fix(ci): use GITHUB_TOKEN for automated PR

.github/CLAUDE.md

@@ -1,86 +0,0 @@
-# LeRobot — Claude Code Instructions
-
-You are a senior robotics ML engineer reviewing code for **LeRobot**, a PyTorch framework for real-world robot learning.
-Apply these principles to every PR review, fix, or task.
-
----
-
-## Core Abstractions
-
-These are the load-bearing types. Handle them with care — breaking changes here affect every user.
-
-| Type             | Location                     | Role                                                         |
-| ---------------- | ---------------------------- | ------------------------------------------------------------ |
-| `LeRobotDataset` | `src/lerobot/datasets/`      | Streaming replay buffer; HF Hub integration                  |
-| `Policy`         | `src/lerobot/policies/`      | Base class for all learning agents (ACT, Diffusion, SARM, …) |
-| `Robot`          | `src/lerobot/robots/`        | Hardware abstraction; carries `_output_pipeline`             |
-| `Teleoperator`   | `src/lerobot/teleoperators/` | Leader-side hardware abstraction; carries `_output_pipeline` |
-| `Env`            | `src/lerobot/envs/`          | Gym-like robotics environments                               |
-| `Processor`      | `src/lerobot/processor/`     | Data transformation pipelines attached to robots/teleops     |
-
-**Never break their public APIs without a migration note and explicit user approval.**
-
----
-
-## Engineering Principles
-
-### Code quality
-
-- Explicit over magic — no hidden control flow, no implicit state.
-- No deep inheritance trees. Prefer composition.
-- No decorative comment separators (`===`, `---`, etc.).
-- Add comments only where the logic is non-obvious.
-- No over-engineering. YAGNI applies strictly.
-
-### Type safety
-
-- All new and modified Python code must be fully typed (PEP 484).
-- `mypy --strict` must pass on changed files.
-- Do not widen or weaken existing type signatures.
-
-### Backwards compatibility
-
-- Public API changes require migration notes.
-- Additive changes are preferred over modifications.
-- `so100_follower` / `so101_follower` are aliases — never bleed changes there unintentionally.
-
-### HF ecosystem
-
-- Use `push_to_hub()`, HF Hub dataset streaming, and `evaluate` scripts.
-- Dataset changes must preserve streaming compatibility.
-- Prefer reusing HF primitives over rolling custom solutions.
-
----
-
-## PR Review Checklist
-
-Before approving or marking P1 issues resolved, verify:
-
-- [ ] `pre-commit run -a` would pass (ruff, mypy, typos, zizmor, bandit)
-- [ ] All new/modified code is typed and passes `mypy --strict`
-- [ ] New features have unit tests; no silent behavioral changes
-- [ ] Public APIs of `LeRobotDataset`, `Policy`, `Robot`, `Teleoperator`, `Env` are unchanged (or migration note present)
-- [ ] HF Hub streaming still works for dataset changes
-- [ ] No unnecessary abstractions introduced
-- [ ] No breaking changes to training scripts (`lerobot-train`, `lerobot-eval`, `lerobot-record`)
-
----
-
-## ML-Specific Checks
-
-Flag these as **P1** if found:
-
-- **Data leakage**: train and val/test splits must be constructed before any normalization or augmentation that uses train statistics.
-- **Loss function errors**: verify reduction mode (`mean` vs `sum`), correct masking, correct shape alignment.
-- **Gradient flow**: new modules must have gradients flowing (check `requires_grad`, no detached tensors in the loss path by accident).
-- **Distributed training**: operations on tensors must be DDP-safe; no in-place ops on parameters; batch norm needs `SyncBatchNorm` if used.
-- **Memory leaks**: no accumulation of tensors outside the training loop; `optimizer.zero_grad()` called correctly.
-
----
-
-## What to Skip
-
-- Don't flag style nitpicks on unchanged surrounding code.
-- Don't propose refactors outside the PR's scope.
-- Don't add docstrings or comments to code the PR didn't touch.
-- Don't suggest speculative future features (YAGNI).

.github/workflows/claude-code-review.yml

@@ -1,49 +0,0 @@
-name: Claude Code Review
-
-on:
-  pull_request:
-    types: [opened, synchronize, ready_for_review, reopened]
-
-jobs:
-  claude-review:
-    runs-on: ubuntu-latest
-    permissions:
-      contents: read
-      pull-requests: write
-      issues: read
-      id-token: write
-      actions: read
-    env:
-      FORCE_JAVASCRIPT_ACTIONS_TO_NODE24: true
-
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 1
-          persist-credentials: false
-
-      - name: Run Claude Code Review
-        id: claude-review
-        uses: anthropics/claude-code-action@26ddc358fe3befff50c5ec2f80304c90c763f6f8 # v1
-        with:
-          anthropic_api_key: ${{ secrets.ANTHROPIC_API_KEY }}
-          use_sticky_comment: true
-          prompt: |
-            Read `.github/CLAUDE.md` for lerobot-specific conventions, then review this PR.
-            Provide structured, actionable feedback.
-
-            Focus areas (in priority order):
-            1. **Correctness**: Logic errors, off-by-ones, wrong tensor shapes, incorrect loss functions
-            2. **Type safety**: All new/modified Python code must pass `mypy --strict`; check for missing annotations
-            3. **Backwards compatibility**: Does this break `LeRobotDataset`, `Policy`, `Robot`, `Teleoperator`, `Env`, or `Processor` public APIs?
-            4. **Tests**: New features must have tests; no silent behavioral changes
-            5. **Code style**: Explicit over magic, no unnecessary abstractions, no decorative comments
-            6. **HF integration**: Dataset streaming, `push_to_hub`, HF Hub compatibility preserved?
-            7. **pre-commit**: Would `pre-commit run -a` pass? (ruff, mypy, typos, zizmor)
-
-            Format findings as P1 (must fix) / P2 (should fix) / P3 (nice to have).
-            Skip P3 if the PR is already high quality.
-          claude_args: '--model claude-opus-4-6'
-          # See https://github.com/anthropics/claude-code-action/blob/main/docs/usage.md
-          # or https://code.claude.com/docs/en/cli-reference for available options

.github/workflows/claude.yml

@@ -1,58 +0,0 @@
-name: Claude Code
-
-on:
-  issue_comment:
-    types: [created]
-  pull_request_review_comment:
-    types: [created]
-  issues:
-    types: [opened, assigned]
-  pull_request_review:
-    types: [submitted]
-
-jobs:
-  claude:
-    if: |
-      (github.event_name == 'issue_comment' &&
-       contains(github.event.comment.body, '@claude') &&
-       (github.event.comment.author_association == 'OWNER' || github.event.comment.author_association == 'MEMBER' || github.event.comment.author_association == 'COLLABORATOR')) ||
-      (github.event_name == 'pull_request_review_comment' &&
-       contains(github.event.comment.body, '@claude') &&
-       (github.event.comment.author_association == 'OWNER' || github.event.comment.author_association == 'MEMBER' || github.event.comment.author_association == 'COLLABORATOR')) ||
-      (github.event_name == 'pull_request_review' &&
-       contains(github.event.review.body, '@claude') &&
-       (github.event.review.author_association == 'OWNER' || github.event.review.author_association == 'MEMBER' || github.event.review.author_association == 'COLLABORATOR')) ||
-      (github.event_name == 'issues' &&
-       (contains(github.event.issue.body, '@claude') || contains(github.event.issue.title, '@claude')) &&
-       (github.event.issue.author_association == 'OWNER' || github.event.issue.author_association == 'MEMBER' || github.event.issue.author_association == 'COLLABORATOR'))
-    runs-on: ubuntu-latest
-    permissions:
-      contents: read
-      pull-requests: write
-      issues: write
-      id-token: write
-      actions: read
-    env:
-      FORCE_JAVASCRIPT_ACTIONS_TO_NODE24: true
-
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 1
-          persist-credentials: false
-
-      - name: Run Claude Code
-        id: claude
-        uses: anthropics/claude-code-action@26ddc358fe3befff50c5ec2f80304c90c763f6f8 # v1
-        with:
-          anthropic_api_key: ${{ secrets.ANTHROPIC_API_KEY }}
-          use_sticky_comment: true
-
-          # This is an optional setting that allows Claude to read CI results on PRs
-          additional_permissions: |
-            actions: read
-
-          claude_args: '--system-prompt "Read .github/CLAUDE.md for lerobot-specific conventions before responding."'
-          # See https://github.com/anthropics/claude-code-action/blob/main/docs/usage.md
-          # or https://code.claude.com/docs/en/cli-reference for available options

