Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
objects: list<item: struct<Object_ID: string, Total_Items: string, Positive_Count: string, Negative_Count: st (... 48 chars omitted)
child 0, item: struct<Object_ID: string, Total_Items: string, Positive_Count: string, Negative_Count: string, Succe (... 36 chars omitted)
child 0, Object_ID: string
child 1, Total_Items: string
child 2, Positive_Count: string
child 3, Negative_Count: string
child 4, Success_Rate_%: string
child 5, Has_Data: string
total: struct<Object_ID: string, Total_Items: string, Positive_Count: string, Negative_Count: string, Succe (... 36 chars omitted)
child 0, Object_ID: string
child 1, Total_Items: string
child 2, Positive_Count: string
child 3, Negative_Count: string
child 4, Success_Rate_%: string
child 5, Has_Data: string
mini_dataset: struct<path: string, object_ids: list<item: string>, train_object_ids: list<item: string>, test_obje (... 27 chars omitted)
child 0, path: string
child 1, object_ids: list<item: string>
child 0, item: string
child 2, train_object_ids: list<item: string>
child 0, item: string
child 3, test_object_ids: list<item: string>
child 0, item: string
external_assets_not_included: list<item: string>
child 0, item: string
release_stage: string
manifest_policy: string
files: list<item: struct<path: string, bytes: int64, category: string>>
child 0, item: struct<path: string, bytes: int64, category: string>
child 0, path: string
child 1, bytes: int64
child 2, category: string
tools: struct<dataset_utils: string, purpose: string, required_dependencies: list<item: string>, optional_d (... 32 chars omitted)
child 0, dataset_utils: string
child 1, purpose: string
child 2, required_dependencies: list<item: string>
child 0, item: string
child 3, optional_dependencies: list<item: string>
child 0, item: string
full_dataset: struct<object_shards: int64, object_ids: list<item: string>, zip_format: string, total_zip_bytes: in (... 63 chars omitted)
child 0, object_shards: int64
child 1, object_ids: list<item: string>
child 0, item: string
child 2, zip_format: string
child 3, total_zip_bytes: int64
child 4, samples: int64
child 5, train_samples: int64
child 6, test_samples: int64
name: string
to
{'name': Value('string'), 'release_stage': Value('string'), 'full_dataset': {'object_shards': Value('int64'), 'object_ids': List(Value('string')), 'zip_format': Value('string'), 'total_zip_bytes': Value('int64'), 'samples': Value('int64'), 'train_samples': Value('int64'), 'test_samples': Value('int64')}, 'mini_dataset': {'path': Value('string'), 'object_ids': List(Value('string')), 'train_object_ids': List(Value('string')), 'test_object_ids': List(Value('string'))}, 'tools': {'dataset_utils': Value('string'), 'purpose': Value('string'), 'required_dependencies': List(Value('string')), 'optional_dependencies': List(Value('string'))}, 'external_assets_not_included': List(Value('string')), 'manifest_policy': Value('string'), 'files': List({'path': Value('string'), 'bytes': Value('int64'), 'category': 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 2815, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2352, in __iter__
for key, pa_table in self._iter_arrow():
^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2377, 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 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 419, 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 310, 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 130, 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 2369, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
objects: list<item: struct<Object_ID: string, Total_Items: string, Positive_Count: string, Negative_Count: st (... 48 chars omitted)
child 0, item: struct<Object_ID: string, Total_Items: string, Positive_Count: string, Negative_Count: string, Succe (... 36 chars omitted)
child 0, Object_ID: string
child 1, Total_Items: string
child 2, Positive_Count: string
child 3, Negative_Count: string
child 4, Success_Rate_%: string
child 5, Has_Data: string
total: struct<Object_ID: string, Total_Items: string, Positive_Count: string, Negative_Count: string, Succe (... 36 chars omitted)
child 0, Object_ID: string
child 1, Total_Items: string
child 2, Positive_Count: string
child 3, Negative_Count: string
child 4, Success_Rate_%: string
child 5, Has_Data: string
mini_dataset: struct<path: string, object_ids: list<item: string>, train_object_ids: list<item: string>, test_obje (... 27 chars omitted)
child 0, path: string
child 1, object_ids: list<item: string>
child 0, item: string
child 2, train_object_ids: list<item: string>
child 0, item: string
