fix(ci): downgrade contents permission to read in claude.yml

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

.github/CLAUDE.md

@@ -0,0 +1,86 @@
+# 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

@@ -0,0 +1,49 @@
+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

@@ -0,0 +1,58 @@
+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

docs/source/adding_benchmarks.mdx

@@ -115,23 +115,22 @@ Each `EnvConfig` subclass declares two dicts that tell the policy what to expect
 ## Step by step
 
 <Tip>
-  At minimum, you need three files: a **gym.Env wrapper**, an **EnvConfig
-  subclass**, and a **factory dispatch branch**. Everything else is optional or
-  documentation.
+  At minimum, you need two files: a **gym.Env wrapper** and an **EnvConfig
+  subclass** with a `create_envs()` override. Everything else is optional or
+  documentation. No changes to `factory.py` are needed.
 </Tip>
 
 ### Checklist
 
-| File                                     | Required | Why                                       |
-| ---------------------------------------- | -------- | ----------------------------------------- |
-| `src/lerobot/envs/<benchmark>.py`        | Yes      | Wraps the simulator as a standard gym.Env |
-| `src/lerobot/envs/configs.py`            | Yes      | Registers your benchmark for the CLI      |
-| `src/lerobot/envs/factory.py`            | Yes      | Tells `make_env()` how to build your envs |
-| `src/lerobot/processor/env_processor.py` | Optional | Custom observation/action transforms      |
-| `src/lerobot/envs/utils.py`              | Optional | Only if you need new raw observation keys |
-| `pyproject.toml`                         | Yes      | Declares benchmark-specific dependencies  |
-| `docs/source/<benchmark>.mdx`            | Yes      | User-facing documentation page            |
-| `docs/source/_toctree.yml`               | Yes      | Adds your page to the docs sidebar        |
+| File                                     | Required | Why                                                          |
+| ---------------------------------------- | -------- | ------------------------------------------------------------ |
+| `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/processor/env_processor.py` | Optional | Custom observation/action transforms                         |
+| `src/lerobot/envs/utils.py`              | Optional | Only if you need new raw observation keys                    |
+| `pyproject.toml`                         | Yes      | Declares benchmark-specific dependencies                     |
+| `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`)
 
@@ -179,7 +178,10 @@ 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>`:
+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:
+
+- **`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>")
@@ -204,6 +206,20 @@ 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:
@@ -211,36 +227,11 @@ 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. The factory dispatch (`src/lerobot/envs/factory.py`)
+### 3. Env processor (optional — `src/lerobot/processor/env_processor.py`)
 
-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):
+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):
 
 ```python
 @dataclass
@@ -260,7 +251,7 @@ class MyBenchmarkProcessorStep(ObservationProcessorStep):
 
 See `LiberoProcessorStep` for a full example (image rotation, quaternion-to-axis-angle conversion).
 
-### 5. Dependencies (`pyproject.toml`)
+### 4. Dependencies (`pyproject.toml`)
 
 Add a new optional-dependency group:
 
@@ -281,11 +272,11 @@ Users install with:
 pip install -e ".[mybenchmark]"
 ```
 
-### 6. Documentation (`docs/source/<benchmark>.mdx`)
+### 5. 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.
 
-### 7. Table of contents (`docs/source/_toctree.yml`)
+### 6. Table of contents (`docs/source/_toctree.yml`)
 
 Add your benchmark to the "Benchmarks" section:
 

docs/source/env_processor.mdx

@@ -90,11 +90,17 @@ The same policy can work with different environment processors, and the same env
 
 ```python
 # Use SmolVLA policy with LIBERO environment
-libero_preprocessor, libero_postprocessor = make_env_pre_post_processors(libero_cfg)
+# Use SmolVLA policy with LIBERO environment
+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_cfg)
+libero_preprocessor, libero_postprocessor = make_env_pre_post_processors(
+    env_cfg=libero_cfg,
+    policy_cfg=act_cfg,
+)
 act_preprocessor, act_postprocessor = make_pre_post_processors(act_cfg)
 ```
 
@@ -151,7 +157,7 @@ observation = {
 
 ### Factory Function
 
-The `make_env_pre_post_processors` function follows the same pattern as `make_pre_post_processors` for policies:
+The `make_env_pre_post_processors` function delegates to `env_cfg.get_env_processors()`:
 
 ```python
 from lerobot.envs.factory import make_env_pre_post_processors
@@ -159,47 +165,31 @@ 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)
+env_preprocessor, env_postprocessor = make_env_pre_post_processors(libero_cfg, policy_cfg)
 
 # For other environments: Returns identity processors (no-op)
 pusht_cfg = PushtEnv()
-env_preprocessor, env_postprocessor = make_env_pre_post_processors(pusht_cfg)
+env_preprocessor, env_postprocessor = make_env_pre_post_processors(pusht_cfg, policy_cfg)
 ```
 
-### Implementation in `envs/factory.py`
+### How It Works
+
+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
-def make_env_pre_post_processors(
-    env_cfg: EnvConfig,
-) -> tuple[
-    PolicyProcessorPipeline[dict[str, Any], dict[str, Any]],
-    PolicyProcessorPipeline[dict[str, Any], dict[str, Any]],
-]:
-    """
-    Create preprocessor and postprocessor pipelines for environment observations.
-
-    Args:
-        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
+# In your EnvConfig subclass:
+def get_env_processors(self):
+    from lerobot.processor.pipeline import PolicyProcessorPipeline
+    return (
+        PolicyProcessorPipeline(steps=[MyProcessorStep()]),
+        PolicyProcessorPipeline(steps=[]),
+    )
 ```
 
+The factory function `make_env_pre_post_processors` simply delegates to this method,
+with a special case for `XVLAConfig` policies which override the env processors entirely.
+
 ### Integration in Evaluation
 
 In `lerobot_eval.py`, the environment processors are created once and used throughout:
@@ -219,7 +209,10 @@ def eval_main(cfg: EvalPipelineConfig):
     )
 
     # Create environment processors (NEW!)
-    env_preprocessor, env_postprocessor = make_env_pre_post_processors(env_cfg=cfg.env)
+    env_preprocessor, env_postprocessor = make_env_pre_post_processors(
+        env_cfg=cfg.env,
+        policy_cfg=cfg.policy,
+    )
 
     # Run evaluation with both processor types
     eval_policy_all(
@@ -323,21 +316,22 @@ class MyEnvProcessorStep(ObservationProcessorStep):
         return processed
 ```
 
