ema_param.dtype).data)
+                else:
+                    ema_param.mul_(self.alpha)
+                    ema_param.add_(param.data.to(dtype=ema_param.dtype), alpha=1 - self.alpha)
+
+        self.optimization_step += 1

lerobot/common/policies/diffusion/policy.py

@@ -1,199 +0,0 @@
-import copy
-import time
-
-import hydra
-import torch
-import torch.nn as nn
-
-from lerobot.common.policies.diffusion.diffusion_unet_image_policy import DiffusionUnetImagePolicy
-from lerobot.common.policies.diffusion.model.lr_scheduler import get_scheduler
-from lerobot.common.policies.diffusion.model.multi_image_obs_encoder import MultiImageObsEncoder
-
-
-class DiffusionPolicy(nn.Module):
-    def __init__(
-        self,
-        cfg,
-        cfg_device,
-        cfg_noise_scheduler,
-        cfg_rgb_model,
-        cfg_obs_encoder,
-        cfg_optimizer,
-        cfg_ema,
-        shape_meta: dict,
-        horizon,
-        n_action_steps,
-        n_obs_steps,
-        num_inference_steps=None,
-        obs_as_global_cond=True,
-        diffusion_step_embed_dim=256,
-        down_dims=(256, 512, 1024),
-        kernel_size=5,
-        n_groups=8,
-        cond_predict_scale=True,
-        # parameters passed to step
-        **kwargs,
-    ):
-        super().__init__()
-        self.cfg = cfg
-
-        noise_scheduler = hydra.utils.instantiate(cfg_noise_scheduler)
-        rgb_model = hydra.utils.instantiate(cfg_rgb_model)
-        obs_encoder = MultiImageObsEncoder(
-            rgb_model=rgb_model,
-            **cfg_obs_encoder,
-        )
-
-        self.diffusion = DiffusionUnetImagePolicy(
-            shape_meta=shape_meta,
-            noise_scheduler=noise_scheduler,
-            obs_encoder=obs_encoder,
-            horizon=horizon,
-            n_action_steps=n_action_steps,
-            n_obs_steps=n_obs_steps,
-            num_inference_steps=num_inference_steps,
-            obs_as_global_cond=obs_as_global_cond,
-            diffusion_step_embed_dim=diffusion_step_embed_dim,
-            down_dims=down_dims,
-            kernel_size=kernel_size,
-            n_groups=n_groups,
-            cond_predict_scale=cond_predict_scale,
-            # parameters passed to step
-            **kwargs,
-        )
-
-        self.device = torch.device(cfg_device)
-        if torch.cuda.is_available() and cfg_device == "cuda":
-            self.diffusion.cuda()
-
-        self.ema = None
-        if self.cfg.use_ema:
-            self.ema = hydra.utils.instantiate(
-                cfg_ema,
-                model=copy.deepcopy(self.diffusion),
-            )
-
-        self.optimizer = hydra.utils.instantiate(
-            cfg_optimizer,
-            params=self.diffusion.parameters(),
-        )
-
-        # TODO(rcadene): modify lr scheduler so that it doesnt depend on epochs but steps
-        self.global_step = 0
-
-        # configure lr scheduler
-        self.lr_scheduler = get_scheduler(
-            cfg.lr_scheduler,
-            optimizer=self.optimizer,
-            num_warmup_steps=cfg.lr_warmup_steps,
-            num_training_steps=cfg.offline_steps,
-            # pytorch assumes stepping LRScheduler every epoch
-            # however huggingface diffusers steps it every batch
-            last_epoch=self.global_step - 1,
-        )
-
-    @torch.no_grad()
-    def forward(self, observation, step_count):
-        # TODO(rcadene): remove unused step_count
-        del step_count
-
-        # TODO(rcadene): remove unsqueeze hack to add bsize=1
-        observation["image"] = observation["image"].unsqueeze(0)
-        observation["state"] = observation["state"].unsqueeze(0)
-
-        obs_dict = {
-            "image": observation["image"],
-            "agent_pos": observation["state"],
-        }
-        out = self.diffusion.predict_action(obs_dict)
-
-        action = out["action"].squeeze(0)
-        return action
-
-    def update(self, replay_buffer, step):
-        start_time = time.time()
-
-        self.diffusion.train()
-
-        num_slices = self.cfg.batch_size
-        batch_size = self.cfg.horizon * num_slices
-
-        assert batch_size % self.cfg.horizon == 0
-        assert batch_size % num_slices == 0
-
-        def process_batch(batch, horizon, num_slices):
-            # trajectory t = 64, horizon h = 16
-            # (t h) ... -> t h ...
-            batch = batch.reshape(num_slices, horizon)  # .transpose(1, 0).contiguous()
-
-            # |-1|0|1|2|3|4|5|6|7|8|9|10|11|12|13|14| timestamps: 16
-            #  |o|o|                                  observations: 2
-            #  | |a|a|a|a|a|a|a|a|                    actions executed: 8
-            #  |p|p|p|p|p|p|p|p|p|p|p| p| p| p| p| p| actions predicted: 16
-            # note: we predict the action needed to go from t=-1 to t=0 similarly to an inverse kinematic model
-
-            image = batch["observation", "image"]
-            state = batch["observation", "state"]
-            action = batch["action"]
-            assert image.shape[1] == horizon
-            assert state.shape[1] == horizon
-            assert action.shape[1] == horizon
-
-            if not (horizon == 16 and self.cfg.n_obs_steps == 2):
-                raise NotImplementedError()
-
-            # keep first 2 observations of the slice corresponding to t=[-1,0]
-            image = image[:, : self.cfg.n_obs_steps]
-            state = state[:, : self.cfg.n_obs_steps]
-
-            out = {
-                "obs": {
-                    "image": image.to(self.device, non_blocking=True),
-                    "agent_pos": state.to(self.device, non_blocking=True),
-                },
-                "action": action.to(self.device, non_blocking=True),
-            }
-            return out
-
-        batch = replay_buffer.sample(batch_size)
-        batch = process_batch(batch, self.cfg.horizon, num_slices)
-
-        data_s = time.time() - start_time
-
-        loss = self.diffusion.compute_loss(batch)
-        loss.backward()
-
-        grad_norm = torch.nn.utils.clip_grad_norm_(
-            self.diffusion.parameters(),
-            self.cfg.grad_clip_norm,
-            error_if_nonfinite=False,
-        )
-
-        self.optimizer.step()
-        self.optimizer.zero_grad()
-        self.lr_scheduler.step()
-
-        if self.ema is not None:
-            self.ema.step(self.diffusion)
-
-        info = {
-            "loss": loss.item(),
-            "grad_norm": float(grad_norm),
-            "lr": self.lr_scheduler.get_last_lr()[0],
-            "data_s": data_s,
-            "update_s": time.time() - start_time,
-        }
-
-        # TODO(rcadene): remove hardcoding
-        # in diffusion_policy, len(dataloader) is 168 for a batch_size of 64
-        if step % 168 == 0:
-            self.global_step += 1
-
-        return info
-
-    def save(self, fp):
-        torch.save(self.state_dict(), fp)
-
-    def load(self, fp):
-        d = torch.load(fp)
-        self.load_state_dict(d)

