---
license: apache-2.0
authors:
- XenseRobotics
- fza
task_categories:
- robotics
tags:
- tsfile
- timeseries
- tabular
modality:
- timeseries
- tabular
pretty_name: Whiteboard Inspect 0708 TsFile
configs:
- config_name: default
  data_files:
  - split: train
    path: data/xense_whiteboard_inspect_0708.tsfile
size_categories:
- 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)
- 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,
`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()
```