refactor: several fixes

pyproject.toml

@@ -70,6 +70,9 @@ dependencies = [
     "huggingface-hub>=1.0.0,<2.0.0",
 
     # Environments
+    # NOTE: gymnasium is used in lerobot.envs (lerobot-train, lerobot-eval), policies/factory,
+    # and robots/unitree. Moving it to an optional extra would require import guards across many
+    # tightly-coupled modules. Candidate for a future refactor to decouple envs from the core.
     "gymnasium>=1.1.1,<2.0.0",
 
     # Lightweight utilities
@@ -83,11 +86,11 @@ dependencies = [
 # ── Feature-scoped extras ──────────────────────────────────
 dataset = [
     "datasets>=4.0.0,<5.0.0",
-    "av>=15.0.0,<16.0.0",
+    "lerobot[av-dep]",
     "torchcodec>=0.3.0,<0.11.0; sys_platform != 'win32' and (sys_platform != 'linux' or (platform_machine != 'aarch64' and platform_machine != 'arm64' and platform_machine != 'armv7l')) and (sys_platform != 'darwin' or platform_machine != 'x86_64')", # NOTE: Windows support starts at version 0.7 (needs torch==2.8), ffmpeg>=8 support starts at version 0.8.1 (needs torch==2.9), system-wide ffmpeg support starts at version 0.10 (needs torch==2.10).
     "jsonlines>=4.0.0,<5.0.0",
 ]
-train = [
+training = [
     "lerobot[dataset]",
     "accelerate>=1.10.0,<2.0.0",
     "wandb>=0.24.0,<0.25.0",
@@ -110,13 +113,12 @@ build = [
 # lerobot-record, lerobot-replay, lerobot-calibrate, lerobot-teleoperate, etc.
 robot = ["lerobot[dataset]", "lerobot[hardware]", "lerobot[viz]"]
 # lerobot-eval
-evaluation = ["av>=15.0.0,<16.0.0"]
-# lerobot-train
-training = ["lerobot[train]"]
+evaluation = ["lerobot[av-dep]"]
 # lerobot-dataset-viz, lerobot-imgtransform-viz
 dataset_viz = ["lerobot[dataset]", "lerobot[viz]"]
 
 # Common
+av-dep = ["av>=15.0.0,<16.0.0"]
 pygame-dep = ["pygame>=2.5.1,<2.7.0"]
 placo-dep = ["placo>=0.9.6,<0.9.17"]
 transformers-dep = ["transformers==5.3.0"] # TODO(Steven): https://github.com/huggingface/lerobot/pull/3249
@@ -186,8 +188,8 @@ async = ["lerobot[grpcio-dep]", "lerobot[matplotlib-dep]"]
 peft = ["lerobot[transformers-dep]", "lerobot[peft-dep]"]
 
 # Development
-dev = ["lerobot[dataset]", "lerobot[train]", "lerobot[hardware]", "lerobot[viz]", "pre-commit>=3.7.0,<5.0.0", "debugpy>=1.8.1,<1.9.0", "lerobot[grpcio-dep]", "grpcio-tools==1.73.1", "mypy>=1.19.1"]
-test = ["lerobot[dataset]", "lerobot[train]", "lerobot[hardware]", "lerobot[viz]", "pytest>=8.1.0,<9.0.0", "pytest-timeout>=2.4.0,<3.0.0", "pytest-cov>=5.0.0,<8.0.0", "mock-serial>=0.0.1,<0.1.0 ; sys_platform != 'win32'"]
+dev = ["lerobot[dataset]", "lerobot[training]", "lerobot[hardware]", "lerobot[viz]", "pre-commit>=3.7.0,<5.0.0", "debugpy>=1.8.1,<1.9.0", "lerobot[grpcio-dep]", "grpcio-tools==1.73.1", "mypy>=1.19.1"]
+test = ["lerobot[dataset]", "lerobot[training]", "lerobot[hardware]", "lerobot[viz]", "pytest>=8.1.0,<9.0.0", "pytest-timeout>=2.4.0,<3.0.0", "pytest-cov>=5.0.0,<8.0.0", "mock-serial>=0.0.1,<0.1.0 ; sys_platform != 'win32'"]
 video_benchmark = ["scikit-image>=0.23.2,<0.26.0", "pandas>=2.2.2,<2.4.0"]
 
 # Simulation
@@ -201,7 +203,7 @@ metaworld = ["lerobot[dataset]", "metaworld==3.0.0", "lerobot[scipy-dep]"]
 all = [
     # Feature-scoped extras
     "lerobot[dataset]",
-    "lerobot[train]",
+    "lerobot[training]",
     "lerobot[hardware]",
     "lerobot[viz]",
     "lerobot[build]",
@@ -294,7 +296,6 @@ ignore = [
 
 [tool.ruff.lint.per-file-ignores]
 "__init__.py" = ["F401", "F403", "E402"]
-"src/lerobot/scripts/*" = ["E402"] # require_package gates before imports
 "src/lerobot/policies/wall_x/**" = ["N801", "N812", "SIM102", "SIM108", "SIM210", "SIM211", "B006", "B007", "SIM118"] # Supprese these as they are coming from original Qwen2_5_vl code TODO(pepijn): refactor original
 
 [tool.ruff.lint.isort]

src/lerobot/datasets/utils.py

@@ -17,9 +17,7 @@ import contextlib
 import importlib.resources
 import json
 import logging
-from collections.abc import Iterator
 from pathlib import Path
-from typing import Any
 
 import datasets
 import numpy as np
@@ -281,27 +279,6 @@ def get_safe_version(repo_id: str, version: str | packaging.version.Version) ->
     raise ForwardCompatibilityError(repo_id, min(upper_versions))
 
 
-def cycle(iterable: Any) -> Iterator[Any]:
-    """Create a dataloader-safe cyclical iterator.
-
-    This is an equivalent of `itertools.cycle` but is safe for use with
-    PyTorch DataLoaders with multiple workers.
-    See https://github.com/pytorch/pytorch/issues/23900 for details.
-
-    Args:
-        iterable: The iterable to cycle over.
-
-    Yields:
-        Items from the iterable, restarting from the beginning when exhausted.
-    """
-    iterator = iter(iterable)
-    while True:
-        try:
-            yield next(iterator)
-        except StopIteration:
-            iterator = iter(iterable)
-
-
 def create_branch(repo_id: str, *, branch: str, repo_type: str | None = None) -> None:
     """Create a branch on an existing Hugging Face repo.
 

