| --- |
| license: apache-2.0 |
| authors: |
| - Kunalmod |
| task_categories: |
| - robotics |
| tags: |
| - LeRobot |
| - so101 |
| - tutorial |
| - tsfile |
| - format:tsfile |
| - timeseries |
| - tabular |
| modality: |
| - tabular |
| - timeseries |
| pretty_name: SO101 Block-to-Box TsFile |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/kunalmod_so101_groot.tsfile |
| size_categories: |
| - 10K<n<100K |
| --- |
| |
| # SO101 Block-to-Box TsFile |
|
|
| This dataset provides the numeric robot trajectories from |
| [`Kunalmod/so101_groot`](https://huggingface.co/datasets/Kunalmod/so101_groot) as one Apache TsFile table. The LeRobot |
| v2.1 SO101 demonstrations cover grasping a block and placing it in a box. |
|
|
| ## Source Dataset |
|
|
| - Author, repository owner, and uploader: [Kunalmod](https://huggingface.co/Kunalmod) (`Kunalmod`) |
| - License: Apache-2.0 |
| - Split: `train` |
| - Scale: 50 episodes, 47,507 frame rows, 1 task, and 50 source Parquet shards |
| - Sampling frequency: 30 fps |
| - Robot type: `so101` |
| - LeRobot codebase version: `v2.1` |
| - Source data layout: `data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet` |
| - Paper and citation: the source card does not provide either one |
| - Task `0`: `Grasp a block and put it in the box.` |
|
|
| ## Data Layout |
|
|
| - TsFile: `data/kunalmod_so101_groot.tsfile` |
| - Table: `kunalmod_so101_groot` |
| - Rows: 47,507 |
| - Devices: 50, identified by the two TAG columns |
| - Time precision: milliseconds |
| - Per-episode Time range: 0 to 39,600 ms |
|
|
| All 50 episode shards are represented in the table. Filter by `episode_index` |
| and `task_index` to select an episode trajectory. |
|
|
| ## Schema |
|
|
| | Column | TsFile type | Role | Meaning | |
| |---|---|---|---| |
| | `Time` | TIMESTAMP | TIME | `round(timestamp * 1000)` in milliseconds | |
| | `episode_index` | STRING | TAG | Source episode index stored by the TsFile device/tag mechanism | |
| | `task_index` | STRING | TAG | Source task index stored by the TsFile device/tag mechanism | |
| | `frame_index` | INT64 | FIELD | Frame position within the episode | |
| | `sample_index` | INT64 | FIELD | Source global `index` value | |
| | `action_0` ... `action_5` | FLOAT | FIELD | Six SO101 action components | |
| | `observation_state_0` ... `observation_state_5` | FLOAT | FIELD | Six SO101 joint-state components | |
|
|
| The action and state component order is `main_shoulder_pan`, |
| `main_shoulder_lift`, `main_elbow_flex`, `main_wrist_flex`, `main_wrist_roll`, |
| and `main_gripper`. |
|
|
| ## Transform Details |
|
|
| - `Time` is derived from the source timestamp and restarts at zero for every |
| episode. The source `timestamp` is omitted because it equals `Time / 1000` |
| seconds. |
| - `index` is renamed to `sample_index`; `frame_index` is preserved. |
| - `action[6]` is flattened to `action_0` through `action_5`. |
| - `observation.state[6]` is flattened to `observation_state_0` through |
| `observation_state_5`. |
| - Rows are ordered by `episode_index`, `task_index`, and `Time`. |
| - No source trajectory row, episode, task, action dimension, or state dimension |
| is removed. |
|
|
| ## Videos |
|
|
| The 100 source MP4 files remain in the original repository and are not |
| included here. Each camera has 50 files at 640x480 and 30 fps: |
|
|
| - [`observation.images.base`](https://huggingface.co/datasets/Kunalmod/so101_groot/tree/main/videos/chunk-000/observation.images.base) |
| - [`observation.images.wrist`](https://huggingface.co/datasets/Kunalmod/so101_groot/tree/main/videos/chunk-000/observation.images.wrist) |
|
|
| Their source pattern is |
| `videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4`. |
| Use `episode_index` and `frame_index` to align each numeric row with both video |
| streams. |
|
|
| ## Usage |
|
|
| ```python |
| from tsfile import ColumnCategory, TsFileReader |
| |
| path = "data/kunalmod_so101_groot.tsfile" |
| reader = TsFileReader(path) |
| table_name = "kunalmod_so101_groot" |
| schema = reader.get_all_table_schemas()[table_name] |
| columns = [ |
| column.get_column_name() |
| for column in schema.get_columns() |
| if column.get_category() in (ColumnCategory.TAG, ColumnCategory.FIELD) |
| ] |
| |
| with reader.query_table(table_name, columns, batch_size=65536) as result: |
| batch = result.read_arrow_batch() |
| print(batch.to_pandas().head()) |
| reader.close() |
| ``` |
|
|
| ## Attribution |
|
|
| The demonstrations were published by [Kunalmod](https://huggingface.co/Kunalmod) under the |
| Apache-2.0 license and were created with |
| [LeRobot](https://github.com/huggingface/lerobot). Cite the original Hugging |
| Face dataset URL above when using the data. |
|
|