lerobot/common/policies/diffusion/pytorch_utils.py

@@ -1,76 +0,0 @@
-from typing import Callable, Dict
-
-import torch
-import torch.nn as nn
-import torchvision
-
-
-def get_resnet(name, weights=None, **kwargs):
-    """
-    name: resnet18, resnet34, resnet50
-    weights: "IMAGENET1K_V1", "r3m"
-    """
-    # load r3m weights
-    if (weights == "r3m") or (weights == "R3M"):
-        return get_r3m(name=name, **kwargs)
-
-    func = getattr(torchvision.models, name)
-    resnet = func(weights=weights, **kwargs)
-    resnet.fc = torch.nn.Identity()
-    return resnet
-
-
-def get_r3m(name, **kwargs):
-    """
-    name: resnet18, resnet34, resnet50
-    """
-    import r3m
-
-    r3m.device = "cpu"
-    model = r3m.load_r3m(name)
-    r3m_model = model.module
-    resnet_model = r3m_model.convnet
-    resnet_model = resnet_model.to("cpu")
-    return resnet_model
-
-
-def dict_apply(
-    x: Dict[str, torch.Tensor], func: Callable[[torch.Tensor], torch.Tensor]
-) -> Dict[str, torch.Tensor]:
-    result = {}
-    for key, value in x.items():
-        if isinstance(value, dict):
-            result[key] = dict_apply(value, func)
-        else:
-            result[key] = func(value)
-    return result
-
-
-def replace_submodules(
-    root_module: nn.Module, predicate: Callable[[nn.Module], bool], func: Callable[[nn.Module], nn.Module]
-) -> nn.Module:
-    """
-    predicate: Return true if the module is to be replaced.
-    func: Return new module to use.
-    """
-    if predicate(root_module):
-        return func(root_module)
-
-    bn_list = [k.split(".") for k, m in root_module.named_modules(remove_duplicate=True) if predicate(m)]
-    for *parent, k in bn_list:
-        parent_module = root_module
-        if len(parent) > 0:
-            parent_module = root_module.get_submodule(".".join(parent))
-        if isinstance(parent_module, nn.Sequential):
-            src_module = parent_module[int(k)]
-        else:
-            src_module = getattr(parent_module, k)
-        tgt_module = func(src_module)
-        if isinstance(parent_module, nn.Sequential):
-            parent_module[int(k)] = tgt_module
-        else:
-            setattr(parent_module, k, tgt_module)
-    # verify that all BN are replaced
-    bn_list = [k.split(".") for k, m in root_module.named_modules(remove_duplicate=True) if predicate(m)]
-    assert len(bn_list) == 0
-    return root_module

lerobot/common/policies/factory.py

@@ -1,40 +1,61 @@
-def make_policy(cfg):
-    if cfg.policy.name == "tdmpc":
-        from lerobot.common.policies.tdmpc.policy import TDMPC
+import inspect
 
-        policy = TDMPC(cfg.policy, cfg.device)
-    elif cfg.policy.name == "diffusion":
-        from lerobot.common.policies.diffusion.policy import DiffusionPolicy
+from omegaconf import DictConfig, OmegaConf
 