src/lerobot/policies/diffusion/modeling_diffusion.py

@@ -29,19 +29,24 @@ import numpy as np
 import torch
 import torch.nn.functional as F  # noqa: N812
 import torchvision
-from diffusers.schedulers.scheduling_ddim import DDIMScheduler
-from diffusers.schedulers.scheduling_ddpm import DDPMScheduler
-from torch import Tensor, nn
 
-from lerobot.policies.diffusion.configuration_diffusion import DiffusionConfig
-from lerobot.policies.pretrained import PreTrainedPolicy
-from lerobot.policies.utils import (
+from lerobot.utils.import_utils import require_package
+
+require_package("diffusers", extra="training")
+
+from diffusers.schedulers.scheduling_ddim import DDIMScheduler  # noqa: E402
+from diffusers.schedulers.scheduling_ddpm import DDPMScheduler  # noqa: E402
+from torch import Tensor, nn  # noqa: E402
+
+from lerobot.policies.diffusion.configuration_diffusion import DiffusionConfig  # noqa: E402
+from lerobot.policies.pretrained import PreTrainedPolicy  # noqa: E402
+from lerobot.policies.utils import (  # noqa: E402
     get_device_from_parameters,
     get_dtype_from_parameters,
     get_output_shape,
     populate_queues,
 )
-from lerobot.utils.constants import ACTION, OBS_ENV_STATE, OBS_IMAGES, OBS_STATE
+from lerobot.utils.constants import ACTION, OBS_ENV_STATE, OBS_IMAGES, OBS_STATE  # noqa: E402
 
 
 class DiffusionPolicy(PreTrainedPolicy):

src/lerobot/policies/groot/action_head/cross_attention_dit.py

@@ -16,15 +16,20 @@
 
 import torch
 import torch.nn.functional as F  # noqa: N812
-from diffusers import ConfigMixin, ModelMixin
-from diffusers.configuration_utils import register_to_config
-from diffusers.models.attention import Attention, FeedForward
-from diffusers.models.embeddings import (
+
+from lerobot.utils.import_utils import require_package
+
+require_package("diffusers", extra="training")
+
+from diffusers import ConfigMixin, ModelMixin  # noqa: E402
+from diffusers.configuration_utils import register_to_config  # noqa: E402
+from diffusers.models.attention import Attention, FeedForward  # noqa: E402
+from diffusers.models.embeddings import (  # noqa: E402
     SinusoidalPositionalEmbedding,
     TimestepEmbedding,
     Timesteps,
 )
-from torch import nn
+from torch import nn  # noqa: E402
 
 
 class TimestepEncoder(nn.Module):

src/lerobot/policies/multi_task_dit/modeling_multi_task_dit.py

@@ -34,12 +34,17 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F  # noqa: N812
 import torchvision
-from diffusers.schedulers.scheduling_ddim import DDIMScheduler
-from diffusers.schedulers.scheduling_ddpm import DDPMScheduler
-from torch import Tensor
 
-from lerobot.policies.multi_task_dit.configuration_multi_task_dit import MultiTaskDiTConfig
-from lerobot.utils.import_utils import _transformers_available
+from lerobot.utils.import_utils import require_package
+
+require_package("diffusers", extra="training")
+
+from diffusers.schedulers.scheduling_ddim import DDIMScheduler  # noqa: E402
+from diffusers.schedulers.scheduling_ddpm import DDPMScheduler  # noqa: E402
+from torch import Tensor  # noqa: E402
+
+from lerobot.policies.multi_task_dit.configuration_multi_task_dit import MultiTaskDiTConfig  # noqa: E402
+from lerobot.utils.import_utils import _transformers_available  # noqa: E402
 
 # Conditional import for type checking and lazy loading
 if TYPE_CHECKING or _transformers_available:
@@ -47,9 +52,9 @@ if TYPE_CHECKING or _transformers_available:
 else:
     CLIPTextModel = None
     CLIPVisionModel = None
-from lerobot.policies.pretrained import PreTrainedPolicy
-from lerobot.policies.utils import populate_queues
-from lerobot.utils.constants import (
+from lerobot.policies.pretrained import PreTrainedPolicy  # noqa: E402
+from lerobot.policies.utils import populate_queues  # noqa: E402
+from lerobot.utils.constants import (  # noqa: E402
     ACTION,
     OBS_IMAGES,
     OBS_LANGUAGE_ATTENTION_MASK,

src/lerobot/scripts/augment_dataset_quantile_stats.py

@@ -44,10 +44,10 @@ from huggingface_hub import HfApi
 from requests import HTTPError
 from tqdm import tqdm
 
+from lerobot.datasets import LeRobotDataset
 from lerobot.datasets.compute_stats import DEFAULT_QUANTILES, aggregate_stats, get_feature_stats
 from lerobot.datasets.dataset_metadata import CODEBASE_VERSION
 from lerobot.datasets.io_utils import write_stats
-from lerobot.datasets.lerobot_dataset import LeRobotDataset
 from lerobot.utils.utils import init_logging
 
 

src/lerobot/scripts/convert_dataset_v21_to_v30.py

@@ -59,6 +59,7 @@ from datasets import Dataset, Features, Image
 from huggingface_hub import HfApi, snapshot_download
 from requests import HTTPError
 
+from lerobot.datasets import LeRobotDataset
 from lerobot.datasets.compute_stats import aggregate_stats
 from lerobot.datasets.dataset_metadata import CODEBASE_VERSION
 from lerobot.datasets.io_utils import (
@@ -72,7 +73,6 @@ from lerobot.datasets.io_utils import (
     write_stats,
     write_tasks,
 )
-from lerobot.datasets.lerobot_dataset import LeRobotDataset
 from lerobot.datasets.utils import (
     DEFAULT_CHUNK_SIZE,
     DEFAULT_DATA_FILE_SIZE_IN_MB,
@@ -82,12 +82,11 @@ from lerobot.datasets.utils import (
     LEGACY_EPISODES_PATH,
     LEGACY_EPISODES_STATS_PATH,
     LEGACY_TASKS_PATH,
-    flatten_dict,
     update_chunk_file_indices,
 )
 from lerobot.datasets.video_utils import concatenate_video_files, get_video_duration_in_s
 from lerobot.utils.constants import HF_LEROBOT_HOME
-from lerobot.utils.utils import init_logging
+from lerobot.utils.utils import flatten_dict, init_logging
 
