Datasets:

zjt24
/

act_kikobot_block_real

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 68, 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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ACT Kikobot Block Real (TsFile)

This dataset is a numeric time-series conversion of swarajgosavi/act_kikobot_block_real, which was created using LeRobot. The source is a LeRobot v2.0 dataset for an SO-100 robot performing the task pick and place. The source dataset is licensed under Apache-2.0.

Dataset Summary

Modalities: Time-series
Split: train (episodes 0:50)
Episodes: 50
Frames / TsFile rows: 21,685
Tasks: 1 (pick and place)
Sampling rate: 30 fps
Source numeric files: 50 per-episode Parquet files
Converted files: 1 TsFile

The converted file is data/act_kikobot_block_real_train.tsfile.

TsFile Schema

The TsFile table is act_kikobot_block_real_train and contains 19 columns:

Columns	TsFile role	Type	Description
`Time`	TIME	INT64	`round(timestamp * 1000)` milliseconds, restarting within each episode
`episode_index`, `task_index`	TAG	INT64	Original LeRobot episode and task identifiers
`frame_index`, `sample_index`	FIELD	INT64	Original frame index and renamed source `index`
`action_0` ... `action_6`	FIELD	FLOAT	Flattened 7-element `action` vector
`observation_state_0` ... `observation_state_6`	FIELD	FLOAT	Flattened 7-element `observation.state` vector

The action and state indices follow the source feature order: main_shoulder_pan, main_shoulder_lift, main_elbow_flex, main_wrist_flex_1, main_wrist_flex_2, main_wrist_roll, and main_gripper.

Conversion Notes

The 50 source episode Parquet files were merged into one TsFile for the train split. All 21,685 numeric frame rows are retained.
Source timestamp is replaced by the millisecond Time column and is not duplicated as a FIELD because timestamp = Time / 1000 seconds, subject to integer millisecond rounding.
Source index is renamed to sample_index.
The full 7 values of both action and observation.state are retained as scalar FLOAT fields.
episode_index and task_index are retained as original numeric TAG columns. No synthetic episode or task aliases are added.
The mirrored meta/info.json records the converted path, row count, time mapping, TAG columns, flattened features, source episode-file count, and source video references.

Videos

The camera streams observation.images.laptop and observation.images.phone are not duplicated in this converted repository. The 100 source videos remain available from the original dataset videos. Use episode_index and frame_index to align TsFile rows with the original video frames.

Reading the Data

Download the converted file and open it with an Apache TsFile reader:

from huggingface_hub import hf_hub_download
from tsfile import TsFileReader

path = hf_hub_download(
    repo_id="zjt24/act_kikobot_block_real",
    repo_type="dataset",
    filename="data/act_kikobot_block_real_train.tsfile",
)

reader = TsFileReader(path)
print(reader.get_all_table_schemas())

Source and Citation

Original dataset: swarajgosavi/act_kikobot_block_real
Source framework: LeRobot
License: Apache-2.0

The source dataset card does not provide a paper or BibTeX citation.

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