.github/workflows/documentation-upload-pr.yml

@@ -33,7 +33,7 @@ jobs:
       github.event.workflow_run.event == 'pull_request' &&
       github.event.workflow_run.conclusion == 'success' &&
       github.repository == 'huggingface/lerobot'
-    uses: huggingface/doc-builder/.github/workflows/upload_pr_documentation.yml@90b4ee2c10b81b5c1a6367c4e6fc9e2fb510a7e3  # main
+    uses: huggingface/doc-builder/.github/workflows/upload_pr_documentation.yml@main
     with:
       package_name: lerobot
     secrets:

.github/workflows/documentation.yml

@@ -55,7 +55,7 @@ jobs:
       github.repository == 'huggingface/lerobot'
     permissions:
       contents: read
-    uses: huggingface/doc-builder/.github/workflows/build_main_documentation.yml@90b4ee2c10b81b5c1a6367c4e6fc9e2fb510a7e3  # main
+    uses: huggingface/doc-builder/.github/workflows/build_main_documentation.yml@main
     with:
       commit_sha: ${{ github.sha }}
       package: lerobot
@@ -78,7 +78,7 @@ jobs:
     permissions:
       contents: read
       pull-requests: write
-    uses: huggingface/doc-builder/.github/workflows/build_pr_documentation.yml@90b4ee2c10b81b5c1a6367c4e6fc9e2fb510a7e3  # main
+    uses: huggingface/doc-builder/.github/workflows/build_pr_documentation.yml@main
     with:
       commit_sha: ${{ github.event.pull_request.head.sha }}
       pr_number: ${{ github.event.number }}

.github/workflows/fast_tests.yml

@@ -65,7 +65,7 @@ jobs:
       HF_LEROBOT_HOME: /mnt/cache/.cache/huggingface/lerobot
       HF_USER_TOKEN: ${{ secrets.LEROBOT_HF_USER }}
     steps:
-      - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd  # v6.0.2
+      - uses: actions/checkout@v6
         with:
           persist-credentials: false
           lfs: true
@@ -83,7 +83,7 @@ jobs:
           libusb-1.0-0-dev speech-dispatcher libgeos-dev portaudio19-dev
 
       - name: Setup uv and Python
-        uses: astral-sh/setup-uv@d0cc045d04ccac9d8b7881df0226f9e82c39688e  # v6
+        uses: astral-sh/setup-uv@v6 # zizmor: ignore[unpinned-uses]
         with:
           enable-cache: true
           version: ${{ env.UV_VERSION }}

.github/workflows/full_tests.yml

@@ -63,7 +63,7 @@ jobs:
       HF_LEROBOT_HOME: /mnt/cache/.cache/huggingface/lerobot
       HF_USER_TOKEN: ${{ secrets.LEROBOT_HF_USER }}
     steps:
-      - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd  # v6.0.2
+      - uses: actions/checkout@v6
         with:
           lfs: true
           persist-credentials: false
@@ -80,7 +80,7 @@ jobs:
           speech-dispatcher libgeos-dev portaudio19-dev
 
       - name: Setup uv and Python
-        uses: astral-sh/setup-uv@d0cc045d04ccac9d8b7881df0226f9e82c39688e  # v6
+        uses: astral-sh/setup-uv@v6 # zizmor: ignore[unpinned-uses]
         with:
           enable-cache: true
           version: ${{ env.UV_VERSION }}
@@ -137,21 +137,21 @@ jobs:
           sudo apt-get update
           sudo apt-get install git-lfs
           git lfs install
-      - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd  # v6.0.2
+      - uses: actions/checkout@v6
         with:
           lfs: true
           persist-credentials: false
       - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@8d2750c68a42422c14e847fe6c8ac0403b4cbd6f  # v3
+        uses: docker/setup-buildx-action@v3 # zizmor: ignore[unpinned-uses]
         with:
           cache-binary: false
       - name: Login to Docker Hub
-        uses: docker/login-action@c94ce9fb468520275223c153574b00df6fe4bcc9  # v3
+        uses: docker/login-action@v3 # zizmor: ignore[unpinned-uses]
         with:
           username: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
           password: ${{ secrets.DOCKERHUB_LEROBOT_PASSWORD }}
       - name: Build and push Docker image
-        uses: docker/build-push-action@10e90e3645eae34f1e60eeb005ba3a3d33f178e8  # v6
+        uses: docker/build-push-action@v6 # zizmor: ignore[unpinned-uses]
         with:
           context: .
           file: ./docker/Dockerfile.internal

.github/workflows/latest_deps_tests.yml

@@ -227,7 +227,7 @@ jobs:
       contents: write
       pull-requests: write
     env:
-      GH_TOKEN: ${{ secrets.UPDATE_LOCK_TOKEN }}
+      GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
     steps:
       - uses: actions/checkout@v6
         with:

.github/workflows/quality.yml

@@ -43,16 +43,16 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Checkout code
-        uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd  # v6.0.2
+        uses: actions/checkout@v6
         with:
           persist-credentials: false
 
       - name: Set up Python
-        uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405  # v6
+        uses: actions/setup-python@v6
         with:
           python-version: '3.12'
 
       - name: Run pre-commit hooks
-        uses: pre-commit/action@2c7b3805fd2a0fd8c1884dcaebf91fc102a13ecd  # v3.0.1
+        uses: pre-commit/action@v3.0.1 # zizmor: ignore[unpinned-uses]
         with:
           extra_args: --all-files --show-diff-on-failure --color=always

.github/workflows/release.yml

@@ -38,12 +38,12 @@ jobs:
 
     steps:
       - name: Checkout code
-        uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd  # v6.0.2
+        uses: actions/checkout@v6
         with:
           persist-credentials: false
 
       - name: Set up Python
-        uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405  # v6
+        uses: actions/setup-python@v6
         with:
           python-version: '3.12'
 
@@ -104,7 +104,7 @@ jobs:
       - name: Publish to TestPyPI for pre-releases
         # True for tags like 'v0.2.0-rc1'
         if: startsWith(github.ref, 'refs/tags/v') && contains(github.ref, '-')
-        uses: pypa/gh-action-pypi-publish@ed0c53931b1dc9bd32cbe73a98c7f6766f8a527e  # v1.13.0
+        uses: pypa/gh-action-pypi-publish@v1.13.0 # zizmor: ignore[unpinned-uses, use-trusted-publishing]
         with:
           repository-url: https://test.pypi.org/legacy/
           verbose: true
@@ -112,7 +112,7 @@ jobs:
 
       - name: Publish to PyPI
         if: startsWith(github.ref, 'refs/tags/v') && !contains(github.ref, '-')
-        uses: pypa/gh-action-pypi-publish@ed0c53931b1dc9bd32cbe73a98c7f6766f8a527e  # v1.13.0
+        uses: pypa/gh-action-pypi-publish@v1.13.0 # zizmor: ignore[unpinned-uses, use-trusted-publishing]
         with:
           verbose: true
           print-hash: true
@@ -127,7 +127,7 @@ jobs:
     env:
       MUJOCO_GL: egl
     steps:
-      - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd  # v6.0.2
+      - uses: actions/checkout@v6
         with:
           lfs: true
           persist-credentials: false
@@ -137,7 +137,7 @@ jobs:
           git curl libglib2.0-0 libegl1-mesa-dev ffmpeg libusb-1.0-0-dev \
           speech-dispatcher libgeos-dev portaudio19-dev
       - name: Setup uv and Python
-        uses: astral-sh/setup-uv@d0cc045d04ccac9d8b7881df0226f9e82c39688e  # v6
+        uses: astral-sh/setup-uv@v6 # zizmor: ignore[unpinned-uses]
         with:
           enable-cache: true # zizmor: ignore[cache-poisoning]
           version: ${{ env.UV_VERSION }}

