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

# Pouring (TsFile)

This is a time-series conversion of [`garySue/pouring`](https://huggingface.co/datasets/garySue/pouring), a LeRobot v2.0 robotics dataset containing demonstrations of the `pouring` task with a dual-arm `bi_ur5` robot.

- **Modalities:** Time-series
- **Robot:** `bi_ur5`
- **Task:** `pouring`
- **License:** Apache License 2.0

## Dataset scale

| Split | Episodes | Frames / TsFile rows | Sampling rate | Tasks | Source Parquets | Final TsFiles |
|---|---:|---:|---:|---:|---:|---:|
| `train` | 80 | 16,818 | 30 fps | 1 | 80 | 1 |

All numeric frame data is merged into exactly one file, [`data/pouring.tsfile`](data/pouring.tsfile), containing the table `pouring`. The file is approximately 2.59 MB.

## TsFile schema

| Column | TsFile role | Type | Description |
|---|---|---|---|
| `Time` | TIME | `INT64` | Milliseconds, computed as `Time = round(timestamp * 1000)`; time restarts within each episode. |
| `episode_index` | TAG | `INT64` | Original episode identifier (`0` through `79`). |
| `task_index` | TAG | `INT64` | Original task identifier (`0` for the single `pouring` task). |
| `frame_index` | FIELD | `INT64` | Original frame position within the episode. |
| `sample_index` | FIELD | `INT64` | Original global `index`, renamed to avoid ambiguity. |
| `action_0` ... `action_13` | FIELD | `FLOAT` (`float32`) | Complete 14-element source `action` vector. |
| `observation_state_0` ... `observation_state_13` | FIELD | `FLOAT` (`float32`) | Complete 14-element source `observation.state` vector. |
| `observation_velocity_0` ... `observation_velocity_13` | FIELD | `FLOAT` (`float32`) | Complete 14-element source `observation.velocity` vector. |
| `observation_gripper_position_0` ... `observation_gripper_position_1` | FIELD | `FLOAT` (`float32`) | Complete two-element source `observation.gripper_position` vector. |

The source `timestamp` column is dropped after generating `Time` because it is the redundant seconds representation of the same time coordinate (`Time / 1000`). No rows or other numeric source columns are dropped. All four vectors are flattened without truncation, `index` is renamed, and the episode, task, and frame identifiers remain available.

## Videos and metadata

The source contains three 480 x 640 RGB H.264 camera streams: `observation.images.top_rgb`, `observation.images.left_rgb`, and `observation.images.right_rgb`. Their 240 MP4 files are intentionally omitted from this time-series repository and remain in the original dataset's [`videos/` tree](https://huggingface.co/datasets/garySue/pouring/tree/main/videos).

Numeric rows retain `episode_index`, `frame_index`, and the 30 fps time coordinate, so they remain aligned with the corresponding source video frames. The source `meta/` files are mirrored, except that `meta/info.json` is rewritten to describe the single TsFile path, TIME/TAG/FIELD schema, flattened and renamed features, omitted video features, and source-video alignment. No `videos/` directory is included here.

## Reading the data

Install the Apache TsFile Python package, then query the actual table. `query_table` returns the time column automatically in addition to the requested TAG and FIELD columns.

```python
from tsfile import TsFileReader

reader = TsFileReader("data/pouring.tsfile")
columns = [
    "episode_index",
    "task_index",
    "frame_index",
    "sample_index",
    "observation_state_0",
    "observation_velocity_0",
    "action_0",
]

with reader.query_table("pouring", columns, batch_size=65536) as result:
    batch = result.read_arrow_batch()
    if batch is not None:
        print(batch.to_pandas().head())
```

## Source and citation

The source dataset was created with [LeRobot](https://github.com/huggingface/lerobot) and is published at [`garySue/pouring`](https://huggingface.co/datasets/garySue/pouring). Its dataset card does not provide a paper or BibTeX citation; consult the source repository for any future citation updates.