The dataset viewer is not available for this subset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
~~~~~~~~~~~~~~~~~~~~~~~~~^
StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/folder_based_builder/folder_based_builder.py", line 246, in _split_generators
raise ValueError(
"`file_name`, `*_file_name`, `file_names` or `*_file_names` must be present as dictionary key in metadata files"
)
ValueError: `file_name`, `*_file_name`, `file_names` or `*_file_names` must be present as dictionary key in metadata files
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 68, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
~~~~~~~~~~~~~~~~~~~~~~~^
path=dataset,
^^^^^^^^^^^^^
config_name=config,
^^^^^^^^^^^^^^^^^^^
token=hf_token,
^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
path,
...<6 lines>...
**config_kwargs,
)
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.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.
YAML Metadata Warning:The task_categories "imitation-learning" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
Unscrewing Travel Mug Lid Dataset
Total Hours: 5 seconds Total Frames: 288 Number of Episodes: 1
Dataset Description
This dataset contains synchronized .mp4 videos and .h5 data files for the task of unscrewing a travel mug lid. It is designed for training and evaluating robot learning models, particularly in imitation learning and reinforcement learning settings.
Quickstart
from datasets import load_dataset
ds = load_dataset("your-username/unscrewing-travel-mug-lid")
print(ds)
Hardware & Collection Specs
- Camera Model: Iphone 16 Pro
- Resolution: 4K
- FPS: 60
- Robot Arm Model: None
- Provenance: None
Diversity & Data Splits
- Number of Demonstrations: 1
- Number of Objects/Variations: 1
- Number of Environments: 1
- Splits:
- Train: [TRAIN_SPLIT]
- Validation: [VAL_SPLIT]
- Test: [TEST_SPLIT]
Data Dictionary
The .h5 files contain the following structure:
| Key | Type | Description |
|---|---|---|
observations/images |
dict | Camera images (e.g., cam0, cam1) |
observations/state |
array | Robot joint states (angles, velocities) |
actions |
array | Executed actions (joint torques or positions) |
rewards |
array | Reward signal (if applicable) |
dones |
array | Episode termination flags |
Intended Use Cases & Limitations
Intended for:
- Training imitation learning policies (e.g., Behavior Cloning, Action Chunking with Transformers)
- Fine-tuning Vision-Language-Action (VLA) models
- Evaluating robot manipulation algorithms
Limitations:
- The dataset may not cover all possible variations of unscrewing motions.
- The action space may be specific to the robot used.
- The data may contain biases from the collection environment.
Action Space Definition
Actions are defined as: [ABSOLUTE or DELTA]
Coordinate Frame: [COORDINATE_FRAME]
Units: [UNITS]
Failure Trajectories
Contains failed attempts or human interventions: NO
Details: [DETAILS]
Community & Support
For questions or support, please contact: hello@sonictech.com
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