The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
mesh: string
rgb: string
surface_visibility: double
grasps: struct<front_back: struct<json: string, npz: string, overlay: string, n_contacts: int64, fingers: st (... 89 chars omitted)
child 0, front_back: struct<json: string, npz: string, overlay: string, n_contacts: int64, fingers: struct<ring: int64, i (... 69 chars omitted)
child 0, json: string
child 1, npz: string
child 2, overlay: string
child 3, n_contacts: int64
child 4, fingers: struct<ring: int64, index: int64, pinky: int64, middle: int64, palm: int64, thumb: int64>
child 0, ring: int64
child 1, index: int64
child 2, pinky: int64
child 3, middle: int64
child 4, palm: int64
child 5, thumb: int64
n_vertices: int64
n_faces: int64
bounding_box: list<item: list<item: double>>
child 0, item: list<item: double>
child 0, item: double
contacts: list<item: struct<position: list<item: double>, normal: list<item: double>, tangent: list<item: doub (... 41 chars omitted)
child 0, item: struct<position: list<item: double>, normal: list<item: double>, tangent: list<item: double>, finger (... 29 chars omitted)
child 0, position: list<item: double>
child 0, item: double
child 1, normal: list<item: double>
child 0, item: double
child 2, tangent: list<item: double>
child 0, item: double
child 3, finger: string
child 4, visibility: string
strategy: string
n_contacts: int64
to
{'mesh': Value('string'), 'strategy': Value('string'), 'n_contacts': Value('int64'), 'contacts': List({'position': List(Value('float64')), 'normal': List(Value('float64')), 'tangent': List(Value('float64')), 'finger': Value('string'), 'visibility': Value('string')})}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
return get_rows(
^^^^^^^^^
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2227, in __iter__
for key, pa_table in self._iter_arrow():
^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2251, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 494, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 384, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 295, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 128, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2321, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2249, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
mesh: string
rgb: string
surface_visibility: double
grasps: struct<front_back: struct<json: string, npz: string, overlay: string, n_contacts: int64, fingers: st (... 89 chars omitted)
child 0, front_back: struct<json: string, npz: string, overlay: string, n_contacts: int64, fingers: struct<ring: int64, i (... 69 chars omitted)
child 0, json: string
child 1, npz: string
child 2, overlay: string
child 3, n_contacts: int64
child 4, fingers: struct<ring: int64, index: int64, pinky: int64, middle: int64, palm: int64, thumb: int64>
child 0, ring: int64
child 1, index: int64
child 2, pinky: int64
child 3, middle: int64
child 4, palm: int64
child 5, thumb: int64
n_vertices: int64
n_faces: int64
bounding_box: list<item: list<item: double>>
child 0, item: list<item: double>
child 0, item: double
contacts: list<item: struct<position: list<item: double>, normal: list<item: double>, tangent: list<item: doub (... 41 chars omitted)
child 0, item: struct<position: list<item: double>, normal: list<item: double>, tangent: list<item: double>, finger (... 29 chars omitted)
child 0, position: list<item: double>
child 0, item: double
child 1, normal: list<item: double>
child 0, item: double
child 2, tangent: list<item: double>
child 0, item: double
child 3, finger: string
child 4, visibility: string
strategy: string
n_contacts: int64
to
{'mesh': Value('string'), 'strategy': Value('string'), 'n_contacts': Value('int64'), 'contacts': List({'position': List(Value('float64')), 'normal': List(Value('float64')), 'tangent': List(Value('float64')), 'finger': Value('string'), 'visibility': Value('string')})}
because column names don't matchNeed 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 "computer-vision" 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
ποΈ Synthetic Grasp Dataset (Objaverse-LVIS Curated)
This dataset contains high-quality synthetic grasp data generated for robotic manipulation research. It focuses on the fusion of vision and tactile sensing by providing visibility and occlusion analysis for each contact point.
π Dataset Statistics
- Number of objects: 10
- Source: Curated objects from Objaverse-LVIS (categories: cup, bottle, hammer, screwdriver, wrench)
- Grasp Strategies: front_back
- Camera Resolution: 640x480
π οΈ Data Format
Each object folder contains:
rgb.png: Monocular RGB render.grasp_<strategy>.json: Contact points with position, normal, tangent, and visibility status.grasp_<strategy>.npz: NumPy version of the contact points.grasp_<strategy>_overlay.png: Visual overlay of the grasp on the object.metadata.json: Object-specific metadata (surface visibility, bounding box, complexity).
π Visibility Classification
Every contact point is classified based on camera occlusion:
- VISIBLE: Point is directly seen by the camera.
- SILHOUETTE: Point is on the visual horizon (critical for tactile ΨͺΪ©Ω ΫΩ).
- OCCLUDED: Point is hidden by the object itself (back side or self-occlusion).
π How to use
This dataset is designed to train models that predict contact stability from visual data or to simulate-to-real transfer for tactile controllers.
from huggingface_hub import snapshot_download
path = snapshot_download("jack635/grasp-dataset-curated", repo_type="dataset")
Generated using the Grasp Dataset Generator pipeline.
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