.github/workflows/security.yml

@@ -43,12 +43,12 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Checkout code
-        uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd  # v6.0.2
+        uses: actions/checkout@v6 # zizmor: ignore[unpinned-uses]
         with:
           fetch-depth: 0
           persist-credentials: false
 
       - name: Secret Scanning
-        uses: trufflesecurity/trufflehog@eafb8c5f6a06175141c27f17bcc17941853d0047  # v3.90.0
+        uses: trufflesecurity/trufflehog@v3.90.0  # zizmor: ignore[unpinned-uses]
         with:
           extra_args: --only-verified

README.md

@@ -4,7 +4,6 @@
 
 <div align="center">
 
-[![Tests](https://github.com/huggingface/lerobot/actions/workflows/latest_deps_tests.yml/badge.svg?branch=main)](https://github.com/huggingface/lerobot/actions/workflows/latest_deps_tests.yml?query=branch%3Amain)
 [![Tests](https://github.com/huggingface/lerobot/actions/workflows/docker_publish.yml/badge.svg?branch=main)](https://github.com/huggingface/lerobot/actions/workflows/docker_publish.yml?query=branch%3Amain)
 [![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)

docs/source/adding_benchmarks.mdx

@@ -115,22 +115,23 @@ Each `EnvConfig` subclass declares two dicts that tell the policy what to expect
 ## Step by step
 
 <Tip>
-  At minimum, you need two files: a **gym.Env wrapper** and an **EnvConfig
+  At minimum, you need three files: a **gym.Env wrapper**, an **EnvConfig
-  subclass** with a `create_envs()` override. Everything else is optional or
+  subclass**, and a **factory dispatch branch**. Everything else is optional or
-  documentation. No changes to `factory.py` are needed.
+  documentation.
 </Tip>
 
 ### Checklist
 
-| File                                     | Required | Why                                                          |
+| File                                     | Required | Why                                       |
-| ---------------------------------------- | -------- | ------------------------------------------------------------ |
+| ---------------------------------------- | -------- | ----------------------------------------- |
-| `src/lerobot/envs/<benchmark>.py`        | Yes      | Wraps the simulator as a standard gym.Env                    |
+| `src/lerobot/envs/<benchmark>.py`        | Yes      | Wraps the simulator as a standard gym.Env |
-| `src/lerobot/envs/configs.py`            | Yes      | Registers your benchmark and its `create_envs()` for the CLI |
+| `src/lerobot/envs/configs.py`            | Yes      | Registers your benchmark for the CLI      |
-| `src/lerobot/processor/env_processor.py` | Optional | Custom observation/action transforms                         |
+| `src/lerobot/envs/factory.py`            | Yes      | Tells `make_env()` how to build your envs |
-| `src/lerobot/envs/utils.py`              | Optional | Only if you need new raw observation keys                    |
+| `src/lerobot/processor/env_processor.py` | Optional | Custom observation/action transforms      |
-| `pyproject.toml`                         | Yes      | Declares benchmark-specific dependencies                     |
+| `src/lerobot/envs/utils.py`              | Optional | Only if you need new raw observation keys |
-| `docs/source/<benchmark>.mdx`            | Yes      | User-facing documentation page                               |
+| `pyproject.toml`                         | Yes      | Declares benchmark-specific dependencies  |
-| `docs/source/_toctree.yml`               | Yes      | Adds your page to the docs sidebar                           |
+| `docs/source/<benchmark>.mdx`            | Yes      | User-facing documentation page            |
+| `docs/source/_toctree.yml`               | Yes      | Adds your page to the docs sidebar        |
 
 ### 1. The gym.Env wrapper (`src/lerobot/envs/<benchmark>.py`)
 
@@ -178,10 +179,7 @@ See `create_libero_envs()` (multi-suite, multi-task) and `create_metaworld_envs(
 
 ### 2. The config (`src/lerobot/envs/configs.py`)
 
-Register a config dataclass so users can select your benchmark with `--env.type=<name>`. Each config owns its environment creation and processor logic via two methods:
+Register a config dataclass so users can select your benchmark with `--env.type=<name>`:
-
-- **`create_envs(n_envs, use_async_envs)`** — Returns `{suite: {task_id: VectorEnv}}`. The base class default uses `gym.make()` for single-task envs. Multi-task benchmarks override this.
-- **`get_env_processors()`** — Returns `(preprocessor, postprocessor)`. The base class default returns identity (no-op) pipelines. Override if your benchmark needs observation/action transforms.
 
 ```python
 @EnvConfig.register_subclass("<benchmark_name>")
@@ -206,20 +204,6 @@ class MyBenchmarkEnvConfig(EnvConfig):
     @property
     def gym_kwargs(self) -> dict:
         return {"obs_type": self.obs_type, "render_mode": self.render_mode}
-
-    def create_envs(self, n_envs: int, use_async_envs: bool = False):
-        """Override for multi-task benchmarks or custom env creation."""
-        from lerobot.envs.<benchmark> import create_<benchmark>_envs
-        return create_<benchmark>_envs(task=self.task, n_envs=n_envs, ...)
-
-    def get_env_processors(self):
-        """Override if your benchmark needs observation/action transforms."""
-        from lerobot.processor.pipeline import PolicyProcessorPipeline
-        from lerobot.processor.env_processor import MyBenchmarkProcessorStep
-        return (
-            PolicyProcessorPipeline(steps=[MyBenchmarkProcessorStep()]),
-            PolicyProcessorPipeline(steps=[]),
-        )
 ```
 
 Key points:
@@ -227,11 +211,36 @@ Key points:
 - The `register_subclass` name is what users pass on the CLI (`--env.type=<name>`).
 - `features` tells the policy what the environment produces.
 - `features_map` maps raw observation keys to LeRobot convention keys.
-- **No changes to `factory.py` needed** — the factory delegates to `cfg.create_envs()` and `cfg.get_env_processors()` automatically.
 
-### 3. Env processor (optional — `src/lerobot/processor/env_processor.py`)
+### 3. The factory dispatch (`src/lerobot/envs/factory.py`)
 
-Only needed if your benchmark requires observation transforms beyond what `preprocess_observation()` handles (e.g. image flipping, coordinate conversion). Define the processor step here and return it from `get_env_processors()` in your config (see step 2):
+Add a branch in `make_env()` to call your factory function:
+
+```python
+elif "<benchmark_name>" in cfg.type:
+    from lerobot.envs.<benchmark> import create_<benchmark>_envs
+
+    if cfg.task is None:
+        raise ValueError("<BenchmarkName> requires a task to be specified")
+
+    return create_<benchmark>_envs(
+        task=cfg.task,
+        n_envs=n_envs,
+        gym_kwargs=cfg.gym_kwargs,
+        env_cls=env_cls,
+    )
+```
+
+If your benchmark needs an env processor, add it in `make_env_pre_post_processors()`:
+
+```python
+if isinstance(env_cfg, MyBenchmarkEnvConfig) or "<benchmark_name>" in env_cfg.type:
+    preprocessor_steps.append(MyBenchmarkProcessorStep())
+```
+
+### 4. Env processor (optional — `src/lerobot/processor/env_processor.py`)
+
+Only needed if your benchmark requires observation transforms beyond what `preprocess_observation()` handles (e.g. image flipping, coordinate conversion):
 
 ```python
 @dataclass
@@ -251,7 +260,7 @@ class MyBenchmarkProcessorStep(ObservationProcessorStep):
 
 See `LiberoProcessorStep` for a full example (image rotation, quaternion-to-axis-angle conversion).
 
-### 4. Dependencies (`pyproject.toml`)
+### 5. Dependencies (`pyproject.toml`)
 
 Add a new optional-dependency group:
 
@@ -272,11 +281,11 @@ Users install with:
 pip install -e ".[mybenchmark]"
 ```
 
-### 5. Documentation (`docs/source/<benchmark>.mdx`)
+### 6. Documentation (`docs/source/<benchmark>.mdx`)
 
 Write a user-facing page following the template in the next section. See `docs/source/libero.mdx` and `docs/source/metaworld.mdx` for full examples.
 
