`unienv_data.integrations.lerobot`¶

LeRobotAsUniEnvDataset ¶

LeRobotAsUniEnvDataset(dataset_dir: str, schema: LeRobotSchema, index: LeRobotIndex, space: DictSpace, _backend: ComputeBackend, _device: Optional[Any] = None, decode_video: bool = True, video_backend: Literal['pyav', 'torchcodec', 'auto'] = 'auto', include_features: Optional[List[str]] = None, exclude_features: Optional[List[str]] = None)

Bases: BatchBase[BatchT, BArrayType, BDeviceType, BDtypeType, BRNGType]

Wraps a local LeRobot dataset directory as a UniEnv BatchBase.

Parses the LeRobot on-disk format natively (no lerobot package required). Supports v2.0, v2.1, and v3.0 dataset formats.

Usage::

from unienv_interface.backends.numpy import NumpyComputeBackend
dataset = LeRobotAsUniEnvDataset.from_local(
    "/path/to/lerobot/dataset",
    backend=NumpyComputeBackend,
)
frame = dataset[42]
batch = dataset[0:100]

is_mutable `class-attribute` `instance-attribute` ¶

is_mutable = False

num_episodes `property` ¶

num_episodes: int

fps `property` ¶

fps: int

schema `property` ¶

schema: LeRobotSchema

episode_metadata `property` ¶

episode_metadata: List[EpisodeMetadata]

single_space `instance-attribute` ¶

single_space = single_space

single_metadata_space `instance-attribute` ¶

single_metadata_space = single_metadata_space

backend `property` ¶

backend: ComputeBackend[BArrayType, BDeviceType, BDtypeType, BRNGType]

device `property` ¶

device: Optional[BDeviceType]

from_local `staticmethod` ¶

from_local(dataset_dir: str, backend: ComputeBackend[BArrayType, BDeviceType, BDtypeType, BRNGType], device: Optional[BDeviceType] = None, include_features: Optional[List[str]] = None, exclude_features: Optional[List[str]] = None, decode_video: bool = True, video_backend: Literal['pyav', 'torchcodec', 'auto'] = 'auto') -> LeRobotAsUniEnvDataset[BArrayType, BDeviceType, BDtypeType, BRNGType]

Load a LeRobot dataset from a local directory.

from_hub `staticmethod` ¶

from_hub(repo_id: str, backend: ComputeBackend[BArrayType, BDeviceType, BDtypeType, BRNGType], device: Optional[BDeviceType] = None, revision: Optional[str] = None, local_dir: Optional[str] = None, **kwargs) -> LeRobotAsUniEnvDataset[BArrayType, BDeviceType, BDtypeType, BRNGType]

Download a LeRobot dataset from HuggingFace Hub and load it.

Uses huggingface_hub.snapshot_download (not the lerobot package).

get_at ¶

get_at(idx: Union[IndexableType, BArrayType]) -> BatchT

get_at_with_metadata ¶

get_at_with_metadata(idx: Union[IndexableType, BArrayType]) -> Tuple[BatchT, Dict[str, Any]]

get_column ¶

get_column(nested_keys: Sequence[str]) -> BatchBase

Return a view over a single feature column, joining nested keys with dots.

get_episode ¶

get_episode(episode_index: int) -> BatchT

Load all frames for a single episode.

close ¶

close() -> None

get_flattened_at ¶

get_flattened_at(idx: Union[IndexableType, BArrayType]) -> BArrayType

Fetch samples as flattened backend arrays.

get_flattened_at_with_metadata ¶

get_flattened_at_with_metadata(idx: Union[IndexableType, BArrayType]) -> Tuple[BArrayType, Optional[Dict[str, Any]]]

Fetch flattened samples together with optional per-sample metadata.

set_flattened_at ¶

set_flattened_at(idx: Union[IndexableType, BArrayType], value: BArrayType) -> None

Overwrite existing samples using flattened data.

append_flattened ¶

append_flattened(value: BArrayType) -> None

Append one flattened sample to the batch.

extend_flattened ¶

extend_flattened(value: BArrayType) -> None

Append a batched block of flattened samples.

set_at ¶

set_at(idx: Union[IndexableType, BArrayType], value: BatchT) -> None

Overwrite existing samples using structured data.

remove_at ¶

remove_at(idx: Union[IndexableType, BArrayType]) -> None

Remove one or more samples from the batch.

append ¶

append(value: BatchT) -> None

Append one structured sample to the batch.

extend ¶

extend(value: BatchT) -> None

Append a batched block of structured samples.

extend_from ¶

extend_from(other: BatchBase[BatchT, BArrayType, BDeviceType, BDtypeType, BRNGType], chunk_size: int = 8, tqdm: bool = False) -> None

Copy data from another batch in bounded-size chunks.

get_slice ¶

get_slice(idx: Union[IndexableType, BArrayType]) -> BatchBase[BatchT, BArrayType, BDeviceType, BDtypeType, BRNGType]

Create a lazy view over a subset of indices.

unienv_data.integrations.lerobot¶