chore(style): pre-commit envs

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

.github/workflows/full_tests.yml

@@ -173,8 +173,6 @@ jobs:
         shell: bash
         working-directory: /lerobot
     steps:
-      - name: Fix ptxas permissions
-        run: chmod +x /lerobot/.venv/lib/python3.10/site-packages/triton/backends/nvidia/bin/ptxas
       - name: Run pytest on GPU
         run: pytest tests -vv --maxfail=10
       - name: Run end-to-end tests

MANIFEST.in

@@ -1,3 +1,2 @@
 include src/lerobot/templates/lerobot_modelcard_template.md
 include src/lerobot/datasets/card_template.md
-include src/lerobot/envs/metaworld_config.json

docker/Dockerfile.internal

@@ -85,8 +85,6 @@ RUN if [ "$UNBOUND_DEPS" = "true" ]; then \
 
 RUN uv pip install --no-cache ".[all]"
 
-RUN chmod +x /lerobot/.venv/lib/python${PYTHON_VERSION}/site-packages/triton/backends/nvidia/bin/ptxas
-
 # Copy the rest of the application source code
 # Make sure to have the git-LFS files for testing
 COPY --chown=user_lerobot:user_lerobot . .

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
 ```
 

pyproject.toml

@@ -214,9 +214,6 @@ lerobot-edit-dataset="lerobot.scripts.lerobot_edit_dataset:main"
 lerobot-setup-can="lerobot.scripts.lerobot_setup_can:main"
 
 # ---------------- Tool Configurations ----------------
-[tool.setuptools.package-data]
-lerobot = ["envs/*.json"]
-
 [tool.setuptools.packages.find]
 where = ["src"]
 

src/lerobot/async_inference/robot_client.py

@@ -49,18 +49,23 @@ import torch
 
 from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig  # noqa: F401
 from lerobot.cameras.realsense.configuration_realsense import RealSenseCameraConfig  # noqa: F401
-from lerobot.robots import (
+from lerobot.robots import (  # noqa: F401
-    RobotConfig,  # noqa: F401
+    Robot,
+    RobotConfig,
+    bi_so_follower,
+    koch_follower,
     make_robot_from_config,
+    omx_follower,
+    so_follower,
 )
 from lerobot.transport import (
     services_pb2,  # type: ignore
     services_pb2_grpc,  # type: ignore
 )
 from lerobot.transport.utils import grpc_channel_options, send_bytes_in_chunks
-from lerobot.utils.import_utils import register_third_party_plugins
 
 from .configs import RobotClientConfig
+from .constants import SUPPORTED_ROBOTS
 from .helpers import (
     Action,
     FPSTracker,
@@ -480,9 +485,8 @@ class RobotClient:
 def async_client(cfg: RobotClientConfig):
     logging.info(pformat(asdict(cfg)))
 
-    # TODO: Assert if checking robot support is still needed with the plugin system
+    if cfg.robot.type not in SUPPORTED_ROBOTS:
-    # if cfg.robot.type not in SUPPORTED_ROBOTS:
+        raise ValueError(f"Robot {cfg.robot.type} not yet supported!")
-    #     raise ValueError(f"Robot {cfg.robot.type} not yet supported!")
 
     client = RobotClient(cfg)
 
@@ -508,5 +512,4 @@ def async_client(cfg: RobotClientConfig):
 
 
 if __name__ == "__main__":
-    register_third_party_plugins()
     async_client()  # run the client

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/datasets/card_template.md

@@ -7,13 +7,6 @@
 
 This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).
 
-{% if repo_id is defined and repo_id %}
-<a class="flex" href="https://huggingface.co/spaces/lerobot/visualize_dataset?path={{ repo_id }}">
-<img class="block dark:hidden" src="https://huggingface.co/datasets/huggingface/badges/resolve/main/visualize-this-dataset-xl.svg"/>
-<img class="hidden dark:block" src="https://huggingface.co/datasets/huggingface/badges/resolve/main/visualize-this-dataset-xl-dark.svg"/>
-</a>
-{% endif %}
-
 ## Dataset Description
 
 {{ dataset_description | default("", true) }}

src/lerobot/datasets/dataset_tools.py

@@ -567,22 +567,20 @@ def _copy_and_reindex_data(
 def _keep_episodes_from_video_with_av(
     input_path: Path,
     output_path: Path,
-    episodes_to_keep: list[tuple[int, int]],
+    episodes_to_keep: list[tuple[float, float]],
     fps: float,
     vcodec: str = "libsvtav1",
     pix_fmt: str = "yuv420p",
 ) -> None:
     """Keep only specified episodes from a video file using PyAV.
