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.
LIBERO Plus 10 Add Object All IIWA Failures TsFile
This dataset is an Apache TsFile conversion of LSY-lab/libero_plus_10_add_object_all_iiwa_failures, a
LeRobot-format KUKA IIWA manipulation failure dataset with ten task instructions, 174
episodes, and 58,701 frame rows sampled at 20 fps.
Modalities: Time-series; Tabular. Numeric robot observations, actions, frame timing, episode/task tags, and source metadata are included. Camera videos remain in the original Hugging Face dataset.
Source Dataset and Attribution
- Original dataset:
LSY-lab/libero_plus_10_add_object_all_iiwa_failures - Pinned source revision:
fdebd7c9129861b4602c436628481c6ebf61cd0b(files and versions) - Publishing organization: LSY-lab
- Original uploader/author: Maximilian Christof (
max-chr) - License: Apache-2.0
- LeRobot codebase version:
v3.0; robot type:iiwa - The source card does not provide a completed paper or BibTeX citation.
- Frame template:
data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet - Video template:
videos/{video_key}/chunk-{chunk_index:03d}/file-{file_index:03d}.mp4
Tasks and Scale
The train split covers 174 episodes (episode_index
0–173), 58,701 rows, and 10 task indexes.
| task_index | Instruction | Episodes | Frames |
|---|---|---|---|
| 0 | put both the cream cheese box and the butter in the basket | 9 | 2,961 |
| 1 | put both the alphabet soup and the cream cheese box in the basket | 17 | 5,399 |
| 2 | put both moka pots on the stove | 24 | 11,199 |
| 3 | put the white mug on the plate and put the chocolate pudding to the right of the plate | 14 | 4,479 |
| 4 | pick up the book and place it in the back compartment of the caddy | 27 | 6,573 |
| 5 | put both the alphabet soup and the tomato sauce in the basket | 23 | 7,747 |
| 6 | put the white mug on the left plate and put the yellow and white mug on the right plate | 12 | 3,684 |
| 7 | turn on the stove and put the moka pot on it | 11 | 3,519 |
| 8 | put the yellow and white mug in the microwave and close it | 25 | 9,528 |
| 9 | put the black bowl in the bottom drawer of the cabinet and close it | 12 | 3,612 |
The source numeric Parquet is 10.16 MiB. The
original videos/ tree contains 696 MP4 files
(0.699 GiB), 174 per stream
across four 256×256 RGB streams (AV1, 20 fps, no audio):
observation.images.image— 174 MP4 filesobservation.images.wrist_image— 174 MP4 filesobservation.images.frontview_image— 174 MP4 filesobservation.images.sideview_image— 174 MP4 files
Converted Files
- TsFile:
data/lsy_lab_libero_plus_10_add_object_all_iiwa_failures.tsfile - Table:
lsy_lab_libero_plus_10_add_object_all_iiwa_failures - Rows: 58,701; episodes/devices: 174
- TsFile size: 6.68 MiB
meta/is mirrored;meta/info.jsondocuments the converted schema, source revision, mapping, and video policy.
TsFile Schema
Time is INT64 milliseconds computed as round(timestamp * 1000);
it restarts per episode. The source timestamp column is dropped as
redundant with Time / 1000.
TAG columns (TsFile table-model device dimensions):
episode_indextask_index
Scalar FIELD columns:
frame_indexsample_indexobservation_state_0observation_state_1observation_state_2observation_state_3observation_state_4observation_state_5observation_state_6observation_state_7observation_states_ee_state_0observation_states_ee_state_1observation_states_ee_state_2observation_states_ee_state_3observation_states_ee_state_4observation_states_ee_state_5observation_states_joint_state_0observation_states_joint_state_1observation_states_joint_state_2observation_states_joint_state_3observation_states_joint_state_4observation_states_joint_state_5observation_states_joint_state_6observation_states_gripper_state_0observation_states_gripper_state_1action_0action_1action_2action_3action_4action_5action_6
Flattened FLOAT FIELD groups (full source prefix retained; . becomes _):
observation.state(8 values) ->observation_state_0...observation_state_7observation.states.ee_state(6 values) ->observation_states_ee_state_0...observation_states_ee_state_5observation.states.joint_state(7 values) ->observation_states_joint_state_0...observation_states_joint_state_6observation.states.gripper_state(2 values) ->observation_states_gripper_state_0...observation_states_gripper_state_1action(7 values) ->action_0...action_6
Scalar renames and dropped fields:
index->sample_index- Dropped after Time synthesis:
timestamp
Encoding and Conversion Notes
- FLOAT/DOUBLE: GORILLA + LZ4.
- INT32/INT64 and
Time: TS_2DIFF + LZ4. episode_indexandtask_indexuse the TsFile TAG/device mechanism; no synthetic aliases are created.- Source
indexis retained assample_index. - All five fixed-width numeric vectors are flattened to scalar FLOAT fields.
- The train split is merged into one table-model TsFile; filter by TAG values to select an episode or task.
Videos and Frame Alignment
Videos are not copied here. They remain in the original videos/ tree.
Rows align with source frames through episode_index, frame_index,
and mirrored meta/episodes/ timestamps and file indexes.
Validation
A local JSON/Markdown report records source/staged/TsFile row counts, schema, TAG columns, readback status, and output size. Expected rows: 58,701.
Minimal Read Example
from tsfile import TsFileReader
reader = TsFileReader("data/lsy_lab_libero_plus_10_add_object_all_iiwa_failures.tsfile")
with reader.query_table(
"lsy_lab_libero_plus_10_add_object_all_iiwa_failures",
["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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