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refactor(envs): move dispatch logic from factory into EnvConfig subclasses

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Replace hardcoded if/elif chains in factory.py with create_envs() and
get_env_processors() methods on EnvConfig. New benchmarks now only need
to register a config subclass — no factory.py edits required.

Net -23 lines: factory.py shrinks from ~200 to ~70 lines of logic.

Made-with: Cursor
This commit is contained in:
Pepijn
2026-04-03 13:23:44 +02:00
parent 508358749a
commit 24d9b74d81
5 changed files with 310 additions and 192 deletions
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docs/source/adding_benchmarks.mdx
+33 -42
View File
@@ -103,16 +103,15 @@ Each `EnvConfig` subclass declares:
### Checklist
| File | Required | Description |
| ---------------------------------------- | -------- | ----------------------------------------------------------------------------------- |
| `src/lerobot/envs/<benchmark>.py` | Yes | `gym.Env` subclass + `create_<benchmark>_envs()` factory |
| `src/lerobot/envs/configs.py` | Yes | `@EnvConfig.register_subclass("<name>")` dataclass |
| `src/lerobot/envs/factory.py` | Yes | Add dispatch branch in `make_env()` and optionally `make_env_pre_post_processors()` |
| `src/lerobot/processor/env_processor.py` | Optional | `ProcessorStep` subclass for env-specific observation transforms |
| `src/lerobot/envs/utils.py` | Optional | Extend `preprocess_observation()` if new raw keys are needed |
| `pyproject.toml` | Yes | Add optional dependency group |
| `docs/source/<benchmark>.mdx` | Yes | User-facing benchmark documentation |
| `docs/source/_toctree.yml` | Yes | Add entry under the "Benchmarks" section |
| File | Required | Description |
| ---------------------------------------- | -------- | -------------------------------------------------------------------------------- |
| `src/lerobot/envs/<benchmark>.py` | Yes | `gym.Env` subclass + `create_<benchmark>_envs()` factory |
| `src/lerobot/envs/configs.py` | Yes | `@EnvConfig.register_subclass("<name>")` dataclass with `create_envs()` override |
| `src/lerobot/processor/env_processor.py` | Optional | `ProcessorStep` subclass for env-specific observation transforms |
| `src/lerobot/envs/utils.py` | Optional | Extend `preprocess_observation()` if new raw keys are needed |
| `pyproject.toml` | Yes | Add optional dependency group |
| `docs/source/<benchmark>.mdx` | Yes | User-facing benchmark documentation |
| `docs/source/_toctree.yml` | Yes | Add entry under the "Benchmarks" section |
### 1. The gym.Env wrapper (`src/lerobot/envs/<benchmark>.py`)
@@ -169,7 +168,10 @@ See `create_libero_envs()` in `src/lerobot/envs/libero.py` (multi-suite, multi-t
### 2. The config (`src/lerobot/envs/configs.py`)
Register a new config dataclass:
Register a new config dataclass. 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>")
@@ -196,6 +198,20 @@ class MyBenchmarkEnv(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:
@@ -203,36 +219,11 @@ Key points:
- The `register_subclass` name is what users pass as `--env.type=<name>` on the CLI.
- `features` declares what the environment produces (used to configure the policy).
- `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()`:
```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, MyBenchmarkEnv) or "<benchmark_name>" in env_cfg.type:
preprocessor_steps.append(MyBenchmarkProcessorStep())
```
### 4. Env processor (optional) (`src/lerobot/processor/env_processor.py`)
If your benchmark needs observation transforms beyond what `preprocess_observation()` handles (e.g., image flipping, coordinate frame conversion), add a `ProcessorStep`:
If your benchmark needs observation transforms beyond what `preprocess_observation()` handles (e.g., image flipping, coordinate frame conversion), add a `ProcessorStep` and return it from `get_env_processors()` in your config (see step 2):
```python
@dataclass
@@ -253,7 +244,7 @@ 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 under `[project.optional-dependencies]`:
@@ -274,11 +265,11 @@ Users install with:
pip install -e ".[mybenchmark]"
```
### 6. Documentation (`docs/source/<benchmark>.mdx`)
### 5. Documentation (`docs/source/<benchmark>.mdx`)
Follow the template below. 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 under the "Benchmarks" section:
docs/source/env_processor.mdx
+17 -33
View File
@@ -151,7 +151,7 @@ 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 +159,31 @@ 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: