| --- |
| license: apache-2.0 |
| task_categories: |
| - robotics |
| tags: |
| - LeRobot |
| - robotics |
| - tsfile |
| - timeseries |
| - format:tsfile |
| pretty_name: Random50 v3 (TsFile) |
| size_categories: |
| - 10K<n<100K |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/random50_v3_train.tsfile |
| modality: |
| - tabular |
| - timeseries |
| --- |
| |
| # Random50 v3 (TsFile) |
|
|
| This dataset is an Apache TsFile conversion of the Hugging Face dataset |
| [`iiyudana/random50_v3`](https://huggingface.co/datasets/iiyudana/random50_v3). |
| The source dataset was created with |
| [LeRobot](https://github.com/huggingface/lerobot) and contains ALOHA robot |
| demonstrations for transferring a cube between arms. |
|
|
| Modalities: Time-series. The original repository also contains synchronized |
| top-camera videos; videos are not included in this converted repository. |
|
|
| ## Source Dataset |
|
|
| - Original dataset: [`iiyudana/random50_v3`](https://huggingface.co/datasets/iiyudana/random50_v3) |
| - License: `apache-2.0` |
| - LeRobot codebase version: `v2.1` |
| - Robot type: `aloha` |
| - Task: `Pick up the cube with the right arm and transfer it to the left arm.` |
| - Split: `train` (`0:50`) |
| - Scale: `50` episodes, `20,000` frames, `1` task |
| - Sampling rate: `50` fps |
| - Source videos: `50` MP4 files from `observation.images.top` |
| - Source data layout: `data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet` |
| - Source video layout: `videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4` |
|
|
| ## Converted File |
|
|
| - TsFile: `data/random50_v3_train.tsfile` |
| - TsFile table: `random50_v3_train` |
| - Converted rows: `20,000` |
| - Episodes: `50` |
| - Time precision: milliseconds |
| - TAG columns: `episode_index`, `task_index` |
| - File size: `2,195,882` bytes |
|
|
| All source episodes in the train split are merged into one TsFile. The source |
| `episode_index` and `task_index` columns are retained as TAG columns, allowing |
| queries to select an episode without creating synthetic tag aliases. |
|
|
| ## Schema |
|
|
| `Time` is computed as `round(timestamp * 1000)` in milliseconds and restarts in |
| each episode. At 50 fps, consecutive source frames are approximately 20 ms |
| apart. The source `timestamp` column is dropped because it is redundant with |
| `Time / 1000` seconds. No source rows are dropped. |
|
|
| TAG columns: |
|
|
| - `episode_index` |
| - `task_index` |
|
|
| FIELD columns: |
|
|
| - `frame_index` |
| - `sample_index` (renamed from source `index`) |
| - `observation_state_0` through `observation_state_13` (FLOAT) |
| - `action_0` through `action_13` (FLOAT) |
|
|
| The 14 elements in both `observation.state` and `action` use this source order: |
|
|
| 1. `left_waist` |
| 2. `left_shoulder` |
| 3. `left_elbow` |
| 4. `left_forearm_roll` |
| 5. `left_wrist_angle` |
| 6. `left_wrist_rotate` |
| 7. `left_gripper` |
| 8. `right_waist` |
| 9. `right_shoulder` |
| 10. `right_elbow` |
| 11. `right_forearm_roll` |
| 12. `right_wrist_angle` |
| 13. `right_wrist_rotate` |
| 14. `right_gripper` |
|
|
| Vector names preserve the full source feature name: `.` is replaced with `_` |
| and the element index is appended. |
|
|
| ## Video Policy |
|
|
| The source feature `observation.images.top` is not converted or uploaded. It is |
| 480 x 640 RGB AV1 video at 50 fps. Use the original dataset for the synchronized |
| videos: |
| [`iiyudana/random50_v3/videos`](https://huggingface.co/datasets/iiyudana/random50_v3/tree/main/videos). |
|
|
| The numeric rows retain `episode_index`, `frame_index`, `task_index`, and |
| `sample_index`, preserving their alignment with the original per-episode video. |
|
|
| ## Metadata |
|
|
| The source `meta/` files are mirrored in this repository. `meta/info.json` is |
| updated so `data_path` points to `data/random50_v3_train.tsfile`. Its |
| `tsfile_conversion` object records the actual table name, source episode-file |
| count, converted TsFile count, Time formula, TAG columns, row count, flattened |
| features, renamed and dropped fields, and frame/video alignment. The converted |
| `total_videos` value is `0`; the original count of `50` is preserved as |
| `tsfile_conversion.source_video_count`. |
|
|
| ## Validation |
|
|
| The converted file was validated with the project pipeline and read back with |
| the TsFile Python SDK: |
|
|
| - staged Parquet rows: `20,000` |
| - TsFile metadata rows: `20,000` |
| - TsFile query rows: `20,000` |
| - duplicate `(episode_index, task_index, Time)` rows: `0` |
| - TsFile size: `2,195,882` bytes |
|
|
| ## Usage |
|
|
| ```python |
| from tsfile import TsFileReader |
| |
| path = "data/random50_v3_train.tsfile" |
| with TsFileReader(path) as reader: |
| schemas = reader.get_all_table_schemas() |
| table = schemas["random50_v3_train"] |
| print([(column.get_column_name(), column.get_category()) |
| for column in table.get_columns()]) |
| ``` |
|
|