 
-    This function decodes frames from specified frame ranges and re-encodes them with
+    This function decodes frames from specified time ranges and re-encodes them with
     properly reset timestamps to ensure monotonic progression.
 
     Args:
         input_path: Source video file path.
         output_path: Destination video file path.
-        episodes_to_keep: List of (start_frame, end_frame) tuples for episodes to keep.
+        episodes_to_keep: List of (start_time, end_time) tuples for episodes to keep.
-            Ranges are half-open intervals: [start_frame, end_frame), where start_frame
-            is inclusive and end_frame is exclusive.
         fps: Frame rate of the video.
         vcodec: Video codec to use for encoding.
         pix_fmt: Pixel format for output video.
@@ -624,10 +622,9 @@ def _keep_episodes_from_video_with_av(
 
     # Create set of (start, end) ranges for fast lookup.
     # Convert to a sorted list for efficient checking.
-    frame_ranges = sorted(episodes_to_keep)
+    time_ranges = sorted(episodes_to_keep)
 
     # Track frame index for setting PTS and current range being processed.
-    src_frame_count = 0
     frame_count = 0
     range_idx = 0
 
@@ -637,20 +634,21 @@ def _keep_episodes_from_video_with_av(
             if frame is None:
                 continue
 
-            # Check if frame is in any of our desired frame ranges.
+            # Get frame timestamp.
+            frame_time = float(frame.pts * frame.time_base) if frame.pts is not None else 0.0
+
+            # Check if frame is in any of our desired time ranges.
             # Skip ranges that have already passed.
-            while range_idx < len(frame_ranges) and src_frame_count >= frame_ranges[range_idx][1]:
+            while range_idx < len(time_ranges) and frame_time >= time_ranges[range_idx][1]:
                 range_idx += 1
 
             # If we've passed all ranges, stop processing.
-            if range_idx >= len(frame_ranges):
+            if range_idx >= len(time_ranges):
                 break
 
             # Check if frame is in current range.
-            start_frame = frame_ranges[range_idx][0]
+            start_ts, end_ts = time_ranges[range_idx]
-
+            if frame_time < start_ts:
-            if src_frame_count < start_frame:
-                src_frame_count += 1
                 continue
 
             # Frame is in range - create a new frame with reset timestamps.
@@ -663,7 +661,6 @@ def _keep_episodes_from_video_with_av(
             for pkt in v_out.encode(new_frame):
                 out.mux(pkt)
 
-            src_frame_count += 1
             frame_count += 1
 
     # Flush encoder.
@@ -752,17 +749,15 @@ def _copy_and_reindex_videos(
                         f"videos/{video_key}/to_timestamp"
                     ]
             else:
-                # Build list of frame ranges to keep, in sorted order.
+                # Build list of time ranges to keep, in sorted order.
                 sorted_keep_episodes = sorted(episodes_in_file, key=lambda x: episode_mapping[x])
-                episodes_to_keep_ranges: list[tuple[int, int]] = []
+                episodes_to_keep_ranges: list[tuple[float, float]] = []
+
                 for old_idx in sorted_keep_episodes:
                     src_ep = src_dataset.meta.episodes[old_idx]
-                    from_frame = round(src_ep[f"videos/{video_key}/from_timestamp"] * src_dataset.meta.fps)
+                    from_ts = src_ep[f"videos/{video_key}/from_timestamp"]
-                    to_frame = round(src_ep[f"videos/{video_key}/to_timestamp"] * src_dataset.meta.fps)
+                    to_ts = src_ep[f"videos/{video_key}/to_timestamp"]
-                    assert src_ep["length"] == to_frame - from_frame, (
+                    episodes_to_keep_ranges.append((from_ts, to_ts))
-                        f"Episode length mismatch: {src_ep['length']} vs {to_frame - from_frame}"
-                    )
-                    episodes_to_keep_ranges.append((from_frame, to_frame))
 
