---
license: apache-2.0
authors:
  - karthus198
task_categories:
  - robotics
tags:
  - tsfile
  - timeseries
  - tabular
modality:
  - timeseries
  - tabular
pretty_name: G1 WBT Dex1 Put Clothes into Washing Machine TsFile
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/unitreerobotics_g1_wbt_dex1_put_clothes_into_washing_machine.tsfile
size_categories:
  - 100K<n<1M
---

# G1 WBT Dex1 Put Clothes into Washing Machine TsFile

This repository is an Apache TsFile conversion of
[`unitreerobotics/G1_WBT_Dex1_Put_Clothes_into_Washing_Machine`](https://huggingface.co/datasets/unitreerobotics/G1_WBT_Dex1_Put_Clothes_into_Washing_Machine),
a LeRobot v3 robot-manipulation dataset for a Unitree G1 with Dex1 hands.

The converted repository contains numeric robot states, actions, frame timing,
episode/task tags, and source metadata. Camera videos remain in the original
Hugging Face dataset and are not duplicated here.

## Source dataset and attribution

- Source: [Unitree G1 WBT Dex1 Put Clothes into Washing Machine](https://huggingface.co/datasets/unitreerobotics/G1_WBT_Dex1_Put_Clothes_into_Washing_Machine)
- Source organization: [Unitree Robotics (`unitreerobotics`)](https://huggingface.co/unitreerobotics)
- Uploader shown in the repository and video history: [karthus198](https://huggingface.co/karthus198)
- License: Apache-2.0
- Framework: LeRobot `v3.0`
- Robot type: `unitree_g1`
- Task: put clothes into a washing machine using a Unitree G1 with Dex1 hands
- Paper/citation: the source card does not provide a paper or formal citation

## Source scale and split

| Item | Value |
|---|---:|
| Source split | `train` |
| Source Parquet shards | 1 |
| Source data layout | `data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet` |
| Observed Parquet rows | 124,184 |
| Observed episodes | 155 (episode indexes 0–154) |
| Tasks | 1 (task index 0) |
| Sampling rate | 30 fps |
| Converted TsFiles | 1 |

The source `meta/info.json` declares 154 episodes, 123,603 frames, and
`train: 0:154`, while the Parquet shard contains an additional episode 154
with 581 rows. The conversion preserves all 124,184 Parquet rows and all 155
observed episodes. The original declaration is retained in metadata as a
documented source inconsistency.

## Converted file

- TsFile: `data/unitreerobotics_g1_wbt_dex1_put_clothes_into_washing_machine.tsfile`
- Table: `unitreerobotics_g1_wbt_dex1_put_clothes_into_washing_machine`
- Rows: 124,184
- Devices/TAG combinations: 155
- Time precision: milliseconds
- File size: 47,175,737 bytes
- Source Parquet size: 71,575,199 bytes
- Size ratio: 65.91% of the source Parquet

## TsFile schema

The logical `Time` value is `round(timestamp * 1000)` milliseconds.
It restarts for every episode. The Python reader exposes the physical time
column as lowercase `time`; it is the TsFile TIME column, not a normal
FIELD.

| Source / converted column | TsFile type | Role | Notes |
|---|---|---|---|
| `timestamp` → `Time` | TIMESTAMP / INT64 | TIME | Milliseconds; source timestamp is dropped after conversion |
| `episode_index` | STRING | TAG | Source INT64 stored as a TsFile device/tag segment |
| `task_index` | STRING | TAG | Source INT64 stored as a TsFile device/tag segment |
| `frame_index` | INT64 | FIELD | Preserved |
| `index` → `sample_index` | INT64 | FIELD | Renamed to avoid ambiguity |
| `observation.state.ee_state[12]` | FLOAT | FIELD | `observation_state_ee_state_0` … `_11` |
| `observation.state.hand_state[2]` | FLOAT | FIELD | `observation_state_hand_state_0` … `_1` |
| `observation.state.robot_q_current[36]` | FLOAT | FIELD | `observation_state_robot_q_current_0` … `_35` |
| `action.ee_action[12]` | FLOAT | FIELD | `action_ee_action_0` … `_11` |
| `action.hand_cmd[2]` | FLOAT | FIELD | `action_hand_cmd_0` … `_1` |
| `action.robot_q_desired[36]` | FLOAT | FIELD | `action_robot_q_desired_0` … `_35` |

Vector columns are flattened in row-major order. The full source name is
preserved by replacing each dot with an underscore and appending the element
index. The resulting table has 102 FIELD columns, 2 TAG columns, and 1 TIME
column.

