chore(style): pre-commit envs

Signed-off-by: Steven Palma <imstevenpmwork@ieee.org>

docs/source/envhub.mdx

@@ -55,7 +55,8 @@ To make your environment loadable from the Hub, your repository must contain at
 
 **`env.py`** (or custom Python file)
 
-- Must expose a `make_env(n_envs: int, use_async_envs: bool)` function
+- Must expose a `make_env(n_envs: int, use_async_envs: bool, **kwargs)` function
+- The function should accept `**kwargs` to allow users to pass custom configurations
 - This function should return one of:
   - A `gym.vector.VectorEnv` (most common)
   - A single `gym.Env` (will be automatically wrapped)
@@ -99,6 +100,8 @@ Create an `env.py` file with a `make_env` function:
 ```python
 # env.py
 import gymnasium as gym
+from pathlib import Path
+from typing import Any
 
 def make_env(n_envs: int = 1, use_async_envs: bool = False):
     """
@@ -250,6 +253,76 @@ envs_dict = make_env(
 )
 ```
 
+### Custom Configuration via kwargs
+
+Hub environments can accept custom configurations through keyword arguments. This is useful for parameterizing tasks, loading different objects, or overriding default settings:
+
+```python
+from pathlib import Path
+
+# Pass a config file path
+envs_dict = make_env(
+    "nvkartik/isaaclab-arena-envs:envs/microwave_g1.py",
+    n_envs=4,
+    trust_remote_code=True,
+    config_path=Path("/path/to/my_config.yaml"),
+)
+
+# Pass config overrides as a dictionary
+envs_dict = make_env(
+    "nvkartik/isaaclab-arena-envs:envs/microwave_g1.py",
+    n_envs=4,
+    trust_remote_code=True,
+    config_overrides={
+        "scene.object": "microwave",
+        "sim.dt": 0.01,
+    },
+)
+
+# Combine config path with overrides
+envs_dict = make_env(
+    "username/my-env",
+    n_envs=4,
+    trust_remote_code=True,
+    config_path="configs/gr1_pick_place.yaml",
+    config_overrides={"scene.table_objects": ["apple", "banana", "cup"]},
+)
+```
+
+Any keyword arguments you pass will be forwarded to the hub environment's `make_env` function. Check the environment's documentation for supported configuration options.
+
+### Using Custom kwargs with lerobot-eval
+
+When evaluating policies using the `lerobot-eval` CLI, you can pass custom kwargs to hub environments using the `--env_kwargs.` prefix:
+
+```bash
+lerobot-eval \
+    --policy.path=user123/example-policy-checkpoint \
+    --env=user123/example-sim-backend \
+    --eval.batch_size=1 \
+    --eval.n_episodes=10 \
+    --env_kwargs.task_id=demo_task_alpha \
+    --env_kwargs.agent_profile=arm_v2 \
+    --env_kwargs.target_item=object_red \
+    --env_kwargs.run_mode=offscreen \
+    --env_kwargs.enable_sensors=true \
+    --env_kwargs.record_output=true \
+    --env_kwargs.output_horizon=10 \
+    --env_kwargs.output_stride=15 \
+    --env_kwargs.state_features=joint_angles \
+    --env_kwargs.visual_streams=front_camera
+```
+
+All `--env_kwargs.*` arguments will be collected into a dictionary and passed as keyword arguments to the hub environment's `make_env` function. This allows you to:
+
+- Pass configuration file paths
+- Override default settings
+- Specify custom task parameters
+- Control simulation options (headless mode, camera settings, etc.)
+- Select different embodiments or objects
+
+The hub environment's `make_env` function receives these as regular keyword arguments, so check the environment's documentation for the available options.
+
 ## URL Format Reference
 
