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.
ALOHA Play Dataset Feb22 with FK TsFile
Apache TsFile edition of
ishika/aloha_play_dataset_feb22_with_fk, a LeRobot v2.1
ALOHA play dataset containing joint commands, joint observations, and forward-
kinematics poses for both end effectors and the head camera.
Source and Attribution
- Original dataset:
ishika/aloha_play_dataset_feb22_with_fk - Repository owner, uploader, and sole contributor: ishika
- License: not specified by the source repository
- Task label:
play data - Robot:
trossen_ai_stationary; Trossen subversion:v1.0 - LeRobot codebase version:
v2.1 - Split:
train; sampling rate: 30 fps - Scale: 60 episodes, 147,758 frame rows, 1 distinct task
- Source layout: 60 episode Parquet files at
data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet - The source card supplies no paper, citation, homepage, or separate author list.
TsFile Data
- Path:
data/ishika_aloha_play_dataset_feb22_with_fk.tsfile(12,630,997 bytes) - Table:
ishika_aloha_play_dataset_feb22_with_fk - Rows: 147,758; episode devices: 60; tasks: 1
- Time precision: milliseconds
- Size relative to the source train Parquet: 54.56%
- Source metadata remains under
meta/; no source Parquet is placed there.
Schema
| Columns | TsFile role | Type | Meaning |
|---|---|---|---|
Time |
TIME | INT64 | round(timestamp * 1000) milliseconds; restarts at zero per episode |
episode_index, task_index |
TAG | source INT64, stored as TAG values | Original LeRobot device dimensions |
frame_index |
FIELD | INT64 | Frame number within each episode |
sample_index |
FIELD | INT64 | Source index, renamed for clarity |
action_0 ... action_13 |
FIELD | FLOAT | Left and right commanded joint values |
observation_state_0 ... observation_state_13 |
FIELD | FLOAT | Left and right observed joint values |
left_ee_position_0 ... _2 |
FIELD | FLOAT | Left end-effector XYZ position in metres |
left_ee_quat_xyzw_0 ... _3 |
FIELD | FLOAT | Left end-effector quaternion XYZW |
right_ee_position_0 ... _2 |
FIELD | FLOAT | Right end-effector XYZ position in metres |
right_ee_quat_xyzw_0 ... _3 |
FIELD | FLOAT | Right end-effector quaternion XYZW |
head_camera_position_0 ... _2 |
FIELD | FLOAT | Head camera XYZ position in metres |
head_camera_quat_xyzw_0 ... _3 |
FIELD | FLOAT | Head camera quaternion XYZW |
All vector columns are flattened in source order, with dots replaced by
underscores. Source timestamp is omitted after Time synthesis because it is
equivalent to Time / 1000 seconds. No row, episode, task, scalar field, or
vector element is removed.
Encodings and Compression
- FLOAT/DOUBLE: GORILLA + ZSTD
- INT32/INT64: TS_2DIFF + ZSTD
- Time: TS_2DIFF + LZ4
- BOOLEAN: RLE + LZ4 when present; this source has no BOOLEAN field
- TAG: TsFile table/device mechanism
The physical table schema, Time codec, every FIELD codec, and all 147,758 rows were read back with the Apache TsFile Java API.
Videos
Videos are not included here. The 240 original AV1 MP4 files (about 12.02 GB)
remain under the source videos/ directory. They are
arranged across chunk-000 through chunk-015, with 60 files in each stream:
observation.images.cam_highobservation.images.cam_lowobservation.images.cam_left_wristobservation.images.cam_right_wrist
The source path template is
videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4.
Use episode_index and frame_index to align a TsFile row with its original
per-episode video frame.
Usage
from tsfile import TsFileReader
reader = TsFileReader("data/ishika_aloha_play_dataset_feb22_with_fk.tsfile")
with reader.query_table(
"ishika_aloha_play_dataset_feb22_with_fk",
["episode_index", "task_index", "frame_index", "action_0",
"observation_state_0", "left_ee_position_0"],
batch_size=65536,
) as result:
print(result.read_arrow_batch().to_pandas().head())
reader.close()
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