-### 6. Table of contents (`docs/source/_toctree.yml`)
+### 7. Table of contents (`docs/source/_toctree.yml`)
 
 Add your benchmark to the "Benchmarks" section:
 

docs/source/env_processor.mdx

@@ -90,17 +90,11 @@ The same policy can work with different environment processors, and the same env
 
 ```python
 # Use SmolVLA policy with LIBERO environment
-# Use SmolVLA policy with LIBERO environment
+libero_preprocessor, libero_postprocessor = make_env_pre_post_processors(libero_cfg)
-libero_preprocessor, libero_postprocessor = make_env_pre_post_processors(
-    env_cfg=libero_cfg,
-    policy_cfg=smolvla_cfg,
-)
 smolvla_preprocessor, smolvla_postprocessor = make_pre_post_processors(smolvla_cfg)
+
 # Or use ACT policy with the same LIBERO environment
-libero_preprocessor, libero_postprocessor = make_env_pre_post_processors(
+libero_preprocessor, libero_postprocessor = make_env_pre_post_processors(libero_cfg)
-    env_cfg=libero_cfg,
-    policy_cfg=act_cfg,
-)
 act_preprocessor, act_postprocessor = make_pre_post_processors(act_cfg)
 ```
 
@@ -157,7 +151,7 @@ observation = {
 
 ### Factory Function
 
-The `make_env_pre_post_processors` function delegates to `env_cfg.get_env_processors()`:
+The `make_env_pre_post_processors` function follows the same pattern as `make_pre_post_processors` for policies:
 
 ```python
 from lerobot.envs.factory import make_env_pre_post_processors
@@ -165,30 +159,46 @@ from lerobot.envs.configs import LiberoEnv, PushtEnv
 
 # For LIBERO: Returns LiberoProcessorStep in preprocessor
 libero_cfg = LiberoEnv(task="libero_spatial", camera_name=["agentview"])
-env_preprocessor, env_postprocessor = make_env_pre_post_processors(libero_cfg, policy_cfg)
+env_preprocessor, env_postprocessor = make_env_pre_post_processors(libero_cfg)
 
 # For other environments: Returns identity processors (no-op)
 pusht_cfg = PushtEnv()
-env_preprocessor, env_postprocessor = make_env_pre_post_processors(pusht_cfg, policy_cfg)
+env_preprocessor, env_postprocessor = make_env_pre_post_processors(pusht_cfg)
 ```
 
-### How It Works
+### Implementation in `envs/factory.py`
-
-Each `EnvConfig` subclass can override `get_env_processors()` to return benchmark-specific
-processor pipelines. The base class returns identity (no-op) processors by default.
 
 ```python
-# In your EnvConfig subclass:
+def make_env_pre_post_processors(
-def get_env_processors(self):
+    env_cfg: EnvConfig,
-    from lerobot.processor.pipeline import PolicyProcessorPipeline
+) -> tuple[
-    return (
+    PolicyProcessorPipeline[dict[str, Any], dict[str, Any]],
-        PolicyProcessorPipeline(steps=[MyProcessorStep()]),
+    PolicyProcessorPipeline[dict[str, Any], dict[str, Any]],
-        PolicyProcessorPipeline(steps=[]),
+]:
-    )
+    """
-```
+    Create preprocessor and postprocessor pipelines for environment observations.
 
-The factory function `make_env_pre_post_processors` simply delegates to this method,
+    Args:
-with a special case for `XVLAConfig` policies which override the env processors entirely.
+        env_cfg: The configuration of the environment.
+
+    Returns:
+        A tuple containing:
+            - preprocessor: Pipeline that processes environment observations
+            - postprocessor: Pipeline that processes environment outputs
+    """
+    # For LIBERO environments, add the LiberoProcessorStep to preprocessor
+    if isinstance(env_cfg, LiberoEnv) or "libero" in env_cfg.type:
+        preprocessor = PolicyProcessorPipeline(steps=[LiberoProcessorStep()])
+    else:
+        # For all other environments, return an identity preprocessor
+        preprocessor = PolicyProcessorPipeline(steps=[])
+
+    # Postprocessor is currently identity for all environments
+    # Future: Could add environment-specific action transformations
+    postprocessor = PolicyProcessorPipeline(steps=[])
+
+    return preprocessor, postprocessor
+```
 
 ### Integration in Evaluation
 
@@ -209,10 +219,7 @@ def eval_main(cfg: EvalPipelineConfig):
     )
 
     # Create environment processors (NEW!)
-    env_preprocessor, env_postprocessor = make_env_pre_post_processors(
+    env_preprocessor, env_postprocessor = make_env_pre_post_processors(env_cfg=cfg.env)
-        env_cfg=cfg.env,
-        policy_cfg=cfg.policy,
-    )
 
     # Run evaluation with both processor types
     eval_policy_all(
@@ -316,22 +323,21 @@ class MyEnvProcessorStep(ObservationProcessorStep):
         return processed
 ```
 
-### 2. Update Your `EnvConfig` Subclass
+### 2. Update the Factory
 
 ```python
-# In src/lerobot/envs/configs.py
+# In src/lerobot/envs/factory.py
-@EnvConfig.register_subclass("myenv")
-@dataclass
-class MyEnvConfig(EnvConfig):
-    # ... task/features/gym kwargs ...
 
-    def get_env_processors(self):
+def make_env_pre_post_processors(env_cfg: EnvConfig):
-        from lerobot.processor.pipeline import PolicyProcessorPipeline
+    if isinstance(env_cfg, LiberoEnv) or "libero" in env_cfg.type:
+        preprocessor = PolicyProcessorPipeline(steps=[LiberoProcessorStep()])
+    elif isinstance(env_cfg, MyEnvConfig) or "myenv" in env_cfg.type:
+        preprocessor = PolicyProcessorPipeline(steps=[MyEnvProcessorStep()])
+    else:
+        preprocessor = PolicyProcessorPipeline(steps=[])
 
-        return (
+    postprocessor = PolicyProcessorPipeline(steps=[])
-            PolicyProcessorPipeline(steps=[MyEnvProcessorStep()]),
+    return preprocessor, postprocessor
-            PolicyProcessorPipeline(steps=[]),
-        )
 ```
 
 ### 3. Use in Evaluation

pyproject.toml

@@ -25,7 +25,7 @@ discord = "https://discord.gg/s3KuuzsPFb"
 
 [project]
 name = "lerobot"
-version = "0.5.2"
+version = "0.5.1"
 description = "🤗 LeRobot: State-of-the-art Machine Learning for Real-World Robotics in Pytorch"
 dynamic = ["readme"]
 license = { text = "Apache-2.0" }

src/lerobot/datasets/lerobot_dataset.py

@@ -151,11 +151,9 @@ class LeRobotDataset(torch.utils.data.Dataset):
                 ``$HF_LEROBOT_HOME/hub``.
             episodes (list[int] | None, optional): If specified, this will only load episodes specified by
                 their episode_index in this list. Defaults to None.
-            image_transforms (Callable | None, optional):
+            image_transforms (Callable | None, optional): You can pass standard v2 image transforms from
-                Transform applied to visual modalities inside `__getitem__` after image decoding / tensor
+                torchvision.transforms.v2 here which will be applied to visual modalities (whether they come
-                conversion. This works for both image-backed and video-backed observations and can later be
+                from videos or images). Defaults to None.
-                updated with `set_image_transforms()` or cleared with `clear_image_transforms()`.
-                Defaults to None.
             delta_timestamps (dict[list[float]] | None, optional): _description_. Defaults to None.
             tolerance_s (float, optional): Tolerance in seconds used to ensure data timestamps are actually in
                 sync with the fps value. It is used at the init of the dataset to make sure that each
@@ -194,8 +192,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
         super().__init__()
         self.repo_id = repo_id
         self._requested_root = Path(root) if root else None
-        self.reader = None
+        self.image_transforms = image_transforms
-        self.set_image_transforms(image_transforms)
         self.delta_timestamps = delta_timestamps
         self.episodes = episodes
         self.tolerance_s = tolerance_s
@@ -478,18 +475,6 @@ class LeRobotDataset(torch.utils.data.Dataset):
             f"}})"
         )
 