 The hub URL format supports several patterns:
@@ -266,7 +339,7 @@ The hub URL format supports several patterns:
 For benchmarks with multiple tasks (like LIBERO), return a nested dictionary:
 
 ```python
-def make_env(n_envs: int = 1, use_async_envs: bool = False):
+def make_env(n_envs: int = 1, use_async_envs: bool = False, **kwargs):
     env_cls = gym.vector.AsyncVectorEnv if use_async_envs else gym.vector.SyncVectorEnv
 
     # Return dict: {suite_name: {task_id: VectorEnv}}
@@ -388,8 +461,9 @@ pip install gymnasium numpy
 Your `env.py` must expose a `make_env` function:
 
 ```python
-def make_env(n_envs: int, use_async_envs: bool):
+def make_env(n_envs: int, use_async_envs: bool, **kwargs):
     # Your implementation
+    # kwargs can include config_path, config_overrides, etc.
     pass
 ```
 

src/lerobot/configs/eval.py

@@ -38,6 +38,8 @@ class EvalPipelineConfig:
     seed: int | None = 1000
     # Rename map for the observation to override the image and state keys
     rename_map: dict[str, str] = field(default_factory=dict)
+    # Additional kwargs to pass to hub environments (e.g., config_path, config_overrides, custom params)
+    env_kwargs: dict = field(default_factory=dict)
     # Explicit consent to execute remote code from the Hub (required for hub environments).
     trust_remote_code: bool = False
 

src/lerobot/envs/factory.py

@@ -105,6 +105,7 @@ def make_env(
     use_async_envs: bool = False,
     hub_cache_dir: str | None = None,
     trust_remote_code: bool = False,
+    **kwargs,
 ) -> dict[str, dict[int, gym.vector.VectorEnv]]:
     """Makes a gym vector environment according to the config or Hub reference.
 
@@ -118,6 +119,9 @@ def make_env(
         hub_cache_dir (str | None): Optional cache path for downloaded hub files.
         trust_remote_code (bool): **Explicit consent** to execute remote code from the Hub.
             Default False — must be set to True to import/exec hub `env.py`.
+        **kwargs: Additional keyword arguments passed to the hub environment's `make_env` function.
+            Useful for passing custom configurations like `config_path`, `config_overrides`, etc.
+
     Raises:
         ValueError: if n_envs < 1
         ModuleNotFoundError: If the requested env package is not installed
@@ -149,9 +153,11 @@ def make_env(
         # import and surface clear import errors
         module = _import_hub_module(local_file, repo_id)
 
-        # call the hub-provided make_env
+        # call the hub-provided make_env with any additional kwargs
         env_cfg = None if isinstance(cfg, str) else cfg
-        raw_result = _call_make_env(module, n_envs=n_envs, use_async_envs=use_async_envs, cfg=env_cfg)
+        raw_result = _call_make_env(
+            module, n_envs=n_envs, use_async_envs=use_async_envs, cfg=env_cfg, **kwargs
+        )
 
         # normalize the return into {suite: {task_id: vec_env}}
         return _normalize_hub_result(raw_result)

src/lerobot/envs/utils.py

@@ -311,20 +311,27 @@ def _import_hub_module(local_file: str, repo_id: str) -> Any:
     return module
 
 
-def _call_make_env(module: Any, n_envs: int, use_async_envs: bool, cfg: EnvConfig | None) -> Any:
+def _call_make_env(module: Any, n_envs: int, use_async_envs: bool, cfg: EnvConfig | None, **kwargs) -> Any:
     """
-    Ensure module exposes make_env and call it.
+    Ensure module exposes make_env and call it with any additional kwargs.
+
+    Args:
+        module: The imported hub module containing make_env.
+        n_envs: Number of parallel environments.
+        use_async_envs: Whether to use AsyncVectorEnv or SyncVectorEnv.
+        **kwargs: Additional keyword arguments to pass to the hub's make_env function.
+            Common examples include config_path, config_overrides, etc.
     """
     if not hasattr(module, "make_env"):