 V21 = "v2.1"
 V30 = "v3.0"

src/lerobot/scripts/lerobot_dataset_viz.py

@@ -70,7 +70,7 @@ import torch
 import torch.utils.data
 import tqdm
 
-from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.datasets import LeRobotDataset
 from lerobot.utils.constants import ACTION, DONE, OBS_STATE, REWARD
 from lerobot.utils.utils import init_logging
 

src/lerobot/scripts/lerobot_edit_dataset.py

@@ -178,6 +178,7 @@ from pathlib import Path
 import draccus
 
 from lerobot.configs import parser
+from lerobot.datasets import LeRobotDataset
 from lerobot.datasets.dataset_tools import (
     convert_image_to_video_dataset,
     delete_episodes,
@@ -187,7 +188,6 @@ from lerobot.datasets.dataset_tools import (
     remove_feature,
     split_dataset,
 )
-from lerobot.datasets.lerobot_dataset import LeRobotDataset
 from lerobot.utils.constants import HF_LEROBOT_HOME
 from lerobot.utils.utils import init_logging
 

src/lerobot/scripts/lerobot_imgtransform_viz.py

@@ -36,7 +36,7 @@ import draccus
 from torchvision.transforms import ToPILImage
 
 from lerobot.configs.default import DatasetConfig
-from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.datasets import LeRobotDataset
 from lerobot.transforms import (
     ImageTransforms,
     ImageTransformsConfig,

src/lerobot/scripts/lerobot_record.py

@@ -85,9 +85,9 @@ from lerobot.cameras.realsense.configuration_realsense import RealSenseCameraCon
 from lerobot.cameras.zmq.configuration_zmq import ZMQCameraConfig  # noqa: F401
 from lerobot.configs import parser
 from lerobot.configs.policies import PreTrainedConfig
+from lerobot.datasets import LeRobotDataset
 from lerobot.datasets.feature_utils import build_dataset_frame, combine_feature_dicts
 from lerobot.datasets.image_writer import safe_stop_image_writer
-from lerobot.datasets.lerobot_dataset import LeRobotDataset
 from lerobot.datasets.pipeline_features import aggregate_pipeline_dataset_features, create_initial_features
 from lerobot.datasets.video_utils import VideoEncodingManager
 from lerobot.policies.factory import make_policy, make_pre_post_processors

src/lerobot/scripts/lerobot_replay.py

@@ -46,7 +46,7 @@ from pathlib import Path
 from pprint import pformat
 
 from lerobot.configs import parser
-from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.datasets import LeRobotDataset
 from lerobot.processor import (
     make_default_robot_action_processor,
 )

src/lerobot/scripts/lerobot_train.py

@@ -24,35 +24,35 @@ from lerobot.utils.import_utils import require_package
 
 require_package("accelerate", extra="training")
 
-import torch
-from accelerate import Accelerator
-from termcolor import colored
-from torch.optim import Optimizer
-from tqdm import tqdm
+import torch  # noqa: E402
+from accelerate import Accelerator  # noqa: E402
+from termcolor import colored  # noqa: E402
+from torch.optim import Optimizer  # noqa: E402
+from tqdm import tqdm  # noqa: E402
 
-from lerobot.configs import parser
-from lerobot.configs.train import TrainPipelineConfig
-from lerobot.datasets.factory import make_dataset
-from lerobot.datasets.sampler import EpisodeAwareSampler
-from lerobot.datasets.utils import cycle
-from lerobot.envs.factory import make_env, make_env_pre_post_processors
-from lerobot.envs.utils import close_envs
-from lerobot.optim.factory import make_optimizer_and_scheduler
-from lerobot.policies.factory import make_policy, make_pre_post_processors
-from lerobot.policies.pretrained import PreTrainedPolicy
-from lerobot.rl.wandb_utils import WandBLogger
-from lerobot.scripts.lerobot_eval import eval_policy_all
-from lerobot.utils.import_utils import register_third_party_plugins
-from lerobot.utils.logging_utils import AverageMeter, MetricsTracker
-from lerobot.utils.random_utils import set_seed
-from lerobot.utils.train_utils import (
+from lerobot.configs import parser  # noqa: E402
+from lerobot.configs.train import TrainPipelineConfig  # noqa: E402
+from lerobot.datasets import EpisodeAwareSampler  # noqa: E402
+from lerobot.datasets.factory import make_dataset  # noqa: E402
+from lerobot.envs.factory import make_env, make_env_pre_post_processors  # noqa: E402
+from lerobot.envs.utils import close_envs  # noqa: E402
+from lerobot.optim.factory import make_optimizer_and_scheduler  # noqa: E402
+from lerobot.policies.factory import make_policy, make_pre_post_processors  # noqa: E402
+from lerobot.policies.pretrained import PreTrainedPolicy  # noqa: E402
+from lerobot.rl.wandb_utils import WandBLogger  # noqa: E402
+from lerobot.scripts.lerobot_eval import eval_policy_all  # noqa: E402
+from lerobot.utils.import_utils import register_third_party_plugins  # noqa: E402
+from lerobot.utils.logging_utils import AverageMeter, MetricsTracker  # noqa: E402
+from lerobot.utils.random_utils import set_seed  # noqa: E402
+from lerobot.utils.train_utils import (  # noqa: E402
     get_step_checkpoint_dir,
     get_step_identifier,
     load_training_state,
     save_checkpoint,
     update_last_checkpoint,
 )
-from lerobot.utils.utils import (
+from lerobot.utils.utils import (  # noqa: E402
+    cycle,
     format_big_number,
     has_method,
     init_logging,

src/lerobot/scripts/lerobot_train_tokenizer.py

@@ -62,7 +62,7 @@ else:
 
 from lerobot.configs import parser
 from lerobot.configs.types import NormalizationMode
-from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.datasets import LeRobotDataset
 from lerobot.utils.constants import ACTION, OBS_STATE
 
