| --- |
| 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. |
|
|