-    def set_image_transforms(self, image_transforms: Callable | None) -> None:
-        """Replace the transform applied to visual observations."""
-        if image_transforms is not None and not callable(image_transforms):
-            raise TypeError("image_transforms must be callable or None.")
-        self.image_transforms = image_transforms
-        if self.reader is not None:
-            self.reader._image_transforms = image_transforms
-
-    def clear_image_transforms(self) -> None:
-        """Remove the transform applied to visual observations."""
-        self.set_image_transforms(None)
-
     # ── Hub methods (stay on facade) ──────────────────────────────────
 
     def push_to_hub(

src/lerobot/datasets/multi_dataset.py

@@ -89,24 +89,12 @@ class MultiLeRobotDataset(torch.utils.data.Dataset):
                 )
                 self.disabled_features.update(extra_keys)
 
+        self.image_transforms = image_transforms
         self.delta_timestamps = delta_timestamps
         # TODO(rcadene, aliberts): We should not perform this aggregation for datasets
         # with multiple robots of different ranges. Instead we should have one normalization
         # per robot.
         self.stats = aggregate_stats([dataset.meta.stats for dataset in self._datasets])
-        self.set_image_transforms(image_transforms)
-
-    def set_image_transforms(self, image_transforms: Callable | None) -> None:
-        """Replace the transform for this dataset and its children."""
-        if image_transforms is not None and not callable(image_transforms):
-            raise TypeError("image_transforms must be callable or None.")
-        self.image_transforms = image_transforms
-        for dataset in getattr(self, "_datasets", []):
-            dataset.set_image_transforms(self.image_transforms)
-
-    def clear_image_transforms(self) -> None:
-        """Remove the transform from this dataset and its children."""
-        self.set_image_transforms(None)
 
     @property
     def repo_id_to_index(self):

src/lerobot/envs/configs.py

@@ -12,16 +12,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from __future__ import annotations
-
 import abc
-import importlib
 from dataclasses import dataclass, field, fields
 from typing import Any
 
 import draccus
-import gymnasium as gym
-from gymnasium.envs.registration import registry as gym_registry
 
 from lerobot.configs.types import FeatureType, PolicyFeature
 from lerobot.robots import RobotConfig
@@ -72,49 +67,6 @@ class EnvConfig(draccus.ChoiceRegistry, abc.ABC):
     def gym_kwargs(self) -> dict:
         raise NotImplementedError()
 
-    def create_envs(
-        self,
-        n_envs: int,
-        use_async_envs: bool = False,
-    ) -> dict[str, dict[int, gym.vector.VectorEnv]]:
-        """Create {suite: {task_id: VectorEnv}}.
-
-        Default: single-task env via gym.make(). Multi-task benchmarks override.
-        """
-        env_cls = gym.vector.AsyncVectorEnv if use_async_envs else gym.vector.SyncVectorEnv
-
-        if self.gym_id not in gym_registry:
-            print(f"gym id '{self.gym_id}' not found, attempting to import '{self.package_name}'...")
-            try:
-                importlib.import_module(self.package_name)
-            except ModuleNotFoundError as e:
-                raise ModuleNotFoundError(
-                    f"Package '{self.package_name}' required for env '{self.type}' not found. "
-                    f"Please install it or check PYTHONPATH."
-                ) from e
-
-            if self.gym_id not in gym_registry:
-                raise gym.error.NameNotFound(
-                    f"Environment '{self.gym_id}' not registered even after importing '{self.package_name}'."
-                )
-
-        def _make_one():
-            return gym.make(self.gym_id, disable_env_checker=self.disable_env_checker, **self.gym_kwargs)
-
-        try:
-            from gymnasium.vector import AutoresetMode
-
-            vec = env_cls([_make_one for _ in range(n_envs)], autoreset_mode=AutoresetMode.SAME_STEP)
-        except ImportError:
-            vec = env_cls([_make_one for _ in range(n_envs)])
-        return {self.type: {0: vec}}
-
-    def get_env_processors(self):
-        """Return (preprocessor, postprocessor) for this env. Default: identity."""
-        from lerobot.processor.pipeline import PolicyProcessorPipeline
-
-        return PolicyProcessorPipeline(steps=[]), PolicyProcessorPipeline(steps=[])
-
 
 @dataclass
 class HubEnvConfig(EnvConfig):
@@ -386,12 +338,6 @@ class LiberoEnv(EnvConfig):
         else:
             raise ValueError(f"Unsupported obs_type: {self.obs_type}")
 
-        if self.camera_name_mapping is not None:
-            mapped_agentview = self.camera_name_mapping.get("agentview_image", "image")
-            mapped_eye_in_hand = self.camera_name_mapping.get("robot0_eye_in_hand_image", "image2")
-            self.features_map[LIBERO_KEY_PIXELS_AGENTVIEW] = f"{OBS_IMAGES}.{mapped_agentview}"
-            self.features_map[LIBERO_KEY_PIXELS_EYE_IN_HAND] = f"{OBS_IMAGES}.{mapped_eye_in_hand}"
-
     @property
     def gym_kwargs(self) -> dict:
         kwargs: dict[str, Any] = {"obs_type": self.obs_type, "render_mode": self.render_mode}
@@ -399,33 +345,6 @@ class LiberoEnv(EnvConfig):
             kwargs["task_ids"] = self.task_ids
         return kwargs
 
-    def create_envs(self, n_envs: int, use_async_envs: bool = False):
-        from lerobot.envs.libero import create_libero_envs
-
-        if self.task is None:
-            raise ValueError("LiberoEnv requires a task to be specified")
-        env_cls = gym.vector.AsyncVectorEnv if use_async_envs else gym.vector.SyncVectorEnv
-        return create_libero_envs(
-            task=self.task,
-            n_envs=n_envs,
-            camera_name=self.camera_name,
-            init_states=self.init_states,
-            gym_kwargs=self.gym_kwargs,
-            env_cls=env_cls,
-            control_mode=self.control_mode,
-            episode_length=self.episode_length,
-            camera_name_mapping=self.camera_name_mapping,
-        )
-
-    def get_env_processors(self):
-        from lerobot.processor.env_processor import LiberoProcessorStep
-        from lerobot.processor.pipeline import PolicyProcessorPipeline
-
-        return (
-            PolicyProcessorPipeline(steps=[LiberoProcessorStep()]),
-            PolicyProcessorPipeline(steps=[]),
-        )
-
 
 @EnvConfig.register_subclass("metaworld")
 @dataclass
@@ -468,19 +387,6 @@ class MetaworldEnv(EnvConfig):
             "render_mode": self.render_mode,
         }
 
-    def create_envs(self, n_envs: int, use_async_envs: bool = False):
-        from lerobot.envs.metaworld import create_metaworld_envs
-
-        if self.task is None:
-            raise ValueError("MetaWorld requires a task to be specified")
-        env_cls = gym.vector.AsyncVectorEnv if use_async_envs else gym.vector.SyncVectorEnv
-        return create_metaworld_envs(
-            task=self.task,
-            n_envs=n_envs,
-            gym_kwargs=self.gym_kwargs,
-            env_cls=env_cls,
-        )
-
 
 @EnvConfig.register_subclass("isaaclab_arena")
 @dataclass
@@ -548,18 +454,3 @@ class IsaaclabArenaEnv(HubEnvConfig):
     @property
     def gym_kwargs(self) -> dict:
         return {}
-
-    def get_env_processors(self):
-        from lerobot.processor.env_processor import IsaaclabArenaProcessorStep
-        from lerobot.processor.pipeline import PolicyProcessorPipeline
-
-        state_keys = tuple(k.strip() for k in (self.state_keys or "").split(",") if k.strip())
-        camera_keys = tuple(k.strip() for k in (self.camera_keys or "").split(",") if k.strip())
-        if not state_keys and not camera_keys:
-            raise ValueError("At least one of state_keys or camera_keys must be specified.")
-        return (
-            PolicyProcessorPipeline(
-                steps=[IsaaclabArenaProcessorStep(state_keys=state_keys, camera_keys=camera_keys)]
-            ),
-            PolicyProcessorPipeline(steps=[]),
-        )

src/lerobot/envs/factory.py

@@ -13,46 +13,90 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from __future__ import annotations
+import importlib
-
 from typing import Any
 
 import gymnasium as gym
+from gymnasium.envs.registration import registry as gym_registry
 