                 # Use PyAV filters to efficiently re-encode only the desired segments.
                 assert src_dataset.meta.video_path is not None

src/lerobot/datasets/lerobot_dataset.py

@@ -747,7 +747,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
             # Check if cached dataset contains all requested episodes
             if not self._check_cached_episodes_sufficient():
                 raise FileNotFoundError("Cached dataset doesn't contain all requested episodes")
-        except (FileNotFoundError, NotADirectoryError):
+        except (AssertionError, FileNotFoundError, NotADirectoryError):
             if is_valid_version(self.revision):
                 self.revision = get_safe_version(self.repo_id, self.revision)
             self.download(download_videos)
@@ -839,7 +839,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
             hub_api.upload_folder(**upload_kwargs)
 
         card = create_lerobot_dataset_card(
-            tags=tags, dataset_info=self.meta.info, license=license, repo_id=self.repo_id, **card_kwargs
+            tags=tags, dataset_info=self.meta.info, license=license, **card_kwargs
         )
         card.push_to_hub(repo_id=self.repo_id, repo_type="dataset", revision=branch)
 
@@ -1771,7 +1771,6 @@ class MultiLeRobotDataset(torch.utils.data.Dataset):
             )
         for repo_id, ds in zip(self.repo_ids, self._datasets, strict=True):
             extra_keys = set(ds.features).difference(intersection_features)
-            if extra_keys:
             logging.warning(
                 f"keys {extra_keys} of {repo_id} were disabled as they are not contained in all the "
                 "other datasets."

src/lerobot/datasets/video_utils.py

@@ -227,8 +227,7 @@ def decode_video_frames_torchvision(
     min_, argmin_ = dist.min(1)
 
     is_within_tol = min_ < tolerance_s
-    if not is_within_tol.all():
+    assert is_within_tol.all(), (
-        raise FrameTimestampError(
         f"One or several query timestamps unexpectedly violate the tolerance ({min_[~is_within_tol]} > {tolerance_s=})."
         "It means that the closest frame that can be loaded from the video is too far away in time."
         "This might be due to synchronization issues with timestamps during data collection."
@@ -249,11 +248,7 @@ def decode_video_frames_torchvision(
     # convert to the pytorch format which is float32 in [0,1] range (and channel first)
     closest_frames = closest_frames.type(torch.float32) / 255
 
-    if len(timestamps) != len(closest_frames):
+    assert len(timestamps) == len(closest_frames)
-        raise FrameTimestampError(
-            f"Number of retrieved frames ({len(closest_frames)}) does not match "
-            f"number of queried timestamps ({len(timestamps)})"
-        )
     return closest_frames
 
 
@@ -358,8 +353,7 @@ def decode_video_frames_torchcodec(
     min_, argmin_ = dist.min(1)
 
     is_within_tol = min_ < tolerance_s
-    if not is_within_tol.all():
+    assert is_within_tol.all(), (
-        raise FrameTimestampError(
         f"One or several query timestamps unexpectedly violate the tolerance ({min_[~is_within_tol]} > {tolerance_s=})."
         "It means that the closest frame that can be loaded from the video is too far away in time."
         "This might be due to synchronization issues with timestamps during data collection."

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/policies/diffusion/configuration_diffusion.py

@@ -139,10 +139,6 @@ class DiffusionConfig(PreTrainedConfig):
     # Inference
     num_inference_steps: int | None = None
 
-    # Optimization
-    compile_model: bool = False
-    compile_mode: str = "reduce-overhead"
-
     # Loss computation
     do_mask_loss_for_padding: bool = False
 

src/lerobot/policies/diffusion/modeling_diffusion.py

@@ -142,9 +142,6 @@ class DiffusionPolicy(PreTrainedPolicy):
         """Run the batch through the model and compute the loss for training or validation."""
         if self.config.image_features:
             batch = dict(batch)  # shallow copy so that adding a key doesn't modify the original
-            for key in self.config.image_features:
-                if self.config.n_obs_steps == 1 and batch[key].ndim == 4:
-                    batch[key] = batch[key].unsqueeze(1)
             batch[OBS_IMAGES] = torch.stack([batch[key] for key in self.config.image_features], dim=-4)
         loss = self.diffusion.compute_loss(batch)
         # no output_dict so returning None
@@ -185,11 +182,6 @@ class DiffusionModel(nn.Module):
 
         self.unet = DiffusionConditionalUnet1d(config, global_cond_dim=global_cond_dim * config.n_obs_steps)
 
-        if config.compile_model:
-            # Compile the U-Net. "reduce-overhead" is preferred for the small-batch repetitive loops
-            # common in diffusion inference.