-        policy = DiffusionPolicy(
-            cfg=cfg.policy,
-            cfg_device=cfg.device,
-            cfg_noise_scheduler=cfg.noise_scheduler,
-            cfg_rgb_model=cfg.rgb_model,
-            cfg_obs_encoder=cfg.obs_encoder,
-            cfg_optimizer=cfg.optimizer,
-            cfg_ema=cfg.ema,
-            n_action_steps=cfg.n_action_steps + cfg.n_latency_steps,
-            **cfg.policy,
+from lerobot.common.utils.utils import get_safe_torch_device
+
+
+def _policy_cfg_from_hydra_cfg(policy_cfg_class, hydra_cfg):
+    expected_kwargs = set(inspect.signature(policy_cfg_class).parameters)
+    assert set(hydra_cfg.policy).issuperset(
+        expected_kwargs
+    ), f"Hydra config is missing arguments: {set(expected_kwargs).difference(hydra_cfg.policy)}"
+    policy_cfg = policy_cfg_class(
+        **{
+            k: v
+            for k, v in OmegaConf.to_container(hydra_cfg.policy, resolve=True).items()
+            if k in expected_kwargs
+        }
+    )
+    return policy_cfg
+
+
+def make_policy(hydra_cfg: DictConfig):
+    if hydra_cfg.policy.name == "tdmpc":
+        from lerobot.common.policies.tdmpc.policy import TDMPCPolicy
+
+        policy = TDMPCPolicy(
+            hydra_cfg.policy,
+            n_obs_steps=hydra_cfg.n_obs_steps,
+            n_action_steps=hydra_cfg.n_action_steps,
+            device=hydra_cfg.device,
         )
-    elif cfg.policy.name == "act":
-        from lerobot.common.policies.act.policy import ActionChunkingTransformerPolicy
+    elif hydra_cfg.policy.name == "diffusion":
+        from lerobot.common.policies.diffusion.configuration_diffusion import DiffusionConfig
+        from lerobot.common.policies.diffusion.modeling_diffusion import DiffusionPolicy
 
-        policy = ActionChunkingTransformerPolicy(
-            cfg.policy, cfg.device, n_action_steps=cfg.n_action_steps + cfg.n_latency_steps
-        )
+        policy_cfg = _policy_cfg_from_hydra_cfg(DiffusionConfig, hydra_cfg)
+        policy = DiffusionPolicy(policy_cfg, hydra_cfg.offline_steps)
+        policy.to(get_safe_torch_device(hydra_cfg.device))
+    elif hydra_cfg.policy.name == "act":
+        from lerobot.common.policies.act.configuration_act import ActionChunkingTransformerConfig
+        from lerobot.common.policies.act.modeling_act import ActionChunkingTransformerPolicy
+
+        policy_cfg = _policy_cfg_from_hydra_cfg(ActionChunkingTransformerConfig, hydra_cfg)
+        policy = ActionChunkingTransformerPolicy(policy_cfg)
+        policy.to(get_safe_torch_device(hydra_cfg.device))
     else:
-        raise ValueError(cfg.policy.name)
+        raise ValueError(hydra_cfg.policy.name)
 
-    if cfg.policy.pretrained_model_path:
+    if hydra_cfg.policy.pretrained_model_path:
         # TODO(rcadene): hack for old pretrained models from fowm
-        if cfg.policy.name == "tdmpc" and "fowm" in cfg.policy.pretrained_model_path:
-            if "offline" in cfg.pretrained_model_path:
+        if hydra_cfg.policy.name == "tdmpc" and "fowm" in hydra_cfg.policy.pretrained_model_path:
+            if "offline" in hydra_cfg.policy.pretrained_model_path:
                 policy.step[0] = 25000
-            elif "final" in cfg.pretrained_model_path:
+            elif "final" in hydra_cfg.policy.pretrained_model_path:
                 policy.step[0] = 100000
             else:
                 raise NotImplementedError()
-        policy.load(cfg.policy.pretrained_model_path)
+        policy.load(hydra_cfg.policy.pretrained_model_path)
 
     return policy

lerobot/common/policies/policy_protocol.py

@@ -0,0 +1,45 @@
+"""A protocol that all policies should follow.
+
+This provides a mechanism for type-hinting and isinstance checks without requiring the policies classes
+subclass a base class.
+
+The protocol structure, method signatures, and docstrings should be used by developers as a reference for
+how to implement new policies.
+"""
+
+from typing import Protocol, runtime_checkable
+
+from torch import Tensor
+
+
+@runtime_checkable
+class Policy(Protocol):
+    """The required interface for implementing a policy."""
+
+    name: str
+
+    def reset(self):
+        """To be called whenever the environment is reset.
+
+        Does things like clearing caches.
+        """
+
+    def forward(self, batch: dict[str, Tensor]) -> dict:
+        """Run the batch through the model and compute the loss for training or validation.
+
+        Returns a dictionary with "loss" and maybe other information.
+        """
+
+    def select_action(self, batch: dict[str, Tensor]):
+        """Return one action to run in the environment (potentially in batch mode).
+
+        When the model uses a history of observations, or outputs a sequence of actions, this method deals
+        with caching.
+        """
+
+    def update(self, batch):
+        """Does compute_loss then an optimization step.
+
+        TODO(alexander-soare): We will move the optimization step back into the training loop, so this will
+        disappear.
+        """

lerobot/common/policies/tdmpc/policy.py

@@ -1,6 +1,7 @@
 # ruff: noqa: N806
 
 import time
+from collections import deque
 from copy import deepcopy
 
 import einops
@@ -9,6 +10,8 @@ import torch
 import torch.nn as nn
 
 import lerobot.common.policies.tdmpc.helper as h
+from lerobot.common.policies.utils import populate_queues
+from lerobot.common.utils.utils import get_safe_torch_device
 
 FIRST_FRAME = 0
 
@@ -85,24 +88,28 @@ class TOLD(nn.Module):
         return torch.min(Q1, Q2) if return_type == "min" else (Q1 + Q2) / 2
 
 
-class TDMPC(nn.Module):
+class TDMPCPolicy(nn.Module):
     """Implementation of TD-MPC learning + inference."""
 