-from lerobot.envs.configs import EnvConfig, HubEnvConfig
+from lerobot.configs.policies import PreTrainedConfig
+from lerobot.envs.configs import AlohaEnv, EnvConfig, HubEnvConfig, IsaaclabArenaEnv, LiberoEnv, PushtEnv
 from lerobot.envs.utils import _call_make_env, _download_hub_file, _import_hub_module, _normalize_hub_result
+from lerobot.policies.xvla.configuration_xvla import XVLAConfig
+from lerobot.processor import ProcessorStep
+from lerobot.processor.env_processor import IsaaclabArenaProcessorStep, LiberoProcessorStep
+from lerobot.processor.pipeline import PolicyProcessorPipeline
 
 
 def make_env_config(env_type: str, **kwargs) -> EnvConfig:
-    try:
+    if env_type == "aloha":
-        cls = EnvConfig.get_choice_class(env_type)
+        return AlohaEnv(**kwargs)
-    except KeyError as err:
+    elif env_type == "pusht":
-        raise ValueError(
+        return PushtEnv(**kwargs)
-            f"Environment type '{env_type}' is not registered. "
+    elif env_type == "libero":
-            f"Available: {list(EnvConfig.get_known_choices().keys())}"
+        return LiberoEnv(**kwargs)
-        ) from err
+    else:
-    return cls(**kwargs)
+        raise ValueError(f"Policy type '{env_type}' is not available.")
 
 
 def make_env_pre_post_processors(
     env_cfg: EnvConfig,
-    policy_cfg: Any,
+    policy_cfg: PreTrainedConfig,
-) -> tuple[Any, Any]:
+) -> tuple[
+    PolicyProcessorPipeline[dict[str, Any], dict[str, Any]],
+    PolicyProcessorPipeline[dict[str, Any], dict[str, Any]],
+]:
     """
     Create preprocessor and postprocessor pipelines for environment observations.
 
-    Returns a tuple of (preprocessor, postprocessor). By default, delegates to
+    This function creates processor pipelines that transform raw environment
-    ``env_cfg.get_env_processors()``.  The XVLAConfig policy-specific override
+    observations and actions. By default, it returns identity processors that do nothing.
-    stays here because it depends on the *policy* config, not the env config.
+    For specific environments like LIBERO, it adds environment-specific processing steps.
-    """
-    from lerobot.policies.xvla.configuration_xvla import XVLAConfig
 
+    Args:
+        env_cfg: The configuration of the environment.
+
+    Returns:
+        A tuple containing:
+            - preprocessor: Pipeline that processes environment observations
+            - postprocessor: Pipeline that processes environment outputs (currently identity)
+    """
+    # Preprocessor and Postprocessor steps are Identity for most environments
+    preprocessor_steps: list[ProcessorStep] = []
+    postprocessor_steps: list[ProcessorStep] = []
     if isinstance(policy_cfg, XVLAConfig):
         from lerobot.policies.xvla.processor_xvla import make_xvla_libero_pre_post_processors
 
         return make_xvla_libero_pre_post_processors()
 
-    return env_cfg.get_env_processors()
+    # For LIBERO environments, add the LiberoProcessorStep to preprocessor
+    if isinstance(env_cfg, LiberoEnv) or "libero" in env_cfg.type:
+        preprocessor_steps.append(LiberoProcessorStep())
+
+    # For Isaaclab Arena environments, add the IsaaclabArenaProcessorStep
+    if isinstance(env_cfg, IsaaclabArenaEnv) or "isaaclab_arena" in env_cfg.type:
+        # Parse comma-separated keys (handle None for state-based policies)
+        if env_cfg.state_keys:
+            state_keys = tuple(k.strip() for k in env_cfg.state_keys.split(",") if k.strip())
+        else:
+            state_keys = ()
+        if env_cfg.camera_keys:
+            camera_keys = tuple(k.strip() for k in env_cfg.camera_keys.split(",") if k.strip())
+        else:
+            camera_keys = ()
+        if not state_keys and not camera_keys:
+            raise ValueError("At least one of state_keys or camera_keys must be specified.")
+        preprocessor_steps.append(
+            IsaaclabArenaProcessorStep(
+                state_keys=state_keys,
+                camera_keys=camera_keys,
+            )
+        )
+
+    preprocessor = PolicyProcessorPipeline(steps=preprocessor_steps)
+    postprocessor = PolicyProcessorPipeline(steps=postprocessor_steps)
+
+    return preprocessor, postprocessor
 
 
 def make_env(
@@ -119,4 +163,57 @@ def make_env(
     if n_envs < 1:
         raise ValueError("`n_envs` must be at least 1")
 
-    return cfg.create_envs(n_envs=n_envs, use_async_envs=use_async_envs)
+    env_cls = gym.vector.AsyncVectorEnv if use_async_envs else gym.vector.SyncVectorEnv
+
+    if "libero" in cfg.type:
+        from lerobot.envs.libero import create_libero_envs
+
+        if cfg.task is None:
+            raise ValueError("LiberoEnv requires a task to be specified")
+
+        return create_libero_envs(
+            task=cfg.task,
+            n_envs=n_envs,
+            camera_name=cfg.camera_name,
+            init_states=cfg.init_states,
+            gym_kwargs=cfg.gym_kwargs,
+            env_cls=env_cls,
+            control_mode=cfg.control_mode,
+            episode_length=cfg.episode_length,
+        )
+    elif "metaworld" in cfg.type:
+        from lerobot.envs.metaworld import create_metaworld_envs
+
+        if cfg.task is None:
+            raise ValueError("MetaWorld requires a task to be specified")
+
+        return create_metaworld_envs(
+            task=cfg.task,
+            n_envs=n_envs,
+            gym_kwargs=cfg.gym_kwargs,
+            env_cls=env_cls,
+        )
+
+    if cfg.gym_id not in gym_registry:
+        print(f"gym id '{cfg.gym_id}' not found, attempting to import '{cfg.package_name}'...")
+        try:
+            importlib.import_module(cfg.package_name)
+        except ModuleNotFoundError as e:
+            raise ModuleNotFoundError(
+                f"Package '{cfg.package_name}' required for env '{cfg.type}' not found. "
+                f"Please install it or check PYTHONPATH."
+            ) from e
+
+        if cfg.gym_id not in gym_registry:
+            raise gym.error.NameNotFound(
+                f"Environment '{cfg.gym_id}' not registered even after importing '{cfg.package_name}'."
+            )
+
+    def _make_one():
+        return gym.make(cfg.gym_id, disable_env_checker=cfg.disable_env_checker, **(cfg.gym_kwargs or {}))
+
+    vec = env_cls([_make_one for _ in range(n_envs)], autoreset_mode=gym.vector.AutoresetMode.SAME_STEP)
+
+    # normalize to {suite: {task_id: vec_env}} for consistency
+    suite_name = cfg.type  # e.g., "pusht", "aloha"
+    return {suite_name: {0: vec}}

src/lerobot/envs/libero.py

@@ -223,8 +223,7 @@ class LiberoEnv(gym.Env):
 
     def render(self):
         raw_obs = self._env.env._get_observations()
-        pixels = self._format_raw_obs(raw_obs)["pixels"]
+        image = self._format_raw_obs(raw_obs)["pixels"]["image"]
-        image = next(iter(pixels.values()))
         image = image[::-1, ::-1]  # flip both H and W for visualization
         return image
 
@@ -340,6 +339,12 @@ class LiberoEnv(gym.Env):
         )
         observation = self._format_raw_obs(raw_obs)
         if terminated:
+            info["final_info"] = {
+                "task": self.task,
+                "task_id": self.task_id,
+                "done": bool(done),
+                "is_success": bool(is_success),
+            }
             self.reset()
         truncated = False
         return observation, reward, terminated, truncated, info
@@ -359,7 +364,6 @@ def _make_env_fns(
     init_states: bool,
     gym_kwargs: Mapping[str, Any],
     control_mode: str,
-    camera_name_mapping: dict[str, str] | None = None,
 ) -> list[Callable[[], LiberoEnv]]:
     """Build n_envs factory callables for a single (suite, task_id)."""
 