-            self.unet = torch.compile(self.unet, mode=config.compile_mode)
-
         self.noise_scheduler = _make_noise_scheduler(
             config.noise_scheduler_type,
             num_train_timesteps=config.num_train_timesteps,

src/lerobot/policies/pi0/modeling_pi0.py

@@ -995,13 +995,6 @@ class PI0Policy(PreTrainedPolicy):
 
         # Initialize model without loading weights
         # Check if dataset_stats were provided in kwargs
-        if _transformers_available:
-            from transformers.modeling_utils import no_init_weights
-
-            with no_init_weights():
-                model = cls(config, **kwargs)
-            model.model.paligemma_with_expert.paligemma.tie_weights()
-        else:
         model = cls(config, **kwargs)
 
         # Now manually load and remap the state dict

src/lerobot/policies/pi05/modeling_pi05.py

@@ -967,13 +967,6 @@ class PI05Policy(PreTrainedPolicy):
 
         # Initialize model without loading weights
         # Check if dataset_stats were provided in kwargs
-        if _transformers_available:
-            from transformers.modeling_utils import no_init_weights
-
-            with no_init_weights():
-                model = cls(config, **kwargs)
-            model.model.paligemma_with_expert.paligemma.tie_weights()
-        else:
         model = cls(config, **kwargs)
 
         # Now manually load and remap the state dict

src/lerobot/policies/pi0_fast/modeling_pi0_fast.py

@@ -895,13 +895,6 @@ class PI0FastPolicy(PreTrainedPolicy):
 
         # Initialize model without loading weights
         # Check if dataset_stats were provided in kwargs
-        if _transformers_available:
-            from transformers.modeling_utils import no_init_weights
-
-            with no_init_weights():
-                model = cls(config, **kwargs)
-            model.model.paligemma_with_expert.paligemma.tie_weights()
-        else:
         model = cls(config, **kwargs)
 
         # Now manually load and remap the state dict

src/lerobot/policies/sarm/processor_sarm.py

@@ -277,6 +277,8 @@ class SARMEncodingProcessorStep(ProcessorStep):
 
         # When language is perturbed, targets are zero so perturbed samples don't contribute to progress loss
         if self.dataset_meta is not None:
+            episodes_df = None
+            if self.sparse_subtask_names != ["task"]:
                 episodes_df = self.dataset_meta.episodes.to_pandas()
 
             # Generate sparse targets

src/lerobot/policies/smolvla/smolvlm_with_expert.py

@@ -77,6 +77,7 @@ class SmolVLMWithExpertModel(nn.Module):
             print(f"Loading  {model_id} weights ...")
             self.vlm = AutoModelForImageTextToText.from_pretrained(
                 model_id,
+                device_map=device,
                 torch_dtype="bfloat16",
                 low_cpu_mem_usage=True,
             )

src/lerobot/scripts/lerobot_calibrate.py

@@ -56,7 +56,6 @@ from lerobot.teleoperators import (  # noqa: F401
     make_teleoperator_from_config,
     omx_leader,
     openarm_leader,
-    openarm_mini,
     so_leader,
     unitree_g1,
 )

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.")

