---
authors:
- Jiangeng
task_categories:
- robotics
tags:
- tsfile
- timeseries
- tabular
- robotics
- lerobot
- unitree-g1
- whole-body-control
- format:tsfile
modality:
- tabular
- timeseries
pretty_name: G1 06 11 TsFile
configs:
- config_name: default
  data_files:
  - split: train
    path: data/jiangeng_g1_06_11.tsfile
size_categories:
- 10K<n<100K
---

# G1 06 11 TsFile

Apache TsFile edition of [`Jiangeng/G1_06_11`](https://huggingface.co/datasets/Jiangeng/G1_06_11), a LeRobot v2.1 Unitree G1 whole-body dataset for the task: **Put the teddy bear in the bag.** Numeric trajectories are stored in one table-model TsFile.

## Source and attribution

- Original repository owner, publisher, uploader, and sole listed contributor: [Jiangeng](https://huggingface.co/Jiangeng). No separate personal name is provided.
- License: the source repository does not declare one.
- Paper, homepage, and citation: the source repository does not provide them.
- Split: `train`; 51 episodes; 79,948 frame rows; one task; 50 Hz; 51 source Parquet episode shards.
- Task `0`: `Put the teddy bear in the bag.`

## Data layout

The table is `jiangeng_g1_06_11` and contains 79,948 rows across 51 TAG devices. The source Parquet shards total 67,782,033 bytes; the TsFile is 31,817,362 bytes (46.9% of the source Parquet size).

| Column or group | TsFile role | Type | Meaning |
|---|---|---|---|
| `Time` | TIME | INT64 milliseconds | `round(timestamp * 1000)`, restarting at zero for each episode |
| `episode_index` | TAG | STRING from source INT64 | Source episode identity, values 0 through 50 |
| `task_index` | TAG | STRING from source INT64 | Source task identity, value 0 |
| `frame_index` | FIELD | INT64 | Frame position within the episode |
| `sample_index` | FIELD | INT64 | Source `index`, renamed for clarity |
| `teleop_delta_heading` | FIELD | DOUBLE | Source heading delta |
| `teleop_smpl_frame_index` | FIELD | INT64 | Source SMPL frame index |
| `teleop_stream_mode`, `teleop_planner_mode` | FIELD | INT32 | Teleoperation and locomotion planner modes |
| `teleop_planner_speed`, `teleop_planner_height` | FIELD | FLOAT | Planner speed and height |
| `observation_state_0` ... `observation_state_42` | FIELD | FLOAT | G1 joint state; 43 elements |
| `observation_eef_state_0` ... `observation_eef_state_13` | FIELD | FLOAT | left/right wrist position and quaternion state; 14 elements |
| `action_wbc_0` ... `action_wbc_42` | FIELD | FLOAT | whole-body controller action; 43 elements |
| `observation_root_orientation_0` ... `observation_root_orientation_3` | FIELD | FLOAT | flattened source `observation.root_orientation` vector; 4 elements |
| `observation_projected_gravity_0` ... `observation_projected_gravity_2` | FIELD | FLOAT | flattened source `observation.projected_gravity` vector; 3 elements |
| `observation_cpp_rotation_offset_0` ... `observation_cpp_rotation_offset_3` | FIELD | FLOAT | flattened source `observation.cpp_rotation_offset` vector; 4 elements |
| `observation_init_base_quat_0` ... `observation_init_base_quat_3` | FIELD | FLOAT | flattened source `observation.init_base_quat` vector; 4 elements |
| `action_motion_token_0` ... `action_motion_token_63` | FIELD | FLOAT | motion token; 64 elements |
| `teleop_smpl_joints_0` ... `teleop_smpl_joints_71` | FIELD | FLOAT | SMPL joint values; 72 elements |
| `teleop_smpl_pose_0` ... `teleop_smpl_pose_62` | FIELD | FLOAT | SMPL pose values; 63 elements |
| `teleop_body_quat_w_0` ... `teleop_body_quat_w_3` | FIELD | FLOAT | flattened source `teleop.body_quat_w` vector; 4 elements |
| `teleop_target_body_orientation_0` ... `teleop_target_body_orientation_5` | FIELD | FLOAT | flattened source `teleop.target_body_orientation` vector; 6 elements |
| `teleop_left_hand_joints_0` ... `teleop_left_hand_joints_6` | FIELD | FLOAT | flattened source `teleop.left_hand_joints` vector; 7 elements |
| `teleop_right_hand_joints_0` ... `teleop_right_hand_joints_6` | FIELD | FLOAT | flattened source `teleop.right_hand_joints` vector; 7 elements |
| `teleop_left_wrist_joints_0` ... `teleop_left_wrist_joints_2` | FIELD | FLOAT | flattened source `teleop.left_wrist_joints` vector; 3 elements |
| `teleop_right_wrist_joints_0` ... `teleop_right_wrist_joints_2` | FIELD | FLOAT | flattened source `teleop.right_wrist_joints` vector; 3 elements |
| `teleop_planner_movement_0` ... `teleop_planner_movement_2` | FIELD | FLOAT | flattened source `teleop.planner_movement` vector; 3 elements |
| `teleop_planner_facing_0` ... `teleop_planner_facing_2` | FIELD | FLOAT | flattened source `teleop.planner_facing` vector; 3 elements |
| `teleop_vr_3pt_position_0` ... `teleop_vr_3pt_position_8` | FIELD | FLOAT | flattened source `teleop.vr_3pt_position` vector; 9 elements |
| `teleop_vr_3pt_orientation_0` ... `teleop_vr_3pt_orientation_17` | FIELD | FLOAT | three-point VR orientation; 18 elements |

The source `timestamp` column is not stored separately because it is represented by `Time / 1000` seconds. All 79,948 numeric rows, every episode and task identity, `frame_index`, and every numeric vector element are retained. Source vector prefixes are preserved with dots replaced by underscores.

## Encoding and compression

- TIME, INT32, and INT64: `TS_2DIFF + LZ4`
- FLOAT and DOUBLE: `GORILLA + LZ4`
- BOOLEAN, when present: `RLE + LZ4`
- TAG values: TsFile table-model device/tag storage

## Videos and alignment

The 51 source ego-view MP4 files are not included here. They remain in the original repository at [`videos/chunk-000/observation.images.ego_view`](https://huggingface.co/datasets/Jiangeng/G1_06_11/tree/main/videos/chunk-000/observation.images.ego_view), named `episode_000000.mp4` through `episode_000050.mp4`. The source directory is displayed as 257 MB on Hugging Face.

The source template is `videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4`. Use `episode_index` and `frame_index` to align a numeric row with its 50 FPS video frame.

## Read example

```python
from tsfile import TsFileReader

reader = TsFileReader("data/jiangeng_g1_06_11.tsfile")
with reader.query_table(
    "jiangeng_g1_06_11",
    [
        "episode_index",
        "task_index",
        "frame_index",
        "sample_index",
        "observation_state_0",
        "action_wbc_0",
    ],
    batch_size=1024,
) as result:
    batch = result.read_arrow_batch()
    print(batch.to_pandas().head())
reader.close()
```