 

src/lerobot/transforms/__init__.py

@@ -1,6 +1,4 @@
-#!/usr/bin/env python
-
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+# Copyright 2026 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.
@@ -13,248 +11,21 @@
 # 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 collections
-from collections.abc import Callable, Sequence
-from dataclasses import dataclass, field
-from typing import Any
 
-import torch
-from torchvision.transforms import v2
-from torchvision.transforms.v2 import (
-    Transform,
-    functional as F,  # noqa: N812
+from lerobot.transforms.transforms import (
+    ImageTransformConfig,
+    ImageTransforms,
+    ImageTransformsConfig,
+    RandomSubsetApply,
+    SharpnessJitter,
+    make_transform_from_config,
 )
 
-
-class RandomSubsetApply(Transform):
-    """Apply a random subset of N transformations from a list of transformations.
-
-    Args:
-        transforms: list of transformations.
-        p: represents the multinomial probabilities (with no replacement) used for sampling the transform.
-            If the sum of the weights is not 1, they will be normalized. If ``None`` (default), all transforms
-            have the same probability.
-        n_subset: number of transformations to apply. If ``None``, all transforms are applied.
-            Must be in [1, len(transforms)].
-        random_order: apply transformations in a random order.
-    """
-
-    def __init__(
-        self,
-        transforms: Sequence[Callable],
-        p: list[float] | None = None,
-        n_subset: int | None = None,
-        random_order: bool = False,
-    ) -> None:
-        super().__init__()
-        if not isinstance(transforms, Sequence):
-            raise TypeError("Argument transforms should be a sequence of callables")
-        if p is None:
-            p = [1] * len(transforms)
-        elif len(p) != len(transforms):
-            raise ValueError(
-                f"Length of p doesn't match the number of transforms: {len(p)} != {len(transforms)}"
-            )
-
-        if n_subset is None:
-            n_subset = len(transforms)
-        elif not isinstance(n_subset, int):
-            raise TypeError("n_subset should be an int or None")
-        elif not (1 <= n_subset <= len(transforms)):
-            raise ValueError(f"n_subset should be in the interval [1, {len(transforms)}]")
-
-        self.transforms = transforms
-        total = sum(p)
-        self.p = [prob / total for prob in p]
-        self.n_subset = n_subset
-        self.random_order = random_order
-
-        self.selected_transforms = None
-
-    def forward(self, *inputs: Any) -> Any:
-        needs_unpacking = len(inputs) > 1
-
-        selected_indices = torch.multinomial(torch.tensor(self.p), self.n_subset)
-        if not self.random_order:
-            selected_indices = selected_indices.sort().values
-
-        self.selected_transforms = [self.transforms[i] for i in selected_indices]
-
-        for transform in self.selected_transforms:
-            outputs = transform(*inputs)
-            inputs = outputs if needs_unpacking else (outputs,)
-
-        return outputs
-
-    def extra_repr(self) -> str:
-        return (
-            f"transforms={self.transforms}, "
-            f"p={self.p}, "
-            f"n_subset={self.n_subset}, "
-            f"random_order={self.random_order}"
-        )
-
-
-class SharpnessJitter(Transform):
-    """Randomly change the sharpness of an image or video.
-
-    Similar to a v2.RandomAdjustSharpness with p=1 and a sharpness_factor sampled randomly.
-    While v2.RandomAdjustSharpness applies — with a given probability — a fixed sharpness_factor to an image,
-    SharpnessJitter applies a random sharpness_factor each time. This is to have a more diverse set of
-    augmentations as a result.
-
-    A sharpness_factor of 0 gives a blurred image, 1 gives the original image while 2 increases the sharpness
-    by a factor of 2.
-
-    If the input is a :class:`torch.Tensor`,
-    it is expected to have [..., 1 or 3, H, W] shape, where ... means an arbitrary number of leading dimensions.
-
-    Args:
-        sharpness: How much to jitter sharpness. sharpness_factor is chosen uniformly from
-            [max(0, 1 - sharpness), 1 + sharpness] or the given
-            [min, max]. Should be non negative numbers.
-    """
-
-    def __init__(self, sharpness: float | Sequence[float]) -> None:
-        super().__init__()
-        self.sharpness = self._check_input(sharpness)
-
-    def _check_input(self, sharpness):
-        if isinstance(sharpness, (int | float)):
-            if sharpness < 0:
-                raise ValueError("If sharpness is a single number, it must be non negative.")
-            sharpness = [1.0 - sharpness, 1.0 + sharpness]
-            sharpness[0] = max(sharpness[0], 0.0)
-        elif isinstance(sharpness, collections.abc.Sequence) and len(sharpness) == 2:
-            sharpness = [float(v) for v in sharpness]
-        else:
-            raise TypeError(f"{sharpness=} should be a single number or a sequence with length 2.")
-
-        if not 0.0 <= sharpness[0] <= sharpness[1]:
-            raise ValueError(f"sharpness values should be between (0., inf), but got {sharpness}.")
-
-        return float(sharpness[0]), float(sharpness[1])
-
-    def make_params(self, flat_inputs: list[Any]) -> dict[str, Any]:
-        sharpness_factor = torch.empty(1).uniform_(self.sharpness[0], self.sharpness[1]).item()
-        return {"sharpness_factor": sharpness_factor}
-
-    def transform(self, inpt: Any, params: dict[str, Any]) -> Any:
-        sharpness_factor = params["sharpness_factor"]
-        return self._call_kernel(F.adjust_sharpness, inpt, sharpness_factor=sharpness_factor)
-
-
-@dataclass
-class ImageTransformConfig:
-    """
-    For each transform, the following parameters are available:
-      weight: This represents the multinomial probability (with no replacement)
-            used for sampling the transform. If the sum of the weights is not 1,
-            they will be normalized.
-      type: The name of the class used. This is either a class available under torchvision.transforms.v2 or a
-            custom transform defined here.
-      kwargs: Lower & upper bound respectively used for sampling the transform's parameter
-            (following uniform distribution) when it's applied.
-    """
-
-    weight: float = 1.0
-    type: str = "Identity"
-    kwargs: dict[str, Any] = field(default_factory=dict)
-
-
-@dataclass
-class ImageTransformsConfig:
-    """
-    These transforms are all using standard torchvision.transforms.v2
-    You can find out how these transformations affect images here:
-    https://pytorch.org/vision/0.18/auto_examples/transforms/plot_transforms_illustrations.html
-    We use a custom RandomSubsetApply container to sample them.
-    """
-
-    # Set this flag to `true` to enable transforms during training
-    enable: bool = False
-    # This is the maximum number of transforms (sampled from these below) that will be applied to each frame.
-    # It's an integer in the interval [1, number_of_available_transforms].
-    max_num_transforms: int = 3
-    # By default, transforms are applied in Torchvision's suggested order (shown below).
-    # Set this to True to apply them in a random order.
-    random_order: bool = False
-    tfs: dict[str, ImageTransformConfig] = field(
-        default_factory=lambda: {
-            "brightness": ImageTransformConfig(
-                weight=1.0,
-                type="ColorJitter",
-                kwargs={"brightness": (0.8, 1.2)},
-            ),
-            "contrast": ImageTransformConfig(
-                weight=1.0,
-                type="ColorJitter",
-                kwargs={"contrast": (0.8, 1.2)},
-            ),
-            "saturation": ImageTransformConfig(
-                weight=1.0,
-                type="ColorJitter",
-                kwargs={"saturation": (0.5, 1.5)},
-            ),
-            "hue": ImageTransformConfig(
-                weight=1.0,
-                type="ColorJitter",
-                kwargs={"hue": (-0.05, 0.05)},
-            ),
-            "sharpness": ImageTransformConfig(
-                weight=1.0,
-                type="SharpnessJitter",
-                kwargs={"sharpness": (0.5, 1.5)},
-            ),
-            "affine": ImageTransformConfig(
-                weight=1.0,
-                type="RandomAffine",
-                kwargs={"degrees": (-5.0, 5.0), "translate": (0.05, 0.05)},
-            ),
-        }
-    )
-
-
-def make_transform_from_config(cfg: ImageTransformConfig):
-    if cfg.type == "SharpnessJitter":
-        return SharpnessJitter(**cfg.kwargs)
-
-    transform_cls = getattr(v2, cfg.type, None)
-    if isinstance(transform_cls, type) and issubclass(transform_cls, Transform):
-        return transform_cls(**cfg.kwargs)
-
-    raise ValueError(
-        f"Transform '{cfg.type}' is not valid. It must be a class in "
-        f"torchvision.transforms.v2 or 'SharpnessJitter'."
-    )
-
-
-class ImageTransforms(Transform):
-    """A class to compose image transforms based on configuration."""
-
-    def __init__(self, cfg: ImageTransformsConfig) -> None:
-        super().__init__()
-        self._cfg = cfg
-
-        self.weights = []
-        self.transforms = {}
-        for tf_name, tf_cfg in cfg.tfs.items():
-            if tf_cfg.weight <= 0.0:
-                continue
-
-            self.transforms[tf_name] = make_transform_from_config(tf_cfg)
-            self.weights.append(tf_cfg.weight)
-
-        n_subset = min(len(self.transforms), cfg.max_num_transforms)
-        if n_subset == 0 or not cfg.enable:
-            self.tf = v2.Identity()
-        else:
-            self.tf = RandomSubsetApply(
-                transforms=list(self.transforms.values()),
-                p=self.weights,
-                n_subset=n_subset,
-                random_order=cfg.random_order,
-            )
-
-    def forward(self, *inputs: Any) -> Any:
-        return self.tf(*inputs)
+__all__ = [
+    "ImageTransformConfig",
+    "ImageTransforms",
+    "ImageTransformsConfig",
+    "RandomSubsetApply",
+    "SharpnessJitter",
+    "make_transform_from_config",
+]