-### 2. Update the Factory
+### 2. Update Your `EnvConfig` Subclass
 
 ```python
-# In src/lerobot/envs/factory.py
+# In src/lerobot/envs/configs.py
+@EnvConfig.register_subclass("myenv")
+@dataclass
+class MyEnvConfig(EnvConfig):
+    # ... task/features/gym kwargs ...
 
-def make_env_pre_post_processors(env_cfg: EnvConfig):
-    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=[])
+    def get_env_processors(self):
+        from lerobot.processor.pipeline import PolicyProcessorPipeline
 
-    postprocessor = PolicyProcessorPipeline(steps=[])
-    return preprocessor, postprocessor
+        return (
+            PolicyProcessorPipeline(steps=[MyEnvProcessorStep()]),
+            PolicyProcessorPipeline(steps=[]),
+        )
 ```
 
 ### 3. Use in Evaluation

src/lerobot/envs/configs.py

@@ -12,11 +12,16 @@
 # 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
@@ -67,6 +72,49 @@ 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):
@@ -338,6 +386,12 @@ 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}
@@ -345,6 +399,33 @@ 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
@@ -387,6 +468,19 @@ 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
@@ -454,3 +548,18 @@ 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

@@ -13,90 +13,46 @@
 # 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.
-import importlib
+from __future__ import annotations
+
 from typing import Any
 
 import gymnasium as gym
-from gymnasium.envs.registration import registry as gym_registry
 
-from lerobot.configs.policies import PreTrainedConfig
-from lerobot.envs.configs import AlohaEnv, EnvConfig, HubEnvConfig, IsaaclabArenaEnv, LiberoEnv, PushtEnv
+from lerobot.envs.configs import EnvConfig, HubEnvConfig
 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:
-    if env_type == "aloha":
-        return AlohaEnv(**kwargs)
-    elif env_type == "pusht":
-        return PushtEnv(**kwargs)
-    elif env_type == "libero":
-        return LiberoEnv(**kwargs)
-    else:
-        raise ValueError(f"Policy type '{env_type}' is not available.")
+    try:
+        cls = EnvConfig.get_choice_class(env_type)
+    except KeyError as err:
+        raise ValueError(
+            f"Environment type '{env_type}' is not registered. "
+            f"Available: {list(EnvConfig.get_known_choices().keys())}"
+        ) from err
+    return cls(**kwargs)
 
 
 def make_env_pre_post_processors(
     env_cfg: EnvConfig,
-    policy_cfg: PreTrainedConfig,
-) -> tuple[
-    PolicyProcessorPipeline[dict[str, Any], dict[str, Any]],
-    PolicyProcessorPipeline[dict[str, Any], dict[str, Any]],
-]:
+    policy_cfg: Any,
+) -> tuple[Any, Any]:
     """
     Create preprocessor and postprocessor pipelines for environment observations.
 
-    This function creates processor pipelines that transform raw environment
-    observations and actions. By default, it returns identity processors that do nothing.
-    For specific environments like LIBERO, it adds environment-specific processing steps.
-
-    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)
+    Returns a tuple of (preprocessor, postprocessor). By default, delegates to
+    ``env_cfg.get_env_processors()``.  The XVLAConfig policy-specific override
+    stays here because it depends on the *policy* config, not the env config.
     """
-    # Preprocessor and Postprocessor steps are Identity for most environments
-    preprocessor_steps: list[ProcessorStep] = []
-    postprocessor_steps: list[ProcessorStep] = []
+    from lerobot.policies.xvla.configuration_xvla import XVLAConfig
+
     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()
 
-    # 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
+    return env_cfg.get_env_processors()
 
 
 def make_env(
@@ -163,57 +119,4 @@ def make_env(
     if n_envs < 1:
         raise ValueError("`n_envs` must be at least 1")
 
-    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}}
+    return cfg.create_envs(n_envs=n_envs, use_async_envs=use_async_envs)

src/lerobot/envs/libero.py

@@ -223,7 +223,8 @@ class LiberoEnv(gym.Env):
 
     def render(self):
         raw_obs = self._env.env._get_observations()
-        image = self._format_raw_obs(raw_obs)["pixels"]["image"]
+        pixels = self._format_raw_obs(raw_obs)["pixels"]
+        image = next(iter(pixels.values()))
         image = image[::-1, ::-1]  # flip both H and W for visualization
         return image
 
@@ -339,12 +340,6 @@ 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
@@ -364,6 +359,7 @@ 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)."""
 
@@ -379,6 +375,7 @@ def _make_env_fns(
             episode_index=episode_index,
             n_envs=n_envs,
             control_mode=control_mode,
+            camera_name_mapping=camera_name_mapping,
             **local_kwargs,
         )
 
@@ -400,6 +397,7 @@ 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.
@@ -449,6 +447,7 @@ 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

@@ -201,6 +201,11 @@ 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
 

tests/envs/test_dispatch.py

@@ -0,0 +1,143 @@
+"""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)