| ---
|
| license: apache-2.0
|
| authors:
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| - Corneille Marechal
|
| task_categories:
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| - robotics
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| tags:
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| - tsfile
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| - timeseries
|
| - tabular
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| - robotics
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| - lerobot
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| - so101
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| - manipulation
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| - tea-making
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| modality:
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| - timeseries
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| - tabular
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| pretty_name: Cornito SO101 Tea2 TsFile
|
| configs:
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| - config_name: default
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| data_files:
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| - split: train
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| path: data/cornito_so101_tea2.tsfile
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| size_categories:
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| - 10K<n<100K
|
| ---
|
|
|
| # Cornito SO101 Tea2 TsFile
|
|
|
| This dataset is an Apache TsFile conversion of
|
| [`Cornito/so101_tea2`](https://huggingface.co/datasets/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`](https://huggingface.co/datasets/Cornito/so101_tea2)
|
| - Pinned source revision: [`3da2ed9f4d512bf7b5e06821a7a9c70d9c416c99`](https://huggingface.co/datasets/Cornito/so101_tea2/tree/3da2ed9f4d512bf7b5e06821a7a9c70d9c416c99)
|
| - Original repository creator and uploader: [Corneille Marechal (`Cornito`)](https://huggingface.co/Cornito)
|
| - License: Apache-2.0
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| - Robot type: `so101`
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| - LeRobot codebase version: `v2.1`
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| - Task: `Make tea` (`task_index = 0`)
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| - Split: `train`
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| - Sampling rate: 30 fps
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| - Scale: 101 episodes, 78,833 frame rows, 1 task, 101 source Parquet files, 202 source videos
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| - Source frame layout: `data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet`
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| - 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`
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| - Table: `cornito_so101_tea2`
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| - Rows: 78,833
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| - Episodes/devices: 101
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| - TsFile size: 0.86 MiB
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| - Numeric source Parquet size: 3.31 MiB
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| - TsFile/Parquet size ratio: 0.2615
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| - Time precision: milliseconds
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| - 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`
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| - `task_index`
|
|
|
| FIELD columns:
|
|
|
| - `frame_index`
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| - `sample_index`
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| - `action_0`
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| - `action_1`
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| - `action_2`
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| - `action_3`
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| - `action_4`
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| - `action_5`
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| - `observation_state_0`
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| - `observation_state_1`
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| - `observation_state_2`
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| - `observation_state_3`
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| - `observation_state_4`
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| - `observation_state_5`
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|
|
| Flattened vector groups:
|
|
|
| - `action` -> `action_0` ... `action_5` (6 FLOAT fields)
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| - `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:
|
|
|
| - [`observation.images.phone`](https://huggingface.co/datasets/Cornito/so101_tea2/tree/3da2ed9f4d512bf7b5e06821a7a9c70d9c416c99/videos/chunk-000/observation.images.phone) - 101 per-episode MP4 files, 1,623,417,441 bytes
|
| - [`observation.images.wrist`](https://huggingface.co/datasets/Cornito/so101_tea2/tree/3da2ed9f4d512bf7b5e06821a7a9c70d9c416c99/videos/chunk-000/observation.images.wrist) - 101 per-episode MP4 files, 616,287,833 bytes
|
|
|
| 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
|
|
|
| ```python
|
| 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.
|
|
|
