Datasets:

azithromycin
/

sattgle_clean-test

license: apache-2.0
authors:
  - Hugging Face user sattgle
task_categories:
  - robotics
tags:
  - tsfile
  - timeseries
  - tabular
modality:
  - timeseries
  - tabular
pretty_name: Clean Test TsFile
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/sattgle_clean_test.tsfile
size_categories:
  - 10K<n<100K

Clean Test TsFile

This dataset is an Apache TsFile conversion of sattgle/clean-test, a LeRobot v2.1 SO101 follower robot-manipulation dataset for the clean task. It contains numeric trajectories, timing, and episode/task tags. Videos remain in the original Hugging Face repository.

Source Dataset and Attribution

Original dataset: sattgle/clean-test
Repository owner, uploader, and sole contributor: Hugging Face user sattgle (sattgle)
License: Apache-2.0
Task: "clean"
Robot: so101_follower; LeRobot version: v2.1
Split: train; sampling rate: 30 fps
Scale: 50 episodes, 32,220 frames, 1 task
Source shards: 50 Parquet files totaling 1,680,276 bytes
Source frame layout: data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet
Paper, external homepage, and completed citation: not provided by the source card.

The source metadata, all 50 Parquet shards, and episode metadata consistently describe this 50-episode dataset.

TsFile Dataset

TsFile: data/sattgle_clean_test.tsfile (718,233 bytes)
Table: sattgle_clean_test
Rows: 32,220; episodes/devices: 50; tasks: 1
Time precision: milliseconds
TsFile/source-Parquet size ratio: 0.427
meta/ is preserved from the source, with only meta/info.json rewritten to describe the TsFile schema and source-video alignment.

Schema and Mapping

Time = round(timestamp * 1000) milliseconds. Time starts at zero and is strictly increasing inside every episode. The source timestamp is dropped afterward because it is represented by Time / 1000 seconds at the selected precision.

TsFile column	Role	Type	Source mapping
`Time`	TIME	TIMESTAMP	`round(timestamp * 1000)` ms
`episode_index`	TAG	STRING	Original INT64 episode index
`task_index`	TAG	STRING	Original INT64 task index
`frame_index`	FIELD	INT64	Preserved
`sample_index`	FIELD	INT64	Renamed from `index`
`action_0` ... `action_5`	FIELD	FLOAT	Flattened from `action[6]`
`observation_state_0` ... `observation_state_5`	FIELD	FLOAT	Flattened from `observation.state[6]`

The six vector dimensions are shoulder_pan.pos, shoulder_lift.pos, elbow_flex.pos, wrist_flex.pos, wrist_roll.pos, and gripper.pos. Dots in source names are replaced by underscores. No numeric row, episode, task, state dimension, or action dimension is dropped.

Encodings and Compression

FLOAT/DOUBLE: GORILLA + LZ4
INT32/INT64: TS_2DIFF + LZ4
Time: TS_2DIFF + LZ4
BOOLEAN: RLE + LZ4 (the source contains no BOOLEAN field)
TAG: TsFile table/device TAG storage

The physical table schema, every field codec, TAG roles, and all 32,220 rows were read back with the Apache TsFile Java API.

Videos

Videos are not included in this TsFile repository. The source contains 100 frame-aligned AV1 MP4 files (597,891,922 bytes), 640x360 at 30 fps with no audio, in two streams with 50 episode files each:

The source layout is videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4. Use episode_index, frame_index, and meta/episodes.jsonl to align numeric rows with the original video frames.

Quality Checks

Source Parquet, staged Parquet, and complete Java TsFile readback all contain 32,220 rows. Scalar values, all flattened vector values, TAGs, episode indexes, Time mapping and monotonicity, physical codecs, size, and SHA-256 were checked locally. Conversion scripts and local quality-check reports are intentionally excluded from this upload-ready directory.

Minimal Read Example

from tsfile import TsFileReader

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

Citation

The source card provides no personal name, paper, external homepage, or completed citation. Cite the original Hugging Face dataset and Hugging Face user sattgle (sattgle) when using this conversion.