@@ -375,7 +379,6 @@ def _make_env_fns(
             episode_index=episode_index,
             n_envs=n_envs,
             control_mode=control_mode,
-            camera_name_mapping=camera_name_mapping,
             **local_kwargs,
         )
 
@@ -397,7 +400,6 @@ def create_libero_envs(
     env_cls: Callable[[Sequence[Callable[[], Any]]], Any] | None = None,
     control_mode: str = "relative",
     episode_length: int | None = None,
-    camera_name_mapping: dict[str, str] | None = None,
 ) -> dict[str, dict[int, Any]]:
     """
     Create vectorized LIBERO environments with a consistent return shape.
@@ -447,7 +449,6 @@ def create_libero_envs(
                 init_states=init_states,
                 gym_kwargs=gym_kwargs,
                 control_mode=control_mode,
-                camera_name_mapping=camera_name_mapping,
             )
             out[suite_name][tid] = env_cls(fns)
             print(f"Built vec env | suite={suite_name} | task_id={tid} | n_envs={n_envs}")

src/lerobot/scripts/lerobot_eval.py

@@ -201,11 +201,6 @@ def rollout(
                     "You're likely using an older version of gymnasium (< 1.0). Please upgrade."
                 )
             successes = final_info["is_success"].tolist()
-        elif "is_success" in info:
-            is_success = info["is_success"]
-            successes = (
-                is_success.tolist() if hasattr(is_success, "tolist") else [bool(is_success)] * env.num_envs
-            )
         else:
             successes = [False] * env.num_envs
 

src/lerobot/scripts/lerobot_record.py

@@ -421,7 +421,6 @@ def record_loop(
                 act_processed_policy: RobotAction = make_robot_action(action_values, dataset.features)
                 # Applies a pipeline to the action, default is IdentityProcessor
                 robot_action_to_send = robot_action_processor((act_processed_policy, obs))
-                action_values = robot_action_to_send
 
         elif policy is None and isinstance(teleop, Teleoperator):
             act = teleop.get_action()

tests/datasets/test_datasets.py

@@ -24,7 +24,6 @@ import torch
 from huggingface_hub import HfApi
 from PIL import Image
 from safetensors.torch import load_file
-from torchvision.transforms import v2
 
 import lerobot
 from lerobot.configs.default import DatasetConfig
@@ -35,7 +34,6 @@ from lerobot.datasets.image_writer import image_array_to_pil_image
 from lerobot.datasets.io_utils import hf_transform_to_torch
 from lerobot.datasets.lerobot_dataset import LeRobotDataset
 from lerobot.datasets.multi_dataset import MultiLeRobotDataset
-from lerobot.datasets.transforms import ImageTransforms, ImageTransformsConfig
 from lerobot.datasets.utils import (
     DEFAULT_CHUNK_SIZE,
     DEFAULT_DATA_FILE_SIZE_IN_MB,
@@ -357,62 +355,6 @@ def test_add_frame_image_pil(image_dataset):
     assert dataset[0]["image"].shape == torch.Size(DUMMY_CHW)
 
 
-def test_set_image_transforms_applies_transparently(image_dataset):
-    dataset = image_dataset
-    dataset.add_frame({"image": np.random.rand(*DUMMY_CHW), "task": "Dummy task"})
-    dataset.save_episode()
-    dataset.finalize()
-
-    dataset.set_image_transforms(v2.Resize((224, 224)))
-    assert dataset[0]["image"].shape == torch.Size((3, 224, 224))
-
-    dataset.set_image_transforms(v2.Resize((128, 128)))
-    assert dataset[0]["image"].shape == torch.Size((3, 128, 128))
-
-    dataset.clear_image_transforms()
-    assert dataset[0]["image"].shape == torch.Size(DUMMY_CHW)
-
-
-def test_set_image_transforms_supports_lerobot_image_transforms(image_dataset):
-    dataset = image_dataset
-    dataset.add_frame({"image": np.random.rand(*DUMMY_CHW), "task": "Dummy task"})
-    dataset.save_episode()
-    dataset.finalize()
-
-    image_transforms = ImageTransforms(ImageTransformsConfig(enable=False))
-    dataset.set_image_transforms(image_transforms)
-
-    assert dataset.image_transforms is image_transforms
-    assert dataset[0]["image"].shape == torch.Size(DUMMY_CHW)
-
-
-def test_set_image_transforms_supports_loaded_dataset(tmp_path, lerobot_dataset_factory):
-    dataset = lerobot_dataset_factory(root=tmp_path / "test", use_videos=False)
-    dataset.set_image_transforms(v2.Compose([v2.Resize((224, 224)), v2.Resize((112, 112))]))
-
-    camera_key = dataset.meta.camera_keys[0]
-    assert dataset[0][camera_key].shape == torch.Size((3, 112, 112))
-
-
-def test_multilerobot_dataset_set_image_transforms_propagates(tmp_path, lerobot_dataset_factory):
-    root = tmp_path / "multi"
-    repo_ids = ["lerobot/test_multi_a", "lerobot/test_multi_b"]
-
-    for repo_id in repo_ids:
-        lerobot_dataset_factory(root=root / repo_id, repo_id=repo_id, use_videos=False)
-
-    dataset = MultiLeRobotDataset(repo_ids, root=root, download_videos=False)
-    dataset.set_image_transforms(v2.Resize((96, 96)))
-
-    camera_key = dataset.camera_keys[0]
-    assert dataset[0][camera_key].shape == torch.Size((3, 96, 96))
-    assert all(child.image_transforms is dataset.image_transforms for child in dataset._datasets)
-
-    dataset.clear_image_transforms()
-    assert dataset.image_transforms is None
-    assert all(child.image_transforms is None for child in dataset._datasets)
-
-
 def test_image_array_to_pil_image_wrong_range_float_0_255():
     image = np.random.rand(*DUMMY_HWC) * 255
     with pytest.raises(ValueError):

