Datasets:

azithromycin
/

CSI-Agent_foldtowel_merged

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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CSI-Agent Fold Towel Merged TsFile

This repository is an Apache TsFile conversion of CSI-Agent/foldtowel_merged, a LeRobot v3.0 robot-manipulation dataset. The source task is Fold the clothes. with a bi-so_follower (dual SO-101) setup. The conversion contains numeric and phase-label time series; camera videos remain in the original dataset.

Source and attribution

Source: CSI-Agent/foldtowel_merged
Pinned source revision: 1d82051ad4307df6ace2c9e67737768b85299fcd
Original owner/author: CSI-Agent, the Hugging Face organization and repository owner
Author profile: https://huggingface.co/CSI-Agent
License: Apache-2.0
Paper/citation: the source card provides no paper or BibTeX citation
Task: fold clothes; task index 0 maps to Fold the clothes.
Robot type: bi_so_follower; LeRobot codebase: v3.0

Dataset and videos

Split: train
Episodes: 50
Rows/frames: 51,203
Tasks: 1
Sampling rate: 30 fps (source meta/info.json)
Frame Parquet shards: 50
Source episode metadata shards: 1
Video files: 150 total, 50 per stream
Phase labels: step1 15,962, step2 13,895, step3 21,346

The original videos are located at:

Videos are not included in this TsFile repository. episode_index and frame_index preserve alignment with the source episode metadata and the three per-episode MP4 streams.

Converted artifact

TsFile: data/csi_agent_foldtowel_merged.tsfile
Table: csi_agent_foldtowel_merged
Rows: 51,203
Size: 1,813,681 bytes
Source frame Parquet bytes: 3,308,349
TsFile/source Parquet size ratio: 0.548
Granularity: one merged table; each (episode_index, task_index) pair is a TsFile TAG device

Schema

Column(s)	TsFile type	Role	Encoding + compression	Description
`Time`	INT64	TIME	TS_2DIFF + LZ4	`round(timestamp * 1000)` milliseconds; restarts at 0 per episode
`episode_index`	STRING	TAG	TsFile table device/TAG	Source episode index; source dtype INT64
`task_index`	STRING	TAG	TsFile table device/TAG	Source task index; source dtype INT64
`frame_index`, `sample_index`	INT64	FIELD	TS_2DIFF + LZ4	`sample_index` is source `index` renamed
`phase_label`	STRING	FIELD	PLAIN + LZ4	Source phase label (`step1`, `step2`, `step3`)
`action_0..action_11`	FLOAT	FIELD	GORILLA + LZ4	12-dimensional action vector, flattened from `action`
`observation_state_0..observation_state_11`	FLOAT	FIELD	GORILLA + LZ4	12-dimensional state vector, flattened from `observation.state`

The action/state element order follows the source feature names: left_shoulder_pan.pos, left_shoulder_lift.pos, left_elbow_flex.pos, left_wrist_flex.pos, left_wrist_roll.pos, left_gripper.pos, followed by the corresponding six right_* names. Dots in source names are replaced with underscores only for flattened field names.

Conversion details

Time = round(timestamp * 1000) with millisecond precision. The source timestamp column is dropped because it is exactly Time / 1000 seconds; frame_index is preserved.
Source episode_index and task_index remain the TAG columns. No synthetic episode_id or task_id aliases are created.
Vector columns are flattened to scalar FLOAT fields without dropping numeric values. phase_label is retained as a STRING field.
All rows are sorted by TAG columns and then Time; duplicate TAG/Time keys are rejected before writing.
Numeric codecs are explicit: FLOAT/DOUBLE GORILLA + LZ4, INT32/INT64 and Time TS_2DIFF + LZ4, BOOLEAN RLE + LZ4; TAGs use the TsFile table device mechanism. The source contains no BOOLEAN field.
Only timestamp is dropped. Video columns are source metadata references and are intentionally not copied into TsFile.

Read example

from tsfile import TsFileReader

reader = TsFileReader("data/csi_agent_foldtowel_merged.tsfile")
table = reader.get_all_table_schemas()["csi_agent_foldtowel_merged"]
columns = [c.get_column_name() for c in table.get_columns()
           if c.get_column_name() != "Time"]
with reader.query_table("csi_agent_foldtowel_merged", columns, batch_size=65536) as result:
    batch = result.read_arrow_batch()
    print(batch)

Validation

The local validation report confirms 51,203 Java readback rows, 50 TAG devices, 2 TAG columns, 27 FIELD columns, strict per-device Time monotonicity, and the codec policy above. TIME was independently checked from aligned chunk metadata as TS_2DIFF + LZ4. Conversion scripts and JSON/Markdown validation reports are kept locally and are not part of the upload set.

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Total file size:

2.05 MB