Dataset Viewer
The dataset viewer is not available for this dataset.
Cannot get the config names for the dataset.
Error code: ConfigNamesError
Exception: ValueError
Message: Feature type 'Numpy' not found. Available feature types: ['Value', 'ClassLabel', 'Translation', 'TranslationVariableLanguages', 'LargeList', 'Sequence', 'Array2D', 'Array3D', 'Array4D', 'Array5D', 'Audio', 'Image', 'Video']
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 66, in compute_config_names_response
config_names = get_dataset_config_names(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 164, in get_dataset_config_names
dataset_module = dataset_module_factory(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1731, in dataset_module_factory
raise e1 from None
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1688, in dataset_module_factory
return HubDatasetModuleFactoryWithoutScript(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1040, in get_module
dataset_infos = DatasetInfosDict.from_dataset_card_data(dataset_card_data)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/info.py", line 386, in from_dataset_card_data
dataset_info = DatasetInfo._from_yaml_dict(dataset_card_data["dataset_info"])
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/info.py", line 317, in _from_yaml_dict
yaml_data["features"] = Features._from_yaml_list(yaml_data["features"])
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1983, in _from_yaml_list
return cls.from_dict(from_yaml_inner(yaml_data))
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1836, in from_dict
obj = generate_from_dict(dic)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1460, in generate_from_dict
return {key: generate_from_dict(value) for key, value in obj.items()}
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1460, in <dictcomp>
return {key: generate_from_dict(value) for key, value in obj.items()}
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1466, in generate_from_dict
raise ValueError(f"Feature type '{_type}' not found. Available feature types: {list(_FEATURE_TYPES.keys())}")
ValueError: Feature type 'Numpy' not found. Available feature types: ['Value', 'ClassLabel', 'Translation', 'TranslationVariableLanguages', 'LargeList', 'Sequence', 'Array2D', 'Array3D', 'Array4D', 'Array5D', 'Audio', 'Image', 'Video']Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
LeRobot Augmented Dataset
This dataset is an augmented version of the original LeRobot dataset. The augmentation expands the dataset by creating 4 versions of each original episode:
- Original data - preserved as-is
- Horizontally flipped images - original action/state vectors
- Shoulder pan negated - original images with shoulder pan values negated in action/state vectors
- Both flipped and negated - horizontally flipped images with negated shoulder pan values
Augmentation Process
The augmentation process quadruples the size of the dataset by creating three variants of each original episode:
- Image flipping: Horizontally flipping camera frames to simulate the robot operating from the opposite side
- Action negation: Negating shoulder pan values to simulate opposite directional movement
- Combined augmentation: Both flipping images and negating shoulder pan values
This 4x expansion provides more diverse training data for robotic control tasks, helping models generalize better to different perspectives and movement directions.
Original Task
The dataset contains episodes where a robot is performing the task: "Pick up the black pen and place it in the mug."
Structure
The dataset maintains the same structure as the original:
data/: Contains episode data in parquet format (4x the original amount)videos/: Contains video recordings of episodes (4x the original amount)meta/: Contains metadata about the dataset
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