src/lerobot/scripts/lerobot_find_joint_limits.py

@@ -61,7 +61,6 @@ from lerobot.teleoperators import (  # noqa: F401
     make_teleoperator_from_config,
     omx_leader,
     openarm_leader,
-    openarm_mini,
     so_leader,
 )
 from lerobot.utils.robot_utils import precise_sleep

src/lerobot/scripts/lerobot_record.py

@@ -125,7 +125,6 @@ from lerobot.teleoperators import (  # noqa: F401
     make_teleoperator_from_config,
     omx_leader,
     openarm_leader,
-    openarm_mini,
     reachy2_teleoperator,
     so_leader,
     unitree_g1,
@@ -334,7 +333,6 @@ def record_loop(
         preprocessor.reset()
         postprocessor.reset()
 
-    no_action_count = 0
     timestamp = 0
     start_episode_t = time.perf_counter()
     while timestamp < control_time_s:
@@ -382,9 +380,7 @@ def record_loop(
             act = {**arm_action, **base_action} if len(base_action) > 0 else arm_action
             act_processed_teleop = teleop_action_processor((act, obs))
         else:
-            no_action_count += 1
+            logging.info(
-            if no_action_count == 1 or no_action_count % 10 == 0:
-                logging.warning(
                 "No policy or teleoperator provided, skipping action generation."
                 "This is likely to happen when resetting the environment without a teleop device."
                 "The robot won't be at its rest position at the start of the next episode."

src/lerobot/scripts/lerobot_setup_motors.py

@@ -43,7 +43,6 @@ from lerobot.teleoperators import (  # noqa: F401
     koch_leader,
     make_teleoperator_from_config,
     omx_leader,
-    openarm_mini,
     so_leader,
 )
 
@@ -52,7 +51,6 @@ COMPATIBLE_DEVICES = [
     "koch_leader",
     "omx_follower",
     "omx_leader",
-    "openarm_mini",
     "so100_follower",
     "so100_leader",
     "so101_follower",

src/lerobot/scripts/lerobot_teleoperate.py

@@ -94,7 +94,6 @@ from lerobot.teleoperators import (  # noqa: F401
     make_teleoperator_from_config,
     omx_leader,
     openarm_leader,
-    openarm_mini,
     reachy2_teleoperator,
     so_leader,
     unitree_g1,

src/lerobot/scripts/lerobot_train.py

@@ -24,7 +24,6 @@ import torch
 from accelerate import Accelerator
 from termcolor import colored
 from torch.optim import Optimizer
-from tqdm import tqdm
 
 from lerobot.configs import parser
 from lerobot.configs.train import TrainPipelineConfig
@@ -52,7 +51,6 @@ from lerobot.utils.utils import (
     format_big_number,
     has_method,
     init_logging,
-    inside_slurm,
 )
 
 
@@ -392,14 +390,6 @@ def train(cfg: TrainPipelineConfig, accelerator: Accelerator | None = None):
     )
 
     if is_main_process:
-        progbar = tqdm(
-            total=cfg.steps - step,
-            desc="Training",
-            unit="step",
-            disable=inside_slurm(),
-            position=0,
-            leave=True,
-        )
         logging.info(
             f"Start offline training on a fixed dataset, with effective batch size: {effective_batch_size}"
         )
@@ -424,8 +414,6 @@ def train(cfg: TrainPipelineConfig, accelerator: Accelerator | None = None):
         # Note: eval and checkpoint happens *after* the `step`th training update has completed, so we
         # increment `step` here.
         step += 1
-        if is_main_process:
-            progbar.update(1)
         train_tracker.step()
         is_log_step = cfg.log_freq > 0 and step % cfg.log_freq == 0 and is_main_process
         is_saving_step = step % cfg.save_freq == 0 or step == cfg.steps
@@ -519,9 +507,6 @@ def train(cfg: TrainPipelineConfig, accelerator: Accelerator | None = None):
 
             accelerator.wait_for_everyone()
 
-    if is_main_process:
-        progbar.close()
-
     if eval_env:
         close_envs(eval_env)
 

src/lerobot/teleoperators/openarm_mini/__init__.py

@@ -1,20 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# 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 .config_openarm_mini import OpenArmMiniConfig
-from .openarm_mini import OpenArmMini
-
-__all__ = ["OpenArmMini", "OpenArmMiniConfig"]

src/lerobot/teleoperators/openarm_mini/config_openarm_mini.py

@@ -1,30 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# 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 dataclasses import dataclass
-
-from ..config import TeleoperatorConfig
-
-
-@TeleoperatorConfig.register_subclass("openarm_mini")
-@dataclass
-class OpenArmMiniConfig(TeleoperatorConfig):
-    """Configuration for OpenArm Mini teleoperator with Feetech motors (dual arms)."""