-    def __init__(self, cfg, device):
+    name = "tdmpc"
+
+    def __init__(self, cfg, n_obs_steps, n_action_steps, device):
         super().__init__()
         self.action_dim = cfg.action_dim
 
         self.cfg = cfg
-        self.device = torch.device(device)
+        self.n_obs_steps = n_obs_steps
+        self.n_action_steps = n_action_steps
+        self.device = get_safe_torch_device(device)
         self.std = h.linear_schedule(cfg.std_schedule, 0)
-        self.model = TOLD(cfg).cuda() if torch.cuda.is_available() and device == "cuda" else TOLD(cfg)
+        self.model = TOLD(cfg)
+        self.model.to(self.device)
         self.model_target = deepcopy(self.model)
         self.optim = torch.optim.Adam(self.model.parameters(), lr=self.cfg.lr)
         self.pi_optim = torch.optim.Adam(self.model._pi.parameters(), lr=self.cfg.lr)
         # self.bc_optim = torch.optim.Adam(self.model.parameters(), lr=self.cfg.lr)
         self.model.eval()
         self.model_target.eval()
-        self.batch_size = cfg.batch_size
 
         self.register_buffer("step", torch.zeros(1))
 
@@ -123,21 +130,54 @@ class TDMPC(nn.Module):
         self.model.load_state_dict(d["model"])
         self.model_target.load_state_dict(d["model_target"])
 
-    @torch.no_grad()
-    def forward(self, observation, step_count):
-        t0 = step_count.item() == 0
-
-        # TODO(rcadene): remove unsqueeze hack...
-        if observation["image"].ndim == 3:
-            observation["image"] = observation["image"].unsqueeze(0)
-            observation["state"] = observation["state"].unsqueeze(0)
-
-        obs = {
-            # TODO(rcadene): remove contiguous hack...
-            "rgb": observation["image"].contiguous(),
-            "state": observation["state"].contiguous(),
+    def reset(self):
+        """
+        Clear observation and action queues. Should be called on `env.reset()`
+        """
+        self._queues = {
+            "observation.image": deque(maxlen=self.n_obs_steps),
+            "observation.state": deque(maxlen=self.n_obs_steps),
+            "action": deque(maxlen=self.n_action_steps),
         }
-        action = self.act(obs, t0=t0, step=self.step.item())
+
+    @torch.no_grad()
+    def select_action(self, batch, step):
+        assert "observation.image" in batch
+        assert "observation.state" in batch
+        assert len(batch) == 2
+
+        self._queues = populate_queues(self._queues, batch)
+
+        t0 = step == 0
+
+        self.eval()
+
+        if len(self._queues["action"]) == 0:
+            batch = {key: torch.stack(list(self._queues[key]), dim=1) for key in batch}
+
+            if self.n_obs_steps == 1:
+                # hack to remove the time dimension
+                for key in batch:
+                    assert batch[key].shape[1] == 1
+                    batch[key] = batch[key][:, 0]
+
+            actions = []
+            batch_size = batch["observation.image"].shape[0]
+            for i in range(batch_size):
+                obs = {
+                    "rgb": batch["observation.image"][[i]],
+                    "state": batch["observation.state"][[i]],
+                }
+                # Note: unsqueeze needed because `act` still uses non-batch logic.
+                action = self.act(obs, t0=t0, step=self.step)
+                actions.append(action)
+            action = torch.stack(actions)
+
+            # tdmpc returns an action for 1 timestep only, so we copy it over `n_action_steps` time
+            if i in range(self.n_action_steps):
+                self._queues["action"].append(action)
+
+        action = self._queues["action"].popleft()
         return action
 
     @torch.no_grad()
@@ -286,117 +326,58 @@ class TDMPC(nn.Module):
     def _td_target(self, next_z, reward, mask):
         """Compute the TD-target from a reward and the observation at the following time step."""
         next_v = self.model.V(next_z)
-        td_target = reward + self.cfg.discount * mask * next_v
+        td_target = reward + self.cfg.discount * mask * next_v.squeeze(2)
         return td_target
 
-    def update(self, replay_buffer, step, demo_buffer=None):
+    def forward(self, batch, step):
+        # TODO(alexander-soare): Refactor TDMPC and make it comply with the policy interface documentation.
+        raise NotImplementedError()
+
+    def update(self, batch, step):
         """Main update function. Corresponds to one iteration of the model learning."""
         start_time = time.time()
 
-        num_slices = self.cfg.batch_size
-        batch_size = self.cfg.horizon * num_slices
+        batch_size = batch["index"].shape[0]
 
-        if demo_buffer is None:
-            demo_batch_size = 0
-        else:
-            # Update oversampling ratio
-            demo_pc_batch = h.linear_schedule(self.cfg.demo_schedule, step)
-            demo_num_slices = int(demo_pc_batch * self.batch_size)
-            demo_batch_size = self.cfg.horizon * demo_num_slices
-            batch_size -= demo_batch_size
-            num_slices -= demo_num_slices
-            replay_buffer._sampler.num_slices = num_slices
-            demo_buffer._sampler.num_slices = demo_num_slices
+        # TODO(rcadene): convert tdmpc with (batch size, time/horizon, channels)
+        # instead of currently (time/horizon, batch size, channels) which is not the pytorch convention
+        # batch size b = 256, time/horizon t = 5
+        # b t ... -> t b ...
+        for key in batch:
+            if batch[key].ndim > 1:
+                batch[key] = batch[key].transpose(1, 0)
 
-            assert demo_batch_size % self.cfg.horizon == 0
-            assert demo_batch_size % demo_num_slices == 0
+        action = batch["action"]
+        reward = batch["next.reward"]
+        #  idxs = batch["index"]  # TODO(rcadene): use idxs to update sampling weights
+        done = torch.zeros_like(reward, dtype=torch.bool, device=reward.device)
+        mask = torch.ones_like(reward, dtype=torch.bool, device=reward.device)
+        weights = torch.ones(batch_size, dtype=torch.bool, device=reward.device)
 
