| ---
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| language:
|
| - en
|
| pretty_name: "Vacuame/train4 (LeRobot SO-100) - TsFile"
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| tags:
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| - time-series
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| - tsfile
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| - robotics
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| - lerobot
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| - so100
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| - manipulation
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| - timeseries
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| modality: timeseries
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| task_categories:
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| - robotics
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| 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/vacuame_train4.tsfile
|
| ---
|
|
|
| # Vacuame/train4 (LeRobot SO-100) - TsFile
|
|
|
| This dataset converts the numeric frame time-series from
|
| [`Vacuame/train4`](https://huggingface.co/datasets/Vacuame/train4) to Apache
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| TsFile format.
|
|
|
| Modalities: Time-series. The original camera videos are not included in this
|
| converted repository; they remain in the source Hugging Face dataset.
|
|
|
| ## Source Dataset
|
|
|
| - Original dataset: [`Vacuame/train4`](https://huggingface.co/datasets/Vacuame/train4)
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| - LeRobot version: v2.0
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| - Robot type: `so100`
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| - Scale: 2 episodes, 119 frames, 1 task (`try`)
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| - Sampling rate: 30 fps
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| - Source frame data: `data/chunk-000/episode_000000.parquet` and
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| `data/chunk-000/episode_000001.parquet`
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| - Source videos: 2 camera streams, `observation.images.laptop` and
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| `observation.images.phone`, available in the source dataset
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| [`videos/`](https://huggingface.co/datasets/Vacuame/train4/tree/main/videos)
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| tree
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| - License: the source dataset does not declare an explicit license
|
|
|
| ## Converted Layout
|
|
|
| ```text
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| README.md
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| data/vacuame_train4.tsfile
|
| meta/info.json
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| meta/tasks.jsonl
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| meta/episodes.jsonl
|
| meta/stats.json
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| ```
|
|
|
| The converted TsFile contains all 119 numeric frame rows in one table named
|
| `vacuame_train4`.
|
|
|
| ## Schema
|
|
|
| | Role | Column(s) | Type | Notes |
|
| |------|-----------|------|-------|
|
| | Time | `Time` | INT64 ms | `round(timestamp * 1000)` |
|
| | TAG | `episode_index`, `task_index` | INT64 | Source columns used as TsFile device tags |
|
| | FIELD | `frame_index`, `sample_index` | INT64 | `index` is renamed to `sample_index` |
|
| | FIELD | `action_0` ... `action_5` | FLOAT | Flattened from `action[6]` |
|
| | FIELD | `observation_state_0` ... `observation_state_5` | FLOAT | Flattened from `observation.state[6]` |
|
|
|
| ## Conversion Notes
|
|
|
| - The generic `lerobot` converter was used.
|
| - `timestamp` is dropped because it is redundant with `Time / 1000` seconds.
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| - `frame_index` is kept so rows can be aligned back to source video frames.
|
| - Vector columns preserve the source feature name with `.` replaced by `_`:
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| `observation.state` becomes `observation_state_0..observation_state_5`, and
|
| `action` becomes `action_0..action_5`.
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| - Video features are omitted from the converted repository. Use the original
|
| dataset's `videos/` tree for camera data.
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| - Source `meta/` files are mirrored; `meta/info.json` is updated with a
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| `tsfile_conversion` object documenting the converted path, schema roles,
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| flattened features, dropped features, omitted videos, and row count.
|
|
|
| ## Usage
|
|
|
| ```python
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| from tsfile import TsFileReader
|
|
|
| reader = TsFileReader("data/vacuame_train4.tsfile")
|
| schemas = reader.get_all_table_schemas()
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| table_name = next(iter(schemas))
|
|
|
| columns = [
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| "episode_index",
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| "task_index",
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| "frame_index",
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| "observation_state_0",
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| "action_0",
|
| ]
|
|
|
| with reader.query_table(table_name, columns, batch_size=65536) as result:
|
| while (batch := result.read_arrow_batch()) is not None:
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| df = batch.to_pandas()
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| # Process rows here.
|
|
|
| reader.close()
|
| ```
|
|
|
| ## Citation
|
|
|
| ```bibtex
|
| @misc{vacuame_train4,
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| title = {train4 (LeRobot SO-100)},
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| author = {Vacuame},
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| url = {https://huggingface.co/datasets/Vacuame/train4},
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| publisher = {Hugging Face}
|
| }
|
| ```
|
|
|