-
-    port_right: str = "/dev/ttyUSB0"
-    port_left: str = "/dev/ttyUSB1"
-
-    use_degrees: bool = True

src/lerobot/teleoperators/openarm_mini/openarm_mini.py

@@ -1,296 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# 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 logging
-import time
-from typing import Any
-
-from lerobot.motors import Motor, MotorCalibration, MotorNormMode
-from lerobot.motors.feetech import (
-    FeetechMotorsBus,
-    OperatingMode,
-)
-from lerobot.processor import RobotAction
-from lerobot.utils.decorators import check_if_already_connected, check_if_not_connected
-
-from ..teleoperator import Teleoperator
-from .config_openarm_mini import OpenArmMiniConfig
-
-logger = logging.getLogger(__name__)
-
-# Motors whose direction is inverted during readout
-RIGHT_MOTORS_TO_FLIP = ["joint_1", "joint_2", "joint_3", "joint_4", "joint_5"]
-LEFT_MOTORS_TO_FLIP = ["joint_1", "joint_3", "joint_4", "joint_5", "joint_6", "joint_7"]
-
-
-class OpenArmMini(Teleoperator):
-    """
-    OpenArm Mini Teleoperator with dual Feetech-based arms (8 motors per arm).
-
-    Each arm has 7 joints plus a gripper, using Feetech STS3215 servos.
-    """
-
-    config_class = OpenArmMiniConfig
-    name = "openarm_mini"
-
-    def __init__(self, config: OpenArmMiniConfig):
-        super().__init__(config)
-        self.config = config
-
-        norm_mode_body = MotorNormMode.DEGREES
-
-        motors_right = {
-            "joint_1": Motor(1, "sts3215", norm_mode_body),
-            "joint_2": Motor(2, "sts3215", norm_mode_body),
-            "joint_3": Motor(3, "sts3215", norm_mode_body),
-            "joint_4": Motor(4, "sts3215", norm_mode_body),
-            "joint_5": Motor(5, "sts3215", norm_mode_body),
-            "joint_6": Motor(6, "sts3215", norm_mode_body),
-            "joint_7": Motor(7, "sts3215", norm_mode_body),
-            "gripper": Motor(8, "sts3215", MotorNormMode.RANGE_0_100),
-        }
-
-        motors_left = {
-            "joint_1": Motor(1, "sts3215", norm_mode_body),
-            "joint_2": Motor(2, "sts3215", norm_mode_body),
-            "joint_3": Motor(3, "sts3215", norm_mode_body),
-            "joint_4": Motor(4, "sts3215", norm_mode_body),
-            "joint_5": Motor(5, "sts3215", norm_mode_body),
-            "joint_6": Motor(6, "sts3215", norm_mode_body),
-            "joint_7": Motor(7, "sts3215", norm_mode_body),
-            "gripper": Motor(8, "sts3215", MotorNormMode.RANGE_0_100),
-        }
-
-        cal_right = {
-            k.replace("right_", ""): v for k, v in (self.calibration or {}).items() if k.startswith("right_")
-        }
-        cal_left = {
-            k.replace("left_", ""): v for k, v in (self.calibration or {}).items() if k.startswith("left_")
-        }
-
-        self.bus_right = FeetechMotorsBus(
-            port=self.config.port_right,
-            motors=motors_right,
-            calibration=cal_right,
-        )
-
-        self.bus_left = FeetechMotorsBus(
-            port=self.config.port_left,
-            motors=motors_left,
-            calibration=cal_left,
-        )
-
-    @property
-    def action_features(self) -> dict[str, type]:
-        features: dict[str, type] = {}
-        for motor in self.bus_right.motors:
-            features[f"right_{motor}.pos"] = float
-        for motor in self.bus_left.motors:
-            features[f"left_{motor}.pos"] = float
-        return features
-
-    @property
-    def feedback_features(self) -> dict[str, type]:
-        return {}
-
-    @property
-    def is_connected(self) -> bool:
-        return self.bus_right.is_connected and self.bus_left.is_connected
-
-    @check_if_already_connected
-    def connect(self, calibrate: bool = True) -> None:
-        logger.info(f"Connecting right arm on {self.config.port_right}...")
