Datasets:
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 "tsfile/tsfile_py_cpp.pyx", line 567, in tsfile.tsfile_py_cpp.tsfile_reader_new_c
tsfile.exceptions.FileOpenError: 28:
The above exception was the direct cause of the following exception:
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/tsfile/tsfile.py", line 271, in _split_generators
scan = self._scan_metadata(all_files)
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/tsfile/tsfile.py", line 318, in _scan_metadata
with self._open_reader(file) as reader:
~~~~~~~~~~~~~~~~~^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/tsfile/tsfile.py", line 742, in _open_reader
return TsFileReader(file)
File "tsfile/tsfile_reader.pyx", line 323, in tsfile.tsfile_reader.TsFileReaderPy.__init__
SystemError: <class '_weakrefset.WeakSet'> returned a result with an exception set
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 71, 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.
G1 Dex3 Object Placement Dataset TsFile
Apache TsFile conversion of unitreerobotics/G1_Dex3_ObjectPlacement_Dataset, a LeRobot v3 robot-manipulation dataset.
Source and attribution
- Original dataset: https://huggingface.co/datasets/unitreerobotics/G1_Dex3_ObjectPlacement_Dataset
- Original uploader/data author shown in the Hugging Face repository history: Henry-Ellis (https://huggingface.co/Henry-Ellis)
- Repository owner/organization: unitreerobotics (Unitree Robotics)
- License: Apache-2.0; no paper or formal citation is supplied by the source card.
- Task: pick up toothpaste and a trash bag and place them into the blue storage container.
- Robot: 7-DOF dual-arm Unitree_G1 with three-fingered dexterous hands.
- Recording frequency: 30 Hz; source frame resolution 640x480.
Source layout and videos
The source train split is one Parquet shard (data/chunk-000/file-000.parquet) with 210 episodes, 98,266 frames, one task, and 30 fps. Source metadata paths:
- Numeric data: data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet
- Videos: videos/{video_key}/chunk-{chunk_index:03d}/file-{file_index:03d}.mp4
- Four camera streams: observation.images.cam_left_high, observation.images.cam_right_high, observation.images.cam_left_wrist, and observation.images.cam_right_wrist. They are listed in the original repository videos tree at https://huggingface.co/datasets/unitreerobotics/G1_Dex3_ObjectPlacement_Dataset/tree/main/videos.
- Current source revision contains 9 physical MP4 shards and 840 logical episode videos.
Videos are not included in this TsFile repository. They remain at the original Hugging Face videos paths; episode_index and frame_index preserve frame alignment.
Converted artifact
- TsFile: data/g1_dex3_objectplacement_dataset_train.tsfile
- Table: g1_dex3_objectplacement_dataset_train
- Rows: 98,266; episodes: 210; source split: train
- Time precision: milliseconds; Time = round(timestamp * 1000) and restarts from zero for each episode.
- Original timestamp is dropped because it is redundant with Time / 1000; source index is renamed to sample_index.
Schema
| Role | Columns | Source / notes |
|---|---|---|
| TIME | Time | INT64, milliseconds, TS_2DIFF + LZ4 |
| TAG | episode_index, task_index | Original source columns; TsFile stores TAG values as strings while source_dtype: int64 is recorded in meta/info.json |
| FIELD | frame_index, sample_index | INT64, TS_2DIFF + LZ4; sample_index comes from source index |
| FIELD | observation_state_0 ... observation_state_27 | Flattened source observation.state[28], FLOAT, GORILLA + LZ4 |
| FIELD | action_0 ... action_27 | Flattened source action[28], FLOAT, GORILLA + LZ4 |
All source numeric rows and 28-dimensional state/action elements are retained. The only dropped source column is timestamp; video/image columns are intentionally omitted because videos remain in the original dataset.
Conversion and validation
The dataset-specific converter in conversion_support/scripts/converters/unitreerobotics_g1_dex3_objectplacement.py uses LeRobot normalization rules, sorts by episode_index, task_index, then Time, and writes one merged TsFile for the train split. Encodings/compression are explicit: FLOAT/DOUBLE GORILLA, INT32/INT64/Time TS_2DIFF, BOOLEAN RLE (no boolean field is present), and LZ4 compression.
Local validation passed: source/staged/TsFile row counts are all 98,266; 210 TAG devices are present; Time exactly matches round(timestamp * 1000); vector dimensions round-trip exactly; and the TsFile is non-empty and readable. See VALIDATION.md and validation_report.json.
Minimal read example
from tsfile import TsFileReader
path = "data/g1_dex3_objectplacement_dataset_train.tsfile"
reader = TsFileReader(path)
columns = ["episode_index", "task_index", "frame_index", "sample_index", "action_0", "observation_state_0"]
with reader.query_table("g1_dex3_objectplacement_dataset_train", columns, batch_size=65536) as result:
batch = result.read_arrow_batch()
print(batch.to_pandas().head())
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