| --- |
| pretty_name: eval2_compositional_augmented TsFile |
| tags: |
| - TsFile |
| - time-series |
| - robotics |
| - LeRobot |
| - SO-101 |
| - imitation-learning |
| modality: timeseries |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/eval2_compositional_augmented.tsfile |
| --- |
| |
| # eval2_compositional_augmented |
|
|
| This dataset was converted from |
| [`robot-learning-group47/eval2_compositional_augmented`](https://huggingface.co/datasets/robot-learning-group47/eval2_compositional_augmented) |
| to Apache TsFile format. |
|
|
| Modalities: Time-series. |
|
|
| ## Source Dataset |
|
|
| - Original dataset: [`robot-learning-group47/eval2_compositional_augmented`](https://huggingface.co/datasets/robot-learning-group47/eval2_compositional_augmented) |
| - Source format: LeRobot v3 parquet frames with metadata and one front-camera video stream |
| - Robot type: `so_follower` |
| - Split: `train` |
| - Episodes: 720 |
| - Frames / TsFile rows: 161,780 |
| - Tasks: 22 |
| - Sampling rate: 15 fps |
|
|
| The source contains compositional prompt augmentations for banana-and-bowl |
| manipulation tasks. The file `combo_augmented_map.csv` is included with this |
| conversion and maps prompt-combination groups to source and augmented episode |
| ranges. |
|
|
| ## Files |
|
|
| - `data/eval2_compositional_augmented.tsfile`: converted numeric time-series table |
| - `meta/`: source metadata mirrored from the original dataset, with `meta/info.json` |
| rewritten to describe the converted TsFile layout |
| - `combo_augmented_map.csv`: source augmentation map |
|
|
| Videos are not included in this repository. The original camera video stream |
| `observation.images.front` remains in the source dataset under |
| [`videos/`](https://huggingface.co/datasets/robot-learning-group47/eval2_compositional_augmented/tree/main/videos). |
|
|
| ## Schema |
|
|
| The TsFile table name is `eval2_compositional_augmented`. |
|
|
| - `Time`: integer timestamp in milliseconds, synthesized as `round(timestamp * 1000)` |
| - TAG columns: `episode_index`, `task_index` |
| - FIELD columns: `frame_index`, `sample_index`, `action_0..action_5`, |
| `observation_state_0..observation_state_5` |
|
|
| The six action and observation-state dimensions use the original LeRobot joint |
| order: `shoulder_pan.pos`, `shoulder_lift.pos`, `elbow_flex.pos`, |
| `wrist_flex.pos`, `wrist_roll.pos`, and `gripper.pos`. |
|
|
| ## Conversion Notes |
|
|
| - Converted with the generic `lerobot` converter in the HuggingFace-to-TsFile |
| pipeline. |
| - All 720 episodes are stored in one TsFile. Use `episode_index` and |
| `task_index` as device TAG filters when querying. |
| - Source `action[6]` was flattened to `action_0..action_5` as FLOAT fields. |
| - Source `observation.state[6]` was flattened to `observation_state_0..5` as |
| FLOAT fields. |
| - Source `index` was renamed to `sample_index`. |
| - Source `timestamp` is not retained as a separate field because the same |
| information is represented by `Time / 1000` seconds. |
| - Source video field `observation.images.front` is omitted from this TsFile |
| conversion and remains available in the original dataset. |
| - No rows were dropped. |
|
|
| ## Minimal Read Example |
|
|
| ```python |
| from tsfile import TsFileReader |
| |
| reader = TsFileReader("data/eval2_compositional_augmented.tsfile") |
| schemas = reader.get_all_table_schemas() |
| print(schemas.keys()) |
| ``` |
|
|