-        assert batch_size % self.cfg.horizon == 0
-        assert batch_size % num_slices == 0
+        obses = {
+            "rgb": batch["observation.image"],
+            "state": batch["observation.state"],
+        }
 
-        # Sample from interaction dataset
-
-        def process_batch(batch, horizon, num_slices):
-            # trajectory t = 256, horizon h = 5
-            # (t h) ... -> h t ...
-            batch = batch.reshape(num_slices, horizon).transpose(1, 0).contiguous()
-
-            obs = {
-                "rgb": batch["observation", "image"][FIRST_FRAME].to(self.device, non_blocking=True),
-                "state": batch["observation", "state"][FIRST_FRAME].to(self.device, non_blocking=True),
-            }
-            action = batch["action"].to(self.device, non_blocking=True)
-            next_obses = {
-                "rgb": batch["next", "observation", "image"].to(self.device, non_blocking=True),
-                "state": batch["next", "observation", "state"].to(self.device, non_blocking=True),
-            }
-            reward = batch["next", "reward"].to(self.device, non_blocking=True)
-
-            idxs = batch["index"][FIRST_FRAME].to(self.device, non_blocking=True)
-            weights = batch["_weight"][FIRST_FRAME, :, None].to(self.device, non_blocking=True)
-
-            # TODO(rcadene): rearrange directly in offline dataset
-            if reward.ndim == 2:
-                reward = einops.rearrange(reward, "h t -> h t 1")
-
-            assert reward.ndim == 3
-            assert reward.shape == (horizon, num_slices, 1)
-            # We dont use `batch["next", "done"]` since it only indicates the end of an
-            # episode, but not the end of the trajectory of an episode.
-            # Neither does `batch["next", "terminated"]`
-            done = torch.zeros_like(reward, dtype=torch.bool, device=reward.device)
-            mask = torch.ones_like(reward, dtype=torch.bool, device=reward.device)
-            return obs, action, next_obses, reward, mask, done, idxs, weights
-
-        batch = replay_buffer.sample(batch_size) if self.cfg.balanced_sampling else replay_buffer.sample()
-
-        obs, action, next_obses, reward, mask, done, idxs, weights = process_batch(
-            batch, self.cfg.horizon, num_slices
-        )
-
-        # Sample from demonstration dataset
-        if demo_batch_size > 0:
-            demo_batch = demo_buffer.sample(demo_batch_size)
-            (
-                demo_obs,
-                demo_action,
-                demo_next_obses,
-                demo_reward,
-                demo_mask,
-                demo_done,
-                demo_idxs,
-                demo_weights,
-            ) = process_batch(demo_batch, self.cfg.horizon, demo_num_slices)
-
-            if isinstance(obs, dict):
-                obs = {k: torch.cat([obs[k], demo_obs[k]]) for k in obs}
-                next_obses = {k: torch.cat([next_obses[k], demo_next_obses[k]], dim=1) for k in next_obses}
-            else:
-                obs = torch.cat([obs, demo_obs])
-                next_obses = torch.cat([next_obses, demo_next_obses], dim=1)
-            action = torch.cat([action, demo_action], dim=1)
-            reward = torch.cat([reward, demo_reward], dim=1)
-            mask = torch.cat([mask, demo_mask], dim=1)
-            done = torch.cat([done, demo_done], dim=1)
-            idxs = torch.cat([idxs, demo_idxs])
-            weights = torch.cat([weights, demo_weights])
+        shapes = {}
+        for k in obses:
+            shapes[k] = obses[k].shape
+            obses[k] = einops.rearrange(obses[k], "t b ... -> (t b) ... ")
 
         # Apply augmentations
         aug_tf = h.aug(self.cfg)
-        obs = aug_tf(obs)
+        obses = aug_tf(obses)
 
-        for k in next_obses:
-            next_obses[k] = einops.rearrange(next_obses[k], "h t ... -> (h t) ...")
-        next_obses = aug_tf(next_obses)
-        for k in next_obses:
-            next_obses[k] = einops.rearrange(
-                next_obses[k],
-                "(h t) ... -> h t ...",
-                h=self.cfg.horizon,
-                t=self.cfg.batch_size,
-            )
+        for k in obses:
+            t, b = shapes[k][:2]
+            obses[k] = einops.rearrange(obses[k], "(t b) ... -> t b ... ", b=b, t=t)
 
-        horizon = self.cfg.horizon
+        obs, next_obses = {}, {}
+        for k in obses:
+            obs[k] = obses[k][0]
+            next_obses[k] = obses[k][1:].clone()
+
+        horizon = next_obses["rgb"].shape[0]
         loss_mask = torch.ones_like(mask, device=self.device)
         for t in range(1, horizon):
             loss_mask[t] = loss_mask[t - 1] * (~done[t - 1])
@@ -414,7 +395,7 @@ class TDMPC(nn.Module):
             td_targets = self._td_target(next_z, reward, mask)
 
         # Latent rollout
-        zs = torch.empty(horizon + 1, self.batch_size, self.cfg.latent_dim, device=self.device)
+        zs = torch.empty(horizon + 1, batch_size, self.cfg.latent_dim, device=self.device)
         reward_preds = torch.empty_like(reward, device=self.device)
         assert reward.shape[0] == horizon
         z = self.model.encode(obs)
@@ -423,22 +404,21 @@ class TDMPC(nn.Module):
         for t in range(horizon):
             z, reward_pred = self.model.next(z, action[t])
             zs[t + 1] = z
-            reward_preds[t] = reward_pred
+            reward_preds[t] = reward_pred.squeeze(1)
 
         with torch.no_grad():
             v_target = self.model_target.Q(zs[:-1].detach(), action, return_type="min")
 