src/lerobot/transforms/transforms.py

@@ -0,0 +1,260 @@
+#!/usr/bin/env python
+
+# Copyright 2024 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 collections
+from collections.abc import Callable, Sequence
+from dataclasses import dataclass, field
+from typing import Any
+
+import torch
+from torchvision.transforms import v2
+from torchvision.transforms.v2 import (
+    Transform,
+    functional as F,  # noqa: N812
+)
+
+
+class RandomSubsetApply(Transform):
+    """Apply a random subset of N transformations from a list of transformations.
+
+    Args:
+        transforms: list of transformations.
+        p: represents the multinomial probabilities (with no replacement) used for sampling the transform.
+            If the sum of the weights is not 1, they will be normalized. If ``None`` (default), all transforms
+            have the same probability.
+        n_subset: number of transformations to apply. If ``None``, all transforms are applied.
+            Must be in [1, len(transforms)].
+        random_order: apply transformations in a random order.
+    """
+
+    def __init__(
+        self,
+        transforms: Sequence[Callable],
+        p: list[float] | None = None,
+        n_subset: int | None = None,
+        random_order: bool = False,
+    ) -> None:
+        super().__init__()
+        if not isinstance(transforms, Sequence):
+            raise TypeError("Argument transforms should be a sequence of callables")
+        if p is None:
+            p = [1] * len(transforms)
+        elif len(p) != len(transforms):
+            raise ValueError(
+                f"Length of p doesn't match the number of transforms: {len(p)} != {len(transforms)}"
+            )
+
+        if n_subset is None:
+            n_subset = len(transforms)
+        elif not isinstance(n_subset, int):
+            raise TypeError("n_subset should be an int or None")
+        elif not (1 <= n_subset <= len(transforms)):
+            raise ValueError(f"n_subset should be in the interval [1, {len(transforms)}]")
+
+        self.transforms = transforms
+        total = sum(p)
+        self.p = [prob / total for prob in p]
+        self.n_subset = n_subset
+        self.random_order = random_order
+
+        self.selected_transforms = None
+
+    def forward(self, *inputs: Any) -> Any:
+        needs_unpacking = len(inputs) > 1
+
+        selected_indices = torch.multinomial(torch.tensor(self.p), self.n_subset)
+        if not self.random_order:
+            selected_indices = selected_indices.sort().values
+
+        self.selected_transforms = [self.transforms[i] for i in selected_indices]
+
+        for transform in self.selected_transforms:
+            outputs = transform(*inputs)
+            inputs = outputs if needs_unpacking else (outputs,)
+
+        return outputs
+
+    def extra_repr(self) -> str:
+        return (
+            f"transforms={self.transforms}, "
+            f"p={self.p}, "
+            f"n_subset={self.n_subset}, "
+            f"random_order={self.random_order}"
+        )
+
+
+class SharpnessJitter(Transform):
+    """Randomly change the sharpness of an image or video.
+
+    Similar to a v2.RandomAdjustSharpness with p=1 and a sharpness_factor sampled randomly.
+    While v2.RandomAdjustSharpness applies — with a given probability — a fixed sharpness_factor to an image,
+    SharpnessJitter applies a random sharpness_factor each time. This is to have a more diverse set of
+    augmentations as a result.
+
+    A sharpness_factor of 0 gives a blurred image, 1 gives the original image while 2 increases the sharpness
+    by a factor of 2.
+
+    If the input is a :class:`torch.Tensor`,
+    it is expected to have [..., 1 or 3, H, W] shape, where ... means an arbitrary number of leading dimensions.
+
+    Args:
+        sharpness: How much to jitter sharpness. sharpness_factor is chosen uniformly from
+            [max(0, 1 - sharpness), 1 + sharpness] or the given
+            [min, max]. Should be non negative numbers.
+    """
+
+    def __init__(self, sharpness: float | Sequence[float]) -> None:
+        super().__init__()
+        self.sharpness = self._check_input(sharpness)
+
+    def _check_input(self, sharpness):
+        if isinstance(sharpness, (int | float)):
+            if sharpness < 0:
+                raise ValueError("If sharpness is a single number, it must be non negative.")
+            sharpness = [1.0 - sharpness, 1.0 + sharpness]
+            sharpness[0] = max(sharpness[0], 0.0)
+        elif isinstance(sharpness, collections.abc.Sequence) and len(sharpness) == 2:
+            sharpness = [float(v) for v in sharpness]
+        else:
+            raise TypeError(f"{sharpness=} should be a single number or a sequence with length 2.")
+
+        if not 0.0 <= sharpness[0] <= sharpness[1]:
+            raise ValueError(f"sharpness values should be between (0., inf), but got {sharpness}.")
+
+        return float(sharpness[0]), float(sharpness[1])
+
+    def make_params(self, flat_inputs: list[Any]) -> dict[str, Any]:
+        sharpness_factor = torch.empty(1).uniform_(self.sharpness[0], self.sharpness[1]).item()
+        return {"sharpness_factor": sharpness_factor}