## Encoding and compression

| Data type | Encoding | Compression |
|---|---|---|
| FLOAT, DOUBLE | GORILLA | LZ4 |
| INT32, INT64 | TS_2DIFF | LZ4 |
| TIME | TS_2DIFF | LZ4 |
| BOOLEAN | RLE | LZ4 |
| TAG | TsFile table device/tag mechanism | TsFile-supported TAG storage |

This dataset contains FLOAT and INT64 FIELD data and no BOOLEAN FIELD columns.
The writer is configured explicitly before table creation; TAG values are
stored as STRING device segments while their source INT64 types are recorded in
`meta/info.json`.

## Dropped and omitted content

- `timestamp` is dropped only after generating `Time`, because it is
  redundant with `Time / 1000` seconds.
- No numeric rows, state/action dimensions, `frame_index`, episode indexes,
  or task indexes are dropped.
- The four video features are omitted from the TsFile and remain in the source
  repository.

## Videos

The original video tree is approximately 8.41 GB and follows
`videos/{video_key}/chunk-{chunk_index:03d}/file-{file_index:03d}.mp4`.
It contains 43 physical MP4 shards:

- [`observation.images.head_stereo_left`](https://huggingface.co/datasets/unitreerobotics/G1_WBT_Dex1_Put_Clothes_into_Washing_Machine/tree/main/videos/observation.images.head_stereo_left): 10 files, `file-000.mp4`–`file-009.mp4`
- [`observation.images.head_stereo_right`](https://huggingface.co/datasets/unitreerobotics/G1_WBT_Dex1_Put_Clothes_into_Washing_Machine/tree/main/videos/observation.images.head_stereo_right): 13 files, `file-000.mp4`–`file-012.mp4`
- [`observation.images.wrist_left`](https://huggingface.co/datasets/unitreerobotics/G1_WBT_Dex1_Put_Clothes_into_Washing_Machine/tree/main/videos/observation.images.wrist_left): 10 files, `file-000.mp4`–`file-009.mp4`
- [`observation.images.wrist_right`](https://huggingface.co/datasets/unitreerobotics/G1_WBT_Dex1_Put_Clothes_into_Washing_Machine/tree/main/videos/observation.images.wrist_right): 10 files, `file-000.mp4`–`file-009.mp4`

Numeric rows remain aligned with the original videos through
`episode_index`, `frame_index`, and the per-episode offsets in
`meta/episodes/`.

## Validation

Local validation confirmed:

- TsFile metadata rows: 124,184
- Query readback rows: 124,184
- TAG/device combinations: 155
- Time is monotonic within every episode after TAG/Time sorting
- 102 FIELD + 2 TAG + 1 TIME columns are present
- The generated TsFile is smaller than the source Parquet

The detailed local files `validation_report.json` and `VALIDATION.md`
are conversion artifacts and should not be uploaded with the dataset.

## Usage

```python
from tsfile import TsFileReader

path = "data/unitreerobotics_g1_wbt_dex1_put_clothes_into_washing_machine.tsfile"
table_name = "unitreerobotics_g1_wbt_dex1_put_clothes_into_washing_machine"

reader = TsFileReader(path)
columns = [
    "episode_index",
    "task_index",
    "frame_index",
    "sample_index",
    "action_ee_action_0",
    "observation_state_ee_state_0",
]

with reader.query_table(table_name, columns, batch_size=65536) as result:
    batch = result.read_arrow_batch()
    print(batch.to_pandas().head())
```