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Cornito_so101_tea2

Tasks:
Robotics
Modalities:
Time-series
Tabular
Size:
10K<n<100K
Tags:
tsfile
timeseries
tabular
robotics
lerobot
so101
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metadata 
license: apache-2.0
authors:
  - Corneille Marechal
task_categories:
  - robotics
tags:
  - tsfile
  - timeseries
  - tabular
  - robotics
  - lerobot
  - so101
  - manipulation
  - tea-making
modality:
  - timeseries
  - tabular
pretty_name: Cornito SO101 Tea2 TsFile
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/cornito_so101_tea2.tsfile
size_categories:
  - 10K<n<100K

Cornito SO101 Tea2 TsFile

This dataset is an Apache TsFile conversion of Cornito/so101_tea2, a LeRobot v2.1 SO101 robot-manipulation dataset containing 101 demonstrations for the task Make tea.

The converted repository contains numeric robot states, actions, frame timing, and episode/task tags. The two camera streams remain in the original Hugging Face dataset and are linked below.

Source Dataset and Provenance

  • Original dataset: Cornito/so101_tea2
  • Pinned source revision: 3da2ed9f4d512bf7b5e06821a7a9c70d9c416c99
  • Original repository creator and uploader: Corneille Marechal (Cornito)
  • License: Apache-2.0
  • Robot type: so101
  • LeRobot codebase version: v2.1
  • Task: Make tea (task_index = 0)
  • Split: train
  • Sampling rate: 30 fps
  • Scale: 101 episodes, 78,833 frame rows, 1 task, 101 source Parquet files, 202 source videos
  • Source frame layout: data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet
  • Source video layout: videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4

The source dataset card provides no paper or completed citation.

Converted Files

  • TsFile: data/cornito_so101_tea2.tsfile
  • Table: cornito_so101_tea2
  • Rows: 78,833
  • Episodes/devices: 101
  • TsFile size: 0.86 MiB
  • Numeric source Parquet size: 3.31 MiB
  • TsFile/Parquet size ratio: 0.2615
  • Time precision: milliseconds
  • Metadata: meta/ is mirrored from the source, with meta/info.json rewritten for the TsFile artifact.

TsFile Schema

Time is an INT64 millisecond timestamp computed as round(timestamp * 1000) and restarts at zero for each episode.

TAG columns, stored through the TsFile table-model device/tag mechanism:

  • episode_index
  • task_index

FIELD columns:

  • frame_index
  • sample_index
  • action_0
  • action_1
  • action_2
  • action_3
  • action_4
  • action_5
  • observation_state_0
  • observation_state_1
  • observation_state_2
  • observation_state_3
  • observation_state_4
  • observation_state_5

Flattened vector groups:

  • action -> action_0 ... action_5 (6 FLOAT fields)
  • observation.state -> observation_state_0 ... observation_state_5 (6 FLOAT fields)

Conversion Notes

  • The train split is merged into one table-model TsFile. Filter by episode_index and task_index to select an episode or task.
  • Storage profile: Time uses TS_2DIFF + LZ4; FLOAT/DOUBLE use GORILLA + ZSTD; INT32/INT64 use TS_2DIFF + ZSTD; BOOLEAN, if present, uses RLE + LZ4. Physical codecs were checked from the generated TsFile.
  • episode_index and task_index are TsFile TAG/device columns.
  • action[6] and observation.state[6] are flattened to scalar FLOAT fields; the full source prefix is retained and . is replaced with _.
  • The source timestamp column is dropped after Time synthesis because it is redundant with Time / 1000 seconds.
  • The source index column is retained as sample_index; frame_index is retained unchanged.
  • No rows or numeric trajectory dimensions are dropped.
  • Source video features are intentionally omitted from the TsFile because they are external MP4 assets.

Videos

Videos are not duplicated in this converted repository. The pinned source contains two frame-aligned camera streams:

Together the 202 MP4 files occupy 2,239,705,274 bytes. Numeric rows remain aligned with the original videos through episode_index, frame_index, and the source per-episode metadata.

Validation

The generated TsFile passed row-count equality, schema/TAG checks, per-device Time monotonicity, Java full-table readback, physical codec inspection, and file-size comparison against the 101 source Parquet shards. Local JSON and Markdown validation reports are retained with the conversion workspace and are not part of the eventual Hugging Face upload set.

Minimal Read Example 

from tsfile import TsFileReader

reader = TsFileReader("data/cornito_so101_tea2.tsfile")
columns = [
    "episode_index",
    "task_index",
    "frame_index",
    "sample_index",
    "action_0",
    "observation_state_0",
]

with reader.query_table("cornito_so101_tea2", columns, batch_size=65536) as result:
    batch = result.read_arrow_batch()
    print(batch.to_pandas().head())
reader.close()

Citation

The source dataset card provides no paper or completed BibTeX citation. Cite the original Hugging Face dataset and Corneille Marechal (Cornito) when using this converted artifact.