+
+    def transform(self, inpt: Any, params: dict[str, Any]) -> Any:
+        sharpness_factor = params["sharpness_factor"]
+        return self._call_kernel(F.adjust_sharpness, inpt, sharpness_factor=sharpness_factor)
+
+
+@dataclass
+class ImageTransformConfig:
+    """
+    For each transform, the following parameters are available:
+      weight: This represents the multinomial probability (with no replacement)
+            used for sampling the transform. If the sum of the weights is not 1,
+            they will be normalized.
+      type: The name of the class used. This is either a class available under torchvision.transforms.v2 or a
+            custom transform defined here.
+      kwargs: Lower & upper bound respectively used for sampling the transform's parameter
+            (following uniform distribution) when it's applied.
+    """
+
+    weight: float = 1.0
+    type: str = "Identity"
+    kwargs: dict[str, Any] = field(default_factory=dict)
+
+
+@dataclass
+class ImageTransformsConfig:
+    """
+    These transforms are all using standard torchvision.transforms.v2
+    You can find out how these transformations affect images here:
+    https://pytorch.org/vision/0.18/auto_examples/transforms/plot_transforms_illustrations.html
+    We use a custom RandomSubsetApply container to sample them.
+    """
+
+    # Set this flag to `true` to enable transforms during training
+    enable: bool = False
+    # This is the maximum number of transforms (sampled from these below) that will be applied to each frame.
+    # It's an integer in the interval [1, number_of_available_transforms].
+    max_num_transforms: int = 3
+    # By default, transforms are applied in Torchvision's suggested order (shown below).
+    # Set this to True to apply them in a random order.
+    random_order: bool = False
+    tfs: dict[str, ImageTransformConfig] = field(
+        default_factory=lambda: {
+            "brightness": ImageTransformConfig(
+                weight=1.0,
+                type="ColorJitter",
+                kwargs={"brightness": (0.8, 1.2)},
+            ),
+            "contrast": ImageTransformConfig(
+                weight=1.0,
+                type="ColorJitter",
+                kwargs={"contrast": (0.8, 1.2)},
+            ),
+            "saturation": ImageTransformConfig(
+                weight=1.0,
+                type="ColorJitter",
+                kwargs={"saturation": (0.5, 1.5)},
+            ),
+            "hue": ImageTransformConfig(
+                weight=1.0,
+                type="ColorJitter",
+                kwargs={"hue": (-0.05, 0.05)},
+            ),
+            "sharpness": ImageTransformConfig(
+                weight=1.0,
+                type="SharpnessJitter",
+                kwargs={"sharpness": (0.5, 1.5)},
+            ),
+            "affine": ImageTransformConfig(
+                weight=1.0,
+                type="RandomAffine",
+                kwargs={"degrees": (-5.0, 5.0), "translate": (0.05, 0.05)},
+            ),
+        }
+    )
+
+
+def make_transform_from_config(cfg: ImageTransformConfig):
+    if cfg.type == "SharpnessJitter":
+        return SharpnessJitter(**cfg.kwargs)
+
+    transform_cls = getattr(v2, cfg.type, None)
+    if isinstance(transform_cls, type) and issubclass(transform_cls, Transform):
+        return transform_cls(**cfg.kwargs)
+
+    raise ValueError(
+        f"Transform '{cfg.type}' is not valid. It must be a class in "
+        f"torchvision.transforms.v2 or 'SharpnessJitter'."
+    )
+
+
+class ImageTransforms(Transform):
+    """A class to compose image transforms based on configuration."""
+
+    def __init__(self, cfg: ImageTransformsConfig) -> None:
+        super().__init__()
+        self._cfg = cfg
+
+        self.weights = []
+        self.transforms = {}
+        for tf_name, tf_cfg in cfg.tfs.items():
+            if tf_cfg.weight <= 0.0:
+                continue
+
+            self.transforms[tf_name] = make_transform_from_config(tf_cfg)
+            self.weights.append(tf_cfg.weight)
+
+        n_subset = min(len(self.transforms), cfg.max_num_transforms)
+        if n_subset == 0 or not cfg.enable:
+            self.tf = v2.Identity()
+        else:
+            self.tf = RandomSubsetApply(
+                transforms=list(self.transforms.values()),
+                p=self.weights,
+                n_subset=n_subset,
+                random_order=cfg.random_order,
+            )
+
+    def forward(self, *inputs: Any) -> Any:
+        return self.tf(*inputs)

src/lerobot/utils/import_utils.py

@@ -70,10 +70,11 @@ def is_package_available(
 
 
 def get_safe_default_codec():
+    logger = logging.getLogger(__name__)
     if importlib.util.find_spec("torchcodec"):
         return "torchcodec"
     else:
-        logging.warning(
+        logger.warning(
             "'torchcodec' is not available in your platform, falling back to 'pyav' as a default decoder"
         )
         return "pyav"

src/lerobot/utils/io_utils.py

@@ -14,9 +14,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import json
+import logging
 from pathlib import Path
 from typing import Any
 