         # Predictions
         qs = self.model.Q(zs[:-1], action, return_type="all")
+        qs = qs.squeeze(3)
         value_info["Q"] = qs.mean().item()
         v = self.model.V(zs[:-1])
         value_info["V"] = v.mean().item()
 
         # Losses
-        rho = torch.pow(self.cfg.rho, torch.arange(horizon, device=self.device)).view(-1, 1, 1)
-        consistency_loss = (rho * torch.mean(h.mse(zs[1:], z_targets), dim=2, keepdim=True) * loss_mask).sum(
-            dim=0
-        )
+        rho = torch.pow(self.cfg.rho, torch.arange(horizon, device=self.device)).view(-1, 1)
+        consistency_loss = (rho * torch.mean(h.mse(zs[1:], z_targets), dim=2) * loss_mask).sum(dim=0)
         reward_loss = (rho * h.mse(reward_preds, reward) * loss_mask).sum(dim=0)
         q_value_loss, priority_loss = 0, 0
         for q in range(self.cfg.num_q):
@@ -446,7 +426,9 @@ class TDMPC(nn.Module):
             priority_loss += (rho * h.l1(qs[q], td_targets) * loss_mask).sum(dim=0)
 
         expectile = h.linear_schedule(self.cfg.expectile, step)
-        v_value_loss = (rho * h.l2_expectile(v_target - v, expectile=expectile) * loss_mask).sum(dim=0)
+        v_value_loss = (rho * h.l2_expectile(v_target - v, expectile=expectile).squeeze(2) * loss_mask).sum(
+            dim=0
+        )
 
         total_loss = (
             self.cfg.consistency_coef * consistency_loss
@@ -455,7 +437,7 @@ class TDMPC(nn.Module):
             + self.cfg.value_coef * v_value_loss
         )
 
-        weighted_loss = (total_loss.squeeze(1) * weights).mean()
+        weighted_loss = (total_loss * weights).mean()
         weighted_loss.register_hook(lambda grad: grad * (1 / self.cfg.horizon))
         has_nan = torch.isnan(weighted_loss).item()
         if has_nan:
@@ -468,19 +450,20 @@ class TDMPC(nn.Module):
         )
         self.optim.step()
 
-        if self.cfg.per:
-            # Update priorities
-            priorities = priority_loss.clamp(max=1e4).detach()
-            has_nan = torch.isnan(priorities).any().item()
-            if has_nan:
-                print(f"priorities has nan: {priorities=}")
-            else:
-                replay_buffer.update_priority(
-                    idxs[:num_slices],
-                    priorities[:num_slices],
-                )
-                if demo_batch_size > 0:
-                    demo_buffer.update_priority(demo_idxs, priorities[num_slices:])
+        # TODO(rcadene): implement PrioritizedSampling by modifying sampler.weights with priorities computed by a criterion
+        # if self.cfg.per:
+        #     # Update priorities
+        #     priorities = priority_loss.clamp(max=1e4).detach()
+        #     has_nan = torch.isnan(priorities).any().item()
+        #     if has_nan:
+        #         print(f"priorities has nan: {priorities=}")
+        #     else:
+        #         replay_buffer.update_priority(
+        #             idxs[:num_slices],
+        #             priorities[:num_slices],
+        #         )
+        #         if demo_batch_size > 0:
+        #             demo_buffer.update_priority(demo_idxs, priorities[num_slices:])
 
         # Update policy + target network
         _, pi_update_info = self.update_pi(zs[:-1].detach(), acts=action)
@@ -503,7 +486,7 @@ class TDMPC(nn.Module):
             "data_s": data_s,
             "update_s": time.time() - start_time,
         }
-        info["demo_batch_size"] = demo_batch_size
+        # info["demo_batch_size"] = demo_batch_size
         info["expectile"] = expectile
         info.update(value_info)
         info.update(pi_update_info)

lerobot/common/policies/utils.py

@@ -0,0 +1,30 @@
+import torch
+from torch import nn
+
+
+def populate_queues(queues, batch):
+    for key in batch:
+        if len(queues[key]) != queues[key].maxlen:
+            # initialize by copying the first observation several times until the queue is full
+            while len(queues[key]) != queues[key].maxlen:
+                queues[key].append(batch[key])
+        else:
+            # add latest observation to the queue
+            queues[key].append(batch[key])
+    return queues
+
+
+def get_device_from_parameters(module: nn.Module) -> torch.device:
+    """Get a module's device by checking one of its parameters.
+
+    Note: assumes that all parameters have the same device
+    """
+    return next(iter(module.parameters())).device
+
+
+def get_dtype_from_parameters(module: nn.Module) -> torch.dtype:
+    """Get a module's parameter dtype by checking one of its parameters.
+
+    Note: assumes that all parameters have the same dtype.
+    """
+    return next(iter(module.parameters())).dtype

