README.md

---
license: apache-2.0
task_categories:
  - robotics
tags:
  - tsfile
  - timeseries
  - tabular
modality:
  - timeseries
  - tabular
  - video
pretty_name: G1 Dex1 Clean Table TsFile
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/unitreerobotics_G1_Dex1_Clean_Table.tsfile
size_categories:
  - 100K<n<1M
---

# G1 Dex1 Clean Table — Apache TsFile conversion

This repository is a compact Apache TsFile representation of the
[`unitreerobotics/G1_Dex1_Clean_Table`](https://huggingface.co/datasets/unitreerobotics/G1_Dex1_Clean_Table)
LeRobot dataset. It contains the robot state/action time series and episode/task
metadata. The source camera videos are intentionally not copied here.

## Source dataset and attribution

- **Publisher/authors:** Unitree Robotics; dataset contributors shown by Hugging Face are `wangcong` and `wangcong627`.
- **License:** Apache-2.0.
- **Homepage:** [UnifoLM-VLA-0](https://unigen-x.github.io/unifolm-vla.github.io/).
- **Task:** organize and tidy items on a table (7-DOF dual-arm G1, gripper end effectors).
- **Acquisition:** 30 Hz; 640×480 images; approximately 20–40 seconds per operation.
- **Citation/paper:** the original dataset card does not provide a BibTeX citation or paper reference.

The source has one `train` split with 200 episodes, 265,701 rows and one task.
There are 200 source Parquet episode files in `data/chunk-000/` and 800 source
video files (four streams × 200 episodes). The original video layout is:

```
videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4
```

For this dataset, `video_key` is one of
`observation.images.cam_left_high`, `cam_right_high`, `cam_left_wrist`, or
`cam_right_wrist`. See the [original videos directory](https://huggingface.co/datasets/unitreerobotics/G1_Dex1_Clean_Table/tree/main/videos).
Videos remain in the original Hugging Face dataset and are **not included** in
this TsFile repository; align them to rows with `episode_index` and
`frame_index`.

## Converted artifact

- **TsFile:** `data/unitreerobotics_G1_Dex1_Clean_Table.tsfile`
- **Table:** `unitreerobotics_G1_Dex1_Clean_Table` (the Python SDK exposes the normalized lower-case table name)
- **Rows:** 265,701
- **Episodes/devices:** 200
- **Time precision:** integer milliseconds
- **Source Parquet shards:** 200 episode files, merged into one TsFile

### Schema

| Category | Columns |
| --- | --- |
| TIME | `Time` (`INT64`, ms) |
| TAG/device | `episode_index` (`INT64`), `task_index` (`INT64`) |
| FIELD scalars | `frame_index`, `sample_index` (`INT64`); four gripper scalars (`FLOAT`) |
| FIELD vectors | `observation.left_arm`/`right_arm` (7 each), `observation.left_ee`/`right_ee` (6 each), `observation.body` (29), and matching `action.*` vectors, flattened to scalar `*_0` … `*_{N-1}` FLOAT fields |

Dots in source names are replaced by underscores while preserving the full
prefix (for example, `observation.left_arm` →
`observation_left_arm_0..observation_left_arm_6`). The source `index` is
renamed to `sample_index`.

## Conversion details

- `Time = round(timestamp * 1000)` with millisecond precision. `timestamp` is
  dropped because it is redundant (`Time / 1000` seconds); `frame_index` is kept.
- Rows are sorted by `episode_index`, `task_index`, then `Time`; `Time` is
  monotonic within each episode and restarts from zero at the first frame.
- `episode_index` and `task_index` are stored as TsFile TAG/device dimensions,
  not duplicated as ordinary fields.
- Numeric codec profile: FLOAT/DOUBLE → **GORILLA**, INT32/INT64 and Time →
  **TS_2DIFF**, all with **LZ4** compression. No BOOLEAN source fields exist in
  this dataset; the configured BOOLEAN policy is RLE + LZ4.
- Dropped/omitted source data: only redundant `timestamp` is dropped from the
  tabular rows; four video columns are omitted from TsFile and remain at the
  source URL above. No numeric rows or measurements are intentionally removed.

## Reading

```python
from tsfile import TsFileReader

reader = TsFileReader("data/unitreerobotics_G1_Dex1_Clean_Table.tsfile")
table = next(iter(reader.get_all_table_schemas()))
columns = [c.get_column_name() for c in reader.get_all_table_schemas()[table].get_columns()
           if c.get_column_name() != "Time"]
with reader.query_table(table, columns, batch_size=65536) as result:
    batch = result.read_arrow_batch()
```

## Local conversion and validation files

The dataset-specific script is `D:\\code\\scripts\\convert_unitreerobotics_G1_Dex1_Clean_Table.py` and
the config is `D:\\code\\config\\unitreerobotics_G1_Dex1_Clean_Table.yaml`. Local validation
reports are kept under `conversion_reports/`; they are not part of the upload set.