-        self.bus_right.connect()
-        logger.info(f"Connecting left arm on {self.config.port_left}...")
-        self.bus_left.connect()
-
-        if calibrate:
-            self.calibrate()
-
-        self.configure()
-        logger.info(f"{self} connected.")
-
-    @property
-    def is_calibrated(self) -> bool:
-        return self.bus_right.is_calibrated and self.bus_left.is_calibrated
-
-    def calibrate(self) -> None:
-        """
-        Run calibration procedure for OpenArm Mini.
-
-        1. Disable torque
-        2. Ask user to position arms in hanging position with grippers closed
-        3. Set this as zero position via half-turn homing
-        4. Interactive gripper calibration (open/close positions)
-        5. Save calibration
-        """
-        if self.calibration:
-            user_input = input(
-                f"Press ENTER to use existing calibration for {self.id}, "
-                f"or type 'c' and press ENTER to run new calibration: "
-            )
-            if user_input.strip().lower() != "c":
-                logger.info(f"Using existing calibration for {self.id}")
-                cal_right = {
-                    k.replace("right_", ""): v for k, v in self.calibration.items() if k.startswith("right_")
-                }
-                cal_left = {
-                    k.replace("left_", ""): v for k, v in self.calibration.items() if k.startswith("left_")
-                }
-                self.bus_right.write_calibration(cal_right)
-                self.bus_left.write_calibration(cal_left)
-                return
-
-        logger.info(f"\nRunning calibration for {self}")
-
-        self._calibrate_arm("right", self.bus_right)
-        self._calibrate_arm("left", self.bus_left)
-
-        self._save_calibration()
-        print(f"\nCalibration complete and saved to {self.calibration_fpath}")
-
-    def _calibrate_arm(self, arm_name: str, bus: FeetechMotorsBus) -> None:
-        """Calibrate a single arm with Feetech motors."""
-        logger.info(f"\n=== Calibrating {arm_name.upper()} arm ===")
-
-        bus.disable_torque()
-
-        logger.info(f"Setting Phase to 12 for all motors in {arm_name.upper()} arm...")
-        for motor in bus.motors:
-            bus.write("Phase", motor, 12)
-
-        for motor in bus.motors:
-            bus.write("Operating_Mode", motor, OperatingMode.POSITION.value)
-
-        input(
-            f"\nCalibration: Zero Position ({arm_name.upper()} arm)\n"
-            "Position the arm in the following configuration:\n"
-            "  - Arm hanging straight down\n"
-            "  - Gripper closed\n"
-            "Press ENTER when ready..."
-        )
-
-        homing_offsets = bus.set_half_turn_homings()
-        logger.info(f"{arm_name.capitalize()} arm zero position set.")
-
-        print(f"\nSetting motor ranges for {arm_name.upper()} arm\n")
-
-        if self.calibration is None:
-            self.calibration = {}
-
-        motor_resolution = bus.model_resolution_table[list(bus.motors.values())[0].model]
-        max_res = motor_resolution - 1
-
-        for motor_name, motor in bus.motors.items():
-            prefixed_name = f"{arm_name}_{motor_name}"
-
-            if motor_name == "gripper":
-                input(
-                    f"\nGripper Calibration ({arm_name.upper()} arm)\n"
-                    f"Step 1: CLOSE the gripper fully\n"
-                    f"Press ENTER when gripper is closed..."
-                )
-                closed_pos = bus.read("Present_Position", motor_name, normalize=False)
-                logger.info(f"  Gripper closed position recorded: {closed_pos}")
-
-                input("\nStep 2: OPEN the gripper fully\nPress ENTER when gripper is fully open...")
