Datasets:

zjt24
/

svla_so100_stacking

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 66, 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.

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SVLA SO100 Stacking (TsFile)

This dataset is a TsFile conversion of the Hugging Face dataset lerobot/svla_so100_stacking. It contains the numeric LeRobot frame data for an so100 robot dataset. The source dataset was created using LeRobot.

Modalities: Time-series. The original dataset also contains videos; those video files are not mirrored in this converted repository.

Source dataset

Original dataset: lerobot/svla_so100_stacking
Source commit used for conversion: 476c6810591b5e75b385d6e8d9c648525a153eb9
License: apache-2.0
LeRobot codebase version: v3.0
Robot type: so100
Published task metadata: one task index (0); the source task table does not include a text label

Dataset scale

Episodes: 56
Frames / converted TsFile rows: 22,956
Tasks: 1
Split: train (0:56)
Sampling rate: 30 FPS
Converted files: 1 TsFile file (429,148 bytes)
Original video streams: observation.images.top and observation.images.wrist

Converted layout

data/svla_so100_stacking_train.tsfile
meta/
  episodes/chunk-000/file-000.parquet
  info.json
  stats.json
  tasks.parquet

The uploaded meta/info.json mirrors the source metadata and adds a tsfile_conversion object describing the source-to-TsFile mappings.

TsFile schema

Table name: svla_so100_stacking_train

Time: millisecond timestamp, computed as round(timestamp * 1000).
TAG columns: episode_index, task_index.
FIELD columns: frame_index, sample_index, action_0 to action_5, and observation_state_0 to observation_state_5.

The six action_* and six observation_state_* fields preserve the source joint order:

main_shoulder_pan
main_shoulder_lift
main_elbow_flex
main_wrist_flex
main_wrist_roll
main_gripper

Conversion notes

Source vector column action is flattened to action_0 through action_5.
Source vector column observation.state is flattened to observation_state_0 through observation_state_5.
Source column timestamp is mapped to the TsFile Time column in milliseconds and is not retained as a separate field because it is equivalent to Time / 1000 seconds.
Source column index is renamed to sample_index; frame_index is retained.
Source video features observation.images.top and observation.images.wrist are omitted from this TsFile conversion. The original videos remain available in the source dataset under videos/.

Minimal read example

import pyarrow as pa
from tsfile import ColumnCategory, TsFileReader

path = "data/svla_so100_stacking_train.tsfile"
table_name = "svla_so100_stacking_train"

reader = TsFileReader(path)
schema = reader.get_all_table_schemas()[table_name]
columns = [
    column.get_column_name()
    for column in schema.get_columns()
    if column.get_category() in (ColumnCategory.FIELD, ColumnCategory.TAG)
]

batches = []
with reader.query_table(table_name, columns, batch_size=65536) as result_set:
    while True:
        batch = result_set.read_arrow_batch()
        if batch is None:
            break
        if batch.num_rows:
            batches.append(batch)

table = pa.concat_tables(batches)
print(table.to_pandas().head())

Downloads last month: 62

Total file size:

524 kB