+logger = logging.getLogger(__name__)
+
 JsonLike = str | int | float | bool | None | list["JsonLike"] | dict[str, "JsonLike"] | tuple["JsonLike", ...]
 
 
@@ -58,10 +61,18 @@ def write_video(video_path: str | Path, stacked_frames: list, fps: int) -> None:
     import av
 
     with av.open(str(video_path), mode="w") as container:
-        height, width = stacked_frames[0].shape[:2]
-        # Ensure dimensions are even for yuv420p compatibility
-        height = height if height % 2 == 0 else height - 1
-        width = width if width % 2 == 0 else width - 1
+        orig_height, orig_width = stacked_frames[0].shape[:2]
+        # yuv420p requires even dimensions; crop by one pixel if needed
+        height = orig_height if orig_height % 2 == 0 else orig_height - 1
+        width = orig_width if orig_width % 2 == 0 else orig_width - 1
+        if height != orig_height or width != orig_width:
+            logger.warning(
+                "Frame dimensions %dx%d are not even; cropping to %dx%d for yuv420p compatibility.",
+                orig_width,
+                orig_height,
+                width,
+                height,
+            )
         stream = container.add_stream("libx264", rate=fps)
         stream.width = width
         stream.height = height

src/lerobot/utils/utils.py

@@ -22,11 +22,12 @@ import select
 import subprocess
 import sys
 import time
+from collections.abc import Iterator
 from copy import copy, deepcopy
 from datetime import datetime
 from pathlib import Path
 from statistics import mean
-from typing import TYPE_CHECKING
+from typing import TYPE_CHECKING, Any
 
 import numpy as np
 
@@ -252,6 +253,27 @@ def unflatten_dict(d: dict, sep: str = "/") -> dict:
     return outdict
 
 
+def cycle(iterable: Any) -> Iterator[Any]:
+    """Create a dataloader-safe cyclical iterator.
+
+    This is an equivalent of `itertools.cycle` but is safe for use with
+    PyTorch DataLoaders with multiple workers.
+    See https://github.com/pytorch/pytorch/issues/23900 for details.
+
+    Args:
+        iterable: The iterable to cycle over.
+
+    Yields:
+        Items from the iterable, restarting from the beginning when exhausted.
+    """
+    iterator = iter(iterable)
+    while True:
+        try:
+            yield next(iterator)
+        except StopIteration:
+            iterator = iter(iterable)
+
+
 class SuppressProgressBars:
     """
     Context manager to suppress progress bars.

tests/policies/test_policies.py

@@ -29,7 +29,6 @@ from lerobot.configs.train import TrainPipelineConfig
 from lerobot.configs.types import FeatureType, PolicyFeature
 from lerobot.datasets.factory import make_dataset
 from lerobot.datasets.feature_utils import dataset_to_policy_features
-from lerobot.datasets.utils import cycle
 from lerobot.envs.factory import make_env, make_env_config
 from lerobot.envs.utils import close_envs, preprocess_observation
 from lerobot.optim.factory import make_optimizer_and_scheduler
@@ -46,6 +45,7 @@ from lerobot.policies.vqbet.configuration_vqbet import VQBeTConfig
 from lerobot.policies.vqbet.modeling_vqbet import VQBeTHead
 from lerobot.utils.constants import ACTION, OBS_IMAGES, OBS_STATE
 from lerobot.utils.random_utils import seeded_context
+from lerobot.utils.utils import cycle
 from tests.artifacts.policies.save_policy_to_safetensors import get_policy_stats
 from tests.utils import DEVICE, require_cpu, require_env, require_x86_64_kernel
 

