README.md

---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
- tsfile
- timeseries
- robotics
- so101
- format:tsfile
modality:
- tabular
- timeseries
pretty_name: Record Test TsFile
size_categories:
- 10K<n<100K
configs:
- config_name: default
  data_files:
  - split: train
    path: data/record_test.tsfile
---

# Record Test TsFile

This dataset is a TsFile conversion of the Hugging Face dataset
[`sattgle/record-test`](https://huggingface.co/datasets/sattgle/record-test),
which was created using [LeRobot](https://github.com/huggingface/lerobot).

Modalities: Time-series. The original repository also contains front and rear
camera video streams. Videos are not included in this repository and remain
available at the source.

## Source Dataset

- Original dataset: [`sattgle/record-test`](https://huggingface.co/datasets/sattgle/record-test)
- License: Apache-2.0
- Task category: robotics
- Robot type: `so101_follower`
- Task: `clean`
- LeRobot codebase version: `v2.1`
- Sampling rate: 30 fps
- Original video features: `observation.images.front` and
  `observation.images.rear` (640 x 360 RGB, AV1, 30 fps)

The source dataset card does not provide a paper, homepage, or detailed task
protocol beyond the LeRobot provenance and task metadata above.

## Source Consistency Note

The source repository contains inconsistent trailing files. Its
`meta/info.json` declares 43 episodes and 45,113 frames, while the repository
tree contains 50 frame Parquet files. Direct inspection found that files 41 and
42 were overwritten with embedded episode indexes 0 and 1, and files 43-49 are
outside `meta/episodes.jsonl` and also duplicate earlier episode indexes.

This conversion therefore uses the contiguous, internally consistent files
`episode_000000.parquet` through `episode_000040.parquet`: 41 episodes and
44,533 frames. The adjusted upload metadata and `meta/info.json` document the
excluded files. The corresponding aligned source subset has 82 videos; all
source videos remain upstream.

## Converted Files

- TsFile: `data/record_test.tsfile`
- Rows: 44,533
- Episodes: 41 (`0:41`)
- Tasks: 1 (`task_index = 0`)
- Table name: `record_test`
- Time precision: milliseconds
- Metadata: `meta/` is based on the source metadata and filtered to the
  validated episode range; `meta/info.json` records the TsFile mapping and
  source-repository anomalies.

## Schema

| Column | Role | Type | Notes |
|---|---|---|---|
| `Time` | TIME | INT64 | `round(timestamp * 1000)`, in milliseconds; restarts per episode |
| `episode_index` | TAG | INT64 | Source episode identifier |
| `task_index` | TAG | INT64 | Source task identifier |
| `frame_index` | FIELD | INT64 | Source frame index, preserved |
| `sample_index` | FIELD | INT64 | Renamed from source `index` |
| `action_0` ... `action_5` | FIELD | FLOAT | Flattened from `action[6]` |
| `observation_state_0` ... `observation_state_5` | FIELD | FLOAT | Flattened from `observation.state[6]` |

The action and observation-state element order is:
`shoulder_pan.pos`, `shoulder_lift.pos`, `elbow_flex.pos`, `wrist_flex.pos`,
`wrist_roll.pos`, and `gripper.pos`.

`episode_index` and `task_index` are TAG columns, so an individual episode can
be selected by filtering on `episode_index`.

## Conversion Notes

- The 41 validated LeRobot frame Parquet files were merged into one TsFile.
- Vector columns were flattened by preserving the source column name, replacing
  `.` with `_`, and appending the element index.
- The source `timestamp` column is dropped because it is redundant with
  `Time / 1000` seconds.
- The source `index` column is renamed to `sample_index`.
- Front and rear videos are omitted from the converted schema and are not
  uploaded here. Use the original dataset videos:
  https://huggingface.co/datasets/sattgle/record-test/tree/main/videos

## Read Example

```python
from tsfile import TsFileReader

path = "data/record_test.tsfile"
table = "record_test"

with TsFileReader(path) as reader:
    columns = [
        "episode_index",
        "task_index",
        "frame_index",
        "sample_index",
        "action_0",
        "observation_state_0",
    ]
    with reader.query_table(table, columns, batch_size=4096) as result:
        batch = result.read_arrow_batch()
        print(batch)
```

## Citation

The original dataset card does not provide a BibTeX citation.