         raise AttributeError(
-            f"The hub module {getattr(module, '__name__', 'hub_module')} must expose `make_env(n_envs=int, use_async_envs=bool)`."
+            f"The hub module {getattr(module, '__name__', 'hub_module')} must expose `make_env(n_envs=int, use_async_envs=bool, **kwargs)`."
         )
     entry_fn = module.make_env
     # Only pass cfg if it's not None (i.e., when an EnvConfig was provided, not a string hub ID)
     if cfg is not None:
-        return entry_fn(n_envs=n_envs, use_async_envs=use_async_envs, cfg=cfg)
+        return entry_fn(n_envs=n_envs, use_async_envs=use_async_envs, cfg=cfg, **kwargs)
     else:
-        return entry_fn(n_envs=n_envs, use_async_envs=use_async_envs)
+        return entry_fn(n_envs=n_envs, use_async_envs=use_async_envs, **kwargs)
 
 
 def _normalize_hub_result(result: Any) -> dict[str, dict[int, gym.vector.VectorEnv]]:

src/lerobot/scripts/lerobot_eval.py

@@ -43,6 +43,17 @@ lerobot-eval \
 
 Note that in both examples, the repo/folder should contain at least `config.json` and `model.safetensors` files.
 
+You can also evaluate a model on a Hub environment with custom kwargs:
+```
+lerobot-eval \
+    --policy.path=HF_USER/HF_REPO \
+    --env=HF_USER/HF_REPO \
+    --eval.batch_size=1 \
+    --eval.n_episodes=10 \
+    --env_kwargs.environment=env_A \
+    --env_kwargs.embodiment=emb_B \
+```
+
 You can learn about the CLI options for this script in the `EvalPipelineConfig` in lerobot/configs/eval.py
 """
 
@@ -521,6 +532,7 @@ def eval_main(cfg: EvalPipelineConfig):
         n_envs=cfg.eval.batch_size,
         use_async_envs=cfg.eval.use_async_envs,
         trust_remote_code=cfg.trust_remote_code,
+        **cfg.env_kwargs,
     )
 
     logging.info("Making policy.")

tests/envs/test_envs.py

@@ -266,3 +266,65 @@ def test_make_env_from_hub_async():
 
     # clean up
     env.close()
+
+
+def test_make_env_from_hub_with_kwargs():
+    """Test that kwargs are correctly passed to hub environment's make_env."""
+    hub_id = "lerobot/dummy-hub-env"
+
+    # Test with config_path kwarg
+    envs_dict = make_env(
+        hub_id,
+        n_envs=1,
+        trust_remote_code=True,
+        config_path="/path/to/config.yaml",
+    )
+    env = envs_dict["cartpole_suite"][0]
+
+    assert hasattr(env, "hub_config")
+    assert env.hub_config["config_path"] == "/path/to/config.yaml"
+    env.close()
+
+    # Test with config_overrides dict
+    envs_dict = make_env(
+        hub_id,
+        n_envs=1,
+        trust_remote_code=True,
+        config_overrides={"scene.object": "microwave", "sim.dt": 0.01},
+    )
+    env = envs_dict["cartpole_suite"][0]
+
+    assert env.hub_config["config_overrides"]["scene.object"] == "microwave"
+    assert env.hub_config["config_overrides"]["sim.dt"] == 0.01
+    env.close()
+
+    # Test with arbitrary extra kwargs
+    envs_dict = make_env(
+        hub_id,
+        n_envs=1,
+        trust_remote_code=True,
+        custom_param="value",
+        another_param=42,
+    )
+    env = envs_dict["cartpole_suite"][0]
+
+    assert env.hub_config["extra_kwargs"]["custom_param"] == "value"
+    assert env.hub_config["extra_kwargs"]["another_param"] == 42
+    env.close()
+
+    # Test combining config_path, config_overrides, and extra kwargs
+    envs_dict = make_env(
+        hub_id,
+        n_envs=2,
+        trust_remote_code=True,
+        config_path="my_config.yaml",
+        config_overrides={"robot": "gr1"},
+        task_name="pick_and_place",
+    )
+    env = envs_dict["cartpole_suite"][0]
+
+    assert env.hub_config["config_path"] == "my_config.yaml"
+    assert env.hub_config["config_overrides"]["robot"] == "gr1"
+    assert env.hub_config["extra_kwargs"]["task_name"] == "pick_and_place"
+    assert env.num_envs == 2
+    env.close()