| ---
|
| license: apache-2.0
|
| authors:
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| - XenseRobotics
|
| - fza
|
| task_categories:
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| - robotics
|
| tags:
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| - tsfile
|
| - timeseries
|
| - tabular
|
| modality:
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| - timeseries
|
| - tabular
|
| pretty_name: Whiteboard Inspect 0708 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/xense_whiteboard_inspect_0708.tsfile
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| size_categories:
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| - 10K<n<100K
|
| ---
|
|
|
| # Whiteboard Inspect 0708 TsFile
|
|
|
| Apache TsFile edition of [`Xense/whiteboard_inspect_0708`](https://huggingface.co/datasets/Xense/whiteboard_inspect_0708), a
|
| LeRobot v3.0 bimanual Flexiv Rizon 4 robotics dataset.
|
|
|
| ## Source and attribution
|
|
|
| - Original dataset: [https://huggingface.co/datasets/Xense/whiteboard_inspect_0708](https://huggingface.co/datasets/Xense/whiteboard_inspect_0708)
|
| - Publishing organization: [XenseRobotics (`Xense`)](https://huggingface.co/Xense)
|
| - Repository contributor: [fza (`fza1796262052`)](https://huggingface.co/fza1796262052)
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| - License: Apache-2.0
|
| - Task: `Pick up the inspection probe, press it on each red-marked spot on the whiteboard one by one, put down the probe, pick up the eraser, and wipe off all the red marks.`
|
| - Robot: `bi_flexiv_rizon4_rt`
|
| - Train split: 19 episodes, 67,684 frame rows, one task, 30 fps, 19 Parquet files
|
| - Source frame layout: `data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet`
|
|
|
| The source dataset card does not provide a paper or BibTeX citation. Cite the
|
| original Hugging Face dataset, XenseRobotics, and contributor fza when using
|
| this data.
|
|
|
| ## TsFile schema
|
|
|
| `Time = round(timestamp * 1000)` as INT64 milliseconds and restarts at zero in
|
| each episode. The source `timestamp` column is removed because it is exactly
|
| represented by `Time / 1000` seconds. `index` is renamed to `sample_index`, and
|
| `frame_index` is retained.
|
|
|
| | Columns | TsFile type | Role |
|
| |---|---|---|
|
| | `Time` | INT64/TIMESTAMP | TIME |
|
| | `episode_index`, `task_index` | STRING device segments | TAG |
|
| | `frame_index`, `sample_index` | INT64 | FIELD |
|
| | `action_0` ... `action_19` | FLOAT | FIELD |
|
| | `observation_state_0` ... `observation_state_19` | FLOAT | FIELD |
|
|
|
| The 20 action and 20 state elements follow the source feature order. Each arm
|
| contains TCP x/y/z, six rotation representation values, r1 through r6, in the |
| source-defined order, and a gripper position value. The left-arm values precede
|
| the right-arm values. Dots in source vector names are represented by the scalar
|
| field prefix and element index. No numeric rows, action dimensions, or state
|
| dimensions are omitted.
|
|
|
| Storage uses `GORILLA + LZ4` for FLOAT/DOUBLE, `TS_2DIFF + LZ4` for INT32/INT64,
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| `TS_2DIFF + LZ4` for Time, and `RLE + LZ4` for BOOLEAN fields. TAG values use
|
| the TsFile table/device mechanism.
|
|
|
| ## Original videos
|
|
|
| The 133 source MP4 files remain at [`videos/`](https://huggingface.co/datasets/Xense/whiteboard_inspect_0708/tree/main/videos) and
|
| are not included here. The path template is
|
| `videos/{video_key}/chunk-{chunk_index:03d}/file-{file_index:03d}.mp4`
|
| for these streams:
|
|
|
| - `observation.images.head` (640 x 480, H.264, 30 fps)
|
| - `observation.images.left_wrist` (640 x 480, H.264, 30 fps)
|
| - `observation.images.right_wrist` (640 x 480, H.264, 30 fps)
|
| - `observation.images.left_tactile_0` (700 x 400, H.264, 30 fps)
|
| - `observation.images.left_tactile_1` (700 x 400, H.264, 30 fps)
|
| - `observation.images.right_tactile_0` (700 x 400, H.264, 30 fps)
|
| - `observation.images.right_tactile_1` (700 x 400, H.264, 30 fps)
|
|
|
| Use `episode_index` and `frame_index` to align numeric rows with the matching
|
| frame in each original per-episode video.
|
|
|
| ## Read example
|
|
|
| ```python
|
| from tsfile import TsFileReader
|
|
|
| reader = TsFileReader("data/xense_whiteboard_inspect_0708.tsfile")
|
| with reader.query_table(
|
| "xense_whiteboard_inspect_0708",
|
| ["episode_index", "task_index", "Time", "frame_index", "action_0"],
|
| batch_size=4096,
|
| ) as result:
|
| print(result.read_arrow_batch().to_pandas().head())
|
| reader.close()
|
| ```
|
|
|