uv.lock

@@ -2275,6 +2275,9 @@ async = [
     { name = "matplotlib" },
     { name = "protobuf" },
 ]
+av-dep = [
+    { name = "av" },
+]
 build = [
     { name = "cmake" },
     { name = "setuptools" },
@@ -2483,15 +2486,6 @@ test = [
     { name = "torchcodec", marker = "(platform_machine != 'aarch64' and platform_machine != 'arm64' and platform_machine != 'armv7l' and sys_platform == 'linux') or (platform_machine != 'x86_64' and sys_platform == 'darwin') or (sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32')" },
     { name = "wandb" },
 ]
-train = [
-    { name = "accelerate" },
-    { name = "av" },
-    { name = "datasets" },
-    { name = "diffusers" },
-    { name = "jsonlines" },
-    { name = "torchcodec", marker = "(platform_machine != 'aarch64' and platform_machine != 'arm64' and platform_machine != 'armv7l' and sys_platform == 'linux') or (platform_machine != 'x86_64' and sys_platform == 'darwin') or (sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32')" },
-    { name = "wandb" },
-]
 training = [
     { name = "accelerate" },
     { name = "av" },
@@ -2534,16 +2528,15 @@ xvla = [
 [package.metadata]
 requires-dist = [
     { name = "accelerate", marker = "extra == 'smolvla'", specifier = ">=1.7.0,<2.0.0" },
-    { name = "accelerate", marker = "extra == 'train'", specifier = ">=1.10.0,<2.0.0" },
-    { name = "av", marker = "extra == 'dataset'", specifier = ">=15.0.0,<16.0.0" },
-    { name = "av", marker = "extra == 'evaluation'", specifier = ">=15.0.0,<16.0.0" },
+    { name = "accelerate", marker = "extra == 'training'", specifier = ">=1.10.0,<2.0.0" },
+    { name = "av", marker = "extra == 'av-dep'", specifier = ">=15.0.0,<16.0.0" },
     { name = "cmake", marker = "extra == 'build'", specifier = ">=3.29.0.1,<4.2.0" },
     { name = "contourpy", marker = "extra == 'matplotlib-dep'", specifier = ">=1.3.0,<2.0.0" },
     { name = "datasets", marker = "extra == 'dataset'", specifier = ">=4.0.0,<5.0.0" },
     { name = "debugpy", marker = "extra == 'dev'", specifier = ">=1.8.1,<1.9.0" },
     { name = "decord", marker = "(platform_machine == 'AMD64' and extra == 'groot') or (platform_machine == 'x86_64' and extra == 'groot')", specifier = ">=0.6.0,<1.0.0" },
     { name = "deepdiff", marker = "extra == 'hardware'", specifier = ">=7.0.1,<9.0.0" },
-    { name = "diffusers", marker = "extra == 'train'", specifier = ">=0.27.2,<0.36.0" },
+    { name = "diffusers", marker = "extra == 'training'", specifier = ">=0.27.2,<0.36.0" },
     { name = "dm-tree", marker = "extra == 'groot'", specifier = ">=0.1.8,<1.0.0" },
     { name = "draccus", specifier = "==0.10.0" },
     { name = "dynamixel-sdk", marker = "extra == 'dynamixel'", specifier = ">=3.7.31,<3.9.0" },
@@ -2565,6 +2558,8 @@ requires-dist = [
     { name = "jsonlines", marker = "extra == 'dataset'", specifier = ">=4.0.0,<5.0.0" },
     { name = "lerobot", extras = ["aloha"], marker = "extra == 'all'" },
     { name = "lerobot", extras = ["async"], marker = "extra == 'all'" },
+    { name = "lerobot", extras = ["av-dep"], marker = "extra == 'dataset'" },
+    { name = "lerobot", extras = ["av-dep"], marker = "extra == 'evaluation'" },
     { name = "lerobot", extras = ["build"], marker = "extra == 'all'" },
     { name = "lerobot", extras = ["can-dep"], marker = "extra == 'damiao'" },
     { name = "lerobot", extras = ["can-dep"], marker = "extra == 'robstride'" },
@@ -2578,7 +2573,7 @@ requires-dist = [
     { name = "lerobot", extras = ["dataset"], marker = "extra == 'pusht'" },
     { name = "lerobot", extras = ["dataset"], marker = "extra == 'robot'" },
     { name = "lerobot", extras = ["dataset"], marker = "extra == 'test'" },
-    { name = "lerobot", extras = ["dataset"], marker = "extra == 'train'" },
+    { name = "lerobot", extras = ["dataset"], marker = "extra == 'training'" },
     { name = "lerobot", extras = ["dev"], marker = "extra == 'all'" },
     { name = "lerobot", extras = ["dynamixel"], marker = "extra == 'all'" },
     { name = "lerobot", extras = ["feetech"], marker = "extra == 'hopejr'" },
@@ -2625,10 +2620,9 @@ requires-dist = [
     { name = "lerobot", extras = ["scipy-dep"], marker = "extra == 'wallx'" },
     { name = "lerobot", extras = ["smolvla"], marker = "extra == 'all'" },
     { name = "lerobot", extras = ["test"], marker = "extra == 'all'" },
-    { name = "lerobot", extras = ["train"], marker = "extra == 'all'" },
-    { name = "lerobot", extras = ["train"], marker = "extra == 'dev'" },
-    { name = "lerobot", extras = ["train"], marker = "extra == 'test'" },
-    { name = "lerobot", extras = ["train"], marker = "extra == 'training'" },
+    { name = "lerobot", extras = ["training"], marker = "extra == 'all'" },
+    { name = "lerobot", extras = ["training"], marker = "extra == 'dev'" },
+    { name = "lerobot", extras = ["training"], marker = "extra == 'test'" },
     { name = "lerobot", extras = ["transformers-dep"], marker = "extra == 'groot'" },
     { name = "lerobot", extras = ["transformers-dep"], marker = "extra == 'hilserl'" },
     { name = "lerobot", extras = ["transformers-dep"], marker = "extra == 'libero'" },
@@ -2694,9 +2688,9 @@ requires-dist = [
     { name = "torchdiffeq", marker = "extra == 'wallx'", specifier = ">=0.2.4,<0.3.0" },
     { name = "torchvision", specifier = ">=0.22.0,<0.26.0" },
     { name = "transformers", marker = "extra == 'transformers-dep'", specifier = "==5.3.0" },
-    { name = "wandb", marker = "extra == 'train'", specifier = ">=0.24.0,<0.25.0" },
+    { name = "wandb", marker = "extra == 'training'", specifier = ">=0.24.0,<0.25.0" },
 ]
-provides-extras = ["dataset", "train", "hardware", "viz", "build", "robot", "evaluation", "training", "dataset-viz", "pygame-dep", "placo-dep", "transformers-dep", "grpcio-dep", "can-dep", "peft-dep", "scipy-dep", "qwen-vl-utils-dep", "matplotlib-dep", "feetech", "dynamixel", "damiao", "robstride", "openarms", "gamepad", "hopejr", "lekiwi", "unitree-g1", "reachy2", "kinematics", "intelrealsense", "phone", "wallx", "pi", "smolvla", "multi-task-dit", "groot", "sarm", "xvla", "hilserl", "async", "peft", "dev", "test", "video-benchmark", "aloha", "pusht", "libero", "metaworld", "all"]
+provides-extras = ["dataset", "training", "hardware", "viz", "build", "robot", "evaluation", "dataset-viz", "av-dep", "pygame-dep", "placo-dep", "transformers-dep", "grpcio-dep", "can-dep", "peft-dep", "scipy-dep", "qwen-vl-utils-dep", "matplotlib-dep", "feetech", "dynamixel", "damiao", "robstride", "openarms", "gamepad", "hopejr", "lekiwi", "unitree-g1", "reachy2", "kinematics", "intelrealsense", "phone", "wallx", "pi", "smolvla", "multi-task-dit", "groot", "sarm", "xvla", "hilserl", "async", "peft", "dev", "test", "video-benchmark", "aloha", "pusht", "libero", "metaworld", "all"]
 
 [[package]]
 name = "librt"