Datasets:

jzpeng
/

kivod_so100_cam2

Tasks:

Robotics

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The dataset viewer is not available for this subset.

Cannot get the split names for the config 'default' of the dataset.

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.

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SO100 Cam2 TsFile

This is an Apache TsFile conversion of kivod/so100_cam2, a LeRobot v2.1 SO100 dataset for “Grasp a cucumber and put it in the hole.”

Modalities: Time-series. Numeric robot state, actions, timing, and episode/task metadata are stored in TsFile. The original dataset has two camera streams; videos are intentionally not copied here.

Source and provenance

Source revision: e52e39a1adfea61bade108a17a366455bbe542d0
Original repository owner/uploader: kivod
License: Apache-2.0; robot: so100; split: train; sampling: 30 fps
Scale: 52 episodes, 24,655 frame rows, 1 task, 52 source Parquet files, 104 source videos
Source frame path: data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet
Source video path: videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4

The source card provides no separate formal author, paper, or BibTeX citation; repository ownership is the available authorship attribution.

Converted artifact

TsFile: data/kivod_so100_cam2_train.tsfile
Table: kivod_so100_cam2_train
Rows: 24,655; episodes/devices: 52
Time precision: milliseconds
meta/ is mirrored from the source. meta/info.json records source/converted paths, row count, mappings, and video policy.

Schema and encoding

Time is round(timestamp * 1000) (INT64 milliseconds), restarting at zero for each episode. Source timestamp is redundant and dropped; frame_index and source index (renamed sample_index) are retained.

TAG columns (TsFile table/device tags):

episode_index
task_index

FIELD columns:

frame_index
sample_index
action_0 ... action_5
observation_state_0 ... observation_state_5

Flattened vectors:

action -> action_0 ... action_5 (FLOAT)
observation.state -> observation_state_0 ... observation_state_5 (FLOAT)

Encoding profile: FLOAT/DOUBLE = GORILLA + LZ4; INT32/INT64 and Time = TS_2DIFF + LZ4; TAGs use TsFile device/tag storage. No source rows or numeric fields are dropped other than redundant timestamp.

Videos and frame alignment

Videos remain upstream:

Each stream has 52 per-episode MP4 files. Join numeric rows to source videos using episode_index and frame_index.

Minimal read example

from tsfile import TsFileReader
reader = TsFileReader("data/kivod_so100_cam2_train.tsfile")
with reader.query_table("kivod_so100_cam2_train", [
    "episode_index", "task_index", "frame_index", "sample_index",
    "action_0", "observation_state_0",
], batch_size=65536) as result:
    print(result.read_arrow_batch().to_pandas().head())
reader.close()

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Total file size:

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