lerobot/common/transforms.py

@@ -0,0 +1,65 @@
+from torchvision.transforms.v2 import Compose, Transform
+
+
+def apply_inverse_transform(item, transform):
+    transforms = transform.transforms if isinstance(transform, Compose) else [transform]
+    for tf in transforms[::-1]:
+        if tf.invertible:
+            item = tf.inverse_transform(item)
+        else:
+            raise ValueError(f"Inverse transform called on a non invertible transform ({tf}).")
+    return item
+
+
+class NormalizeTransform(Transform):
+    invertible = True
+
+    def __init__(
+        self,
+        stats: dict,
+        in_keys: list[str] = None,
+        out_keys: list[str] | None = None,
+        in_keys_inv: list[str] | None = None,
+        out_keys_inv: list[str] | None = None,
+        mode="mean_std",
+    ):
+        super().__init__()
+        self.in_keys = in_keys
+        self.out_keys = in_keys if out_keys is None else out_keys
+        self.in_keys_inv = self.out_keys if in_keys_inv is None else in_keys_inv
+        self.out_keys_inv = self.in_keys if out_keys_inv is None else out_keys_inv
+        self.stats = stats
+        assert mode in ["mean_std", "min_max"]
+        self.mode = mode
+
+    def forward(self, item):
+        for inkey, outkey in zip(self.in_keys, self.out_keys, strict=False):
+            if inkey not in item:
+                continue
+            if self.mode == "mean_std":
+                mean = self.stats[inkey]["mean"]
+                std = self.stats[inkey]["std"]
+                item[outkey] = (item[inkey] - mean) / (std + 1e-8)
+            else:
+                min = self.stats[inkey]["min"]
+                max = self.stats[inkey]["max"]
+                # normalize to [0,1]
+                item[outkey] = (item[inkey] - min) / (max - min)
+                # normalize to [-1, 1]
+                item[outkey] = item[outkey] * 2 - 1
+        return item
+
+    def inverse_transform(self, item):
+        for inkey, outkey in zip(self.in_keys_inv, self.out_keys_inv, strict=False):
+            if inkey not in item:
+                continue
+            if self.mode == "mean_std":
+                mean = self.stats[inkey]["mean"]
+                std = self.stats[inkey]["std"]
+                item[outkey] = item[inkey] * std + mean
+            else:
+                min = self.stats[inkey]["min"]
+                max = self.stats[inkey]["max"]
+                item[outkey] = (item[inkey] + 1) / 2
+                item[outkey] = item[outkey] * (max - min) + min
+        return item

lerobot/common/utils.py

@@ -1,45 +0,0 @@
-import logging
-import random
-from datetime import datetime
-
-import numpy as np
-import torch
-
-
-def set_seed(seed):
-    """Set seed for reproducibility."""
-    random.seed(seed)
-    np.random.seed(seed)
-    torch.manual_seed(seed)
-    torch.cuda.manual_seed_all(seed)
-
-
-def init_logging():
-    def custom_format(record):
-        dt = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-        fnameline = f"{record.pathname}:{record.lineno}"
-        message = f"{record.levelname} {dt} {fnameline[-15:]:>15} {record.msg}"
-        return message
-
-    logging.basicConfig(level=logging.INFO)
-
-    for handler in logging.root.handlers[:]:
-        logging.root.removeHandler(handler)
-
-    formatter = logging.Formatter()
-    formatter.format = custom_format
-    console_handler = logging.StreamHandler()
-    console_handler.setFormatter(formatter)
-    logging.getLogger().addHandler(console_handler)
-
-
-def format_big_number(num):
-    suffixes = ["", "K", "M", "B", "T", "Q"]
-    divisor = 1000.0
-
-    for suffix in suffixes:
-        if abs(num) < divisor:
-            return f"{num:.0f}{suffix}"
-        num /= divisor
-
-    return num

lerobot/common/utils/import_utils.py

@@ -0,0 +1,44 @@
+import importlib
+import logging
+
+
+def is_package_available(pkg_name: str, return_version: bool = False) -> tuple[bool, str] | bool:
+    """Copied from https://github.com/huggingface/transformers/blob/main/src/transformers/utils/import_utils.py
+    Check if the package spec exists and grab its version to avoid importing a local directory.
+    **Note:** this doesn't work for all packages.
+    """
+    package_exists = importlib.util.find_spec(pkg_name) is not None
+    package_version = "N/A"
+    if package_exists:
+        try:
+            # Primary method to get the package version
+            package_version = importlib.metadata.version(pkg_name)
+        except importlib.metadata.PackageNotFoundError:
+            # Fallback method: Only for "torch" and versions containing "dev"
+            if pkg_name == "torch":
+                try:
+                    package = importlib.import_module(pkg_name)
+                    temp_version = getattr(package, "__version__", "N/A")
+                    # Check if the version contains "dev"
+                    if "dev" in temp_version:
+                        package_version = temp_version
+                        package_exists = True
+                    else:
+                        package_exists = False
+                except ImportError:
+                    # If the package can't be imported, it's not available
+                    package_exists = False
+            else:
+                # For packages other than "torch", don't attempt the fallback and set as not available
+                package_exists = False
+        logging.debug(f"Detected {pkg_name} version: {package_version}")
+    if return_version:
+        return package_exists, package_version
+    else:
+        return package_exists
+
+
+_torch_available, _torch_version = is_package_available("torch", return_version=True)
+_gym_xarm_available = is_package_available("gym_xarm")
+_gym_aloha_available = is_package_available("gym_aloha")
+_gym_pusht_available = is_package_available("gym_pusht")