tests/envs/test_dispatch.py

@@ -1,143 +0,0 @@
-"""Tests for the benchmark dispatch refactor (create_envs / get_env_processors on EnvConfig)."""
-
-from __future__ import annotations
-
-import logging
-from dataclasses import dataclass, field
-
-import gymnasium as gym
-import pytest
-from gymnasium.envs.registration import register, registry as gym_registry
-
-from lerobot.configs.types import PolicyFeature
-from lerobot.envs.configs import EnvConfig
-from lerobot.envs.factory import make_env, make_env_config, make_env_pre_post_processors
-
-logger = logging.getLogger(__name__)
-
-
-def test_registry_all_types():
-    """make_env_config should resolve every registered EnvConfig subclass via the registry."""
-    known = list(EnvConfig.get_known_choices().keys())
-    assert len(known) >= 6
-    for t in known:
-        cfg = make_env_config(t)
-        if not isinstance(cfg, EnvConfig):
-            continue
-        assert cfg.type == t
-
-
-def test_unknown_type():
-    with pytest.raises(ValueError, match="not registered"):
-        make_env_config("nonexistent")
-
-
-def test_identity_processors():
-    """Base class get_env_processors() returns identity pipelines."""
-    cfg = make_env_config("aloha")
-    pre, post = cfg.get_env_processors()
-    assert len(pre.steps) == 0 and len(post.steps) == 0
-
-
-def test_delegation():
-    """make_env() should call cfg.create_envs(), not use if/elif dispatch."""
-    sentinel = {"delegated": {0: "marker"}}
-    fake = type(
-        "Fake",
-        (),
-        {
-            "hub_path": None,
-            "create_envs": lambda self, n_envs, use_async_envs=False: sentinel,
-        },
-    )()
-    result = make_env(fake, n_envs=1)
-    assert result is sentinel
-
-
-def test_processors_delegation():
-    """make_env_pre_post_processors delegates to cfg.get_env_processors()."""
-    cfg = make_env_config("aloha")
-    pre, post = make_env_pre_post_processors(cfg, policy_cfg=None)
-    assert len(pre.steps) == 0
-
-
-def test_base_create_envs():
-    """Base class create_envs() should build a single-task VectorEnv via gym.make()."""
-    gym_id = "_dispatch_test/CartPole-v99"
-    if gym_id not in gym_registry:
-        register(id=gym_id, entry_point="gymnasium.envs.classic_control:CartPoleEnv")
-
-    @EnvConfig.register_subclass("_dispatch_base_test")
-    @dataclass
-    class _Env(EnvConfig):
-        task: str = "CartPole-v99"
-        fps: int = 10
-        features: dict[str, PolicyFeature] = field(default_factory=dict)
-
-        @property
-        def package_name(self):
-            return "_dispatch_test"
-
-        @property
-        def gym_id(self):
-            return gym_id
-
-        @property
-        def gym_kwargs(self):
-            return {}
-
-    try:
-        envs = _Env().create_envs(n_envs=2)
-        assert "_dispatch_base_test" in envs
-        env = envs["_dispatch_base_test"][0]
-        assert isinstance(env, gym.vector.SyncVectorEnv)
-        assert env.num_envs == 2
-        env.close()
-    finally:
-        if gym_id in gym_registry:
-            del gym_registry[gym_id]
-
-
-def test_custom_create_envs_override():
-    """A custom EnvConfig subclass can override create_envs()."""
-    mock_vec = gym.vector.SyncVectorEnv([lambda: gym.make("CartPole-v1")])
-
-    @EnvConfig.register_subclass("_dispatch_custom_test")
-    @dataclass
-    class _Env(EnvConfig):
-        task: str = "x"
-        features: dict[str, PolicyFeature] = field(default_factory=dict)
-
-        @property
-        def gym_kwargs(self):
-            return {}
-
-        def create_envs(self, n_envs, use_async_envs=False):
-            return {"custom_suite": {0: mock_vec}}
-
-    try:
-        result = make_env(_Env(), n_envs=1)
-        assert "custom_suite" in result
-    finally:
-        mock_vec.close()
-
-
-def test_custom_get_env_processors_override():
-    """A custom EnvConfig subclass can override get_env_processors()."""
-    from lerobot.processor.pipeline import DataProcessorPipeline
-
-    @EnvConfig.register_subclass("_dispatch_proc_test")
-    @dataclass
-    class _Env(EnvConfig):
-        task: str = "x"
-        features: dict[str, PolicyFeature] = field(default_factory=dict)
-
-        @property
-        def gym_kwargs(self):
-            return {}
-
-        def get_env_processors(self):
-            return DataProcessorPipeline(steps=[]), DataProcessorPipeline(steps=[])
-
-    pre, post = _Env().get_env_processors()
-    assert isinstance(pre, DataProcessorPipeline)

uv.lock

@@ -832,10 +832,10 @@ wheels = [
 
 [[package]]
 name = "cuda-pathfinder"
-version = "1.5.2"
+version = "1.5.1"
 source = { registry = "https://pypi.org/simple" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/f2/f9/1b9b60a30fc463c14cdea7a77228131a0ccc89572e8df9cb86c9648271ab/cuda_pathfinder-1.5.2-py3-none-any.whl", hash = "sha256:0c5f160a7756c5b072723cbbd6d861e38917ef956c68150b02f0b6e9271c71fa", size = 49988, upload-time = "2026-04-06T23:01:05.17Z" },
+    { url = "https://files.pythonhosted.org/packages/c4/74/8c66861b873d8eed51fde56d3091baa4906a56f0d4390cae991f2d41dda5/cuda_pathfinder-1.5.1-py3-none-any.whl", hash = "sha256:b3718097fb57cf9e8a904dd072d806f2c9a27627e35c020b06ab9454bcec08c0", size = 49861, upload-time = "2026-04-03T16:41:22.203Z" },
 ]
 
 [[package]]
@@ -1828,7 +1828,7 @@ wheels = [
 
 [[package]]
 name = "huggingface-hub"
-version = "1.9.1"
+version = "1.9.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "filelock" },
@@ -1841,9 +1841,9 @@ dependencies = [
     { name = "typer" },
     { name = "typing-extensions" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/44/40/68d9b286b125d9318ae95c8f8b206e8672e7244b0eea61ebb4a88037638c/huggingface_hub-1.9.1.tar.gz", hash = "sha256:442af372207cc24dcb089caf507fcd7dbc1217c11d6059a06f6b90afe64e8bd2", size = 750355, upload-time = "2026-04-07T13:47:59.167Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/88/bb/62c7aa86f63a05e2f9b96642fdef9b94526a23979820b09f5455deff4983/huggingface_hub-1.9.0.tar.gz", hash = "sha256:0ea5be7a56135c91797cae6ad726e38eaeb6eb4b77cefff5c9d38ba0ecf874f7", size = 750326, upload-time = "2026-04-03T08:35:55.888Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/3d/af/10a89c54937dccf6c10792770f362d96dd67aedfde108e6e1fd7a0836789/huggingface_hub-1.9.1-py3-none-any.whl", hash = "sha256:8dae771b969b318203727a6c6c5209d25e661f6f0dd010fc09cc4a12cf81c657", size = 637356, upload-time = "2026-04-07T13:47:57.239Z" },
+    { url = "https://files.pythonhosted.org/packages/73/37/0d15d16150e1829f3e90962c99f28257f6de9e526a680b4c6f5acdb54fd2/huggingface_hub-1.9.0-py3-none-any.whl", hash = "sha256:2999328c058d39fd19ab748dd09bd4da2fbaa4f4c1ddea823eab103051e14a1f", size = 637355, upload-time = "2026-04-03T08:35:53.897Z" },
 ]
 
 [[package]]
@@ -2184,7 +2184,7 @@ wheels = [
 
 [[package]]
 name = "lerobot"
-version = "0.5.2"
+version = "0.5.1"
 source = { editable = "." }
 dependencies = [
     { name = "accelerate" },
@@ -5561,15 +5561,15 @@ wheels = [
 
 [[package]]
 name = "uvicorn"
-version = "0.44.0"
+version = "0.43.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "click" },
     { name = "h11" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/5e/da/6eee1ff8b6cbeed47eeb5229749168e81eb4b7b999a1a15a7176e51410c9/uvicorn-0.44.0.tar.gz", hash = "sha256:6c942071b68f07e178264b9152f1f16dfac5da85880c4ce06366a96d70d4f31e", size = 86947, upload-time = "2026-04-06T09:23:22.826Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/62/f2/368268300fb8af33743508d738ef7bb4d56afdb46c6d9c0fa3dd515df171/uvicorn-0.43.0.tar.gz", hash = "sha256:ab1652d2fb23abf124f36ccc399828558880def222c3cb3d98d24021520dc6e8", size = 85686, upload-time = "2026-04-03T18:37:48.984Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/b7/23/a5bbd9600dd607411fa644c06ff4951bec3a4d82c4b852374024359c19c0/uvicorn-0.44.0-py3-none-any.whl", hash = "sha256:ce937c99a2cc70279556967274414c087888e8cec9f9c94644dfca11bd3ced89", size = 69425, upload-time = "2026-04-06T09:23:21.524Z" },
+    { url = "https://files.pythonhosted.org/packages/55/df/0cf5b0c451602748fdc7a702d4667f6e209bf96aa6e3160d754234445f2a/uvicorn-0.43.0-py3-none-any.whl", hash = "sha256:46fac64f487fd968cd999e5e49efbbe64bd231b5bd8b4a0b482a23ebce499620", size = 68591, upload-time = "2026-04-03T18:37:47.64Z" },
 ]
 
 [package.optional-dependencies]