child 3, test_object_ids: list<item: string>
child 0, item: string
external_assets_not_included: list<item: string>
child 0, item: string
release_stage: string
manifest_policy: string
files: list<item: struct<path: string, bytes: int64, category: string>>
child 0, item: struct<path: string, bytes: int64, category: string>
child 0, path: string
child 1, bytes: int64
child 2, category: string
tools: struct<dataset_utils: string, purpose: string, required_dependencies: list<item: string>, optional_d (... 32 chars omitted)
child 0, dataset_utils: string
child 1, purpose: string
child 2, required_dependencies: list<item: string>
child 0, item: string
child 3, optional_dependencies: list<item: string>
child 0, item: string
full_dataset: struct<object_shards: int64, object_ids: list<item: string>, zip_format: string, total_zip_bytes: in (... 63 chars omitted)
child 0, object_shards: int64
child 1, object_ids: list<item: string>
child 0, item: string
child 2, zip_format: string
child 3, total_zip_bytes: int64
child 4, samples: int64
child 5, train_samples: int64
child 6, test_samples: int64
name: string
to
{'name': Value('string'), 'release_stage': Value('string'), 'full_dataset': {'object_shards': Value('int64'), 'object_ids': List(Value('string')), 'zip_format': Value('string'), 'total_zip_bytes': Value('int64'), 'samples': Value('int64'), 'train_samples': Value('int64'), 'test_samples': Value('int64')}, 'mini_dataset': {'path': Value('string'), 'object_ids': List(Value('string')), 'train_object_ids': List(Value('string')), 'test_object_ids': List(Value('string'))}, 'tools': {'dataset_utils': Value('string'), 'purpose': Value('string'), 'required_dependencies': List(Value('string')), 'optional_dependencies': List(Value('string'))}, 'external_assets_not_included': List(Value('string')), 'manifest_policy': Value('string'), 'files': List({'path': Value('string'), 'bytes': Value('int64'), 'category': 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.
3DA-VTG
3DA-VTG is a visuo-tactile grasp-stability dataset prepared for the public SGA-GSN release. It contains paired visual and tactile observations, object-level metadata, and binary grasp-stability labels.
License: pending. Do not assume MIT, Apache-2.0, or another permissive license until the final dataset license is explicitly added here.
Contents
- Full dataset shards:
data/000.ziptodata/087.zip. - Split files:
data/train.csv,data/test.csv,data/train-ids.txt, anddata/test-ids.txt. - Background image for 2D VTG loaders:
data/bg_sim.jpg. - Metadata and format documentation:
metadata/. - Mini dataset for smoke tests:
samples/3da_vtg_mini.zip.
Current staging statistics:
| Split | Objects | Samples |
|---|---|---|
| Train | 68 | 318,532 |
| Test | 19 | 95,000 |
| Total | 87 objects with data / 88 shards | 413,532 |
Object 046 is kept as an empty shard for release completeness but is not part
of the train or test split.
Download And Restore
For full SGA-GSN use, download the data/ directory from this dataset repo.
Then restore it with the SGA-GSN helper script:
bash /SGA-GSN/install/extract_3da_vtg.sh <downloaded_repo>/data /SGA-GSN/data
This creates:
/SGA-GSN/data/3DA-VTG
The SGA-GSN dataset configs expect data/3DA-VTG relative to the SGA-GSN repo
root.
For a quick local smoke test, download and unzip:
unzip samples/3da_vtg_mini.zip -d /SGA-GSN/data
The mini dataset restores the same top-level 3DA-VTG/ directory structure.
File Format
Each object shard expands to one zero-padded object directory, for example
006/. Each object directory contains _metadata.json and sensor folders:
tac_rgb, tac_dep, vis_rgb, vis_dep, and vis_seg.
Split CSV files use columns:
id,global_index
See metadata/file_format.md and metadata/sample_schema.json for details.
Utilities
The read-only helper script tools/dataset_utils.py can load restored samples
and reconstruct visual/tactile point clouds from the released depth images and
metadata:
from tools.dataset_utils import sample_to_pointclouds
pcs = sample_to_pointclouds("data/3DA-VTG", "006", "23476304")
The helper requires NumPy and OpenCV for data loading. Open3D is optional and is
only imported for interactive 3D visualization. Complete object mesh point
clouds still require the separate graspnet-vhacd asset package.
External Assets
This dataset repository does not include object mesh assets or model weights. SGA-GSN still requires:
- object meshes from the separate
graspnet-vhacdasset package; - the AdaPoinTr shape checkpoint
ckpts/ap_ps55.pthfrom the model release.
Integrity
Use the published checksums after download:
sha256sum --check checksums.sha256
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