lerobot/common/utils/utils.py

@@ -0,0 +1,111 @@
+import logging
+import os.path as osp
+import random
+from datetime import datetime
+from pathlib import Path
+
+import hydra
+import numpy as np
+import torch
+from omegaconf import DictConfig
+
+
+def get_safe_torch_device(cfg_device: str, log: bool = False) -> torch.device:
+    """Given a string, return a torch.device with checks on whether the device is available."""
+    match cfg_device:
+        case "cuda":
+            assert torch.cuda.is_available()
+            device = torch.device("cuda")
+        case "mps":
+            assert torch.backends.mps.is_available()
+            device = torch.device("mps")
+        case "cpu":
+            device = torch.device("cpu")
+            if log:
+                logging.warning("Using CPU, this will be slow.")
+        case _:
+            device = torch.device(cfg_device)
+            if log:
+                logging.warning(f"Using custom {cfg_device} device.")
+
+    return device
+
+
+def set_global_seed(seed):
+    """Set seed for reproducibility."""
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+
+
+def init_logging():
+    def custom_format(record):
+        dt = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+        fnameline = f"{record.pathname}:{record.lineno}"
+        message = f"{record.levelname} {dt} {fnameline[-15:]:>15} {record.msg}"
+        return message
+
+    logging.basicConfig(level=logging.INFO)
+
+    for handler in logging.root.handlers[:]:
+        logging.root.removeHandler(handler)
+
+    formatter = logging.Formatter()
+    formatter.format = custom_format
+    console_handler = logging.StreamHandler()
+    console_handler.setFormatter(formatter)
+    logging.getLogger().addHandler(console_handler)
+
+
+def format_big_number(num):
+    suffixes = ["", "K", "M", "B", "T", "Q"]
+    divisor = 1000.0
+
+    for suffix in suffixes:
+        if abs(num) < divisor:
+            return f"{num:.0f}{suffix}"
+        num /= divisor
+
+    return num
+
+
+def _relative_path_between(path1: Path, path2: Path) -> Path:
+    """Returns path1 relative to path2."""
+    path1 = path1.absolute()
+    path2 = path2.absolute()
+    try:
+        return path1.relative_to(path2)
+    except ValueError:  # most likely because path1 is not a subpath of path2
+        common_parts = Path(osp.commonpath([path1, path2])).parts
+        return Path(
+            "/".join([".."] * (len(path2.parts) - len(common_parts)) + list(path1.parts[len(common_parts) :]))
+        )
+
+
+def init_hydra_config(config_path: str, overrides: list[str] | None = None) -> DictConfig:
+    """Initialize a Hydra config given only the path to the relevant config file.
+
+    For config resolution, it is assumed that the config file's parent is the Hydra config dir.
+    """
+    # TODO(alexander-soare): Resolve configs without Hydra initialization.
+    hydra.core.global_hydra.GlobalHydra.instance().clear()
+    # Hydra needs a path relative to this file.
+    hydra.initialize(
+        str(_relative_path_between(Path(config_path).absolute().parent, Path(__file__).absolute().parent))
+    )
+    cfg = hydra.compose(Path(config_path).stem, overrides)
+    return cfg
+
+
+def print_cuda_memory_usage():
+    """Use this function to locate and debug memory leak."""
+    import gc
+
+    gc.collect()
+    # Also clear the cache if you want to fully release the memory
+    torch.cuda.empty_cache()
+    print("Current GPU Memory Allocated: {:.2f} MB".format(torch.cuda.memory_allocated(0) / 1024**2))
+    print("Maximum GPU Memory Allocated: {:.2f} MB".format(torch.cuda.max_memory_allocated(0) / 1024**2))
+    print("Current GPU Memory Reserved: {:.2f} MB".format(torch.cuda.memory_reserved(0) / 1024**2))
+    print("Maximum GPU Memory Reserved: {:.2f} MB".format(torch.cuda.max_memory_reserved(0) / 1024**2))

lerobot/configs/default.yaml

@@ -5,11 +5,14 @@ defaults:
 
 hydra:
   run:
-    dir: outputs/${now:%Y-%m-%d}/${now:%H-%M-%S}_${env.name}_${policy.name}_${hydra.job.name}
+    dir: outputs/train/${now:%Y-%m-%d}/${now:%H-%M-%S}_${env.name}_${policy.name}_${hydra.job.name}
   job:
     name: default
 
 seed: 1337
+# batch size for TorchRL SerialEnv. Each underlying env will get the seed = seed + env_index
+# NOTE: only diffusion policy supports rollout_batch_size > 1
+rollout_batch_size: 1
 device: cuda  # cpu
 prefetch: 4
 eval_freq: ???
@@ -23,12 +26,17 @@ fps: ???
 
 offline_prioritized_sampler: true
 
+dataset_id: ???
+
 n_action_steps: ???
+n_obs_steps: ???
 env: ???
 
 policy: ???
 
 wandb:
   enable: true
+  # Set to true to disable saving an artifact despite save_model == True
+  disable_artifact: false
   project: lerobot
   notes: ""

lerobot/configs/env/aloha.yaml

@@ -4,19 +4,20 @@ eval_episodes: 50
 eval_freq: 7500
 save_freq: 75000
 log_freq: 250
-# TODO: same as simxarm, need to adjust
+# TODO: same as xarm, need to adjust
 offline_steps: 25000
 online_steps: 25000
 
 fps: 50
 
+dataset_id: aloha_sim_insertion_human
+
 env:
   name: aloha
-  task: sim_insertion_human
+  task: AlohaInsertion-v0
   from_pixels: True
   pixels_only: False
   image_size: [3, 480, 640]
-  action_repeat: 1
   episode_length: 400
   fps: ${fps}
 

lerobot/configs/env/pusht.yaml

@@ -4,19 +4,20 @@ eval_episodes: 50
 eval_freq: 7500
 save_freq: 75000
 log_freq: 250
-# TODO: same as simxarm, need to adjust
+# TODO: same as xarm, need to adjust
 offline_steps: 25000
 online_steps: 25000
 
 fps: 10
 
+dataset_id: pusht
+
 env:
   name: pusht
-  task: pusht
+  task: PushT-v0
   from_pixels: True
   pixels_only: False
   image_size: 96
-  action_repeat: 1
   episode_length: 300
   fps: ${fps}