-                open_pos = bus.read("Present_Position", motor_name, normalize=False)
-                logger.info(f"  Gripper open position recorded: {open_pos}")
-
-                if closed_pos < open_pos:
-                    range_min = int(closed_pos)
-                    range_max = int(open_pos)
-                    drive_mode = 0
-                else:
-                    range_min = int(open_pos)
-                    range_max = int(closed_pos)
-                    drive_mode = 1
-
-                logger.info(
-                    f"  {prefixed_name}: range set to [{range_min}, {range_max}] "
-                    f"(0=closed, 100=open, drive_mode={drive_mode})"
-                )
-            else:
-                range_min = 0
-                range_max = max_res
-                drive_mode = 0
-                logger.info(f"  {prefixed_name}: range set to [0, {max_res}] (full motor range)")
-
-            self.calibration[prefixed_name] = MotorCalibration(
-                id=motor.id,
-                drive_mode=drive_mode,
-                homing_offset=homing_offsets[motor_name],
-                range_min=range_min,
-                range_max=range_max,
-            )
-
-        cal_for_bus = {
-            k.replace(f"{arm_name}_", ""): v
-            for k, v in self.calibration.items()
-            if k.startswith(f"{arm_name}_")
-        }
-        bus.write_calibration(cal_for_bus)
-
-    def configure(self) -> None:
-        self.bus_right.disable_torque()
-        self.bus_right.configure_motors()
-        for motor in self.bus_right.motors:
-            self.bus_right.write("Operating_Mode", motor, OperatingMode.POSITION.value)
-
-        self.bus_left.disable_torque()
-        self.bus_left.configure_motors()
-        for motor in self.bus_left.motors:
-            self.bus_left.write("Operating_Mode", motor, OperatingMode.POSITION.value)
-
-    def setup_motors(self) -> None:
-        print("\nSetting up RIGHT arm motors...")
-        for motor in reversed(self.bus_right.motors):
-            input(f"Connect the controller board to the RIGHT '{motor}' motor only and press enter.")
-            self.bus_right.setup_motor(motor)
-            print(f"RIGHT '{motor}' motor id set to {self.bus_right.motors[motor].id}")
-
-        print("\nSetting up LEFT arm motors...")
-        for motor in reversed(self.bus_left.motors):
-            input(f"Connect the controller board to the LEFT '{motor}' motor only and press enter.")
-            self.bus_left.setup_motor(motor)
-            print(f"LEFT '{motor}' motor id set to {self.bus_left.motors[motor].id}")
-
-    @check_if_not_connected
-    def get_action(self) -> RobotAction:
-        """Get current action from both arms (read positions from all motors)."""
-        start = time.perf_counter()
-
-        right_positions = self.bus_right.sync_read("Present_Position")
-        left_positions = self.bus_left.sync_read("Present_Position")
-
-        action: dict[str, Any] = {}
-        for motor, val in right_positions.items():
-            action[f"right_{motor}.pos"] = -val if motor in RIGHT_MOTORS_TO_FLIP else val
-        for motor, val in left_positions.items():
-            action[f"left_{motor}.pos"] = -val if motor in LEFT_MOTORS_TO_FLIP else val
-
-        dt_ms = (time.perf_counter() - start) * 1e3
-        logger.debug(f"{self} read action: {dt_ms:.1f}ms")
-        return action
-
-    def send_feedback(self, feedback: dict[str, float]) -> None:
-        raise NotImplementedError("Feedback is not yet implemented for OpenArm Mini.")
-
-    @check_if_not_connected
-    def disconnect(self) -> None:
-        self.bus_right.disconnect()
-        self.bus_left.disconnect()
-        logger.info(f"{self} disconnected.")

src/lerobot/teleoperators/utils.py

@@ -95,10 +95,6 @@ def make_teleoperator_from_config(config: TeleoperatorConfig) -> "Teleoperator":
         from .bi_openarm_leader import BiOpenArmLeader
 
         return BiOpenArmLeader(config)
-    elif config.type == "openarm_mini":
-        from .openarm_mini import OpenArmMini
-
-        return OpenArmMini(config)
     else:
         try:
             return cast("Teleoperator", make_device_from_device_class(config))

src/lerobot/utils/control_utils.py

@@ -189,7 +189,7 @@ def sanity_check_dataset_name(repo_id, policy_cfg):
     # Check if dataset_name starts with "eval_" but policy is missing
     if dataset_name.startswith("eval_") and policy_cfg is None:
         raise ValueError(
-            f"Your dataset name begins with 'eval_' ({dataset_name}), but no policy is provided."
+            f"Your dataset name begins with 'eval_' ({dataset_name}), but no policy is provided ({policy_cfg.type})."
         )
 
     # Check if dataset_name does not start with "eval_" but policy is provided

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()