---
license: mit
authors:
- Chenxiao Gao
task_categories:
- robotics
tags:
- tsfile
- timeseries
- tabular
- robotics
- lerobot
- vifailback
- aloha
- piper
modality:
- timeseries
- tabular
pretty_name: VIFailBack (TsFile)
configs:
- config_name: default
  data_files:
  - split: train
    path: data/typoverflow_vifailback_train.tsfile
size_categories:
- 1M<n<10M
---

# VIFailBack (TsFile)

This dataset is an Apache TsFile conversion of
[`typoverflow/vifailback`](https://huggingface.co/datasets/typoverflow/vifailback), a LeRobot v3.0 ALOHA AgileX Piper robot dataset.

**Modalities: Time-series, Tabular.** The converted repository contains numeric
robot state, target, timing, episode/task tags, debug signals, and mirrored
source metadata. Camera videos remain in the original Hugging Face dataset.

## Source Dataset and Author

- Original dataset: [`typoverflow/vifailback`](https://huggingface.co/datasets/typoverflow/vifailback)
- Pinned source revision: [`9145e6c58471ec61d5a11d7a1bbb39d540fc5710`](https://huggingface.co/datasets/typoverflow/vifailback/tree/9145e6c58471ec61d5a11d7a1bbb39d540fc5710)
- Original author, repository owner, uploader, and commit author: [Chenxiao Gao (`typoverflow`)](https://huggingface.co/typoverflow)
- License: MIT
- Robot type: `aloha_agilex_piper`
- LeRobot codebase version: `v3.0`
- Sampling rate: 25 fps
- Split: `train`, episodes 0 through 4713
- Source scale: 4,714 episodes, 1,190,262 frames, 98 tasks
- Source frame shards: 8 Parquet files totaling 516,628,399 bytes
- Paper, homepage, and completed citation: not provided by the source card

The source card states that the dataset was created using
[LeRobot](https://github.com/huggingface/lerobot). Task text for all 98 task
indices is preserved in `meta/tasks.parquet`; the source card does not provide a
more detailed dataset narrative, so none is inferred here.

## Converted Files

- TsFile: `data/typoverflow_vifailback_train.tsfile`
- Table: `typoverflow_vifailback_train`
- Rows: 1,190,262
- Episode/task devices: 4,714
- Columns: 183 total — 1 TIME, 2 TAG, 180 FIELD
- TsFile size: 467,828,268 bytes (90.55% of the source frame-Parquet size)
- Time precision: milliseconds
- Metadata: `meta/` is mirrored from the pinned source, with only
  `meta/info.json` rewritten to describe the TsFile artifact and conversion.
- Conversion config: `typoverflow_vifailback.yaml`

The latest pinned source metadata includes `meta/critical_frames.jsonl` and
`meta/qc_warnings.jsonl`; both are preserved unchanged.

## TsFile Schema

`Time` is an INT64 millisecond timestamp computed as
`round(timestamp * 1000)`. It restarts within each episode and spans
0..36,960 ms across the dataset.

TAG columns (TsFile table-model device segments):

- `episode_index` — source episode number, 0 through 4713
- `task_index` — source task number, 0 through 97

Scalar FIELD columns:

- `frame_index` — frame number within an episode
- `sample_index` — source global `index`, renamed to avoid ambiguity

The remaining 178 FLOAT FIELD columns are flattened from the source's scalar
and vector signals while preserving their full source prefixes. Dots are
replaced by underscores and vector positions receive a numeric suffix. They
cover these source groups:

- `raw_state.*` and `raw_target.*`: joint position/velocity, end-effector XYZ
  and quaternion, gripper state, and base velocity
- `state.*` and `target.*`: joint position/velocity, end-effector XYZ and 9D
  rotation, gripper state, and base velocity
- `debug.*`: left/right gripper end-effector XYZ and 6D rotation

For example, `raw_state.left_joint_pos[6]` becomes
`raw_state_left_joint_pos_0` through `raw_state_left_joint_pos_5`. The complete
machine-readable list and roles are recorded in `meta/info.json`.

## Encoding and Conversion Notes

- FLOAT/DOUBLE FIELD: `GORILLA + ZSTD`
- INT32/INT64 FIELD: `TS_2DIFF + ZSTD`
- Time: `TS_2DIFF + LZ4`
- BOOLEAN FIELD policy: `RLE + LZ4`; this source schema contains no BOOLEAN field
- TAG: stored through the TsFile device/TAG mechanism, not duplicated as ordinary FIELD columns
- All eight source frame shards are merged into one train-split TsFile. Filter
  by `episode_index` and `task_index` to select a trajectory or task.
- The source `timestamp` column is the only numeric source value not stored as a
  separate FIELD. It is redundant after the exact millisecond mapping
  (`timestamp = Time / 1000` seconds at 25 fps).
- The source `index` is retained as `sample_index`; `frame_index` is unchanged.
- No published rows, episodes, tasks, or numeric signal dimensions are dropped.

## Videos

Videos are not duplicated in the converted repository. The pinned source has
145 frame-aligned MP4 files totaling 27,938,508,868
bytes (26.02 GiB) in three streams:

- [`videos/observation.images.cam_high/`](https://huggingface.co/datasets/typoverflow/vifailback/tree/9145e6c58471ec61d5a11d7a1bbb39d540fc5710/videos/observation.images.cam_high) — 56 MP4 files, 11,040,155,298 bytes (10.28 GiB)
- [`videos/observation.images.cam_left_wrist/`](https://huggingface.co/datasets/typoverflow/vifailback/tree/9145e6c58471ec61d5a11d7a1bbb39d540fc5710/videos/observation.images.cam_left_wrist) — 45 MP4 files, 8,526,126,153 bytes (7.94 GiB)
- [`videos/observation.images.cam_right_wrist/`](https://huggingface.co/datasets/typoverflow/vifailback/tree/9145e6c58471ec61d5a11d7a1bbb39d540fc5710/videos/observation.images.cam_right_wrist) — 44 MP4 files, 8,372,227,417 bytes (7.80 GiB)

The source template is
`videos/{video_key}/chunk-{chunk_index:03d}/file-{file_index:03d}.mp4`.
Numeric rows remain aligned with the original videos through `episode_index`,
`frame_index`, and the source episode metadata under `meta/episodes/`.

## Validation

The generated TsFile passed Apache TsFile Java 2.2.1 schema inspection and a
full 1,190,262-row query readback. Validation confirmed:

- source, staged, and TsFile row-count equality
- exactly two TAG columns and 180 FIELD columns
- the requested physical encoding/compression profile
- TsFile size below the combined source frame-Parquet size
- SHA-256 `4580f9bfa834395dba4298be61761a3a5d97dd77d93f009e7c3eef0cbad493a8`

The local JSON and Markdown validation reports are intentionally not included
in this upload-ready dataset.

## Minimal Read Example

```python
from tsfile import TsFileReader

reader = TsFileReader("data/typoverflow_vifailback_train.tsfile")
table_name = "typoverflow_vifailback_train"
columns = ["episode_index", "task_index", "frame_index", "sample_index"]

with reader.query_table(table_name, 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 citation. Cite the
original Hugging Face dataset and Chenxiao Gao (`typoverflow`) when using this
converted artifact.